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Background: Extracorporeal cardiopulmonary resuscitation (ECPR) improves the prognosis of in-hospital cardiac arrest (IHCA). The six-factor RESCUE-IHCA score (resuscitation using ECPR during IHCA) was developed to predict outcomes of post-IHCA ECPR-treated adult patients. Our goal was to validate the score in an Asian medical center with a high volume and experience of ECPR performance and to compare the differences in patient characteristics between the current study and the original cohort in a 2022 observational study.Method: For this single-center, retrospective cohort study we enrolled 324 ECPR-treated adult IHCA patients. The primary outcome was in-hospital mortality. We used the area under the receiver operating curve (AUROC) to externally validate the RESCUE-IHCA score. The calibration of the model was tested by the decile calibration plot as well as Hosmer–Lemeshow goodness-of-fit with an associated P-value.Results: Of the 324 participants, 231 (71%) died before hospital discharge. The discriminative performance of the RESCUE-IHCA score was comparable with the originally validated cohort, with an AUC of 0.63. A prolonged duration of cardiac arrest was associated with an increased risk of mortality (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.01–1.03, P = .006). An initial rhythm of ventricular tachycardia (OR 0.14, 95% CI 0.04–0.51, P = .003), ventricular fibrillation (OR 0.11, 95% CI 0.03–0.46, P = .003), and palpable pulse (OR 0.26, 95% CI 0.07–0.92, P = 0.04) were associated with a reduced mortality risk compared to asystole or pulseless electrical activity. In contrast to the original study, age (P = 0.28), resuscitation timing (P = 0.14), disease category (P = 0.18), and pre-existing renal insufficiency (P = 0.12) were not associated with in-hospital death.Conclusion: In external validation, the RESCUE-IHCA score exhibited performance comparable to its original validation within the single-center population. Further investigation on hospital experience, time-of-day effect, and specific disease categories is warranted to improve the selection criteria for ECPR candidates during IHCA.

Background: Aortic dissection (AD) is a life‐threatening cardiovascular emergency that is often misdiagnosed as other chest pain conditions. Physiologically, AD may cause abnormalities in peripheral blood flow, which can be detected using pulse oximetry waveforms. Purpose: This study aimed to assess the feasibility of identifying AD based on pulse oximetry waveforms and to highlight the key waveform features that play a crucial role in this diagnostic method. Methods: This prospective study employed high‐risk chest pain cohorts from two emergency departments. The initial cohort was enriched with AD patients (n = 258, 47% AD) for model development, while the second cohort consisted of chest pain patients awaiting angiography (n = 71, 25% AD) and was used for external validation. Pulse oximetry waveforms from the four extremities were collected for each patient. After data preprocessing, a recognition model based on the random forest algorithm was trained using patients' gender, age, and waveform difference features extracted from the pulse oximetry waveforms. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The importance of features was also assessed using Shapley Value and Gini importance. Results: The model demonstrated strong performance in identifying AD in both the training and external validation sets. In the training set, the model achieved an area under the ROC curve of 0.979 (95% CI: 0.961–0.990), sensitivity of 0.918 (95% CI: 0.873–0.955), specificity of 0.949 (95% CI: 0.912–0.985), and accuracy of 0.933 (95% CI: 0.904–0.959). In the external validation set, the model attained an area under the ROC curve of 0.855 (95% CI: 0.720–0.965), sensitivity of 0.889 (95% CI: 0.722–1.000), specificity of 0.698 (95% CI: 0.566–0.812), and accuracy of 0.794 (95% CI: 0.672–0.878). Decision curve analysis (DCA) further showed that the model provided a substantial net benefit for identifying AD. The median mean and median variance of the four limbs' signals were the most influential features in the recognition model. Conclusions: This study demonstrated the feasibility and strong performance of identifying AD based on peripheral pulse oximetry waveforms in high‐risk chest pain populations in the emergency setting. The findings also provided valuable insights for future human fluid dynamics simulations to elucidate the impact of AD on blood flow in greater detail. [ABSTRACT FROM AUTHOR]

Objective To test the ability of the 2015 modified version of the European Network for the Study of Adrenal Tumors staging system (mENSAT) in predicting cancer-specific mortality (CSM), as well as overall mortality (OM) in adrenocortical carcinoma (ACC) patients of all stages, in a large-scale, and contemporary United States cohort. Methods We relied on the Surveillance, Epidemiology, and End Results (SEER) database (2004–2020) to test the accuracy and calibration of the mENSAT and subsequently compared it to the 8th edition of the American Joint Committee on Cancer staging system (AJCC). Results In 858 ACC patients, mENSAT accuracy was 74.7% for 3-year CSM predictions and 73.8% for 3-year OM predictions. The maximum departures from ideal predictions in mENSAT were +17.2% for CSM and +11.8% for OM. Conversely, AJCC accuracy was 74.5% for 3-year CSM predictions and 73.5% for 3-year OM predictions. The maximum departures from ideal predictions in AJCC were −6.7% for CSM and −7.1% for OM. Conclusion The accuracy of mENSAT is virtually the same as that of AJCC in predicting CSM (74.7% vs 74.5%) and OM (73.7% vs 73.5%). However, calibration is lower for mENSAT than for AJCC. In consequence, no obvious benefit appears to be associated with the use of mENSAT relative to AJCC in US ACC patients. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Methods Results Conclusions To assess the validity of the Fetal Medicine Foundation (FMF) chorionicity‐specific models for fetal growth in twin pregnancy.This was an external validation study of the FMF models using a nationwide Danish cohort of twin pregnancies. The cohort included all dichorionic (DC) and monochorionic diamniotic (MCDA) twin pregnancies with an estimated delivery date between 2008 and 2018, which satisfied the following inclusion criteria: two live fetuses at the first‐trimester ultrasound scan (11–14 weeks' gestation); biometric measurements available for the calculation of estimated fetal weight (EFW) using the Hadlock‐3 formula; and delivery of two liveborn infants. Validation involved assessing the distributional properties of the models and estimating the mean EFW Z‐score deviations. Additionally, the models were applied to pregnancies that delivered preterm and attended non‐scheduled visits (complicated pregnancies).Overall, 8542 DC and 1675 MCDA twin pregnancies met the inclusion criteria. In DC twins, 17 084 fetuses were evaluated at a total of 95 346 ultrasound scans, of which 44.5% were performed at scheduled visits in pregnancies carried to 37 + 0 weeks or later. The median number of growth scans per DC twin fetus from 20 + 0 weeks onwards was four. The model showed good agreement with the validation cohort for scheduled visits in DC twins delivered at 37 + 0 weeks or later (mean EFW Z‐score, –0.14 ± 1.05). In MCDA twins, 3350 fetuses underwent 31 632 eligible ultrasound scans, of which 59.5% were performed at scheduled visits in pregnancies carried to 36 + 0 weeks or later. The median number of growth scans per MCDA twin fetus from 16 + 0 weeks onwards was 10. The model showed favorable agreement with the validation cohort for scheduled visits in MCDA twins delivered at 36 + 0 weeks or later (mean EFW Z‐score, –0.09 ± 1.01). Non‐scheduled visits and preterm delivery before 37 + 0 weeks for DC twins and before 36 + 0 weeks for MCDA twins corresponded with smaller weight estimates, which was consistent with the study's definition of complicated pregnancy.The FMF models provide a good fit for EFW measurements in our Danish national cohort of uncomplicated twin pregnancies assessed at routine scans. Therefore, the FMF models establish robust criteria for subsequent investigations and potential clinical applications. Future research should focus on exploring the consequences of clinical implementation, particularly regarding the identification of twins that are small‐for‐gestational age, as they are especially susceptible to adverse perinatal outcome. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Intermittent Explosive Disorder (IED) is a common, chronic, and impairing psychological condition characterized by recurrent, affective aggressive behavior. IED is associated with a host of cognitive and affective symptoms not included in the diagnostic criteria which may be a valuable indicator of heterogeneity in IED—such information can be useful to enhance understanding and treatment of this disorder in mental health settings. A preliminary investigation conducted on cognitive-affective symptom heterogeneity in individuals with a history of IED demonstrated that level of emotional dysregulation primarily differentiated IED subgroups, however the sample size was limited, and almost half of the individuals did not have current IED (only lifetime IED). The present study addressed these limitations by conducting a latent class analysis of cognitive-affective symptoms among a large (n = 504) sample of individuals diagnosed with current IED. The latent IED classes were then externally validated on several adverse outcomes, historical precursors, and demographic variables. Statistical and clinical indicators supported a four-class model, with classes primarily distinguished by patients' severity of emotion dysregulation. The two moderate emotion-dysregulated classes both endorsed callous-unemotional traits and low empathy relative to other classes, a finding which differs from the initial investigation. An external validation of the four classes revealed that they significantly differed on severity outcomes (e.g., aggression, suicide attempts, antisocial behavior, global functioning, comorbidities) and historical precursors (e.g., aversive parental care, childhood maltreatment). These findings provide further insight into the heterogeneity within IED and the associations of such variability with important precursors and clinical outcomes. • Intermittent Explosive Disorder (IED) involves recurrent and impairing aggression. • It is important to examine heterogeneity among those diagnosed with current IED. • Latent class analysis of cognitive-affective tendencies reveals four IED subgroups. • IED subgroups confer varying risk for adverse outcomes and historical precursors. [ABSTRACT FROM AUTHOR]

