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OBJECTIVE: The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization After Significant Head Injury (CRASH) prognostic models for mortality and outcome after traumatic brain injury (TBI) were developed using data from 1984 to 2004. This study examined IMPACT and CRASH model performances in a contemporary cohort of US patients. METHODS: The prospective 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study (enrollment years 2014-2018) enrolled subjects aged ≥ 17 years who presented to level I trauma centers and received head CT within 24 hours of TBI. Data were extracted from the subjects who met the model criteria (for IMPACT, Glasgow Coma Scale [GCS] score 3-12 with 6-month Glasgow Outcome Scale-Extended [GOSE] data [n = 441]; for CRASH, GCS score 3-14 with 2-week mortality data and 6-month GOSE data [n = 831]). Analyses were conducted in the overall cohort and stratified on the basis of TBI severity (severe/moderate/mild TBI defined as GCS score 3-8/9-12/13-14), age (17-64 years or ≥ 65 years), and the 5 top enrolling sites. Unfavorable outcome was defined as GOSE score 1-4. Original IMPACT and CRASH model coefficients were applied, and model performances were assessed by calibration (intercept [< 0 indicated overprediction; > 0 indicated underprediction] and slope) and discrimination (c-statistic). RESULTS: Overall, the IMPACT models overpredicted mortality (intercept -0.79 [95% CI -1.05 to -0.53], slope 1.37 [1.05-1.69]) and acceptably predicted unfavorable outcome (intercept 0.07 [-0.14 to 0.29], slope 1.19 [0.96-1.42]), with good discrimination (c-statistics 0.84 and 0.83, respectively). The CRASH models overpredicted mortality (intercept -1.06 [-1.36 to -0.75], slope 0.96 [0.79-1.14]) and unfavorable outcome (intercept -0.60 [-0.78 to -0.41], slope 1.20 [1.03-1.37]), with good discrimination (c-statistics 0.92 and 0.88, respectively). IMPACT overpredicted mortality and acceptably predicted unfavorable outcome in the severe and moderate TBI subgroups, with good discrimination (c-statistic ≥ 0.81). CRASH overpredicted mortality in the severe and moderate TBI subgroups and acceptably predicted mortality in the mild TBI subgroup, with good discrimination (c-statistic ≥ 0.86); unfavorable outcome was overpredicted in the severe and mild TBI subgroups with adequate discrimination (c-statistic ≥ 0.78), whereas calibration was nonlinear in the moderate TBI subgroup. In subjects ≥ 65 years of age, the models performed variably (IMPACT-mortality, intercept 0.28, slope 0.68, and c-statistic 0.68; CRASH-unfavorable outcome, intercept -0.97, slope 1.32, and c-statistic 0.88; nonlinear calibration for IMPACT-unfavorable outcome and CRASH-mortality). Model performance differences were observed across the top enrolling sites for mortality and unfavorable outcome. CONCLUSIONS: The IMPACT and CRASH models adequately discriminated mortality and unfavorable outcome. Observed overestimations of mortality and unfavorable outcome underscore the need to update prognostic models to incorporate contemporary changes in TBI management and case-mix. Investigations to elucidate the relationships between increased survival, outcome, treatment intensity, and site-specific practices will be relevant to improve models in specific TBI subpopulations (e.g., older adults), which may benefit from the inclusion of blood-based biomarkers, neuroimaging features, and treatment data.

Introduction Telephone triage is pivotal for evaluating the urgency of patient care, and in the Netherlands, the Netherlands Triage Standard (NTS) demonstrates moderate discrimination for chest pain. To address this, the Safety First Prediction Rule (SFPR) was developed to improve the safety of ruling out acute coronary syndrome (ACS) during telephone triage. Methods We conducted an external validation of the SFPR using data from the TRACE study, a retrospective cohort study in out-of-hours primary care. We evaluated the diagnostic accuracy assessment for ACS, major adverse cardiovascular events (MACE), and major events within 6 weeks. Moreover, we compared its performance with that of the NTS algorithm. Results Among 1404 included patients (57.3% female, 6.8% ACS, 8.6% MACE), the SFPR demonstrated good discrimination for ACS (C-statistic: 0.79; 95%-CI: 0.75–0.83) and MACE (C-statistic: 0.79; 95%-CI: 0.0.76–0.82). Calibration was satisfactory, with overestimation observed in high-risk patients for ACS. The SFPR (risk threshold 2.5%) trended toward higher sensitivity (95.8% vs. 86.3%) and negative predictive value (99.3% vs. 97.6%) with a lower negative likelihood ratio (0.10 vs. 0.34) than the NTS algorithm. Conclusion The SFPR proved robust for risk stratification in patients with acute chest pain seeking out-of-hours primary care in the Netherlands. Further prospective validation and implementation are warranted to refine and establish the rule's clinical utility. [ABSTRACT FROM AUTHOR]

Introduction: Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients undergoing maintenance hemodialysis (MHD). However, complications such as thrombosis may occur. This study aimed to construct and validate a machine learning-based risk-prediction model for AVF thrombosis, hypothesizing that such a model can effectively predict occurrences, providing a foundation for early clinical intervention. Methods: The retrospective longitudinal study included a total of 270 patients who underwent MHD at the Hemodialysis Center of the Second Affiliated Hospital of Harbin Medical University between March 2021 and December 2022. During this study, baseline data and scale information of patients between March 2020 and December 2021 were collected. We recorded outcome indicators between March 2021 and December 2022 for subsequent analyses. Five machine learning models were developed (artificial neural network, logistic regression, ridge classification, random forest, and adaptive boosting). The sensitivity (recall), specificity, accuracy, and precision of each model were evaluated. The effect size of each variable was analyzed and ranked. Models were assessed using the area under the receiver-operating characteristic (AUROC) curve. Results: Among the 270 included patients, 105 had AVF thrombosis (55 male and 50 female patients; age range, 29–79 years; mean age, 56.72 years; standard deviation [SDs], ±13.10 years). Conversely, 165 patients did not have AVF thrombosis (99 male and 66 female patients; age range, 23–79 years; mean age, 53.58 years; SD, ± 13.33 years). During the observation period, approximately 52.6% of patients with AVF experienced long-term complications. The most common complications associated with AVF were thrombosis (105; 38.9%), aneurysm formation (27; 10%), and excessively high output flow (10; 3.7%). Fifty-four (20%) patients with AVF required intervention because of complications associated with vascular access. The AUROC curve of the testing set was between 0.858 and 0.903. Conclusion: In this study, we developed five machine learning models to predict the risk of AVF thrombosis, providing a reference for early clinical intervention. [ABSTRACT FROM AUTHOR]

