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Background: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analyze the risk factors of AEFV and develop and select the optimal risk prediction model for AEFV to provide guidance for the prevention of this condition and enhancement of clinical outcomes. Methods: This work included 383 patients with primary osteoporotic vertebral compression fracture (OVCF) who underwent PKP. The patients were grouped based on the occurrence of AEFV postsurgery, and data were collected. Group comparisons and correlation analysis were conducted to identify potential risk factors, which were then included in the five prediction models. The performance indicators served as basis for the selection of the best model. Results: Multivariate logistic regression analysis revealed the following independent risk factors for AEFV: kissing spine (odds ratio (OR) = 8.47, 95% confidence interval (CI) 1.46–49.02), high paravertebral muscle fat infiltration grade (OR = 29.19, 95% CI 4.83–176.04), vertebral body computed tomography value (OR = 0.02, 95% CI 0.003–0.13, P < 0.001), and large Cobb change (OR = 5.31, 95% CI 1.77–15.77). The support vector machine (SVM) model exhibited the best performance in the prediction of the risk of AEFV. Conclusion: Four independent risk factors were identified of AEFV, and five risk prediction models that can aid clinicians in the accurate identification of high-risk patients and prediction of the occurrence of AEFV were developed. [ABSTRACT FROM AUTHOR]

Background: Haemoglobin variation (ΔHb) induced by fluid transfer through the intestitium has been proposed as a useful tool for detecting hydrostatic pulmonary oedema (HPO). However, its use in the emergency department (ED) setting still needs to be determined. Methods: In this observational retrospective monocentric study, ED patients admitted for acute dyspnoea were enrolled. Hb values were recorded both at ED presentation (T0) and after 4 to 8 h (T1). ΔHb between T1 and T0 (ΔHbT1-T0) was calculated as absolute and relative value. Two investigators, unaware of Hb values, defined the cause of dyspnoea as HPO and non-HPO. ΔHbT1-T0 ability to detect HPO was evaluated. A machine learning approach was used to develop a predictive tool for HPO, by considering the ability of ΔHb as covariate, together with baseline patient characteristics. Results: Seven-hundred-and-six dyspnoeic patients (203 HPO and 503 non-HPO) were enrolled over 19 months. Hb levels were significantly different between HPO and non-HPO patients both at T0 and T1 (p < 0.001). ΔHbT1-T0 were more pronounced in HPO than non-HPO patients, both as relative (-8.2 [-11.2 to -5.6] vs. 0.6 [-2.1 to 3.3] %) and absolute (-1.0 [-1.4 to -0.8] vs. 0.1 [-0.3 to 0.4] g/dL) values (p < 0.001). A relative ΔHbT1-T0 of -5% detected HPO with an area under the receiver operating characteristic curve (AUROC) of 0.901 [0.896–0.906]. Among the considered models, Gradient Boosting Machine showed excellent predictive ability in identifying HPO patients and was used to create a web-based application. ΔHbT1-T0 was confirmed as the most important covariate for HPO prediction. Conclusions: ΔHbT1-T0 in patients admitted for acute dyspnoea reliably identifies HPO in the ED setting. The machine learning predictive tool may represent a performing and clinically handy tool for confirming HPO. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is well known as the commonest cancer in men and the second leading cause of cancer-related death. CT, MRI and bone scintigraphy are considered the commonly widely used imaging diagnostic tools for detection, staging and follow-up of prostate cancer. Prostate-specific membrane antigen (PSMA) is a membrane glycoprotein, that can be concentrated in prostate cancer cells up to 100 times higher than in normal cells. PSMA-targeted imaging modalities have now proven their efficacy in diagnosis, staging and follow-up of prostate cancer. The use of 68Ga PSMA PET-CT has efficiently improved the detection of loco-regional and metastatic disease. 68Ga PSMA PET-CT also has an effective role in the primary diagnosis, staging, and detecting biochemical recurrence after curative treatment and in metastasis-targeted therapy. This work aims to review the role of 68Ga PSMA PET-CT in anatomical staging of prostate cancer in correlation with histopathological staging. Results: Zonal correlation between 68Ga PSMA findings and biopsy results showed sensitivity ranging between 76.9 and 90.6% and specificity ranging from 85.7 to 100%. There was high significant correlation between the SUVmax uptake and the biopsy results, between the SUVmax uptake and the local staging as well as between the Gleason score and 68Ga PSMA PET/CT findings. Conclusions: 68Ga PSMA PET/CT is a highly promising imaging modality with an effective role in detection of prostate cancer showing high sensitivity and specificity in prediction of zonal histopathological results and loco-regional Gleason score staging with significant positive correlation between the SUV uptake results, Gleason score and the PSA levels. [ABSTRACT FROM AUTHOR]

Background: Labor pain is the nastiest conceivable pain women are involved in during labor and delivery. In this way, the end of labor pain is frequently outlined by myths and equivocalness. Hence, giving a compelling absence of pain in labor remained a challenge specifically in developing countries including Ethiopia. Objectives: This systematic review and meta-analysis aimed to explore the pooled prevalence of labor analgesia and associated factors to pregnant women in Ethiopia. Design: A systematic review and meta-analysis were utilized in agreement with the Preferred Reporting Items for Systematic Reviews. Data Sources: PubMed/Medline, SCOPUS, EMBASE, Web of Science, Google Scholars, and the Cochrane Library and supplemented it with manual were deliberately looked at until January 1–30, 2024. Methods: Two authors independently extricated all principal information utilizing standardized data extraction designs, and the analysis was done utilizing STATA version 17. Heterogeneity over the studies was evaluated utilizing I 2 measurement. The funnel plot and Egger's weighted regression tests were utilized to assess subjective and objective publication biases respectively. Also, the pooled effect of labor pain management and the associations were evaluated utilizing a random-effects model. Results: The general pooled prevalence of labor analgesia in the present study was 23.3% with a (95% confidence interval (CI): 13.5, 33.1). Maternal age (odds ratio (OR): 1.91; 95% CI: 1.11, 2.77), parity of the mother (OR: 0.28; 95% CI: 0.06, 0.63), history of pregnancy misfortune (OR: 0.12; 95% CI: 0.11, 0.36), length of labor (OR: 2.09; 95% CI: 1.06, 3.13), and awareness about labor analgesia (OR: 1.91; 95% CI: 0.34, 3.49) were significantly related with labor analgesia among pregnant women in Ethiopia. Conclusions: The generally pooled prevalence of labor analgesia among pregnant women in Ethiopia was low. Maternal age, parity of the mother, history of pregnancy loss, length of labor, and awareness of labor analgesia were factors influencing labor analgesia among pregnant women in Ethiopia. This finding proposes exceptional consideration to make laboring mothers free of pain by scaling up the strategies and utilizing labor pain administration in a way that universally recognized standards are met. Registration number: PROSPERO CRD: 42024525636 [ABSTRACT FROM AUTHOR]

Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems. [ABSTRACT FROM AUTHOR]

Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation. [ABSTRACT FROM AUTHOR]

Objectives: Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model. Methods: This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University. Results: The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility. Conclusions: An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients. [ABSTRACT FROM AUTHOR]

Background: While home is frequently expressed as the favorite place of death (PoD) among terminally ill cancer patients, various factors affect the fulfillment of this wish. The determinants of the PoD of cancer patients in countries without healthcare system-integrated palliative and supportive care have not been studied before. This study aimed at identifying the predictors of the PoD of patients who suffer from advanced cancer by developing a reliable predictive model among who received home-based palliative care in Iran as a representative of the countries with isolated provision of palliative care services. Methods: In a cross-sectional study, electronic records of 4083 advanced cancer patients enrolled in the Iranian Cancer Control Center (MACSA) palliative homecare program, who died between February 2018 and February 2020 were retrieved. Multivariable binary logistic regression analysis as well as subgroup analyses (location, sex, marital status, and tumor topography) was performed to identify the predictors of PoD. Results: Of the 2398 cases included (mean age (SD) = 64.17 (14.45) year, 1269 (%52.9) male), 1216 (50.7%) patients died at home. Older age, presence and intensity of medical homecare in the last two weeks and registration in the Tehran site of the program were associated with dying at home (P < 0.05). Gynecological or hematological cancers, presence and intensity of the calls received from the remote palliative care unit in the last two weeks were predictors of death at the hospital (p < 0.05). The model was internally and externally validated (AUC = 0.723 (95% CI = 0.702–0.745; P < 0.001) and AUC = 0.697 (95% CI = 0.631–0.763; P < 0.001) respectively). Conclusion: Our model highlights the demographic, illness-related and environmental determinants of the PoD in communities with patchy provision of palliative care. It also urges policymakers and service providers to identify and take the local determinant of the place of death into account to match the goals of palliative and supportive services with the patient preferences. [ABSTRACT FROM AUTHOR]