Background: Advances in medical imaging have made it possible to classify ankle fractures using Artificial Intelligence (AI). Recent studies have demonstrated good internal validity for machine learning algorithms using the AO/OTA 2018 classification. This study aimed to externally validate one such model for ankle fracture classification and ways to improve external validity. Methods: In this retrospective observation study, we trained a deep-learning neural network (7,500 ankle studies) to classify traumatic malleolar fractures according to the AO/OTA classification. Our internal validation dataset (IVD) contained 409 studies collected from Danderyd Hospital in Stockholm, Sweden, between 2002 and 2016. The external validation dataset (EVD) contained 399 studies collected from Flinders Medical Centre, Adelaide, Australia, between 2016 and 2020. Our primary outcome measures were the area under the receiver operating characteristic (AUC) and the area under the precision-recall curve (AUPR) for fracture classification of AO/OTA malleolar (44) fractures. Secondary outcomes were performance on other fractures visible on ankle radiographs and inter-observer reliability of reviewers. Results: Compared to the weighted mean AUC (wAUC) 0.86 (95%CI 0.82–0.89) for fracture detection in the EVD, the network attained wAUC 0.95 (95%CI 0.94–0.97) for the IVD. The area under the precision-recall curve (AUPR) was 0.93 vs. 0.96. The wAUC for individual outcomes (type 44A-C, group 44A1-C3, and subgroup 44A1.1-C3.3) was 0.82 for the EVD and 0.93 for the IVD. The weighted mean AUPR (wAUPR) was 0.59 vs 0.63. Throughout, the performance was superior to that of a random classifier for the EVD. Conclusion: Although the two datasets had considerable differences, the model transferred well to the EVD and the alternative clinical scenario it represents. The direct clinical implications of this study are that algorithms developed elsewhere need local validation and that discrepancies can be rectified using targeted training. In a wider sense, we believe this opens up possibilities for building advanced treatment recommendations based on exact fracture types that are more objective than current clinical decisions, often influenced by who is present during rounds. [ABSTRACT FROM AUTHOR]

Background and Purpose: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non‐invasive system for identifying HER2 and HR in breast cancer using dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). Methods: In light of the absence of high‐performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I‐SPY 1) and I‐SPY 2, were incorporated. I‐SPY 2 was utilized for model training and validation, while I‐SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction. Results: The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I‐SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I‐SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction. Conclusion: This study proposes a non‐invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre‐trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic. [ABSTRACT FROM AUTHOR]