To develop and validate a prediction model utilizing clinical and ultrasound (US) data for preoperative assessment of efficacy following US-guided thermal ablation (TA) in patients with benign thyroid nodules (BTNs) ≥ 2 cm. We retrospectively assessed 962 patients with 1011 BTNs who underwent TA at four tertiary centers between May 2018 and July 2022. Ablation efficacy was categorized into therapeutic success (volume reduction rate [VRR] > 50%) and non-therapeutic success (VRR ≤ 50%). We identified independent factors influencing the ablation efficacy of BTNs ≥ 2 cm in the training set using multivariate logistic regression. On this basis, a prediction model was established. The performance of model was further evaluated by discrimination (area under the curve [AUC]) in the validation set. Of the 1011 nodules included, 952 (94.2%) achieved therapeutic success at the 12-month follow-up after TA. Independent factors influencing VRR > 50% included sex, nodular composition, calcification, volume, and largest diameter (all p < 0.05). The prediction equation was established as follows: p = 1/1 + Exp∑[8.113 −2.720 × (if predominantly solid) −2.790 × (if solid) −1.275 × (if 10 mL < volume ≤ 30mL) −1.743 × (if volume > 30 mL) −1.268 × (if with calcification) −2.859 × (if largest diameter > 3 cm) +1.143 × (if female)]. This model showed great discrimination, with AUC of 0.908 (95% confidence interval [CI]: 0.868–0.947) and 0.850 (95% CI: 0.748–0.952) in the training and validation sets, respectively. A clinical prediction model was successfully developed to preoperatively predict the therapeutic success of BTNs larger than 2 cm in size following US-guided TA. This model aids physicians in evaluating treatment efficacy and devising personalized prognostic plans. [ABSTRACT FROM AUTHOR]

Background: External validation studies create evidence about a clinical prediction rule's (CPR's) generalizability by evaluating and updating the CPR in populations different from those used in the derivation, and also by contributing to estimating its overall performance when meta-analysed in a systematic review. While most cardiovascular CPRs do not have any external validation, some CPRs have been externally validated repeatedly. Hence, we examined whether external validation studies of the Framingham Wilson coronary heart disease (CHD) risk rule contributed to generating evidence to their full potential. Methods: A forward citation search of the Framingham Wilson CHD risk rule's derivation study was conducted to identify studies that evaluated the Framingham Wilson CHD risk rule in different populations. For external validation studies of the Framingham Wilson CHD risk rule, we examined whether authors updated the Framingham Wilson CHD risk rule when it performed poorly. We also assessed the contribution of external validation studies to understanding the Predicted/Observed (P/O) event ratio and c statistic of the Framingham Wilson CHD risk rule. Results: We identified 98 studies that evaluated the Framingham Wilson CHD risk rule; 40 of which were external validation studies. Of these 40 studies, 27 (67.5%) concluded the Framingham Wilson CHD risk rule performed poorly but did not update it. Of 23 external validation studies conducted with data that could be included in meta-analyses, 13 (56.5%) could not fully contribute to the meta-analyses of P/O ratio and/or c statistic because these performance measures were neither reported nor could be calculated from provided data. Discussion: Most external validation studies failed to generate evidence about the Framingham Wilson CHD risk rule's generalizability to their full potential. Researchers might increase the value of external validation studies by presenting all relevant performance measures and by updating the CPR when it performs poorly. [ABSTRACT FROM AUTHOR]

Objective: Emergency physicians are always faced with the challenge of choosing the appropriate disposition for elderly patients in order to ensure an acceptable care plan and effective use of resources. A clinical decision rule, Geriatric Fever Score (GFS) has been proposed but not validated to help ED physicians with decision-making. This rule employs leukocytosis, severe coma, and thrombocytopenia as predictors of 30-day mortality. Through our study determines the performance of this clinical prediction rule in a prospective study in a setting different from where it was developed. Method and materials: In this prospective cohort study in a 1200-bed tertiary care, patients older than 65 years old who visited the ED with fever were enrolled. All elements of the rule were collected and the total score was calculated for each patient. Patients were also categorized as low risk (score 0–1) or high risk (score ≥ 2). Thirty-day follow-up was performed to determine the patient outcome (survival or mortality). Results: A total of 296 patients were included in our final analysis. The mortality rate was 33.1% for patients with a Score of 0, 42.1% for a score of 1, 57.1% for a score of 2, and 100% for a score of 3. When divided into two risk groups, patients' mortality rates were as follows: low risk group 37.9% and high-risk group 40.5%. Conclusion: Our study showed that elderly patients who present to ED with fever and have a score of 2 or higher on the Geriatric Fever Score are at higher risk of mortality at 30 days. [ABSTRACT FROM AUTHOR]

The article provides an overview of the relaunch of the Clinical Trials Information System (CTIS) and the updated transparency rules following the implementation of the EU Clinical Trials Regulation (CTR) in January 2023. It discusses the balance between transparency and protection of commercially confidential information, resulting in the non-publication of certain documents. The article also mentions the CORE Reference Project, which offers resources and professional development for regulatory medical writers. It further highlights selected regulatory information and updates in the field of regulatory public disclosure, including the FDA's transition to the Modernized ClinicalTrials.gov and the WHO's introduction of an online platform for medical device information. Library patrons researching these topics may find the underlying text valuable for further exploration. [Extracted from the article]

This article presents a deep-learning model that uses RNA-seq data to predict the chemoresistance of platinum-based therapy in high-grade serous ovarian carcinoma (HGSOC). The model, which was developed using diverse transcriptome datasets and deep ensemble learning techniques, achieved high performance in predicting chemo-resistant HGSOC patients. The study highlights the importance of accurate prediction methods to guide treatment decisions and improve patient outcomes in HGSOC, a disease with high mortality rates worldwide. The identified genes and prediction models have potential biological relevance and may be useful for further research and application in other ovarian cancer studies with limited sample sizes. [Extracted from the article]

Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon—nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms. [ABSTRACT FROM AUTHOR]

Background: Several clinical decision rules have been devised to guide head computed tomography (CT) use in patients with minor head injuries, but none have been validated in patients 65 years or older. We aimed to derive and validate a head injury clinical decision rule for older adults. Methods: We conducted a secondary analysis of an existing dataset of consecutive emergency department (ED) patients >65 years old with blunt head trauma. The main predictive outcomes were significant intracranial injury and Need for Neurosurgical Intervention on CT. The secondary outcomes also considered in the model development and validation were All Injuries and All Intracranial Injuries. Predictor variables were identified using multiple variable logistic regression, and clinical decision rule models were developed in a split‐sample derivation cohort and then tested in an independent validation cohort. Results: Of 5776 patients, 233 (4.0%) had significant intracranial injury and an additional 104 (1.8%) met CT criteria for Need for Neurosurgical Intervention. The best performing model, the Florida Geriatric Head Trauma CT Clinical Decision Rule, assigns points based on several clinical variables. If the points totaled 25 or more, a CT scan is indicated. The included predictors were arrival via Emergency Medical Services (+30 points), Glasgow Coma Scale (GCS) <15 (+20 points), GCS <14 (+50 points), antiplatelet medications (+17 points), loss of consciousness (+16 points), signs of basilar skull fracture (+50 points), and headache (+20 points). Utilizing this clinical decision rule in the validation cohort, a point total ≥25 had a sensitivity and specificity of 100.0% (95% CI: 96.0–100) and 12.3% (95% CI: 10.9–13.8), respectively, for significant intracranial injury and Need for Neurosurgical Intervention. Conclusions: The Florida Geriatric Head Trauma CT Clinical Decision Rule has the potential to reduce unnecessary CT scans in older adults, without compromising safe emergency medicine practice. [ABSTRACT FROM AUTHOR]