Background: Severe trauma is the leading cause of disability and mortality in the patients under 35 years of age. Surgical site infections (SSI) represent a significant complication in this patient population. However, they are often inadequately investigated, potentially impacting the quality of patient outcomes. The aim of this study was to investigate the epidemiology of SSI and risk factors in severe trauma patients. Methods: We conducted a multicenter retrospective cohort study screening the severe trauma patients (STP) admitted to two intensive care units of an academic institution in Marseille between years2018 and 2019. Those who underwent orthopedic or spinal surgery within 5 days after admission were included and classified into two groups according to the occurrence of SSI (defined by the Centers for Disease Control (CDC) international diagnostic criteria) or not. Our secondary goal was to evaluate STP survival at 48 months, risk factors for SSI and microbiological features of SSI. Results: Forty-seven (23%) out of 207 STP developed an SSI. Mortality at 48-months did not differ between SSI and non-SSI patients (12.7% vs. 10.0%; p = 0.59). The fractures of 22 (47%) severe trauma patients with SSI were classified as Cauchoix 3 grade and 18 (38%) SSI were associated with the need for external fixators. Thirty (64%) severe trauma patients with SSI had polymicrobial infection, including 34 (72%) due to Gram-positive cocci. Empirical antibiotic therapy was effective in 31 (66%) cases. Multivariate analysis revealed that risk factors such as low hemoglobin, arterial oxygenation levels, hyperlactatemia, high serum creatinine and glycemia, and Cauchoix 3 grade on the day of surgery were associated with SSI in severe trauma patients. The generated predictive model showed a good prognosis performance with an AUC of 0.80 [0.73–0.88] and a high NPV of 95.9 [88.6–98.5] %. Conclusions: Our study found a high rate of SSI in severe trauma patients, although SSI was not associated with 48-month mortality. Several modifiable risk factors for SSI may be effectively managed through enhanced perioperative monitoring and the implementation of a patient blood management strategy. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) is extensively employed for forecasting the outcome of various illnesses. The objective of the study was to develop ML based classifiers using a stacking ensemble strategy to predict the Japanese Orthopedic Association (JOA) recovery rate for patients with degenerative cervical myelopathy (DCM). Methods: A total of 672 patients with DCM were included in the study and labeled with JOA recovery rate by 1-year follow-up. All data were collected during 2012–2023 and were randomly divided into training and testing (8:2) sub-datasets. A total of 91 initial ML classifiers were developed, and the top 3 initial classifiers with the best performance were further stacked into an ensemble classifier with a supported vector machine (SVM) classifier. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicted outcome was the JOA recovery rate. Results: By applying an ensemble learning strategy (e.g., stacking), the accuracy of the ML classifier improved following combining three widely used ML models (e.g., RFE-SVM, EmbeddingLR-LR, and RFE-AdaBoost). Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top 3 initial classifiers varied a lot in predicting JOA recovery rate in DCM patients. Conclusions: The ensemble classifiers successfully predict the JOA recovery rate in DCM patients, which showed a high potential for assisting physicians in managing DCM patients and making full use of medical resources. [ABSTRACT FROM AUTHOR]

Simple Summary: Seizures are one of the most common and severe symptoms of meningioma, leading to increased morbidity and mortality in the affected patients. Therefore, seizure prevention represents an important goal in the treatment of meningioma patients. For this purpose, our study aims to identify predictors for the occurrence of preoperative and postoperative seizures in meningioma. Neurosurgical tumor resection was demonstrated as an effective treatment of seizures in meningioma patients but is also associated with a moderate risk of new-onset seizures after surgery. The present study identified several independent predictors for seizures in meningioma that could contribute to improved seizure treatment and a deeper understanding of the occurrence of seizures in meningioma patients. Seizure is a common symptom of meningioma that has a major impact on patients' quality of life. The purpose of this study was to identify predictive factors for the occurrence of preoperative and postoperative seizures. The data of patients with resection of histologically confirmed meningioma at University Hospital Leipzig from 2009 to 2018 were retrospectively examined. Univariate and multivariate logistic regression analyses of different factors influencing seizure outcome were performed. The male gender was identified as an independent positive predictor for preoperative seizures (odds ratio [OR] 1.917 [95% confidence interval {CI} 1.044–3.521], p = 0.036), whereas headache (OR 0.230 [95% CI 0.091–0.582], p = 0.002) and neurological deficits (OR 0.223; [95% CI 0.121–0.410], p < 0.001) were demonstrated to be negative predictive factors. Sensorimotor deficit after surgery (OR 4.490 [95% CI 2.231–9.037], p < 0.001) was found to be a positive predictor for the occurrence of postoperative seizures. The identified predictors for the occurrence of seizures in meningioma can contribute to improving seizure treatment and patients' quality of life. [ABSTRACT FROM AUTHOR]

Simple Summary: Preoperative risk prediction prior to oncologic esophagectomy is crucial for assisting surgeons in accurate patient selection and patients in their informed decision making. A new risk stratification tool, the IESG prediction model, was recently introduced, categorizing patients into different risk levelsMachine learning is a subfield of artificial intelligence and may allow for a more accurate identification of patients at risk. Therefore, we evaluated the IESG risk model and compared its performance with ML models. We found that the IESG risk model provided an overall adequate risk estimation. However, ML showed better results in the accurate risk stratification of patients, demonstrating its potential as a novel and powerful approach for future patient assessment. Risk prediction prior to oncologic esophagectomy is crucial for assisting surgeons and patients in their joint informed decision making. Recently, a new risk prediction model for 90-day mortality after esophagectomy using the International Esodata Study Group (IESG) database was proposed, allowing for the preoperative assignment of patients into different risk categories. However, given the non-linear dependencies between patient- and tumor-related risk factors contributing to cumulative surgical risk, machine learning (ML) may evolve as a novel and more integrated approach for mortality prediction. We evaluated the IESG risk model and compared its performance to ML models. Multiple classifiers were trained and validated on 552 patients from two independent centers undergoing oncologic esophagectomies. The discrimination performance of each model was assessed utilizing the area under the receiver operating characteristics curve (AUROC), the area under the precision–recall curve (AUPRC), and the Matthews correlation coefficient (MCC). The 90-day mortality rate was 5.8%. We found that IESG categorization allowed for adequate group-based risk prediction. However, ML models provided better discrimination performance, reaching superior AUROCs (0.64 [0.63–0.65] vs. 0.44 [0.32–0.56]), AUPRCs (0.25 [0.24–0.27] vs. 0.11 [0.05–0.21]), and MCCs (0.27 ([0.25–0.28] vs. 0.15 [0.03–0.27]). Conclusively, ML shows promising potential to identify patients at risk prior to surgery, surpassing conventional statistics. Still, larger datasets are needed to achieve higher discrimination performances for large-scale clinical implementation in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate mpMRI is currently the most widely used image diagnosis approach to detect prostate cancer, while the PI-RADS system was developed to standardize and improve the accuracy of suspicious lesion identification on MRI. However, there still remain several limitations including inter-individual inconsistencies and naked-eye insufficiency. This study aims to apply AI technology to image interpretation to enhance diagnostic efficiency and explore the use of T2-weighted image-based stimulated biopsy in predicting prostate cancer (PCa). Using 820 lesions from The Cancer Imaging Archive database and 83 lesions from Hong Kong Queen Mary Hospital, we constructed 18 machine-learning models based on three algorithms and conducted both internal and external validation. We found that the logistic regression-based model provides additional diagnostic value to the PI-RADS in predicting PCa. Background: Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. Method: A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. Results: All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. Conclusions: The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa. [ABSTRACT FROM AUTHOR]

Simple Summary: This study introduces a deep learning model designed to predict the 5-year recurrence risk of lung adenocarcinoma based on histopathology images. Using a dataset of 189 patients with 455 histopathology slides, our model demonstrated superior performance in risk stratification, achieving a hazard ratio of 2.29 (95% CI: 1.69–3.09, p < 0.005). This outperforms several existing deep learning methods, showcasing the potential of deep learning in automatically predicting lung adenocarcinoma recurrence risk. The superior performance of this model underscores the potential for deep learning models to be integrated into clinical workflows for more accurate and automated risk assessment in lung adenocarcinoma. This could lead to more personalized treatment strategies and better patient outcomes. Lung cancer is the leading cause of cancer-related death in the United States. Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer that can be treated with resection. While resection can be curative, there is a significant risk of recurrence, which necessitates close monitoring and additional treatment planning. Traditionally, microscopic evaluation of tumor grading in resected specimens is a standard pathologic practice that informs subsequent therapy and patient management. However, this approach is labor-intensive and subject to inter-observer variability. To address the challenge of accurately predicting recurrence, we propose a deep learning-based model to predict the 5-year recurrence of LUAD in patients following surgical resection. In our model, we introduce an innovative dual-attention architecture that significantly enhances computational efficiency. Our model demonstrates excellent performance in recurrent risk stratification, achieving a hazard ratio of 2.29 (95% CI: 1.69–3.09, p < 0.005), which outperforms several existing deep learning methods. This study contributes to ongoing efforts to use deep learning models for automatically learning histologic patterns from whole slide images (WSIs) and predicting LUAD recurrence risk, thereby improving the accuracy and efficiency of treatment decision making. [ABSTRACT FROM AUTHOR]