Background Non-traumatic lower extremity amputation (LEA) is a severe complication during dialysis. To inform decision-making for physicians, we developed a multivariable prediction model for LEA after starting dialysis. Methods Data from the Swedish Renal Registry (SNR) between 2010 and 2020 were geographically split into a development and validation cohort. Data from Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) between 1997 and 2009 were used for validation targeted at Dutch patients. Inclusion criteria were no previous LEA and kidney transplant and age ≥40 years at baseline. A Fine–Gray model was developed with LEA within 3 years after starting dialysis as the outcome of interest. Death and kidney transplant were treated as competing events. One coefficient, ordered by expected relevance, per 20 events was estimated. Performance was assessed with calibration and discrimination. Results SNR was split into an urban development cohort with 4771 individuals experiencing 201 (4.8%) events and a rural validation cohort with 4.876 individuals experiencing 155 (3.2%) events. NECOSAD contained 1658 individuals experiencing 61 (3.7%) events. Ten predictors were included: female sex, age, diabetes mellitus, peripheral artery disease, cardiovascular disease, congestive heart failure, obesity, albumin, haemoglobin and diabetic retinopathy. In SNR, calibration intercept and slope were –0.003 and 0.912, respectively. The C-index was estimated as 0.813 (0.783–0.843). In NECOSAD, calibration intercept and slope were 0.001 and 1.142 respectively. The C-index was estimated as 0.760 (0.697–0.824). Calibration plots showed good calibration. Conclusion A newly developed model to predict LEA after starting dialysis showed good discriminatory performance and calibration. By identifying high-risk individuals this model could help select patients for preventive measures. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Can a simplified ovarian hyperstimulation syndrome (OHSS) risk assessment index be developed and validated with sufficient discrimination of moderate/severe OHSS from those without OHSS? SUMMARY ANSWER This easy-to-use OHSS risk assessment index shows good discriminative power and high calibration accuracy in internal and external validation cohorts. WHAT IS KNOWN ALREADY An early alert and risk stratification is critical to prevent the occurrence of OHSS. We have previously developed a multi-stage smartphone app-based prediction model to evaluate the risk of OHSS, but app use might not be so convenient in many primary institutions. A simplified OHSS risk assessment index has been required. STUDY DESIGN, SIZE, DURATION This training and internal validation of an OHSS risk assessment index used retrospective cohort data from January 2016 to December 2020. External validation was performed with a prospective cohort database from January 2021 to May 2022. There were 15 066 cycles in the training cohort, 6502 cycles in the internal validation cohort, and 8097 cycles in the external validation cohort. PARTICIPANTS/MATERIALS, SETTING, METHODS This study was performed in the reproductive medicine center of a tertiary hospital. Infertile women who underwent ovarian stimulation were included. Data were extracted from the local database with detailed medical records. A multi-stage risk assessment index was constructed at multiple stages. The first stage was before the initiation of ovarian stimulation, the second was before the ovulation trigger, the third was after oocyte retrieval, and the last stage was on the embryo transfer day if fresh embryo transfer was scheduled. MAIN RESULTS AND THE ROLE OF CHANCE We established a simplified multi-stage risk assessment index for moderate/severe OHSS, the performance of which was further evaluated with discrimination and calibration abilities in training and internal and external validation cohorts. The discrimination abilities of the OHSS risk assessment index were determined with C-statistics. C-statistics in training (Stages 1–4: 0.631, 0.692, 0.751, 0.788, respectively) and internal (Stages 1–4: 0.626, 0.642, 0.755, 0.771, respectively) and external validation (Stages 1–4: 0.668, 0.670, 0.754, 0.773, respectively) cohorts were all increased from Stage 1 to 3 with similar trends, and were comparable between Stages 3 and 4. Calibration plots showed high agreement between observed and predicted cases in all three cohorts. Incidences of OHSS based on diverse risk stratification (negligible risk, low risk, medium risk, and high risk) were 0%, 0.6%, 2.7%, and 8.3% in the training cohort, 0%, 0.6%, 3.3%, and 8.5% in the internal validation cohort, and 0.1%, 1.1%, 4.1%, and 7.2% in the external validation cohort. LIMITATIONS, REASONS FOR CAUTION The influence from clinical interventions including cryopreservation of all embryos cannot be eliminated and thus certain risk factors like estrogen level on trigger day might be assigned with a lower risk score. Another weakness of the study is that several preventive treatments, for instance oral aspirin and letrozole, were not recorded and evaluated in the model. Despite the robust reliability of OHSS assessment index, this tool cannot be used directly for clinical decision-making or as a diagnostic tool. Its value lies in its capacity to evaluate the prognosis of various interventions and to facilitate clinician–patient communication. The combination of this tool and further symptoms and examinations should be all taken into consideration for accurate and personalized management of OHSS. WIDER IMPLICATIONS OF THE FINDINGS The OHSS risk assessment index can be implemented to facilitate personalized counseling and management of OHSS. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by National Key R&D Program of China (2022YFC2702504), Medical Research Fund Guangdong Provincial (A2024003), and Xinjiang Support Rural Science and Technology (Special Correspondent) Program in Guangdong Province (KTPYJ 2023014). All authors had nothing to disclose. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Background: CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods: We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non‐PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results: A total of 29,422 points were analyzed (PV lesions [n = 22 418], non‐PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non‐PV lesions (PV lesions vs. non‐PV lesions, %; sensitivity, 75.3 vs. 67.5, p <.05; PPV, 91.2 vs. 67.9, p <.05). CTI and superior vena cava could not be recognized or analyzed. In the potential reconnection prediction model, the incidence of potential reconnections was highest in the posterior, while the confidence was the highest in the roof. Conclusion: The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites. [ABSTRACT FROM AUTHOR]

Background: IgA nephropathy (IgAN) is the most common primary glomerular disease in chronic kidney disease (CKD), exhibiting significant heterogeneity in both clinical and pathological presentations. We aimed to explore the risk factors influencing short-term prognosis (≥90 days) and to construct a nomogram model for evaluating the risk of CKD progression in IgAN patients. Methods: Clinical and pathological data of patients diagnosed with IgAN through biopsy at two centers were retrospectively collected. Logistic regression was employed to analyze the training cohort dataset and identify the independent predictors to construct a nomogram model based on the final variables. The predictive model was validated both internally and externally, with its performance assessed using the area under the curve (AUC), calibration curves, and decision curve analysis. Results: Out of the patients in the modeling group, 129 individuals (41.6%) did not achieve remission following 3 months of treatment, indicating a high risk of CKD progression. A multivariate logistic regression analysis demonstrated that body mass index, urinary protein excretion, and tubular atrophy/interstitial fibrosis were identified as independent predictors for risk stratification. A nomogram model was formulated utilizing the final variables. The AUCs for the training set, internal validation set, and external validation set were 0.746 (95% confidence intervals (CI) [0.691–0.8]), 0.764 (95% CI [0.68–0.85]), and 0.749 (95% CI [0.65–0.85]), respectively. The validation of the subgroup analysis also demonstrated a satisfactory AUC. Conclusion: This study developed and validated a practical nomogram that can individually predict short-term treatment outcomes (≥90 days) and the risk of CKD progression in IgAN patients. It provides reliable guidance for timely and personalized intervention and treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Stress urinary incontinence (SUI), the prevalent form of urinary incontinence, significantly impairs women's quality of life. This study aims to create a visual nomogram to estimate the risk of SUI within one year postpartum for early intervention in high-risk Chinese women. Methods: We recruited 1,531 postpartum women who gave birth at two hospitals in Kunshan City from 2021 to 2022. Delivery details were meticulously extracted from the hospitals' medical records system, while one-year postpartum follow-ups were conducted via phone surveys specifically designed to ascertain SUI status. Utilizing data from one hospital as the training set, logistic regression analysis was performed to pinpoint significant factors and subsequently construct the nomogram. To ensure robustness, an independent dataset sourced from the second hospital served as the external validation cohort. The model's performance was rigorously evaluated using calibration plots, ROC curves, AUC values, and DCA curves. Results: The study population was 1,125 women. The SUI incidence within one year postpartum was 26% (293/1125). According to the regression analysis, height, pre-pregnancy BMI, method of induction, mode of delivery, perineal condition, neonatal weight, SUI during pregnancy, and SUI during the first pregnancy were incorporated into the nomogram. The AUC of the nomogram was 0.829 (95% CI 0.790–0.867), and the external validation set was 0.746 (95% CI 0.689–0.804). Subgroup analysis based on parity showed good discrimination. The calibration curve indicated concordance. The DCA curve showed a significant net benefit. Conclusion: Drawing from real-world data, we have successfully developed an SUI predictive model tailored for postpartum Chinese women. Upon successful external validation, this model holds immense potential as an effective screening tool for SUI, enabling timely interventions and ultimately may improve women's quality of life. [ABSTRACT FROM AUTHOR]