Background: Adjuvant chemotherapy following curative surgery for locally advanced gastric cancer (AGC) significantly improves long-term patient prognosis. However, delayed chemotherapy (DC), in which patients are unable to receive timely treatment, is a common phenomenon in clinical practice for various reasons. This study aimed to investigate the impact of DC on the prognosis of patients with stage II–III locally AGC and explore the associated risk factors. Methods: Data from four prospective studies were included in the pooled analysis. The planned chemotherapy (PC) group was defined as the time interval between surgery and the first chemotherapy ≤ 49 d, while the DC group was defined as the time interval between surgery and chemotherapy > 49 d. The prognosis, recurrence, and risk factors were compared, and a nomogram for predicting DC was established. Results: In total, 596 patients were included, of whom 531 (89.1%) had PC and 65 (10.9%) had DC. Survival analysis revealed that the 5-year overall survival (OS) and disease-free survival (DFS) were significantly lower in the DC group than those in the PC group (log-rank P < 0.001). Cox univariable and multivariable analyses showed that DC was an independent risk factor for OS and DFS in stage II–III patients (P < 0.05). Based on the significant factors for DC, a prediction model was established that had a good fit, high accuracy (AUC = 0.780), and clinical applicability in both the training and validation sets. Conclusion: Delayed chemotherapy after gastrectomy is associated with poor long-term prognosis in patients with locally advanced stage II–III GC disease. But standardized, full-cycle adjuvant chemotherapy after surgery may play a remedial role, and can to a certain extent compensate the poor effects caused by delayed chemotherapy. [ABSTRACT FROM AUTHOR]

Prompt identification of large vessel occlusion (LVO) in acute ischemic stroke (AIS) is crucial for expedited endovascular therapy (EVT) and improved patient outcomes. Prehospital stroke scales, such as the 3-Item Stroke Scale (3I-SS), could be beneficial in detecting LVO in suspected patients. This meta-analysis evaluates the diagnostic accuracy of 3I-SS for LVO detection in AIS. A systematic search was conducted in Medline, Embase, Scopus, and Web of Science databases until February 2024 with no time and language restrictions. Prehospital and in-hospital studies reporting diagnostic accuracy were included. Review articles, studies without reported 3I-SS cut-offs, and studies lacking the required data were excluded. Pooled effect sizes, including area under the curve (AUC), sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR and NLR) with 95% confidence intervals (CI) were calculated. Twenty-two studies were included in the present meta-analysis. A 3I-SS score of 2 or higher demonstrated sensitivity of 76% (95% CI: 52%–90%) and specificity of 74% (95% CI: 57%–86%) as the optimal cut-off, with an AUC of 0.81 (95% CI: 0.78–0.84). DOR, PLR, and NLR, were 9 (95% CI: 5–15), 2.9 (95% CI: 2.0–4.3) and 0.32 (95% CI: 0.17–0.61), respectively. Sensitivity analysis confirmed the analyses' robustness in suspected to stroke patients, anterior circulation LVO, assessment by paramedics, and pre-hospital settings. Meta-regression analyses pinpointed LVO definition (anterior circulation, posterior circulation) and patient setting (suspected stroke, confirmed stroke) as potential sources of heterogeneity. 3I-SS demonstrates good diagnostic accuracy in identifying LVO stroke and may be valuable in the prompt identification of patients for direct transfer to comprehensive stroke centers. [ABSTRACT FROM AUTHOR]

Introduction: Pulmonary embolism (PE) is a potentially fatal condition. Several non-invasive clinical decision rules (CDRs) were developed for the safe exclusion of PE. All CDRs used to safely rule out PE have been created and tested within hospital or acute care environments. However, CDRs that are designed in one specific setting may not perform as effectively when used in a different setting. In this study, we aimed to compare the performance of four common CDRs; Wells Score, Simplified Wells Score, revised Geneva Score, and simplified revised Geneva Score. Methods: This was a cross-sectional study in which patients suspected of PE presenting to Imam Reza Hospital or Ghaem Hospital were recruited from September 23, 2013, to March 19, 2016 in Mashhad, Iran. The specificity, sensitivity, and accuracy were utilized as metrics to compare the CDRs in our region. Results: Two hundred and forty patients were included in the study. The mean age of patients was 57.91±19.97 years, and 54.16% of them (n=130) were female. 120 patients were confirmed to have PE with CT angiography. Wells score showed the highest sensitivity (90.4%) and revised Geneva score represented the highest specificity (84.9%). The highest accuracy belongs to the simplified Wells score (62.3%). Conclusion: In this study, we demonstrated that the Wells criteria with its high sensitivity, can be used as a score for screening, and the revised Geneva score with its high specificity, can be used in the second stage for healthy people who have been diagnosed as unhealthy by the Wells score. [ABSTRACT FROM AUTHOR]

To analyze the influence of diabetes mellitus on the clinical outcomes of moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP). Methods: This retrospective study included patients diagnosed with MSAP and SAP at Shanxi Bethune Hospital from January 1, 2017, to December 31, 2021. Clinical data were collected, including patient demographics, 24-hour laboratory indicators, and inflammation indices. Propensity score matching (PSM) was used to compare outcomes before and after matching. Patients were randomized into training and validation sets (7:3) to develop and validate a clinical prediction model for infected pancreatic necrosis (IPN). Results: Among 421 patients, 79 had diabetes at admission. Before PSM, diabetic patients had higher incidences of peripancreatic fluid (71% vs 47%, p< 0.001) and IPN (48% vs 10%, p< 0.001), higher surgical intervention rates (24% vs 12%, p=0.008), and significant differences in abdominocentesis (22% vs 11%, p=0.014). After PSM, 174 patients were matched, and the diabetes group still showed higher incidences of peripancreatic fluid (69% vs 47%, p=0.008), IPN (48% vs 11%, p< 0.001), and surgical intervention rates (27% vs 13%, p=0.037). Diabetes, modified CT severity index (MCTSI), serum calcium, and HDL-c were identified as independent risk factors for IPN. The prediction model demonstrated good predictive value. Conclusion: In MSAP and SAP patients, diabetes mellitus can exert an influence on their clinical outcome and is an independent risk factor for IPN. The alignment diagram and web calculator constructed on the basis of diabetes mellitus, modified CT severity index (MCTSI), serum calcium and high-density lipoprotein cholesterol (HDL-c) have good predictive value and clinical guidance for the occurrence of IPN in MSAP and SAP. [ABSTRACT FROM AUTHOR]