Objective: To compare the Guy’s, S.T.O.N.E, and CROES nomograms for predicting stone-free status in patients who underwent percutaneous nephrolithotomy for renal stones. Materials and Methods: The data of 1114 patients who underwent percutaneous nephrolithotomy for renal calculi between 11/2008 and 08/2018 in our clinic were retrospectively reviewed. Various parameters evaluated by preoperative computed tomography and the scoring systems of the patients and postoperative stone-free status were compared. Results: Out of 1000 patients who met the study criteria. Gender, body mass index, and stone density were not statistically different between the group with residual stones and the stone-free group. However, stone size, number of renal accesses, duration of fluoroscopy usage, duration of operation, number of stones, and complication rate were significantly higher in the group with residual stones than in the stone-free group. A statistically significant correlation was found between the postoperative stone-free rate and scoring systems. The applicability and preoperative prediction ability of all three systems were evaluated by receiver operating characteristic analysis. The area under the curve (AUC) was detected in the Guy’s, CROES, and S.T.O.N.E scoring system (AUC: 0.642, 0.665, 0.592 respectively). Conclusion: In this study, where the perioperative and postoperative results of 1000 patients were evaluated, we found that all three scoring systems could predict the stone-free rate. “We believe that the use of these scoring systems before surgery can guide surgeons.” [ABSTRACT FROM AUTHOR]

The six‐minute walking test (6MWT) is commonly used to measure functional capacity in field settings, primarily through the distance covered. This study aims to establish reference curves for the six‐minute walking distance (6MWD) and peak heart rate (PHR) and develop a predictive equation for cardiovascular capacity in Tunisian children and adolescents. A total of 1501 participants (706 boys and 795 girls), aged 10–18 years, were recruited from schools in Tunisia. The Lambda (L), Mu (M), and Sigma (S) methods (LMS method) were employed to develop smoothed percentile curves for 6MWD and PHR. Multivariate linear regression was utilized to formulate a prediction equation for 6MWD. Smoothed percentiles (3rd, 10th, 25th, 50th, 75th, 90th, and 97th) for 6MWD and PHR were presented with age. All variables showed a strong positive correlation (p < 0.001) with a six‐minute walking distance (r ranged from 0.227 to 0.558 for girls and from 0.309 to 0.610 for boys), except resting heart rate, which showed a strong negative correlation (girls: r = −0.136; boys: r = −0.201; p < 0.001). Additionally, PHR showed a weak correlation (p > 0.05). The prediction equations, based on age as the primary variable, were established for both genders. For boys: 6MWD = 66.181 + 38.142 × Age (years) (R2 = 0.372; Standard Error of Estimate (SEE) = 122.13), and for girls: 6MWD = 105.535 + 28.390 × Age (years) (R2 = 0.312; SEE = 103.66). The study provides normative values and predictive equations for 6MWD and PHR in Tunisian children and adolescents. These findings offer essential tools for identifying, monitoring, and interpreting cardiovascular functional deficits in clinical and research settings. Highlights: Established reference curves for six‐minute walking distance (6MWD) and peak heart rate (PHR) from the data of 1501 children and adolescents aged 10–18 years were recruited from multicenter schools in Tunisia.Developed predictive equations for 6MWD based on age using multivariate linear regression.Provides normative values and predictive equations for 6MWD and PHR, offering valuable tools for identifying and monitoring cardiovascular functional deficits in clinical and research settings. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) increases the risk complications in addition to mortality. Quantifying the risk of complications using artificial intelligence could be a way to design comprehensive patient healthcare programs. Objective: Predicting the probability of macro and microvascular complications in patients with DM through Machine Learning. Methods: Retrospective cohort study. Based on an outpatient follow-up program for diabetic patients, 64,081 records and 287 variables were identified, with highly unbalanced data. Predictive models for chronic kidney disease (CKD), lower extremity amputation (LEA), coronary heart disease (CHD), and early mortality (MOR) were developed. An exhaustive computational method was conducted to find the best combination between machine learning (ML) algorithms and sampling method. Results: The best model was determined by assessing its performance through the heuristics obtained from a comprehensive analysis of the accuracy and F1 values for ML, sampling, and dataset. Regarding each complication, 99.9% accuracy was obtained for LEA, 94.3% for CHD, 97.4% for MOR, and 98.8% for CKD. F1 was assessed to identify false positives, with 84.5% for CKD, 63.6% for MOR, 46.2% for LEA, and 44.8% for CHD. Conclusions: This ML model can be applied to predict CHD, CKD, and MOR. The success of ML predictions lies in the clinical definition of initial variables and their simplification for obtaining variables based on which the algorithms can identify patients that are likely to develop a complication. For clinical application of this system, it is necessary to assess the cross performance of metrics, as found here (accuracy higher 95% and F1-Score higher than 80%). [ABSTRACT FROM AUTHOR]

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. [ABSTRACT FROM AUTHOR]

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methods: A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. Results: The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Conclusions: Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. [ABSTRACT FROM AUTHOR]

Introduction: A dynamic molecular biomarker that can identify early efficacy of immune checkpoint inhibitor (ICI) therapy remains an unmet clinical need. Here we evaluate if a novel circulating tumor DNA (ctDNA) assay, xM, used for treatment response monitoring (TRM), that quantifies changes in ctDNA tumor fraction (TF), can predict outcome benefits in patients treated with ICI alone or in combination with chemotherapy in a real-world (RW) cohort. Methods: This retrospective study consisted of patients with advanced cancer from the Tempus de-identified clinical genomic database who received longitudinal liquid-based next-generation sequencing. Eligible patients had a blood sample ≤ 40 days prior to the start of ICI initiation and an on-treatment blood sample 15–180 days post ICI initiation. TF was calculated via an ensemble algorithm that utilizes TF estimates derived from variants and copy number information. Patients with molecular response (MR) were defined as patients with a ≥ 50% decrease in TF between tests. In the subset of patients with rw-imaging data between 2 and 18 weeks of ICI initiation, the predictive value of MR in addition to rw-imaging was compared to a model of rw-imaging alone. Results: The evaluable cohort (N = 86) was composed of 14 solid cancer types. Patients received either ICI monotherapy (38.4%, N = 33) or ICI in combination with chemotherapy (61.6%, N = 53). Patients with MR had significantly longer rw-overall survival (rwOS) (hazard ratio (HR) 0.4, P = 0.004) and rw-progression free survival (rwPFS) (HR 0.4, P = 0.005) than patients with molecular non-response (nMR). Similar results were seen in the ICI monotherapy subcohort; HR 0.2, P = 0.02 for rwOS and HR 0.2, P = 0.01 for rwPFS. In the subset of patients with matched rw-imaging data (N = 51), a model incorporating both MR and rw-imaging was superior in predicting rwOS than rw-imaging alone (P = 0.02). Conclusions: xM used for TRM is a novel serial quantitative TF algorithm that can be used clinically to evaluate ICI therapy efficacy. [ABSTRACT FROM AUTHOR]

Background: High lactate to albumin ratio (LAR) has been reported to be associated to with poor prognosis in patients admitted to the intensive care unit (ICU). However, its role in predicting in-hospital mortality in AF patients admitted to ICU has not been explored. Methods: The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to retrieve information on patients who had been diagnosed with AF. X-tile software was utilized to determine the optimal cut-off LAR. Area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA) were conducted to assess the prediction performance of LAR for in-hospital mortality. Results: Finally, 8,287 AF patients were included and 1,543 death (18.6%) occurred. The optimal cut-off value of LAR is 0.5. Patients in lower LAR (< 0.5) group showed a better in-hospital survival compared to patients in higher LAR (≥ 0.5) group (HR: 2.67, 95%CI:2.39–2.97, P < 0.001). A nomogram for in-hospital mortality in patients with AF was constructed based on multivariate Cox analysis including age, CCI, β blockers usage, APSIII, hemoglobin and LAR. This nomogram exhibited excellent discrimination and calibration abilities in predicting in-hospital mortality for critically ill AF patients. Conclusion: LAR, as a readily available biomarker, can predict in-hospital mortality in AF patients admitted to the ICU. The nomogram that combined LAR with other relevant variables performed exceptionally well in terms of predicting in-hospital mortality. [ABSTRACT FROM AUTHOR]