Objective: The unique metastatic pattern of skip lateral lymph node metastasis (SLLNM) in PTC patients may lead to missed diagnosis of lateral cervical metastatic lymph nodes. Therefore, many different SLLNM prediction models were constructed. In this study, partially eligible models (Hu 2020, Wang 2020, and Zhao 2023 nomograms) were selected for external validation, and then new variables were incorporated for model reconstruction to extend clinical applicability. Methods: 576 PTC patients from our center were selected to evaluate the performance of the three nomograms using the receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA). Three new variables were added to calibrate the model, including assessment of LN status on ultrasound (US-SLLNM), the distance from the tumor to the capsule (Capsular distance), and the number of central lymph node dissections (CLND number). Univariate and multivariate logistic regression analyses were used to screen independent predictors to reconstruct the model, and 1000 Bootstrap internal validations were performed. Results: SLLNM were present in 69/576 patients (12.0%). In external validation, the area under the ROC curves (AUCs) for Hu 2020, Wang 2020, and Zhao 2023 nomograms were 0.695 (95% CI:0.633-0.766), 0.792 (95% CI=0.73-0.845), and 0.769 (95% CI:0.713-0.824), respectively. The calibration curves for the three models were overall poorly fitted; DCA showed some net clinical benefit. Model differentiation and net clinical benefit improved by adding three new variables. Based on multivariate analysis, female, age, and maximum tumor diameter = 10 mm, located at the upper pole, Capsular distance < 0mm, US-SLLNM, CLND number = 5 were identified as independent predictors of SLLNM and were used to construct the new model. After 1000 Bootstrap internal validations, the mean AUC of the model was 0.870 (95% CI:0.839-0.901), the calibration curve was close to the ideal curve, and the net clinical benefit was significant. Conclusion: Overall, these nomograms were well differentiated and provided some net clinical benefit, but with varying degrees of underestimation or overestimation of the actual risk and high false-negative rates. New dynamic nomogram was constructed based on the addition of new variables and larger samples, showing better performance. [ABSTRACT FROM AUTHOR]

Introduction: Pretreatment hemoglobin and neutrophil levels were previously reported to be important indicators for predicting the effectiveness of ipilimumab plus nivolumab (IPI + NIVO) therapy for renal cell carcinoma (RCC). Therefore, we aimed to validate this in a large external cohort. Methods: In total, 172 patients with RCC who underwent IPI + NIVO treatment at a multicenter setting were divided into three groups according to their pretreatment hemoglobin and neutrophil levels (group 1: non-anemia; group 2: anemia and low-neutrophil; and group 3: anemia and high-neutrophil). Results: Group 1 showed better survival than groups 2 and 3 (overall survival: 52.3 vs. 21.4 vs. 9.4 months, respectively; progression-free survival: 12.1 vs. 7.0 vs. 3.4 months, respectively). Discussion: In this large cohort, we validated our earlier observation that hemoglobin and neutrophil levels can be reliable predictors of the effectiveness of IPI + NIVO in advanced RCC. Thus, our approach may aid in selecting the optimal first-line therapy for RCC. [ABSTRACT FROM AUTHOR]

Background Fatty liver index (FLI) and hepatic steatosis index (HSI) are serologic scores used to detect liver steatosis. However, their diagnostic performance in people with HIV (PWH) remains unclear. We performed an external validation of FLI and HSI in the Swiss HIV Cohort Study. Methods We systematically performed vibration-controlled transient elastography (VCTE) among Swiss HIV Cohort Study participants at Bern University Hospital between November 2019 and August 2021. Individuals with viral hepatitis and pregnant women were excluded. We defined liver steatosis as controlled attenuation parameter ≥248 dB/m using VCTE. Model discrimination was assessed with the C-index and model calibration with calibration plots. A decision curve analysis was performed to compare the clinical usefulness of both scores. Results Of 321 participants, 91 (28.4%) were female, the median age was 51.4 years (IQR, 42–59), 230 (71.7%) were Caucasian, and 164 (51.1%) had a body mass index >25 kg/m2. VCTE-confirmed liver steatosis was present in 158 (49.2%). Overall, 125 (38.9%) had an FLI ≥60, and 128 (39.9%) had an HSI ≥36. At these cutoffs, the C-index to diagnose liver steatosis was 0.85 for FLI (95% CI,.80–.89) and 0.78 for HSI (95% CI,.73–.83). Whereas FLI was well calibrated, HSI overestimated the risk for steatosis. Both models showed a positive net benefit, with FLI having a greater net benefit when compared with HSI. Conclusions FLI and HSI are valid tools to detect liver steatosis in PWH. FLI should be the preferred score, given its better performance and greater clinical usefulness. [ABSTRACT FROM AUTHOR]

Background: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). Methods: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer–Lemeshow (HL), AUC, sensitivity and specificity. Results: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. Conclusions: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting. [ABSTRACT FROM AUTHOR]

In the context of the COVID-19 pandemic, the early and accurate identification of patients at risk of deterioration was crucial in overcrowded and resource-limited emergency departments. This study conducts an external validation for the evaluation of the performance of the National Early Warning Score 2 (NEWS2), the S/F ratio, and the ROX index at ED admission in a large cohort of COVID-19 patients from Colombia, South America, assessing the net clinical benefit with decision curve analysis. A prospective cohort study was conducted on 6907 adult patients with confirmed COVID-19 admitted to a tertiary care ED in Colombia. The study evaluated the diagnostic performance of NEWS2, S/F ratio, and ROX index scores at ED admission using the area under the receiver operating characteristic curve (AUROC) for discrimination, calibration, and decision curve analysis for the prediction of intensive care unit admission, invasive mechanical ventilation, and in-hospital mortality. We included 6907 patients who presented to the ED with confirmed SARS-CoV-2 infection from March 2020 to November 2021. Mean age was 51 (35–65) years and 50.4% of patients were males. The rate of intensive care unit admission was 28%, and in-hospital death was 9.8%. All three scores have good discriminatory performance for the three outcomes based on the AUROC. S/F ratio showed miscalibration at low predicted probabilities and decision curve analysis indicated that the NEWS2 score provided a greater net benefit compared to other scores across at a 10% threshold to decide ED admission at a high-level of care facility. The NEWS2, S/F ratio, and ROX index at ED admission have good discriminatory performances in COVID-19 patients for the prediction of adverse outcomes, but the NEWS2 score has a higher net benefit underscoring its clinical utility in optimizing patient management and resource allocation in emergency settings. [ABSTRACT FROM AUTHOR]

Abstract Background CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non‐PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results A total of 29,422 points were analyzed (PV lesions [n = 22 418], non‐PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non‐PV lesions (PV lesions vs. non‐PV lesions, %; sensitivity, 75.3 vs. 67.5, p

Abstract Background Advances in medical imaging have made it possible to classify ankle fractures using Artificial Intelligence (AI). Recent studies have demonstrated good internal validity for machine learning algorithms using the AO/OTA 2018 classification. This study aimed to externally validate one such model for ankle fracture classification and ways to improve external validity. Methods In this retrospective observation study, we trained a deep-learning neural network (7,500 ankle studies) to classify traumatic malleolar fractures according to the AO/OTA classification. Our internal validation dataset (IVD) contained 409 studies collected from Danderyd Hospital in Stockholm, Sweden, between 2002 and 2016. The external validation dataset (EVD) contained 399 studies collected from Flinders Medical Centre, Adelaide, Australia, between 2016 and 2020. Our primary outcome measures were the area under the receiver operating characteristic (AUC) and the area under the precision-recall curve (AUPR) for fracture classification of AO/OTA malleolar (44) fractures. Secondary outcomes were performance on other fractures visible on ankle radiographs and inter-observer reliability of reviewers. Results Compared to the weighted mean AUC (wAUC) 0.86 (95%CI 0.82–0.89) for fracture detection in the EVD, the network attained wAUC 0.95 (95%CI 0.94–0.97) for the IVD. The area under the precision-recall curve (AUPR) was 0.93 vs. 0.96. The wAUC for individual outcomes (type 44A-C, group 44A1-C3, and subgroup 44A1.1-C3.3) was 0.82 for the EVD and 0.93 for the IVD. The weighted mean AUPR (wAUPR) was 0.59 vs 0.63. Throughout, the performance was superior to that of a random classifier for the EVD. Conclusion Although the two datasets had considerable differences, the model transferred well to the EVD and the alternative clinical scenario it represents. The direct clinical implications of this study are that algorithms developed elsewhere need local validation and that discrepancies can be rectified using targeted training. In a wider sense, we believe this opens up possibilities for building advanced treatment recommendations based on exact fracture types that are more objective than current clinical decisions, often influenced by who is present during rounds.