Background and objective: Surgical site infection (SSI) after dermatological surgery is associated with poor outcomes. Developing clinical prediction rules based on the predicted probability of infection might encourage guided prophylaxis and judicious prescribing. The purpose of this study was to develop a clinical prediction rule based on identified risk factors for SSI in a large general practice patient cohort. Methods: We examined a large, pooled dataset from four randomised controlled trials performed in a regional centre of North Queensland, Australia. Multivariable logistic regression identified a prediction model. Bootstrapping was used for internal validation. A scoring system was based on predicted probabilities of infection. Results: The final prediction rule included age >55 years and the anatomical site, histology and complexity of the excision. The area under the curve was 0.704. Discussion: Our prediction rule encourages judicious use of prophylaxis in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to develop a nomogram for the personalized prediction of postoperative complication risks in patients with middle and low rectal cancer who are undergoing transanal total mesorectal excision (taTME). This tool aims to assist clinicians in early identification of high-risk patients and in addressing preoperative risk factors to enhance surgical safety. Methods: In this case–control study, 207 patients diagnosed with middle and low rectal cancer and undergoing taTME between February 2018 and November 2023 at The First Affiliated Hospital of Xiamen University were included. Independent risk factors for postoperative complications were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multifactorial logistic regression models. A predictive nomogram was constructed using R Studio. Results: Among the 207 patients, 57 (27.5%) experienced postoperative complications. The LASSO and multifactorial logistic regression analyses identified operation time (OR = 1.010, P = 0.007), smoking history (OR = 9.693, P < 0.001), anastomotic technique (OR = 0.260, P = 0.004), and ASA score (OR = 9.077, P = 0.051) as significant predictors. These factors were integrated into the nomogram. The model's accuracy was validated through receiver operating characteristic curves, calibration curves, consistency indices, and decision curve analysis. Conclusion: The developed nomogram, incorporating operation time, smoking history, anastomotic technique, and ASA score, effectively forecasts postoperative complication risks in taTME procedures. It is a valuable tool for clinicians to identify patients at heightened risk and initiate timely interventions, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

Persistent symptoms are common after a mild traumatic brain injury (mTBI). The Post-Concussion Symptoms (PoCS) Rule is a newly developed clinical decision rule for the prediction of persistent post-concussion symptoms (PPCS) 3 months after an mTBI. The PoCS Rule includes assessment of demographic and clinical characteristics and headache presence in the emergency department (ED), and follow-up assessment of symptoms at 7 days post-injury using two thresholds (lower/higher) for symptom scoring. We examined the PoCS Rule in an independent sample. We analyzed a clinical trial that recruited participants with mTBI from EDs in Greater Vancouver, Canada. The primary analysis used data from 236 participants, who were randomized to a usual care control group, and completed the Rivermead Postconcussion Symptoms Questionnaire at 3 months. The primary outcome was PPCS, as defined by the PoCS authors. We assessed the overall performance of the PoCS rule (area under the receiver operating characteristic curve [AUC]), sensitivity, and specificity. More than 40% of participants (median age 38 years, 59% female) reported PPCS at 3 months. Most participants (88%) were categorized as being at medium risk based on the ED assessment, and a majority were considered as being at high risk according to the final PoCS Rule (81% using a lower threshold and 72% using a higher threshold). The PoCS Rule showed a sensitivity of 93% (95% confidence interval [CI], 88–98; lower threshold) and 85% (95% CI, 78–92; higher threshold), and a specificity of 28% (95% CI, 21–36) and 37% (95% CI, 29–46), respectively. The overall performance was modest (AUC 0.61, 95% CI 0.59, 0.65). In conclusion, the PoCS Rule was sensitive for PPCS, but had a low specificity in our sample. Follow-up assessment of symptoms can improve risk stratification after mTBI. [ABSTRACT FROM AUTHOR]

Although exercise is the mainstay of treatment for neck pain (NP), uncertainty remains over optimal decision-making concerning who may benefit most from such, particularly in the long term. To identify the subgroup of patients with nonspecific NP most likely to benefit from stretching and muscle-performance exercises. This was a secondary analysis of treatment outcomes of 70 patients (10 of whom dropped out) with a primary complaint of nonspecific NP in one treatment arm of a prospective, randomized, controlled trial. All patients performed the exercises, twice weekly for 6 weeks, and a home exercise program. Blinded outcome measurements were collected at baseline, after the 6-week program, and at a 6-month follow-up. Patients rated their perceived recovery on a 15-point global rating of change scale; a rating of "quite a bit better" (+5) or higher was defined as a successful outcome. Clinical predictor variables were developed via logistic regression analysis to classify patients with NP that may benefit from exercise-based treatment. NP duration since onset≤6 months, no cervicogenic headache, and shoulder protraction were independent predictor variables. The pretest probability of success was 47% after the 6-week intervention and 40% at the 6-month follow-up. The corresponding posttest probabilities of success for participants with all three variables were 86% and 71%, respectively; such participants were likely to recover. The clinical predictor variables developed in this study may identify patients with nonspecific NP likely to benefit most from stretching and muscle-performance exercises in the short and long terms. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the risk factors for uterine clear‐cell carcinoma (UCCC) and construct nomograms predicting 1‐, 3‐, and 5‐year overall survival rates of patients with UCCC. Methods: The demographic and clinical information of 1674 patients diagnosed with UCCC between 2004 and 2015, including age, race, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, and details of surgery and radiotherapy/chemotherapy, was collected from the Surveillance, Epidemiology, and End Results (SEER) database. After excluding patients with unknown AJCC stage, race, marital status, or lymph node information, 1469 patients remained. Risk factors were determined using univariate and multivariate analyses, and nomograms were developed to predict 1‐, 3‐, and 5‐year overall survival of UCCC. Various indicators were used to evaluate the performance of the nomogram, such as the C‐index, net classification improvement (NRI) and decision curve analysis (DCA). Results: Age, log odds of positive lymph nodes, AJCC stage, surgery status, and chemotherapy status were independent risk factors for UCCC. The C‐indexes of the training group and AJCC stage groups were 0.771 and 0.697, respectively. The results for the area under the receiver operating characteristics curve, NRI, and calibration curves indicated that the nomogram had good predictive ability. DCA revealed that the nomogram had greater clinical applicability than AJCC stage alone. Internal validation using the validation cohort also demonstrated that this nomogram had good predictive performance. Conclusion: A new nomogram comprising a combination of demographic and clinical characteristics provided better survival predictions than the AJCC staging system alone, which will facilitate prognostic assessments and clinical decision‐making. Synopsis: We identified factors influencing the prognosis of UCCC and constructed a nomogram for predicting the overall survival rate of UCCC patients. [ABSTRACT FROM AUTHOR]