Background: Multiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections. Methods: Buffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study ("Glue Grant") multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction. Results: The LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890–0.987] (training) and 0.833 [0.699–0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997–1.000] (training) and 0.907 [0.816–0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884–0.985] (training) and 0.836 [0.703–0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex—0.649 [0.537–0.762] (training) and 0.493 [0.301–0.685] (test); ISS + sex—0.630 [0.516–0.744] (training) and 0.482 [0.293–0.670] (test); NISS + sex—0.651 [0.540–0.763] (training) and 0.525 [0.335–0.714] (test)). Conclusions: The accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions. [ABSTRACT FROM AUTHOR]

Background: Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict ADspecific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Methods: Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Results: Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Conclusion: Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D. [ABSTRACT FROM AUTHOR]

Background: In sepsis, the relationship between lymphocyte counts and patient outcomes is complex. Lymphocytopenia and lymphocytosis significantly influence survival, illustrating the dual functionality of lymphocytes in responding to infections. This study investigates this complex interaction, focusing on how variations in lymphocyte counts correlate with all-cause hospital mortality among sepsis patients. Methods: This retrospective cohort study analyzed data from two extensive critical care databases: the Medical Information Mart for Intensive Care IV 2.0 (MIMIC-IV 2.0) from Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the eICU Collaborative Research Database (eICU-CRD), which was Multi-center database from over 200 hospitals across the United States conducted by Philips eICU Research Institute. We included adult patients aged 18 years and older who met the Sepsis-3 criteria, characterized by documented or suspected infection and a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. Sepsis patients were categorized into quartiles based on lymphocyte counts. The primary outcome was all-cause mortality in the hospital, with 90 and 60-day all-cause mortality as the secondary outcomes. Univariable and multivariable Cox proportional hazard regressions were utilized to assess lymphocyte counts' impact on hospital mortality. An adjusted restricted cubic spline (RCS) analysis was performed to elucidate this relationship further. Subgroup analyses were also conducted to explore the association across various comorbidity groups among sepsis and septic shock patients. Results: Our study included 37,054 patients, with an observed in-hospital mortality rate of 16.6%. Univariable and multivariable Cox proportional hazard regression models showed that lymphocyte counts were independently associated with in-hospital mortality (HR = 1.04, P < 0.01; HR = 1.06, P < 0.01). RCS regression analysis revealed a U-shaped relationship between lymphocyte levels and hospital mortality risk in sepsis and septic shock patients (P for overall < 0.001, P for nonliner < 0.01; P for overall = 0.002, P for nonliner = 0.014). Subgroup analyses revealed that elevated lymphocyte counts correlated with increased hospital mortality among sepsis patients with liver disease and requiring renal replacement therapy (P for overall = 0.021, P for nonliner = 0.158; P for overall = 0.025, P for nonliner = 0.759). These findings suggest that lymphocytes may have enhanced prognostic value in specific subsets of critically ill sepsis patients. Conclusion: Our findings demonstrate that lymphocyte counts are a significant independent predictor of hospital mortality in sepsis and septic shock patients. We observed a U-shaped association between lymphocyte levels and mortality risk, indicating that high and low counts are linked to increased mortality. This result highlights the complex role of lymphocytes in sepsis outcomes and suggests the need for further investigation into the underlying mechanisms and potential therapeutic approaches. Integrating lymphocyte count assessment into risk stratification algorithms and clinical decision support tools could enhance the early identification of high-risk sepsis patients. [ABSTRACT FROM AUTHOR]

Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval in patients with high-risk genotypes. We developed a deep learning approach combining electrocardiogram (ECG) waveform and electronic health record data to assess whether patients had pathogenic variants causing LQTS. We defined patients with high-risk genotypes as having ≥1 pathogenic variant in one of the LQTS-susceptibility genes. We trained the model using data from United Kingdom Biobank (UKBB) and then fine-tuned in a racially/ethnically diverse cohort using Mount Sinai BioMe Biobank. Following group-stratified 5-fold splitting, the fine-tuned model achieved area under the precision-recall curve of 0.29 (95% confidence interval [CI] 0.28–0.29) and area under the receiver operating curve of 0.83 (0.82–0.83) on independent testing data from BioMe. Multimodal fusion learning has promise to identify individuals with pathogenic genetic mutations to enable patient prioritization for further work up. [ABSTRACT FROM AUTHOR]

Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea and colitis. It is recognized as one of the most significant hospital-acquired infections. Although CDI can develop severe complications and spores of Clostridioides difficile can be transmitted by the fecal-oral route, CDI is occasionally overlooked in clinical settings. Thus, it is necessary to monitor high CDI risk groups, particularly those undergoing antibiotic treatment, to prevent complications and spread. We developed and validated a deep learning-based model to predict the occurrence of CDI within 28 days after starting antibiotic treatment using longitudinal electronic health records. For each patient, timelines of vital signs and laboratory tests with a 35-day monitoring period and a patient information vector consisting of age, sex, comorbidities, and medications were constructed. Our model achieved the prediction performance with an area under the receiver operating characteristic curve of 0.952 (95% CI: 0.932–0.973) in internal validation and 0.972 (95% CI: 0.968–0.975) in external validation. Platelet count and body temperature emerged as the most important features. The risk score, the output value of the model, exhibited a consistent increase in the CDI group, while the risk score in the non-CDI group either maintained its initial value or decreased. Using our CDI prediction model, high-risk patients requiring symptom monitoring can be identified. This could help reduce the underdiagnosis of CDI, thereby decreasing transmission and preventing complications. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. Methods: A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong's algorithm. Models were validated externally using an international database. Results: Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07–2.05), 0.81 (0.72–0.90), 9.13 (3.29–28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2–9.8] versus 9.6 [6.8–12.9], p < 0.001, respectively). In the external validation population, the area under the curve (AUC) of the ROX index was 0.683 (accuracy 63%), the AIC model showed an AUC of 0.703 (accuracy 69%), and the lowest AIC model with significant p values had an AUC of 0.725 (accuracy 79%). Conclusions: In the development population of ICU patients with COVID-19, SOFA score, SpO2, and respiratory effort signals predicted the need for IMV better than the ROX index. In the external validation population, although the AUCs did not differ significantly, the accuracy was higher when using SOFA score, SpO2, and respiratory effort signals compared to the ROX index. This suggests that these variables may be more useful in predicting the need for IMV in ICU patients with COVID-19. ClinicalTrials.gov identifier: NCT05663528. [ABSTRACT FROM AUTHOR]

Background: The Achilles tendon is the body's strongest and largest tendon. It is commonly injured, particularly among athletes, accounting for a significant portion of serious tendon injuries. Several factors play a precipitating role in increasing the risk of these injuries. Objective: Our objective is to derive and validate a risk calculator for the prediction of incidence of any complication following Achilles tendon repair. Methods: We used de-identified data from the American College of Surgeons' National Surgical Quality Improvement Project (NSQIP) database from 2005 to 2021. It comprises 7010 individuals who had undergone Achilles tendon rupture repair. Demographic and risk factors information was collected. To develop the calculator, the sample was divided into a derivation cohort (40%) and a validation cohort (60%). Multivariate logistic regression was used for statistical analysis, and a risk calculator for incidence of any complication was derived from the derivation cohort and validated on the remaining 60% of the sample. Patients with missing data were excluded, and the significance level was set at p < 0.05. Results: We analyzed the derivation cohort of 2245 individuals who underwent Achilles tendon repair surgery between 2005 and 2021, with a 5.5% overall complication. Multivariate logistic regression identified anesthesia type, ASA classification, certain co-morbidities (pre-operative dialysis and medication-requiring hypertension), and wound classification as significant predictors of complications. The developed risk calculator model had an area under the curve (AUC) of 0.685 in the derivation cohort and 0.655 in the validation cohort, surpassing the widely used and validated modified frailty index. A cut-off score threshold of 0.06 was established using Youden's index to dichotomize individuals into low and high risk for developing any postoperative complications. Conclusion: Our risk calculator includes factors that most significantly affect the incidence of any complication following Achilles tendon repair. [ABSTRACT FROM AUTHOR]