Abstract Background Stress urinary incontinence (SUI), the prevalent form of urinary incontinence, significantly impairs women’s quality of life. This study aims to create a visual nomogram to estimate the risk of SUI within one year postpartum for early intervention in high-risk Chinese women. Methods We recruited 1,531 postpartum women who gave birth at two hospitals in Kunshan City from 2021 to 2022. Delivery details were meticulously extracted from the hospitals’ medical records system, while one-year postpartum follow-ups were conducted via phone surveys specifically designed to ascertain SUI status. Utilizing data from one hospital as the training set, logistic regression analysis was performed to pinpoint significant factors and subsequently construct the nomogram. To ensure robustness, an independent dataset sourced from the second hospital served as the external validation cohort. The model’s performance was rigorously evaluated using calibration plots, ROC curves, AUC values, and DCA curves. Results The study population was 1,125 women. The SUI incidence within one year postpartum was 26% (293/1125). According to the regression analysis, height, pre-pregnancy BMI, method of induction, mode of delivery, perineal condition, neonatal weight, SUI during pregnancy, and SUI during the first pregnancy were incorporated into the nomogram. The AUC of the nomogram was 0.829 (95% CI 0.790–0.867), and the external validation set was 0.746 (95% CI 0.689–0.804). Subgroup analysis based on parity showed good discrimination. The calibration curve indicated concordance. The DCA curve showed a significant net benefit. Conclusion Drawing from real-world data, we have successfully developed an SUI predictive model tailored for postpartum Chinese women. Upon successful external validation, this model holds immense potential as an effective screening tool for SUI, enabling timely interventions and ultimately may improve women’s quality of life.

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]

The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort. Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient's risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses. The median follow-up duration within our cohort spanned 5.1 years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models. Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility. [ABSTRACT FROM AUTHOR]

Abstract A scoring system to discriminate between uncomplicated and complicated appendicitis is beneficial to determine the optimal treatment for acute appendicitis. We developed a scoring system to discriminate between uncomplicated and complicated appendicitis and assessed the clinical usefulness of the scoring system using external validation. A total of 299 patients with acute appendicitis were retrospectively reviewed. One hundred and ninety-nine patients were assigned to the model development group, while the other 100 patients were assigned to an external validation group. A scoring system for complicated appendicitis was created using a final multivariate logistic regression model with six independent predictors. The area under the receiver operating characteristic curve of the scoring system was 0.882 (95% confidence interval: 0.835–0.929). The cutoff point of the scoring system was 12, and the sensitivity and specificity were 82.9% and 86.2%, respectively. In the external validation group, the area under the receiver operating characteristic curve of the scoring system was 0.868 (95% confidence interval 0.794–0.942), and there was no significant difference between the groups in the area under the receiver operating characteristic curve (P = 0.750). Our newly developed scoring system may contribute to prompt determination of the optimal treatment for acute appendicitis.

Abstract 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

Abstract Background A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

Aim. Development and external validation of a risk prediction model for acute decompensated heart failure (ADHF) in patients with low left ventricular ejection fraction.Material and methods. The model development group was represented by patients with heart failure with reduced ejection fraction (HFrEF) included in a registry observational study from 2015 to 2019, a total of 260 patients, age 59 (53; 66) years, 214 (82.3%) — men. External validation of the model was carried out in a cohort of independent prospective observation of 94 patients with HFrEF from the same registry for the period from 2020 to 2021, median age 66 (52;73) years, of which 73 (77.6%) were men. The prospective follow-up period was 4.6 (2.3; 4.9) years in the internal validation group, 2.5 (1.7; 2.9) years in the external validation group. Data were obtained on the status of patients, causes of death, and the frequency of hospitalizations for ADHF. The actual and predicted incidence of ADHF using the evaluated prognostic model was compared.Results. During the observation period in the internal validation group, ADHF developed in 69 (26.5%) patients, and 47 (18.1%) died due to ADHF. The prognostic regression model included LA enlargement of more than 45 mm, male gender, left ventricular ejection fraction less than 35%, absence of renin-angiotensin system blocker and amiodarone. When performing ROC analysis, the area under the ROC curve (AUC) of the created model was 0.8, sensitivity model — 69.2%, specificity — 80%, accuracy — 75.3%. In the external validation group, 34 (36.2%) cases of ADHF were registered; mortality from ADHF in the external validation group was 15.9%, which is comparable to the development group (p > 0.05). The diagnostic value of the developed model during external validation showed to be high and was comparable to the results obtained in the development group: the area under the ROC curve (AUC) was 0.8, sensitivity — 73.3%, specificity — 82.5%, accuracy 76.1%, (p=0.102, McNeil test).Conclusion. The developed regression model has sufficient statistical power to predict the risk of ADHF in patients with low left ventricular ejection fraction in the long term, which is confirmed by external validation.

Ye Rim Kim,1,2,&ast; Sung Won Chung,1,2,&ast; Min-Ju Kim,3 Won-Mook Choi,1,2 Jonggi Choi,1,2 Danbi Lee,1,2 Han Chu Lee,1,2 Ju Hyun Shim1,2,4 1Division of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; 2Liver Cancer Center, Asan Cancer Institute, Asan Medical Center, Seoul, Republic of Korea; 3Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; 4Digestive Diseases Research Center, University of Ulsan College of Medicine, Seoul, Republic of Korea&ast;These authors contributed equally to this workCorrespondence: Ju Hyun Shim, Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea, Tel +82-02-3010-3190, Fax +82-02-485-5782, Email s5854@amc.seoul.krIntroduction: We aimed to evaluate the generalizability of retrospective single-center cohort studies on prognosis of hepatocellular carcinoma (HCC) by comparing overall survival (OS) after various treatments between a nationwide multicenter cohort and a single-center cohort of HCC patients.Methods: Patients newly diagnosed with HCC between January 2008 and December 2018 were analyzed using data from the Korean Primary Liver Cancer Registry (multicenter cohort, n=16,443), and the Asan Medical Center HCC registry (single-center cohort, n=15,655). The primary outcome, OS after initial treatment, was compared between the two cohorts for both the entire population and for subcohorts with Child-Pugh A liver function (n=2797 and n=5151, respectively) treated according to the Barcelona-Clinic-Liver-Cancer (BCLC) strategy, using Log rank test and Cox proportional hazard models.Results: Patients of BCLC stages 0 and A (59.3% vs 35.2%) and patients who received curative treatment (42.1% vs 32.1%) were more frequently observed in the single-center cohort (Ps< 0.001). Multivariable analysis revealed significant differences between the two cohorts in OS according to type of treatment: the multicenter cohort was associated with higher risk of mortality among patients who received curative (adjusted hazard ratio [95% confidence interval], 1.48 [1.39– 1.59]) and non-curative (1.22 [1.17– 1.27]) treatments, whereas the risk was lower in patients treated with systemic therapy (0.83 [0.74– 0.92]) and best supportive care (0.85 [0.79– 0.91]). Subcohort analysis also demonstrated significantly different OS between the two cohorts, with a higher risk of mortality in multicenter cohort patients who received chemoembolization (1.72 [1.48– 2.00]) and ablation (1.44 [1.08– 1.92]).Conclusion: Comparisons of single-center and multicenter cohorts of HCC patients revealed significant differences in OS according to treatment modality after adjustment for prognostic variables. Therefore, the results of retrospective single-center cohort studies of HCC treatments may not be generalizable to real-world practice.Keywords: BCLC, UICC, liver cancer, retrospective cohort, external validation