Typical first-line management of children with intussusception is enema reduction; however, failure necessitates surgical intervention. The number of attempts varies by clinician, and predictors of failed nonoperative management are not routinely considered in practice. The purpose of this study is to create a scoring system that predicts risk of nonoperative failure and need for surgical intervention. Children diagnosed with intussusception upon presentation to the emergency department of a tertiary children's hospital between 2019 and 2022 were retrospectively identified. Univariable logistic regression identified predictors of nonoperative failure used as starting covariates for multivariable logistic regression with final model determined by backwards elimination. Regression coefficients for final predictors were used to create the scoring system and optimal cut-points were delineated. We identified 143 instances of ultrasound-documented intussusception of which 28 (19.6%) required operative intervention. Predictors of failed nonoperative management included age ≥4 y (odds ratio [OR] 32.83, 95% confidence interval [CI]: 1.91-564.23), ≥1 failed enema reduction attempts (OR 189.53, 95% CI: 19.07-1884.11), presenting heart rate ≥128 (OR 3.38, 95% CI: 0.74-15.36), presenting systolic blood pressure ≥115 mmHg (OR 6.59, 95% CI: 0.93-46.66), and trapped fluid between intussuscepted loops on ultrasound (OR 17.54, 95% CI: 0.77-397.51). Employing these factors, a novel risk scoring system was developed (area under the curve 0.96, 95% CI: 0.93-0.99). Scores range from 0 to 8; ≤2 have low (1.1%), 3-4 moderate (50.0%), and ≥5 high (100%) failure risk. Using known risk factors for enema failure, we produced a risk scoring system with outstanding discriminate ability for children with intussusception necessitating surgical intervention. Prospective validation is warranted prior to clinical integration. [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusion There has been debate over whether the existing World Health Organization (WHO) criteria accurately represent the severity of maternal near misses.This study assessed the diagnostic accuracy of two WHO clinical and laboratory organ dysfunction markers for determining the best cutoff values in a Latin American setting.A prospective multicenter cohort study was conducted in five Latin American countries. Patients with severe maternal complications were followed up from admission to discharge. Organ dysfunction was determined using clinical and laboratory data, and participants were classified according to severe maternal outcomes. This study compares the diagnostic criteria of Latin American Centre for Perinatology, Network for Adverse Maternal Outcomes (CLAP/NAMO) to WHO standards.Of the 698 women studied, 15.2% had severe maternal outcomes. Most measured variables showed significant differences between individuals with and without severe outcomes (all P‐values <0.05). Alternative cutoff values suggested by CLAP/NAMOs include pH ≤7.40, lactate ≥2.3 mmol/L, respiratory rate ≥ 24 bpm, oxygen saturation ≤ 96%, PaO2/FiO2 ≤ 342 mmHg, platelet count ≤189 × 109 × mm3, serum creatinine ≥0.8 mg/dL, and total bilirubin ≥0.67 mg/dL. No significant differences were found when comparing the diagnostic performance of the CLAP/NAMO criteria to that of the WHO standards.The CLAP/NAMO values were comparable to the WHO maternal near‐miss criteria, indicating that the WHO standards might not be superior in this population. These findings suggest that maternal near‐miss thresholds can be adapted regionally, improving the identification and management of severe maternal complications in Latin America. [ABSTRACT FROM AUTHOR]

Purpose: Preoperative prudent patient selection plays a crucial role in knee osteoarthritis management but faces challenges in appropriate referrals such as total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA) and nonoperative intervention. Deep learning (DL) techniques can build prediction models for treatment decision‐making. The aim is to develop and evaluate a knee arthroplasty prediction pipeline using three‐view X‐rays to determine the suitable candidates for TKA, UKA or are not arthroplasty candidates. Methods: A study was conducted using three‐view (anterior‐posterior, lateral and patellar) X‐rays and surgical data of patients undergoing TKA, UKA or nonarthroplasty interventions from sites A and B. Data from site A were used to derive and validate models. Data from site B were used as external test set. A DL pipeline combining YOLOv3 and ResNet‐18 with confident learning (CL) was developed. Multiview Convolutional Neural Network, EfficientNet‐b4, ResNet‐101 and the proposed model without CL were also trained and tested. The models were evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity and F1 score. Results: The data set comprised a total of 1779 knees. Of which 1645 knees were from site A as a derivation set and an internal validation cohort. The external validation cohort consisted of 134 knees. The internal validation cohort demonstrated superior performance for the proposed model augmented with CL, achieving an AUC of 0.94 and an accuracy of 85.9%. External validation further confirmed the model's generalisation, with an AUC of 0.93 and an accuracy of 82.1%. Comparative analysis with other neural network models showed the proposed model's superiority. Conclusions: The proposed DL pipeline, integrating YOLOv3, ResNet‐18 and CL, provides accurate predictions for knee arthroplasty candidates based on three‐view X‐rays. This prediction model could be useful in performing decision making for the type of arthroplasty procedure in an automated fashion. Level of Evidence: Level III, diagnostic study. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm. Methods: We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort. Results: In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively. Conclusions: Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy. [ABSTRACT FROM AUTHOR]

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use. [ABSTRACT FROM AUTHOR]

Objectives: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. Methods: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). Results: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784–0.844) in the training cohort, 0.776 (95% CI, 0.727–0.825) in the testing cohort, and 0.702 (95% CI, 0.614–0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810–0.866) in the training cohort, 0.788 (95% CI, 0.741–0.835) in the testing cohort, and 0.722 (95% CI, 0.637–0.811) in the external validation cohort. Conclusions: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. Clinical relevance statement: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. Key Points: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data. [ABSTRACT FROM AUTHOR]

Postoperative pain is a common complication in endoscopic submucosal dissection (ESD) patients. This study aimed to develop and validate predictive models for postoperative pain associated ESD. Methods: We retrospectively constructed a development cohort comprising 2162 patients who underwent ESD at our hospital between January 2015 and April 2022. The dataset was randomly divided into a training set (n = 1541) and a validation set (n = 621) in a 7:3 ratio. The bidirectional stepwise regression with Akaike's information criterion (AIC) and multivariate logistic regression analysis were used to screen the predictors of post-ESD pain and construct three nomograms. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, Hosmer–Lemeshow (HL) goodness-of-fit test and decision curve analysis (DCA) in internal validation. Results: The proportion of patients developing postoperative pain in the training and testing data set was 25.6% and 28.5%, respectively. Three nomograms were constructed according to the final logistic regression models. The clinical prediction models for preoperative risks, preoperative and intraoperative risks, and perioperative risks consisted of seven, nine and six independent predictors, respectively, after bidirectional stepwise elimination. The models demonstrated the AUC of 0.794 (95% CI 0.768– 0.820), 0.823 (95% CI 0.799– 0.847) and 0.817 (95% CI 0.792– 0.842) in the training cohort and 0.702 (95% CI 0.655– 0.748), 0.705 (95% CI 0.659– 0.752) and 0.747 (95% CI 0.703– 0.790) in the validation cohort. The calibration plot, HL and DCA demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated three robust nomogram models, which might identify patients at risk of post-ESD pain and promising for clinical applications. [ABSTRACT FROM AUTHOR]

(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides valuable insights into GV. Unlike in type 1 diabetes mellitus, the use of CGM-derived GV indices has not been widely adopted in the management of T2DM due to the limited evidence of their effectiveness in predicting clinical outcomes. This study aimed to explore the associations between GV metrics and short- or long-term vascular and clinical complications in T2DM. (2) Methods: A rapid literature review was conducted using the Cochrane Library, MEDLINE, and Scopus databases to seek high-level evidence. Lower-quality studies such as cross-sectional studies were excluded, but their content was reviewed. (3) Results: Six studies (five prospective cohort studies and one clinical trial) reported associations between GV indices (coefficient of variation (CV), standard deviation (SD), Mean Amplitude of Glycaemic Excursions (MAGE), Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR)), and clinical complications. However, since most evidence came from moderate to low-quality studies, the results should be interpreted with caution. (4) Conclusions: Limited but significant evidence suggests that GV indices may predict clinical compilations in T2DM both in the short term and long term. There is a need for longitudinal studies in larger and more diverse populations, longer follow-ups, and the use of numerous CGM-derived GV indices while collecting information about all microvascular and macrovascular complications. [ABSTRACT FROM AUTHOR]

Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment. A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model. The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%. Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions. [ABSTRACT FROM AUTHOR]