Purpose: To identify the key risk factors for venous thromboembolism (VTE) in urological inpatients based on the Caprini scale using an interpretable machine learning method. Methods: VTE risk data of urological inpatients were obtained based on the Caprini scale in the case hospital. Based on the data, the Boruta method was used to further select the key variables from the 37 variables in the Caprini scale. Furthermore, decision rules corresponding to each risk level were generated using the rough set (RS) method. Finally, random forest (RF), support vector machine (SVM), and backpropagation artificial neural network (BPANN) were used to verify the data accuracy and were compared with the RS method. Results: Following the screening, the key risk factors for VTE in urology were "(C1) Age," "(C2) Minor Surgery planned," "(C3) Obesity (BMI > 25)," "(C8) Varicose veins," "(C9) Sepsis (< 1 month)," (C10) "Serious lung disease incl. pneumonia (< 1month) " (C11) COPD," "(C16) Other risk," "(C18) Major surgery (> 45 min)," "(C19) Laparoscopic surgery (> 45 min)," "(C20) Patient confined to bed (> 72 h)," "(C18) Malignancy (present or previous)," "(C23) Central venous access," "(C31) History of DVT/PE," "(C32) Other congenital or acquired thrombophilia," and "(C34) Stroke (< 1 month." According to the decision rules of different risk levels obtained using the RS method, "(C1) Age," "(C18) Major surgery (> 45 minutes)," and "(C21) Malignancy (present or previous)" were the main factors influencing mid- and high-risk levels, and some suggestions on VTE prevention were indicated based on these three factors. The average accuracies of the RS, RF, SVM, and BPANN models were 79.5%, 87.9%, 92.6%, and 97.2%, respectively. In addition, BPANN had the highest accuracy, recall, F1-score, and precision. Conclusions: The RS model achieved poorer accuracy than the other three common machine learning models. However, the RS model provides strong interpretability and allows for the identification of high-risk factors and decision rules influencing high-risk assessments of VTE in urology. This transparency is very important for clinicians in the risk assessment process. [ABSTRACT FROM AUTHOR]

In this study, we evaluated and forecasted the cumulative opportunities for residents to access radiotherapy services in Cali, Colombia, while accounting for traffic congestion, using a new people-centred methodology with an equity focus. Furthermore, we identified 1–2 optimal locations where new services would maximise accessibility. We utilised open data and publicly available big data. Cali is one of South America's cities most impacted by traffic congestion. Methodology: Using a people-centred approach, we tested a web-based digital platform developed through an iterative participatory design. The platform integrates open data, including the location of radiotherapy services, the disaggregated sociodemographic microdata for the population and places of residence, and big data for travel times from Google Distance Matrix API. We used genetic algorithms to identify optimal locations for new services. We predicted accessibility cumulative opportunities (ACO) for traffic ranging from peak congestion to free-flow conditions with hourly assessments for 6–12 July 2020 and 23–29 November 2020. The interactive digital platform is openly available. Primary and secondary outcomes: We present descriptive statistics and population distribution heatmaps based on 20-min accessibility cumulative opportunities (ACO) isochrones for car journeys. There is no set national or international standard for these travel time thresholds. Most key informants found the 20-min threshold reasonable. These isochrones connect the population-weighted centroid of the traffic analysis zone at the place of residence to the corresponding zone of the radiotherapy service with the shortest travel time under varying traffic conditions ranging from free-flow to peak-traffic congestion levels. Additionally, we conducted a time-series bivariate analysis to assess geographical accessibility based on economic stratum. We identify 1–2 optimal locations where new services would maximize the 20-min ACO during peak-traffic congestion. Results: Traffic congestion significantly diminished accessibility to radiotherapy services, particularly affecting vulnerable populations. For instance, urban 20-min ACO by car dropped from 91% of Cali's urban population within a 20-min journey to the service during free-flow traffic to 31% during peak traffic for the week of 6–12 July 2020. Percentages represent the population within a 20-min journey by car from their residence to a radiotherapy service. Specific ethnic groups, individuals with lower educational attainment, and residents on the outskirts of Cali experienced disproportionate effects, with accessibility decreasing to 11% during peak traffic compared to 81% during free-flow traffic for low-income households. We predict that strategically adding sufficient services in 1–2 locations in eastern Cali would notably enhance accessibility and reduce inequities. The recommended locations for new services remained consistent in both of our measurements. These findings underscore the significance of prioritising equity and comprehensive care in healthcare accessibility. They also offer a practical approach to optimising service locations to mitigate disparities. Expanding this approach to encompass other transportation modes, services, and cities, or updating measurements, is feasible and affordable. The new approach and data are particularly relevant for planning authorities and urban development actors. [ABSTRACT FROM AUTHOR]

Simple Summary: Inflammation-based prognostic markers have been recognized as useful in a range of oncologic conditions. Given the high mortality rate associated with pancreatic ductal adenocarcinoma (PDAC), the demand for accurate prognostic tools is significant. Nonetheless, the literature still shows conflicting results regarding the effectiveness of the various available scoring systems. Our data confirm that NLR and PNI are useful biomarkers regarding overall survival in PDAC. Especially in advanced disease stages, they seem to be of considerable benefit as easily accessible tools for prognosis prediction. Purpose: Prognostic inflammation-based parameters have been reported as useful tools in various oncologic diseases. Pancreatic ductal adenocarcinoma (PDAC) is characterized by a high mortality rate, making reliable prognostic markers highly desirable. However, there is still inconsistency in the literature regarding the efficacy of the different available scores. Methods: A total of 207 patients, who underwent primary resection of PDAC from January 2000 to December 2018 at the University Hospital of Erlangen, were included in this retrospective single-center study. Different biomarkers, including the preoperative neutrophil–lymphocyte ratio (NLR), the platelet–lymphocyte ratio (PLR), the c-reactive protein (CRP)–albumin ratio (CAR), the lymphocyte–CRP ratio (LCR), the prognostic nutritional index (PNI) and the modified Glasgow prognostic score (mGPS) were analyzed for their ability to predict overall survival (OS). Results: In our cohort, the median overall survival was 20.7 months. Among the investigated biomarkers, NLR and PNI were identified as independent prognostic markers (Hazard Ratio (HR) 1.6 (1.0–2.5), p = 0.048 and HR 0.6 (0.4–0.9), p = 0.018), whereas PLR, CAR, LCR and mGPS did not reach significance in the multivariate analysis. Subgroup analysis revealed that the prognostic value of NLR and PNI is particularly evident in locally advanced tumor stages (pT3/4 and pN+). Conclusions: The NLR and PNI could serve as valuable tools for estimating prognosis in patients with PDAC undergoing pancreatic resection in curative intention, especially in locally advanced tumor stages. However, conflicting results in the current literature highlight the need for further prospective studies to validate these findings. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to predict the Length of hospital Stay (LoS) after laparoscopic right hemicolectomy for colon cancer using machine learning techniques. Accurately forecasting LoS is crucial for improving patient care and hospital resource management. The researchers utilized data from two large Italian studies, CoDIG 1 and CoDIG 2, to train and validate various machine learning models. The Random Forest (RF) algorithm demonstrated the best internal performance, while the Support Vector Machine (SVM) outperformed in external validation. Key factors influencing LoS included the use of fast-track protocols, type of anastomosis, and drainage. These findings could help tailor postoperative care and optimize hospital resources, ultimately enhancing patient outcomes and operational efficiency. The evolution of laparoscopic right hemicolectomy, particularly with complete mesocolic excision (CME) and central vascular ligation (CVL), represents a significant advancement in colon cancer surgery. The CoDIG 1 and CoDIG 2 studies highlighted Italy's progressive approach, providing useful findings for optimizing patient outcomes and procedural efficiency. Within this context, accurately predicting postoperative length of stay (LoS) is crucial for improving resource allocation and patient care, yet its determination through machine learning techniques (MLTs) remains underexplored. This study aimed to harness MLTs to forecast the LoS for patients undergoing right hemicolectomy for colon cancer, using data from the CoDIG 1 (1224 patients) and CoDIG 2 (788 patients) studies. Multiple MLT algorithms, including random forest (RF) and support vector machine (SVM), were trained to predict LoS, with CoDIG 1 data used for internal validation and CoDIG 2 data for external validation. The RF algorithm showed a strong internal validation performance, achieving the best performances and a 0.92 ROC in predicting long-term stays (more than 5 days). External validation using the SVM model demonstrated 75% ROC values. Factors such as fast-track protocols, anastomosis, and drainage emerged as key predictors of LoS. Integrating MLTs into predicting postoperative LOS in colon cancer surgery offers a promising avenue for personalized patient care and improved surgical management. Using intraoperative features in the algorithm enables the profiling of a patient's stay based on the planned intervention. This issue is important for tailoring postoperative care to individual patients and for hospitals to effectively plan and manage long-term stays for more critical procedures. [ABSTRACT FROM AUTHOR]