Background Cardiovascular diseases seriously threaten the health of the elderly in our country and bring a huge economic burden to our country. Accurate screening of risk assessment tools for early cardiovascular diseases can specifically prevent and delay the development of cardiovascular diseases, studies have suggested that commonly used cardiovascular risk scoring tools are not effective in predicting risk in the elderly, but there is a lack of relevant external validation experimental studies, and there is no well-recognized effective risk prediction tool for elderly patients. Objective To independently validate and compare the efficacy of five commonly used cardiovascular risk assessment tools in China and abroad in predicting 10-year cardiovascular disease risk in our community elderly population, to explore the cardiovascular disease risk assessment tools suitable for our community elderly population, and to provide theoretical basis and support for the prevention and treatment of cardiovascular disease at the grass-roots level. Methods From January 2012 to December 2013, more than 20 000 people in the Liuyuan Community Health Service Centre and Runda Community Health Service Centre in Suzhou Province took part in community medical examinations. According to the inclusion and exclusion criteria, three hundred and forty-four people were identified, data on age, sex, region of residence, systolic blood pressure, diastolic blood pressure, BMI, waist circumference, total cholesterol, high-density lipoprotein cholesterol, triglyceride, atrial fibrillation, Left ventricular hypertrophy, cardiovascular disease, hypertension, diabetes, and smoking were collected from the start-up medical examination system In the follow-up survey of chronic diseases, the current smoking status and smoking age, whether taking antihypertensive drugs, family history of cardiovascular disease, prevalence of hypertension and drug use of hypertension, prevalence of diabetes mellitus, and whether and when cardiovascular disease occurred from January 2014 to December 2022 were improved. The outcome events were stroke and coronary heart disease, and were divided into positive group and negative group, all subjects were assessed with the Framingham Heart Risk Score (FRS-CVD) , the modified Framingham Stroke Scale (R-FSRS) , the European systemic coronary risk assessment scale 2019 (SCORE) , Chinese 10-year ischemic cardiovascular disease risk (ICVD) , and the China-PAR cardiovascular disease risk assessment, concordance Index (C-index) , Hosmer-Lemeshow χ2 and calibration chart were used to evaluate the relationship between the results and actual cardiovascular disease incidence. Results Of the 344 enrolled investigators, with a mean age of 62 (56, 68) years, as of December 2022, 169 had developed cardiovascular disease and 175 had not. Two groups of people were carried out 5 kinds of cardiovascular disease risk score, cardiovascular disease risk assessment tool forecast and the actual incidence of the comparative analysis results are as follows, discrimination test: C-index of FRS-CVD was 0.711 (95%CI=0.658-0.764) , C-index of R-FSRS was 0.728 (95%CI=0.675-0.781) , SCORE: C-index was 0.724 (95%CI=0.671-0.777) , ICVD: C-index was 0.727 (95%CI=0.674-0.779) , China-PAR: C-index was 0.735 (95%CI=0.682-0.788) ; Hosmer-Lemeshow test calibration status: FRS-CVD: χ2 =16.789 (P=0.032) , R-FSRS: χ2=11.019 (P=0.201) , SCORE: χ2=20.396 (P=0.002) , ICVD: χ2=24.311 (P=0.001) , China-PAR: χ2=15.149 (P=0.056) ; R-FSRS is the best calibration. In men, model discrimination: FRS-CVD: C-index was 0.642 (95%CI=0.577-0.707) , R-FSRS: C-index was 0.646 (95%CI=0.581-0.710) , SCORE: C-index was 0.646 (95%CI=0.581-0.711) , ICVD: C-index was 0.628 (95%CI=0.563-0.693) , China-PAR: C-index was 0.636 (95%CI=0.571-0.700) ; Hosmer-Lemeshow test calibration status: FRS-CVD: χ2=7.371 (P=0.288) , R-FSRS: χ2=8.470 (P=0.293) , SCORE: χ2=5.146 (P=0.525) , ICVD: χ2=6.103 (P=0.412) , China-PAR: χ2=9.555 (P=0.298) , SCORE was calibrated best in the calibration diagram. Model discrimination among women: FRS-CVD: C-index was 0.698 (95%CI=0.633-0.762) , R-FSRS: C-index was 0.731 (95%CI=0.666-0.795) , SCORE: C-index was 0.733 (95%CI=0.668-0.798) , ICVD: C-index was 0.747 (95%CI=0.682-0.811) , China-PAR: C-index was 0.754 (95%CI=0.689-0.818) ; Hosmer-Lemeshow test calibration status: FRS-CVD: χ2=14.515 (P=0.069) , R-FSRS: χ2=12.175 (P=0.032) , SCORE: χ2=9.611 (P=0.022) , ICVD: χ2=19.349 (P=0.007) , China-PAR: χ2=12.372 (P=0.135) , China-PAR calibration is the best in calibration chart. Conclusion R-FSRS model has a good performance in predicting the risk of cardiovascular disease in the elderly, especially in the elderly population. SCORE model did better in predicting cardiovascular disease risk in older men, while China-PAR model did better in predicting cardiovascular disease risk in older women. Therefore, the application of these assessment tools will help doctors to accurately predict the risk of cardiovascular disease in the elderly, and formulate corresponding prevention and treatment strategies.

Abstract Background As global aging accelerates, routinely assessing the functional status and morbidity burden of older patients becomes paramount. The aim of this study is to assess the validity of the comprehensive clinical and functional Health Assessment Tool (HAT) based on four cohorts of older adults (60 + years) from the Swedish National study on Aging and Care (SNAC) spanning urban, suburban, and rural areas. Methods The HAT integrates five health indicators (gait speed, global cognition, number of chronic diseases, and basic and instrumental activities of daily living), providing an individual-level score between 0 and 10. The tool was constructed using nominal response models, first separately for each cohort and then in a harmonized dataset. Outcomes included all-cause mortality over a maximum follow-up of 16 years and unplanned hospital admissions over a maximum of 3 years of follow-up. The predictive capacity was assessed through the area under the curve (AUC) using logistic regressions. For time to death, Cox regressions were performed, and Harrell’s C-indices were reported. Results from the four cohorts were pooled using individual participant data meta-analysis and compared with those from the harmonized dataset. Results The HAT demonstrated high predictive capacity across all cohorts as well as in the harmonized dataset. In the harmonized dataset, the AUC was 0.84 (95% CI 0.81–0.87) for 1-year mortality, 0.81 (95% CI 0.80–0.83) for 3-year mortality, 0.80 (95% CI 0.79–0.82) for 5-year mortality, 0.69 (95% CI 0.67–0.70) for 1-year unplanned admissions, and 0.69 (95% CI 0.68–0.70) for 3-year unplanned admissions. The Harrell’s C for time-to-death throughout 16 years of follow-up was 0.75 (95% CI 0.74–0.75). Conclusions The HAT is a highly predictive, clinically intuitive, and externally valid instrument with potential for better addressing older adults’ health needs and optimizing risk stratification at the population level.