Prediction models help to target patients at risk of multidrug-resistant organism (MDRO) colonization or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, there is limited evidence to identify which among the available models are of low risk of bias and suitable for clinical application. To identify, describe, appraise, and summarise the performance of all prognostic and diagnostic models developed or validated for predicting MDRO colonization or infection. Six electronic literature databases and clinical registration databases were searched until April 2022. Development and validation studies of any multivariable prognostic and diagnostic models to predict MDRO colonization or infection in adults. Adults (≥ 18 years old) without MDRO colonization or infection (in prognostic models) or with unknown or suspected MDRO colonization or infection (in diagnostic models). The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias. Evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. Meta-analyses were conducted to summarize the discrimination and calibration of the models' external validations conducted in at least two non-overlapping datasets. We included 162 models (108 studies) developed for diagnosing (n = 135) and predicting (n = 27) MDRO colonization or infection. Models exhibited a high-risk of bias, especially in statistical analysis. High-frequency predictors were age, recent invasive procedures, antibiotic usage, and prior hospitalization. Less than 25% of the models underwent external validations, with only seven by independent teams. Meta-analyses for one diagnostic and two prognostic models only produced very low to low certainty of evidence. The review comprehensively described the models for identifying patients at risk of MDRO colonization or infection. We cannot recommend which models are ready for application because of the high-risk of bias, limited validations, and low certainty of evidence from meta-analyses, indicating a clear need to improve the conducting and reporting of model development and external validation studies to facilitate clinical application. [ABSTRACT FROM AUTHOR]

Background and Purpose: The tumour-node metastasis (TNM) classification is a common model for evaluating the prognostic value of tumour patients. However, few models have been used to predict the survival outcomes of patients with colorectal cancer liver metastasis (CRLM) with unresectable metastases who received the primary local surgery. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to establish novel nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods: Extracted primary data on CRLM patients by local surgery from SEER database. All prognostic factors of OS and CSS were determined by Cox regression analysis. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were used to further evaluate the accuracy and discrimination of these nomograms. Decision curve analysis (DCA) was executed to evaluate the nomograms for the clinical net benefit. Risk stratification analysis (RSA) was used to evaluate the reliability of them in clinical. Results: 3622 eligible patients were screened and assigned to training cohort (1812) or validation cohort (1810). The age, chemotherapy, tumour grade, primary tumour site, tumour size, lymph node positive rate (LNR), marital status, and carcinoembryonic antigen (CEA) were independent prognostic factors of OS. Additionally, the age, chemotherapy, tumour grade, primary tumour site, tumour size, LNR, and CEA were independent prognostic factors of CSS. The results of C-indexes and ROC curves indicated that the established nomograms exhibited better discrimination power than TNM classification. The calibration curves demonstrated excellent agreement between the predicted and actual survival rates for 1-, 3-, and 5 year OS and CSS. Meanwhile, the validation cohort demonstrated similar results. Background the clinic context, the DCA showed that these nomograms have higher net benefits, and the RSA showed that patients were further divided into low risk, medium risk, and high risk groups according to the predicted scores from nomograms. And, the Kaplan–Meier curve and log-rank test showed that the survival differences among the three groups are statistically significant. Conclusions: The prognostic nomograms showed very high accuracy, identifiability, and clinical practicality in predicting the OS and CSS of CRLM patients with unresectable metastases treated by local surgery at 1-, 3-, and 5 years, which might improve individualized predictions of survival risks and help clinicians formulate treatment plans. [ABSTRACT FROM AUTHOR]

Introduction: Lymph node enlargement is common in children, with 90% of physiologically palpable lymph nodes. This study aimed to develop a predictive model based on clinical characteristics to enhance the diagnosis of pediatric lymphadenopathy and provide insights into biopsy outcomes. Materials and methods: A clinical prediction rule was developed using a retrospective, cross-sectional design for patients under 15 years who underwent lymph node biopsy from 2012 to 2022. Multivariable risk regression was used to analyze benign and malignant lesions, presenting results through risk difference and AUROC for each group. Predicted probabilities were applied in a logistic regression equation to classify patients' lymphadenopathy as reactive hyperplasia, benign, or malignant. Results: Of 188 children, 70 (37.2%) had benign lymphadenopathy beyond reactive hyperplasia, and 27 (14.4%) had malignant lymphadenopathy. The predictive model included 12 characteristics such as size, location, duration, associated symptoms, and lymph node examination. Predictive accuracy was 92.2% for benign cases (AUROC = 0.92; 95% CI 0.87–0.96) and 98.6% for malignancy (AUROC = 0.98; 95% CI 0.94–0.99). Overall accuracy for predicting both benign and malignant tumors was 68.3%. Conclusion: The model demonstrated reasonably accurate predictions for the clinical characteristics of pediatric lymphadenopathy. It tended to overestimate malignancy but did not miss diagnoses, aiding in reducing unnecessary lymph node biopsies in benign cases. [ABSTRACT FROM AUTHOR]

Introduction: Patients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB. Materials and methods: This retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated. Results: Radiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63-0.72) in internal validation and 0.64 (95% confidence interval: 0.59-0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB. Conclusion: This study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla. [ABSTRACT FROM AUTHOR]

Background: The prognosis of advanced gastric cancer (AGC) is relatively poor, and long-term survival depends on timely intervention. Currently, predicting survival rates remains a hot topic. The application of radiomics and immunohistochemistry-related techniques in cancer research is increasingly widespread. However, their integration for predicting long-term survival in AGC patients has not been fully explored. Methods: We Collected 150 patients diagnosed with AGC at the Affiliated Zhongshan Hospital of Dalian University who underwent radical surgery between 2015 and 2019. Following strict inclusion and exclusion criteria, 90 patients were included in the analysis. We Collected postoperative pathological specimens from enrolled patients, analyzed the expression levels of MAOA using immunohistochemical techniques, and quantified these levels as the MAOAHScore. Obtained plain abdominal CT images from patients, delineated the region of interest at the L3 vertebral body level, and extracted radiomics features. Lasso Cox regression was used to select significant features to establish a radionics risk score, convert it into a categorical variable named risk, and use Cox regression to identify independent predictive factors for constructing a clinical prediction model. ROC, DCA, and calibration curves validated the model's performance. Results: The enrolled patients had an average age of 65.71 years, including 70 males and 20 females. Multivariate Cox regression analysis revealed that risk (P = 0.001, HR = 3.303), MAOAHScore (P = 0.043, HR = 2.055), and TNM stage (P = 0.047, HR = 2.273) emerged as independent prognostic risk factors for 3-year overall survival (OS) and The Similar results were found in the analysis of 3-year disease-specific survival (DSS). The nomogram developed could predict 3-year OS and DSS rates, with areas under the ROC curve (AUCs) of 0.81 and 0.797, respectively. Joint calibration and decision curve analyses (DCA) confirmed the nomogram's good predictive performance and clinical utility. Conclusion: Integrating immunohistochemistry and muscle fat features provides a more accurate prediction of long-term survival in gastric cancer patients. This study offers new perspectives and methods for a deeper understanding of survival prediction in AGC. [ABSTRACT FROM AUTHOR]