Simple Summary: Three-dimensional (3D) telomere analysis using TeloView v 2.2 has shown promise in quantifying genomic instability and predicting therapy response. In a study of 156 Classic Hodgkin's lymphoma (cHL), we identified significant 3D telomere parameters that predict patient outcomes. A predictive model using four 3D telomere parameters, including the proportion of t-stumps (very short telomeres), achieved an area under curve (AUC) of 0.83, with a sensitivity and specificity of 0.82 and 0.78, respectively. Classic Hodgkin's lymphoma (cHL) is a curable cancer with a disease-free survival rate of over 10 years. Over 80% of diagnosed patients respond favorably to first-line chemotherapy, but few biomarkers exist that can predict the 15–20% of patients who experience refractory or early relapsed disease. To date, the identification of patients who will not respond to first-line therapy based on disease staging and traditional clinical risk factor analysis is still not possible. Three-dimensional (3D) telomere analysis using the TeloView® software platform has been shown to be a reliable tool to quantify genomic instability and to inform on disease progression and patients' response to therapy in several cancers. It also demonstrated telomere dysfunction in cHL elucidating biological mechanisms related to disease progression. Here, we report 3D telomere analysis on a multicenter cohort of 156 cHL patients. We used the cohort data as a training data set and identified significant 3D telomere parameters suitable to predict individual patient outcomes at the point of diagnosis. Multivariate analysis using logistic regression procedures allowed for developing a predictive scoring model using four 3D telomere parameters as predictors, including the proportion of t-stumps (very short telomeres), which has been a prominent predictor for cHL patient outcome in a previously published study using TeloView® analysis. The percentage of t-stumps was by far the most prominent predictor to identify refractory/relapsing (RR) cHL prior to initiation of adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD) therapy. The model characteristics include an AUC of 0.83 in ROC analysis and a sensitivity and specificity of 0.82 and 0.78 respectively. [ABSTRACT FROM AUTHOR]

Background: While growing evidence suggests a relationship between migraine and cardiovascular disease, the genetic evidence for a causal relationship between migraine and cardiovascular disease is still scarce. Investigating the causal association between migraine and cardiovascular disease is vital. Methods: We carried out a bidirectional Mendelian randomization (MR) study including discovery samples and replication samples using publicly available genome-wide association study (GWAS) summary datasets and stringent screening instrumental variables. Four different MR techniques—Inverse variance weighted (IVW), MR ‒Egger, weighted median, and weighted mode—as well as various sensitivity analyses—Cochran's Q, IVW radial, leave-one-out (LOO), and MR-PRESSO—were utilized to investigate the causal relationship between cardiovascular disease and migraine. Results: The protective causal effects of genetically predicted migraine on coronary artery disease (OR, 0.881; 95% CI 0.790–0.982; p = 0.023) and ischemic stroke (OR, 0.912; 95% CI 0.854–0.974; p = 0.006) were detected in forward MR analysis but not in any other cardiovascular disease. Consistently, we also discovered protective causal effects of coronary atherosclerosis (OR, 0.865; 95% CI 0.797–0.940; p = 0.001) and myocardial infarction (OR, 0.798; 95% CI 0.668–0.952; p = 0.012) on migraine in reverse MR analysis. Conclusion: We found a potential protective effect of migraine on coronary artery disease and ischemic stroke and a potential protective effect of coronary atherosclerosis and myocardial infarction on migraine. We emphasised epidemiological and genetic differences and the need for long-term safety monitoring of migraine medications and future research to improve cardiovascular outcomes in migraine patients. [ABSTRACT FROM AUTHOR]

Objective and background: The early detection of diabetic ketoacidosis (DKA) in patients with type 2 diabetes (T2D) plays a crucial role in enhancing outcomes. We developed a nomogram prediction model for screening DKA in T2D patients. At the same time, the input variables were adjusted to reduce misdiagnosis. Methods: We obtained data on T2D patients from Mimic-IV V0.4 and Mimic-III V1.4 databases. A nomogram model was developed using the training data set, internally validated, subjected to sensitivity analysis, and further externally validated with data from T2D patients in Aviation General Hospital. Results: Based on the established model, we analyzed 1885 type 2 diabetes patients, among whom 614 with DKA. We further additionally identified risk factors for DKA based on literature reports and multivariate analysis. We identified age, glucose, chloride, calcium, and urea nitrogen as predictors in our model. The logistic regression model demonstrated an area under the curve (AUC) of 0.86 (95%CI: 0.85–0.90]. To validate the model, we collected data from 91 T2D patients, including 15 with DKA, at our hospital. The external validation of the model yielded an AUC of 0.68 (95%CI: 0.67–0.70). The calibration plot confirmed that our model was adequate for predicting patients with DKA. The decision-curve analysis revealed that our model offered net benefits for clinical use. Conclusions: Our model offers a convenient and accurate tool for predicting whether DKA is present. Excluding input variables that may potentially hinder patient compliance increases the practical application significance of our model. [ABSTRACT FROM AUTHOR]

Background: The Lipid Accumulation Product (LAP) is a measure that indicates excessive fat accumulation in the body. LAP has been the focus of research in epidemiological studies aimed at forecasting chronic and metabolic diseases. This study aimed to evaluate the association between LAP and type 2 diabetes mellitus (T2DM) among adults in western Iran. Methods: The study involved 9,065 adults who participated in the initial phase of the Ravansar non-communicable diseases study (RaNCD) cohort. To investigate the association between LAP and T2DM, multiple logistic regressions were employed. Additionally, the receiver operating characteristic (ROC) curve was used to evaluate LAP's predictive ability concerning T2DM. Results: The participants had an average age of 47.24 ± 8.27 years, comprising 49.30% men and 50.70% women. The mean LAP was 53.10 ± 36.60 for the healthy group and 75.51 ± 51.34 for the diabetic group (P < 0.001). The multiple regression analysis revealed that the odds of T2DM in the second quartile of LAP were 1.69 (95% CI: 1.25, 2.29) times greater than in the first quartile. Furthermore, the odds in the third and fourth quartiles were 2.67 (95% CI: 2.01, 3.55) and 3.73 (95% CI: 2.83, 4.92) times higher, respectively. The ROC analysis for predicting T2DM showed that the LAP index had an area under the curve (AUC) of 0.66 (95% CI: 0.64, 0.68). Conclusion: A strong association was identified between elevated LAP levels and T2DM in the adult population of western Iran. LAP is recommended as a potential tool for screening diabetes susceptibility. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, with severe cases significantly increasing the risk of complications and long-term mortality. The Geriatric Trauma Outcome Score (GTOS), based on age, injury severity, and transfusion need, has been validated for predicting mortality in older trauma patients, but its utility in predicting mortality for TBI patients remains unexplored. Methods: This retrospective study included 5543 adult trauma patients with isolated moderate to severe TBI, defined by head Abbreviated Injury Scale (AIS) scores of ≥ 3, from 1998 to 2021. GTOS was calculated with the following formula: age + (Injury Severity Score × 2.5) + 22 (if transfused within 24 h). The area under the receiver operating characteristic curve (AUROC) assessed GTOS's ability to predict mortality. The optimal GTOS cutoff value was determined using Youden's index. Mortality rates were compared between high- and low-GTOS groups, separated by the optimal GTOS cutoff value, including a propensity score-matched analysis adjusting for baseline characteristics. Results: Among 5543 patients, mortality was 8.3% (462 deaths). Higher mortality is correlated with male sex, older age, higher GTOS, and comorbidities like hypertension, coronary artery disease, and end-stage renal disease. The optimal GTOS cut-off for mortality prediction was 121.5 (AUC = 0.813). Even when the study population was matched by propensity score, patients with GTOS ≥121.5 had much higher odds of death (odds ratio 2.64, 95% confidence interval 1.93–3.61, p < 0.001) and longer hospital stays (mean 16.7 vs. 12.2 days, p < 0.001) than those with GTOS < 121.5. Conclusions: These findings support the idea that GTOS is a useful tool for risk stratification of in-hospital mortality in isolated moderate to severe TBI patients. However, we encourage further research to refine GTOS for better applicability in TBI patients. [ABSTRACT FROM AUTHOR]

This study examines whether health-related quality of life (HRQoL) scores differ due to the frequency of walking or physical activity (PA) throughout a week in diabetic patients in Korea. This population-based cross-sectional study used data from the 2018–2021 Korea National Health and Nutrition Examination Survey. The dependent variable was HRQoL scores as measured with EuroQol-5D (EQ-5D). The independent variables were defined as three types of PA: (1) walking; (2) moderate; and (3) vigorous. An estimated population size of 2,376,066 was included in this study. The mean (95% confidence interval (CI)) age of patients was 60.12 years (59.23, 60.81), and 53.0% were female. The mean (95% CI) of EQ-5D was 0.867 (0.857, 0.877). The majority of diabetic patients reported walking daily (39.05%, 95% CI; 36.28–41.81%), while a significant proportion did not engage in moderate (65.45%, 95% CI; 62.79–68.11%) or vigorous (78.38%, 95% CI; 73.02–77.73%) PA at all. After controlling for covariates, EQ-5D scores significantly increased when patients had walked once per week for at least 10 min in the Tobit regression model. The frequency of walking was the most significant predicting factor for better HRQoL in patients with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics. [ABSTRACT FROM AUTHOR]