A scoring system to discriminate between uncomplicated and complicated appendicitis is beneficial to determine the optimal treatment for acute appendicitis. We developed a scoring system to discriminate between uncomplicated and complicated appendicitis and assessed the clinical usefulness of the scoring system using external validation. A total of 299 patients with acute appendicitis were retrospectively reviewed. One hundred and ninety-nine patients were assigned to the model development group, while the other 100 patients were assigned to an external validation group. A scoring system for complicated appendicitis was created using a final multivariate logistic regression model with six independent predictors. The area under the receiver operating characteristic curve of the scoring system was 0.882 (95% confidence interval: 0.835–0.929). The cutoff point of the scoring system was 12, and the sensitivity and specificity were 82.9% and 86.2%, respectively. In the external validation group, the area under the receiver operating characteristic curve of the scoring system was 0.868 (95% confidence interval 0.794–0.942), and there was no significant difference between the groups in the area under the receiver operating characteristic curve (P = 0.750). Our newly developed scoring system may contribute to prompt determination of the optimal treatment for acute appendicitis. [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]

Objective: Several population pharmacokinetic models of tacrolimus in liver transplant patients were built, and their predictability was evaluated in their settings. However, the extrapolation in the prediction was unclear. This study aimed to evaluate the predictive performance of published tacrolimus models in adult liver transplant recipients using data from the Thai population as an external dataset. Methods: The selected published models were systematically searched and evaluated for their quality. The external dataset of patients who underwent the first liver transplant and received immediate-release tacrolimus was used to assess the predictive performance of each selected model. Trough concentrations between 3 and 6 months were retrospectively collected to evaluate the predictability of each model using prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. Results: Sixty-seven patients with 360 trough concentrations and eight selected published models were included in this study. None of the models met the predictive precision criteria in prediction-based diagnostics. Meanwhile, four published population pharmacokinetic models showed a normal distribution in NPDE testing. Regarding Bayesian forecasting, all models improved their forecasts with at least one prior information data point. Conclusion: Bayesian forecasting is more accurate and precise than other testing methods for predicting drug concentrations. However, none of the evaluated models provides satisfactory predictive performance for generalization to Thai liver transplant patients. This underscores the need for future research to develop population PK models tailored to the Thai population. Such efforts should consider the inclusion of nonlinear pharmacokinetics and region-specific factors, including genetic variability, to improve model accuracy and applicability. [ABSTRACT FROM AUTHOR]

Objective: This study sought to validate the Rossi nomogram in a Chinese population and then to include the Bishop score to see if it has an effect on the accuracy of the nomogram. Materials and methods: The Rossi predictive model was applied and externally validated in a retrospective cohort from August 2017 and July 2023 in a Chinese tertiary-level medical center. For the revision and updating of the models, the regression coefficients of all the predictors (except race) were re-estimated and then the cervical Bishop score at the time of induction was added. Each model's performance was measured using the receiver-operating characteristic and calibration plots. Decision curve analysis determined the range of the probability threshold for each prediction model that would be of clinical value. Results: A total of 721 women met the inclusion criteria, of whom 183 (25.4%) underwent a cesarean delivery. The calibration demonstrated the underestimation of the original model, with an area under the curve (AUC) of 0.789 (95% confidence interval [CI] 0.753–0.825, p < 0.001). After recalibrating the original model, the discriminative performance was improved from 0.789 to 0.803. Moreover, the discriminatory power of the updated model was further improved when the Bishop score at the time of induction was added to the recalibrated multivariable model. Indeed, the updated model demonstrated good calibration and discriminatory power, with an AUC of 0.811. The decision curve analysis indicated that all the models (original, recalibrated, and updated) provided higher net benefits of between 0 and 60% of the probability threshold, which indicates the benefits of using the models to make decisions concerning patients who fall within the identified range of the probability threshold. The net benefits of the updated model were higher than those of the original model and the recalibrated model. Conclusion: The nomogram used to predict cesarean delivery following induction developed by Rossi et al. has been validated in a Chinese population in this study. More specifically, adaptation to a Chinese population by excluding ethnicity and including the Bishop score prior to induction gave rise to better performance. The three models (original, recalibrated, and updated) offer higher net benefits when the probability threshold is between 0 and 60%. [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]

Dexmedetomidine is a sedative used in both adults and off‐label in children with considerable reported pharmacokinetic (PK) interindividual variability affecting drug exposure across populations. Several published models describe the population PKs of dexmedetomidine in neonates, infants, children, and adolescents, though very few have been externally evaluated. A prospective PK dataset of dexmedetomidine plasma concentrations in children and young adults aged 0.01‐19.9 years was collected as part of a multicenter opportunistic PK study. A PubMed search of studies reporting dexmedetomidine PK identified five population PK models developed with data from demographically similar children that were selected for external validation. A total of 168 plasma concentrations from 102 children were compared with both population (PRED) and individualized (IPRED) predicted values from each of the five published models by quantitative and visual analyses using NONMEM (v7.3) and R (v4.1.3). Mean percent prediction errors from observed values ranged from −1% to 120% for PRED, and −24% to 60% for IPRED. The model by James et al, which was developed using similar "real‐world" data, nearly met the generalizability criteria from IPRED predictions. Other models developed using clinical trial data may have been limited by inclusion/exclusion criteria and a less racially diverse population than this study's opportunistic dataset. The James model may represent a useful, but limited tool for model‐informed dosing of hospitalized children. [ABSTRACT FROM AUTHOR]

Background: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia. [ABSTRACT FROM AUTHOR]

Objective: To externally validate TRISS's probability of survival in a tertiary care hospital in northeastern Thailand. Materials and Methods: A retrospective cohort prognostic study included patients with significant injuries (ISS > 15) admitted to the hospital from 2011 to 2022 from the Khon Kaen trauma registry. Baseline characteristics were identified. AuROC presented the accuracy of the model. The age group was used as a subgroup analysis. The primary outcome was in-hospital mortality. Results: This retrospective cohort study was conducted at a tertiary care public hospital in northeastern Thailand. A total of 20,867 patients were included. Missing primary outcome data were excluded. Most patients were male (75.23%). The mean age was 38.19 ± 19.65 years. The mean ISS was 20.17 ± 5.28. The mortality rate was 15.33%. AuROC was 0.8388. Subgroup analysis by age group showed a statistically significant reduction in AuROC by increasing age. Conclusion: The accuracy of the TRISS model in a tertiary care hospital in Thailand was excellent, as close as MTOS. The accuracy was decreased by age. The TRISS model is applied to trauma quality improvement programs in Thailand. [ABSTRACT FROM AUTHOR]