Background: Delayed neurocognitive recovery (DNR) is a common complication in patients undergoing laparoscopic surgery, and there are currently no effective therapies. It is vital to provide a reliable basis for clinical prediction. This study tried to analyse the risk factors for DNR in patients undergoing laparoscopic colorectal surgery and to establish a risk prediction model. Methods: A retrospective analysis of the clinical data and DNR status of patients undergoing laparoscopic colorectal surgery at Xiangya Hospital of Central South University from March 2018 to July 2020 was conducted. Logistic regression was performed to analyse the related risk factors for DNR post-operatively, and the predictive model of DNR post-operatively was constructed and validated internally. Patients who underwent laparoscopic colorectal surgery between January and July 2021 were also selected for external validation of the predictive model, to ultimately investigate the risk factors for DNR in patients undergoing laparoscopic colorectal surgery. Results: The incidence of DNR in patients undergoing laparoscopic colorectal surgery was 15.2% (31/204). The maximum variability of cerebral oxygen, age, education, and pre-existing diabetes was related to the incidence of DNR (p < 0.05). The risk prediction model of DNR after laparoscopic colorectal surgery was established. The internal and external validation showed that the discrimination was good (the AUCs were 0.751 and 0.694, respectively). Conclusions: The risk prediction model of DNR related to cerebral oxygen saturation monitoring shows good predictive performance and clinical value, providing a basis for postoperative DNR prevention. [ABSTRACT FROM AUTHOR]

Aim: This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas. Methods: Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC). Results: Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000). Conclusion: The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial. [ABSTRACT FROM AUTHOR]

Objective: This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes. Materials and Methods: With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost. Results: During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction. Discussion: The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies. Conclusions: This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk. [ABSTRACT FROM AUTHOR]

Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There remains significant variation in the accuracy and nature of risk adjustment models utilised in international PCI registries/databases. Therefore, the current systematic review aims to summarise preoperative variables associated with 30-day mortality among patients undergoing PCI, and the other methodologies used in risk adjustments. The MEDLINE, EMBASE, CINAHL, and Web of Science databases until October 2022 without any language restriction were systematically searched to identify preoperative independent variables related to 30-day mortality following PCI. Information was systematically summarised in a descriptive manner following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The quality and risk of bias of all included articles were assessed using the Prediction Model Risk Of Bias Assessment Tool. Two independent investigators took part in screening and quality assessment. The search yielded 2,941 studies, of which 42 articles were included in the final assessment. Logistic regression, Cox-proportional hazard model, and machine learning were utilised by 27 (64.3%), 14 (33.3%), and one (2.4%) article, respectively. A total of 74 independent preoperative variables were identified that were significantly associated with 30-day mortality following PCI. Variables that repeatedly used in various models were, but not limited to, age (n=36, 85.7%), renal disease (n=29, 69.0%), diabetes mellitus (n=17, 40.5%), cardiogenic shock (n=14, 33.3%), gender (n=14, 33.3%), ejection fraction (n=13, 30.9%), acute coronary syndrome (n=12, 28.6%), and heart failure (n=10, 23.8%). Nine (9; 21.4%) studies used missing values imputation, and 15 (35.7%) articles reported the model's performance (discrimination) with values ranging from 0.501 (95% confidence interval [CI] 0.472–0.530) to 0.928 (95% CI 0.900–0.956), and four studies (9.5%) validated the model on external/out-of-sample data. Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods. [ABSTRACT FROM AUTHOR]

Purpose: To investigate early structural and mechanical predictors of plantarflexor muscle strength and the magnitude of Achilles tendon (AT) nonuniform displacement at 6 and 12 months after AT rupture. Methods: Thirty‐five participants (28 males and 7 females; mean ± SD age 41.7 ± 11.1 years) were assessed for isometric plantarflexion maximal voluntary contraction (MVC) and AT nonuniformity at 6 and 12 months after rupture. Structural and mechanical AT and plantarflexor muscle properties were measured at 2 months. Limb asymmetry index (LSI) was calculated for all variables. Multiple linear regression was used with the 6 and 12 month MVC LSI and 12 month AT nonuniformity LSI as dependent variables and AT and plantarflexor muscle properties at 2 months as independent variables. The level of pre‐ and post‐injury sports participation was inquired using Tegner score at 2 and 12 months (scale 0–10, 10 = best possible score). Subjective perception of recovery was assessed with Achilles tendon total rupture score (ATRS) at 12 months (scale 0–100, 100=best possible score). Results: Achilles tendon resting angle (ATRA) symmetry at 2 months predicted MVC symmetry at 6 and 12 months after rupture (β = 2.530, 95% CI 1.041–4.018, adjusted R2 = 0.416, p = 0.002; β = 1.659, 95% CI 0.330–2.988, adjusted R2 = 0.418, p = 0.016, respectively). At 12 months, participants had recovered their pre‐injury level of sports participation (Tegner 6 ± 2 points). The median (IQR) ATRS score was 92 (7) points at 12 months. Conclusion: Greater asymmetry of ATRA in the early recovery phase may be a predictor of plantarflexor muscle strength deficits up to 1 year after rupture. Trial Registration: This research is a part of "nonoperative treatment of Achilles tendon rupture in Central Finland: a prospective cohort study" that has been registered in ClinicalTrials.gov (NCT03704532) [ABSTRACT FROM AUTHOR]

Current prediction models for patients with ostosarcoma are restricted to predictions from a single, static point in time, such as diagnosis or surgery. These approaches discard information which becomes available during follow-up and may have an impact on patient's prognosis. This study aims at developing a dynamic prediction model providing 5-year overall survival (OS) predictions from different time points during follow-up. The developed model considers relevant baseline prognostic factors, accounting for where appropriate time-varying effects and time-varying intermediate events such as local recurrence (LR) and new metastatic disease (NM). A landmarking approach is applied to 1965 patients with high-grade resectable osteosarcoma from the EURAMOS-1 trial (NCT00143030). Results show that LR and NM negatively affected 5-year OS (HRs: 2.634, 95% CI 1.845–3.761; 8.558, 95% CI 7.367–9.942, respectively). Baseline factors with strong prognostic value (HRs > 2) included poor histological response (≥10% viable tumor), axial tumor location, and the presence of lung metastases. The effect of poor versus good histological response changed over time, becoming non-significant from 3.25 years post-surgery onwards. This time-varying effect, as well as the strong impact of disease-related time-varying variables, show the importance of including updated information collected during follow-up in the model to provide more accurate survival predictions. [ABSTRACT FROM AUTHOR]