Background: Oral aripiprazole exhibits favorable clinical efficacy and safety in the suppression of tics in children and adolescents with tic disorders. This study aims to evaluate and compare the cost-effectiveness of high-dose and low-dose aripiprazole in children and adolescents with tic disorders from the perspective of the Chinese healthcare system. Methods: A questionnaire survey was conducted on 146 patients with tic disorders, of whom 144 completed EQ-5D-Y and YGTSS. Four models were built to convert YGTSS onto EQ-5D-Y utility using two mapping algorithms. We constructed a decision tree model containing efficacy and safety to compare the cost-effectiveness of high-dose and low-dose aripiprazole based on our mapping function. Results: The GLM with model 1 (YGTSS total tic scores) was selected as the preferred function in our decision tree model. The base case cost-effectiveness analysis showed that compared to low-dose aripiprazole, high-dose aripiprazole improves effectiveness by 0.001QALYs and increases the overall cost by $197.99, resulting in an ICER of $174339.22 per QALY, which exceeds three times the gross domestic product per capita. Hence, high-dose aripiprazole is not likely to be a cost-effective option for child patients with tic disorders. One-way sensitivity analysis and probabilistic sensitivity analysis showed that these results is robust. Conclusion: On the basis of currently available data, low-dose aripiprazole may be a safe, effective, and economical dosage for children and adolescents with tic disorders. Limitations: The main limitation of our study is the lack of utility directly used for cost-effectiveness analysis. We obtained the utility of patients with tic disorders indirectly by the mapping function. This may introduce some bias and uncertainty. And it is a limitation to use the direct medical costs of Germany in our model. Although we converted it to the equivalent value of China using purchasing power parities, caution should be exercised when interpreting the results of this study. [ABSTRACT FROM AUTHOR]

Background: Postoperative urinary retention (POUR) is a common complication of anorectal surgery. This study was to determine the incidence of POUR in anorectal surgery for benign anorectal diseases, identify its risk factors, and establish a nomogram for prediction of POUR. Methods: A nested case–control study was conducted. The clinical data of patients were collected, and the incidence of POUR was analyzed. Univariate analysis was used to identify the risk factors associated with POUR, and multivariate logistic regression analysis was used to determine independent risk factors for POUR. A nomogram for the preoperative prediction of POUR using a logistic regression model was developed (n = 609). Results: The incidence of POUR after anorectal surgery for benign anorectal diseases was 19.05%. The independent risk factors for POUR were: female (P = 0.007); male with benign prostatic hyperplasia (BPH) (P = 0.001); postoperative visual analogue scale (VAS) score > 6 (P = 0.002); patient-controlled epidural analgesia (PCEA) (P = 0.016); and a surgery time > 30 min (P = 0.039). In the nomogram, BPH is the most important factor affecting the occurrence of POUR, followed by a postoperative VAS score > 6, PCEA, surgery time > 30 min, and sex has the least influence. Conclusion: For patients undergoing anorectal surgery for benign anorectal diseases, preventive measures can be taken to reduce the risk of POUR, taking into account the following risk factors: female or male with BPH, severe postoperative pain, PCEA, and surgery time > 30 min. Furthermore, we developed and validated an easy-to-use nomogram for preoperative prediction of POUR in anorectal surgery for benign anorectal diseases. Trial registration: China Clinical Trial Registry: ChiCTR2000039684, 05/11/2020. [ABSTRACT FROM AUTHOR]

Background: Periodontitis is associated with poor pregnancy outcomes, indicating periodontal health as an important health concern for pregnant women. Herein, this study identified risk indicators for periodontitis and developed a nomogram for predicting the risk of periodontitis in pregnancy by analyzing periodontitis and associated factors in pregnant women. Materials and methods: A community-based cross-sectional study was conducted by including 438 pregnant women at 10–36 gestational weeks from Langfang, China. Pregnant women were examined for periodontal status, and their demographic, socioeconomic, and oral health behavior data were collected. Potential influencing factors of periodontitis were analyzed with univariate and multivariate logistic regression analyses. A nomogram was developed, followed by the assessment of its validation and discriminatory abilities. Results: The prevalence of periodontitis was 59.8% in pregnant women. Periodontitis-associated variables in pregnant women were gestational age, non-first pregnancy, daily tooth brushing frequency of ≤ 1 before pregnancy, and annual frequency of periodontal treatment < 1 (including no periodontal treatment). The risk of periodontitis was positively associated with gestational age (OR = 1.28, 95% CI = 1.17–1.39, p < 0.001). Pregnancy history showed a strong positive association (OR = 6.57, 95% CI = 1.22–35.43, p = 0.03). Daily tooth brushing frequency before pregnancy was also positively associated with periodontitis (OR = 1.54, 95% CI = 1.03–2.79, p = 0.05). Additionally, the annual frequency of periodontal treatment exhibited a positive association, with higher odds observed for those with less frequent treatment (OR = 2.28, 95% CI = 1.25–4.14, p = 0.05; OR = 7.37, 95% CI = 3.04–22.06, p < 0.001). These four factors were used to develop a nomogram for predicting periodontitis in pregnant women. The area under the receiver operating characteristic curve of the nomogram was 0.855 and 0.831 in the training and testing cohorts, respectively, reflecting the superior prediction accuracy of the nomogram. The calibration curve and decision curve analysis demonstrated the good performance and net benefit of the nomogram. Conclusion: Risk factors for periodontitis in pregnant Chinese women include gestational age, non-first pregnancy, lower frequency of daily tooth brushing before pregnancy, and lower frequency of periodontal treatment. An easy-to-use nomogram with acceptable accuracy can allow for the prediction of periodontitis risk in pregnant Chinese women. Clinical relevance: With the assistance of this nomogram, clinicians can evaluate the risk of periodontitis in pregnancy, thereby offering more tailored oral health education to women of reproductive age. [ABSTRACT FROM AUTHOR]

The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis. [ABSTRACT FROM AUTHOR]

An ever-growing body of empirical evidence has demonstrated the relationship between depression and cancer. The objective of this study was to examine whether depression trajectories predict mortality risk above and beyond demographics and other general health-related factors. Participants (n = 2,345) were a part of the Health and Retirement Study. The sample consisted of patients who were assessed once before their cancer diagnosis and thrice after. Depressive symptoms and general health-related factors were based on self-reports. Mortality risk was determined based on whether the patient was alive or not at respective time points. Latent Growth Mixture Modeling was performed to map trajectories of depression, assess differences in trajectories based on demographics and general health-related factors, and predict mortality risk. Four trajectories of depression symptoms emerged: resilient (69.7%), emerging (13.5%), recovery (9.5%), and chronic (7.2%). Overall, females, fewer years of education, higher functional impairment at baseline, and high mortality risk characterized the emerging, recovery, and chronic trajectories. In comparison to the resilient trajectory, mortality risk was highest for the emerging trajectory and accounted for more than half of the deaths recorded for the participants in emerging trajectory. Mortality risk was also significantly elevated, although to a lesser degree, for the recovery and chronic trajectories. The data highlights clinically relevant information about the depression-cancer association that can have useful implications towards cancer treatment, recovery, and public health. Highlights: Cancer and depression are highly comorbid. This prospective study examines depression trajectories in cancer patients. Chronic, emerging, and recovery trajectories significantly predicted mortality risk. Resilience was the modal response. Demographic differences and functional impairment distinguished trajectories. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Adherence to medications is important for the management of chronic diseases. Although the proportion of days covered (PDC) is a common metric for measuring adherence, it may be insufficient to distinguish relevant differences in medication-taking behavior. Group-based trajectory models (GBTMs) have been used to better represent adherence over time. This study aims to examine adherence patterns 1 year after initiation among users of sodium-glucose cotransporter 2 (SGLT2) inhibitors using GBTMs and evaluate the ability of baseline characteristics to predict adherence trajectory. STUDY DESIGN: SGLT2 inhibitor new-user cohort study from 2014 to 2018. METHODS: We calculated 12-month PDC and categorized patients with PDC of 80% or greater as adherent. We performed multivariable logistic regression on adherence status controlling for baseline covariates. GBTMs were fit to identify adherence patterns 12 months following SGLT2 inhibitor initiation. Five multinomial logistic regression models including different subsets of predictors were used to predict adherence trajectory group assignment. RESULTS: In a cohort of 228,363 SGLT2 inhibitor users, the mean PDC was 57%, with 36% of the cohort being adherent. Overall, women and patients with anxiety or depression were less likely to be adherent. Six patterns of SGLT2 inhibitor adherence were identified with GBTMs: 1 fill (PDC = 0.08), early discontinuation (PDC = 0.22), consistently low adherence (PDC = 0.35), moderate adherence (PDC = 0.48), high adherence (PDC = 0.79), and near-perfect adherence (PDC = 0.95). All prediction models showed poor predictive accuracy (0.35). CONCLUSIONS: We found wide variation in adherence patterns among SGLT2 inhibitor users in a national cohort. Predictors from a health care claims database were unable to accurately predict adherence trajectory. [ABSTRACT FROM AUTHOR]