External validation is crucial in developing reliable machine learning models. This study aimed to validate three novel indices—Thermographic Joint Inflammation Score (ThermoJIS), Thermographic Disease Activity Index (ThermoDAI), and Thermographic Disease Activity Index-C-reactive protein (ThermoDAI-CRP)—based on hand thermography and machine learning to assess joint inflammation and disease activity in rheumatoid arthritis (RA) patients. A 12-week prospective observational study was conducted with 77 RA patients recruited from rheumatology departments of three hospitals. During routine care visits, indices were obtained at baseline and week 12 visits using a pre-trained machine learning model. The performance of these indices was assessed cross-sectionally and longitudinally using correlation coefficients, the area under the receiver operating curve (AUROC), sensitivity, specificity, and positive and negative predictive values. ThermoDAI and ThermoDAI-CRP correlated with CDAI, SDAI, and DAS28-CRP cross-sectionally (ρ = 0.81; ρ = 0.83; ρ = 0.78) and longitudinally (ρ = 0.55; ρ = 0.61; ρ = 0.60), all p < 0.001. ThermoDAI and ThermoDAI-CRP also outperformed Patient Global Assessment (PGA) and PGA + C-reactive protein (CRP) in detecting changes in 28-swollen joint counts (SJC28). ThermoJIS had an AUROC of 0.67 (95% CI, 0.58 to 0.76) for detecting patients with swollen joints and effectively identified patients transitioning from SJC28 > 1 at baseline visit to SJC28 ≤ 1 at week 12 visit. These results support the effectiveness of ThermoJIS in assessing joint inflammation, as well as ThermoDAI and ThermoDAI-CRP in evaluating disease activity in RA patients. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). Methods: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. Results: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. Conclusions: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy. [ABSTRACT FROM AUTHOR]

This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research. [ABSTRACT FROM AUTHOR]

Background In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database. Methods A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers. Results In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity. Conclusion The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients. [ABSTRACT FROM AUTHOR]

Purpose: Anastomotic recurrence leads to poor prognosis in patients with Siewert II or III adenocarcinoma who undergo radical gastrectomy and do not receive neoadjuvant therapy. We aimed to establish a prognostic model to evaluate the risk of postoperative anastomotic recurrence in patients with Siewert II or III adenocarcinoma who did not receive neoadjuvant therapy. Methods: We included 366 patients with Siewert II or III adenocarcinoma who were treated with radical gastrectomy without neoadjuvant therapy at Fujian Provincial Hospital (FPH) between 2012 and 2018 as the development cohort. Cox regression was used to verify prognostic factors for anastomotic recurrence, and a nomogram was established. The nomogram was externally validated using a combined cohort of two external centers. Patients were classified into high- or low-risk groups according to the diagnostic threshold and nomogram scores, and recurrence-related survival analysis was analyzed. Results: The average age was 64.6 years, and 285 patients were male. All surgeries were successfully performed (185 open vs 181 laparoscopic). The 3-year anastomotic recurrence rate was significantly lower in the low-risk group (3.5% vs 18.8%, P < 0.001). The predictive performance was verified in the external validation cohort. This model better stratified patient survival than the American Joint Committee on Cancer (AJCC) TNM staging system. Conclusions: This novel nomogram with surgical margin, postoperative tumor node metastasis (pTNM) stage, and neural invasion as prognostic factors has a significant predictive performance for the risk of anastomotic recurrence after radical gastrectomy in patients with Siewert II or III adenocarcinoma. [ABSTRACT FROM AUTHOR]

Background IgA nephropathy (IgAN) is the most common primary glomerular disease in chronic kidney disease (CKD), exhibiting significant heterogeneity in both clinical and pathological presentations. We aimed to explore the risk factors influencing short-term prognosis (≥90 days) and to construct a nomogram model for evaluating the risk of CKD progression in IgAN patients. Methods Clinical and pathological data of patients diagnosed with IgAN through biopsy at two centers were retrospectively collected. Logistic regression was employed to analyze the training cohort dataset and identify the independent predictors to construct a nomogram model based on the final variables. The predictive model was validated both internally and externally, with its performance assessed using the area under the curve (AUC), calibration curves, and decision curve analysis. Results Out of the patients in the modeling group, 129 individuals (41.6%) did not achieve remission following 3 months of treatment, indicating a high risk of CKD progression. A multivariate logistic regression analysis demonstrated that body mass index, urinary protein excretion, and tubular atrophy/interstitial fibrosis were identified as independent predictors for risk stratification. A nomogram model was formulated utilizing the final variables. The AUCs for the training set, internal validation set, and external validation set were 0.746 (95% confidence intervals (CI) [0.691–0.8]), 0.764 (95% CI [0.68–0.85]), and 0.749 (95% CI [0.65–0.85]), respectively. The validation of the subgroup analysis also demonstrated a satisfactory AUC. Conclusion This study developed and validated a practical nomogram that can individually predict short-term treatment outcomes (≥90 days) and the risk of CKD progression in IgAN patients. It provides reliable guidance for timely and personalized intervention and treatment strategies.

Summary: Background: As differentiating between lipomas and atypical lipomatous tumors (ALTs) based on imaging is challenging and requires biopsies, radiomics has been proposed to aid the diagnosis. This study aimed to externally and prospectively validate a radiomics model differentiating between lipomas and ALTs on MRI in three large, multi-center cohorts, and extend it with automatic and minimally interactive segmentation methods to increase clinical feasibility. Methods: Three study cohorts were formed, two for external validation containing data from medical centers in the United States (US) collected from 2008 until 2018 and the United Kingdom (UK) collected from 2011 until 2017, and one for prospective validation consisting of data collected from 2020 until 2021 in the Netherlands. Patient characteristics, MDM2 amplification status, and MRI scans were collected. An automatic segmentation method was developed to segment all tumors on T1-weighted MRI scans of the validation cohorts. Segmentations were subsequently quality scored. In case of insufficient quality, an interactive segmentation method was used. Radiomics performance was evaluated for all cohorts and compared to two radiologists. Findings: The validation cohorts included 150 (54% ALT), 208 (37% ALT), and 86 patients (28% ALT) from the US, UK and NL. Of the 444 cases, 78% were automatically segmented. For 22%, interactive segmentation was necessary due to insufficient quality, with only 3% of all patients requiring manual adjustment. External validation resulted in an AUC of 0.74 (95% CI: 0.66, 0.82) in US data and 0.86 (0.80, 0.92) in UK data. Prospective validation resulted in an AUC of 0.89 (0.83, 0.96). The radiomics model performed similar to the two radiologists (US: 0.79 and 0.76, UK: 0.86 and 0.86, NL: 0.82 and 0.85). Interpretation: The radiomics model extended with automatic and minimally interactive segmentation methods accurately differentiated between lipomas and ALTs in two large, multi-center external cohorts, and in prospective validation, performing similar to expert radiologists, possibly limiting the need for invasive diagnostics. Funding: Hanarth fonds.

Abstract Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12–14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

Abstract Background Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort. Methods The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions. Results For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66–0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68–0.70) and 0.89 (0.88–0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63–0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis. Conclusions In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.