To use machine learning methods to develop prediction models of pregnancy complications in women who conceived with assisted reproductive techniques (ART). A nation-wide register-based cohort study with prospectively collected data. Swedish national registers and nationwide quality IVF register. all nulliparous women who achieved birth within the first 3 ART treatment cycles between 2008 and 2016 in Sweden. Characteristics before the use of ART, such as demographics and medical history, were considered potential predictors in the development of before treatment prediction models. ART treatment details were further included in after treatment prediction models. Potential diagnoses of preeclampsia, placental complications (previa, accreta, and abruption), and postpartum hemorrhage were identified using the International Classification of Diseases recorded in the Swedish Medical Birth and Patient registers, respectively. Multiple prediction model algorithms were performed and compared for each outcome and treatment cycle, including logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest, and gradient boosting. The performance of each model was assessed with C statistic, and nested cross-validation was used to aid model selection and hyperparameter tuning. A total of 14,732 women gave birth after the first (N = 7,302), second (N = 4,688), or third (N = 2,742) ART cycle, representing birth rates of 24.1%, 23.8%, and 22.0%. Overall prediction performance did not vary much across the different methods used. In the first cycle, the before treatment prediction performance was at best 66%, 66%, and 60% for preeclampsia, placental complications, and postpartum hemorrhage, respectively. Inclusion of after treatment characteristics conferred slight improvement (approximately 1%–5%), as did prediction in later cycles (approximately 1%–5%). The top influential and consistent predictors included age, region of residence, infertility diagnosis, and type of embryo transfer (fresh or frozen) in the later (2nd and 3rd) cycles. Body mass index was a top predictor of preeclampsia and was also influential for placental complications but not for postpartum hemorrhage. The combined use of demographics, medical history, and ART treatment information was not enough to confidently predict serious pregnancy complications in women who conceived with ART. Future studies are needed to assess if additional longitudinal follow-up during pregnancy can improve the prediction to allow clinical protocol development. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study aimed to evaluate the discriminatory performance of 11 vital signbased early warning scores (EWSs) and three shock indices in early sepsis prediction in the emergency department (ED). METHODS: We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong. The primary outcome was sepsis (Sepsis-3 definition) within 48 h of ED presentation. Using c-statistics and the DeLong test, we compared 11 EWSs, including the National Early Warning Score 2 (NEWS2), Modified Early Warning Score, and Worthing Physiological Scoring System (WPS), etc., and three shock indices (the shock index [SI], modified shock index [MSI], and diastolic shock index [DSI]), with Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the primary outcome, intensive care unit admission, and mortality at different time points. RESULTS: We analyzed 601 patients, of whom 166 (27.6%) developed sepsis. NEWS2 had the highest point estimate (area under the receiver operating characteristic curve [AUROC] 0.75, 95%CI 0.70–0.79) and was significantly better than SIRS, qSOFA, other EWSs and shock indices, except WPS, at predicting the primary outcome. However, the pooled sensitivity and specificity of NEWS2 ≥ 5 for the prediction of sepsis were 0.45 (95%CI 0.37–0.52) and 0.88 (95%CI 0.85–0.91), respectively. The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point. CONCLUSION: NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening. [ABSTRACT FROM AUTHOR]

Surgical risk stratification is crucial for enhancing perioperative assistance and allocating resources efficiently. However, existing models may not capture the complexity of surgical care in Brazil. Using data from various healthcare settings nationwide, we developed a new risk model for 30-day in-hospital mortality (the Ex-Care BR model). A retrospective cohort study was conducted in 10 hospitals from different geographic regions in Brazil. Data were analysed using multilevel logistic regression models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration plots. Derivation and validation cohorts were randomly assigned. A total of 107,372 patients were included, and 30-day in-hospital mortality was 2.1% (n =2261). The final risk model comprised four predictors related to the patient and surgery (age, ASA physical status classification, surgical urgency, and surgical size), and the random effect related to hospitals. The model showed excellent discrimination (AUROC=0.93, 95% confidence interval [CI], 0.93–0.94), calibration, and overall performance (Brier score=0.017) in the derivation cohort (n =75,094). Similar results were observed in the validation cohort (n =32,278) (AUROC=0.93, 95% CI, 0.92–0.93). The Ex-Care BR is the first model to consider regional and organisational peculiarities of the Brazilian surgical scene, in addition to patient and surgical factors. It is particularly useful for identifying high-risk surgical patients in situations demanding efficient allocation of limited resources. However, a thorough exploration of mortality variations among hospitals is essential for a comprehensive understanding of risk. NCT05796024. [ABSTRACT FROM AUTHOR]

Background: Amide proton transfer‐weighted imaging (APTWI) and multiple models intravoxel incoherent motion (IVIM) based 18F‐FDG PET/MR could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non‐small cell lung cancer (NSCLC) still needs to be further explored. Purpose: To determine whether pretreatment APTWI, mono‐, bi‐, and stretched‐exponential model IVIM, and 18F‐FDG PET‐derived parameters of the primary lesion may be associated with progression‐free survival (PFS) in NSCLC. Study Type: Prospective. Population: Seventy‐seven patients (mean age, 62 years, range, 20–81 years) with 37 men and 40 women were included. Field Strength/Sequence: 3.0 T 18F‐FDG PET/MRI, single shot echo planar imaging sequences for IVIM and fast spin‐echo sequences with magnetization transfer pulses for APTWI. Assessment: Patient clinical characteristics (age, sex, smoke, subtype, TNM stage, and surgery), PFS (chest CT every 3 months, median follow‐up was 18 months, range, 4–27 months), and APTWI (MTRasym(3.5 ppm)), IVIM (ADCstand, D, D*, f, DDC, and α), and 18F‐FDG PET (SUVmax, MTV, and TLG) parameters were recorded. Statistical Tests: Proportional hazards model, concordance index, calibration curve, decision curve analysis (DCA), and Log‐rank test. A P value <0.05 was considered statistically significant. Results: Histological subtype, TNM stage, MTV, D*, and MTRasym(3.5 ppm) were all independent predictors of PFS. A prediction model based on these predictors was developed with a C‐index of 0.895 (95% CI: 0.839–0.951), which was significantly superior to each of the above predictors alone (C‐index = 0.629, 0.707, 0.692, 0.678, and 0.558, respectively). The calibration curve and DCA indicated good consistency and clinical utility of the prediction model, respectively. Log‐rank test results showed a significant difference in PFS between the high‐ and low‐risk groups. Data Conclusion: APTWI and multiple models IVIM based 18F‐FDG PET/MRI can be used for PFS assessment in NSCLC. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Objective: This study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization. Methods: We prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm. Results: Ten significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97-0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model. Conclusion: This study successfully constructed a model predicting postoperative hospital stay duration using patients' clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization. [ABSTRACT FROM AUTHOR]

Introduction: Triple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis. Methods: We included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (Cindices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis. Results: In the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately. Conclusion: These nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS. [ABSTRACT FROM AUTHOR]

Background: No reliable clinical tools exist to predict acute kidney injury (AKI) progression. We aim to explore a scoring system for predicting the composite outcome of progression to severe AKI or death within seven days among early AKI patients after cardiac surgery. Methods: In this study, we used two independent cohorts, and patients who experienced mild/moderate AKI within 48 h after cardiac surgery were enrolled. Eventually, 3188 patients from the MIMIC-IV database were used as the derivation cohort, while 499 patients from the Zhongshan cohort were used as external validation. The primary outcome was defined by the composite outcome of progression to severe AKI or death within seven days after enrollment. The variables identified by LASSO regression analysis were entered into logistic regression models and were used to construct the risk score. Results: The composite outcome accounted for 3.7% (n = 119) and 7.6% (n = 38) of the derivation and validation cohorts, respectively. Six predictors were assembled into a risk score (AKI-Pro score), including female, baseline eGFR, aortic surgery, modified furosemide responsiveness index (mFRI), SOFA, and AKI stage. And we stratified the risk score into four groups: low, moderate, high, and very high risk. The risk score displayed satisfied predictive discrimination and calibration in the derivation and validation cohort. The AKI-Pro score discriminated the composite outcome better than CRATE score, Cleveland score, AKICS score, Simplified renal index, and SRI risk score (all P < 0.05). Conclusions: The AKI-Pro score is a new clinical tool that could assist clinicians to identify early AKI patients at high risk for AKI progression or death. [ABSTRACT FROM AUTHOR]

Background: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). Methods: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. Results: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. Conclusion: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT. [ABSTRACT FROM AUTHOR]