Simple Summary: A model predicting responders to corticosteroid switch during abiraterone therapy in patients with metastatic castration-resistant prostate cancer is warranted. The model using logistic regression was developed in a cohort of 67 and validated in another cohort of 42 patients. The use of this model is easy and all needed parameters are available from routine laboratory and history of patients. The overall accuracy was 92%. Multivariate analysis revealed that responsiveness was a marker of longer treatment duration and overall survival. For non-responders a new systemic treatment was indicated. Background: The aim was to elaborate a predictive model to find responders for the corticosteroid switch (from prednisolone to dexamethasone) at the first prostate-specific antigen (PSA) progression (≥25% increase) during abiraterone acetate (AA) treatment of metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: If PSA has decreased (≥25%) after switch, patients were considered responders. Logistic regression of 19 dichotomized parameters from routine laboratory and patients' history was used to find the best model in a cohort of 67 patients. The model was validated in another cohort of 42 patients. Results: The model provided 92.5% and 90.5% accuracy in the testing and the validation cohorts, respectively. Overall the accuracy was 91.7%. The AUC of ROC curve was 0.92 (95% CI 0.85–0.96). After a median follow-up of 27.9 (26.3–84) months, the median AA+dexamethasone treatment duration (TD) in non-responders and responders was 4.7 (3.1–6.5) and 11.1 (8.5–12.9) months and the median overall survival (OS) was 23.2 (15.6–25.8) and 33.5 (26.1–38) months, respectively. Multivariate Cox regression revealed that responsiveness was an independent marker of TD and OS. Conclusions: A high accuracy model was developed for mCRPC patients in predicting cases which might benefit from the switch. For non-responders, induction of the next systemic treatment is indicated. [ABSTRACT FROM AUTHOR]

Simple Summary: Esophageal cancer, a highly lethal tumor, contributes to 5% of all cancer deaths, with its primary subtypes being esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most studies focus on ESCC, this study investigates EAC using single-cell RNA sequencing (scRNA-seq) to analyze CD45+ immune cells from tumors and matched non-tumor tissues in therapy-naïve patients. By examining the transcriptional profiles of these immune cells and the entire transcriptome in a cohort of 23 patients, this study identifies distinct transcriptional signatures. These signatures were used to stratify a large cohort of TCGA EAC patients, revealing strong associations with prognosis and clinical outcomes. The findings suggest that these transcriptional profiles can improve prognosis accuracy post-surgery and potentially guide effective therapies, including immunotherapy, for EAC patients. Esophageal cancer is a highly lethal malignancy, representing 5% of all cancer-related deaths. The two main subtypes are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most research has focused on ESCC, few studies have analyzed EAC for transcriptional signatures linked to diagnosis or prognosis. In this study, we utilized single-cell RNA sequencing and bulk RNA sequencing to identify specific immune cell types that contribute to anti-tumor responses, as well as differentially expressed genes (DEGs). We have characterized transcriptional signatures, validated against a wide cohort of TCGA patients, that are capable of predicting clinical outcomes and the prognosis of EAC post-surgery with efficacy comparable to the currently accepted prognostic factors. In conclusion, our findings provide insights into the immune landscape and therapeutic targets of EAC, proposing novel immunological biomarkers for predicting prognosis, aiding in patient stratification for post-surgical outcomes, follow-up, and personalized adjuvant therapy decisions. [ABSTRACT FROM AUTHOR]

Simple Summary: Response to treatment and prognosis after 223Ra treatment varies among patients. Using a combination of clinical, biochemical, and radiomic data from bone scintigraphy and 18F-Fluorocholine PET/CT obtained from our prospective ChoPET-Rad study, we identified predictive and prognostic variables. The goal was to create a nomogram able to predict therapeutic failure and overall survival, aiding in the selection of more suitable candidates for this treatment. Purpose: We aimed to develop a nomogram able to predict treatment failure, skeletal events, and overall survival (OS) in patients with castration-resistant prostate cancer with bone metastases (CRPC-BM) treated with Radium-223 dichloride (223Ra). Patients and Methods: Patients from the Castilla-La Mancha Spanish region were prospectively included in the ChoPET-Rad multicenter study from January 2015 to December 2022. Patients underwent baseline, interim, and end-of-treatment bone scintigraphy (BS) and 18F-Fluorocholine PET/CT (FCH PET/CT) scans, obtaining multiple imaging radiomics as well as clinical and biochemical variables during follow-up and studying their association with the previously defined end-points. Survival analysis was performed using the Kaplan–Meier method and Cox regression. Multivariate logistic and Cox regression models were calculated, and these models were depicted by means of nomograms. Results: Median progression-free survival (PFS) and OS were 4 and 14 months (mo), respectively. The variables that showed independent and significant association with therapeutic failure were baseline alkaline phosphatase (AP) levels (p = 0.022) and the characteristics of BM on the CT portion of PET/CT (p = 0.017). In the case of OS, the significant variables were therapeutic failure (p = 0.038), the number of lines received after 223Ra (p < 0.001), average SUVmax (p = 0.002), bone marrow infiltration in FCH PET/CT (p = 0.006), and interim FCH PET/CT response (p = 0.048). Final nomograms included these variables, showing good discrimination among the 100 patients included in our study. In the study of skeletal events, only OS showed a significant association in the multivariate analysis, resulting in an inconsistent nomogram design. Conclusions: FCH PET/CT appears to be a good tool for evaluating patients eligible for treatment with 223Ra, as well as for their follow-up. Thus, findings derived from it, such as the morphological characteristics of BM in the CT, bone marrow infiltration, or the response to 223Ra in the interim study, have proven to be solid and useful variables in the creation of nomograms for predicting therapeutic failure and OS. [ABSTRACT FROM AUTHOR]

Simple Summary: Indocyanine green (ICG) is a dye that helps surgeons see the blood supply to tissues. In this study, (MIRRORS ICG) researchers wanted to see if this dye could help find cancer in women with advanced ovarian cancer undergoing robotic surgery after chemotherapy. After injecting ICG, a special camera was used to look at the whole abdomen and pelvic area. In this study, 102 tissue samples were taken to see if ICG helped identify cancerous tissue. The results showed that ICG correctly identified cancer in 91.1% of cases but had a low specificity of 13.0%, meaning it often falsely indicated cancer. This technique did not significantly help in detecting cancer in patients with widespread disease, but it showed some potential in those who had responded well to chemotherapy and had few remaining cancer spots. Molecular imaging with targeted dyes could enhance precision surgery in the future. Indocyanine green (ICG) is a fluorescent dye used for sentinel lymph node assessment and the assessment of perfusion in skin flaps and bowel anastomoses. ICG binds serum proteins and behaves as a macromolecule in the circulation. Tumour tissue has increased vascular permeability and reduced drainage, causing macromolecules to accumulate within it. MIRRORS ICG is designed to determine whether indocyanine green (ICG) helped identify metastatic deposits in women undergoing robotic interval cytoreductive surgery for advanced-stage (3c+) ovarian cancer. Peritoneal surfaces of the abdominal and pelvic cavity were inspected under white light and near-infrared light (da Vinci Si and Xi Firefly Fluorescence imaging, Intuitive Surgical Inc.) following intravenous injection of 20 mg ICG in sterile water. Visibly abnormal areas were excised and sent to histopathology, noting IGC positivity. In total, 102 biopsies were assessed using ICG. Intravenous ICG assessment following neoadjuvant chemotherapy had a sensitivity of 91.1% (95% CI [82.6–96.4%]), a specificity of 13.0% (95% CI [2.8–33.6%]), a positive predictive value of 78.3% (95% CI [68.4–86.2%]), and a negative predictive value of 30.0% (95% CI [6.7–65.2%]) False-positive samples were seen in 9/20 patients. Psammoma bodies were noted in the histopathology reports of seven of nine of these patients with false-positive results, indicating that a tumour had been present (chemotherapy-treated disease). This study demonstrates the appearance of metastatic peritoneal deposits during robotic cytoreductive surgery following the intravenous administration of ICG in women who have undergone neoadjuvant chemotherapy for stage 3c+ advanced ovarian cancer. A perfusion assessment using indocyanine green (ICG) peritoneal angiography during robotic interval cytoreductive surgery for advanced ovarian cancer did not clinically improve metastatic disease identification in patients with high-volume disease. The use of ICG in patients with excellent response to chemotherapy where few tumour deposits remained shows some promise. The potential of molecular imaging to enhance precision surgery and improve disease identification using the robotic platform is a novel avenue for future research. [ABSTRACT FROM AUTHOR]