Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning.

BACKGROUND: Precise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT) and can offer clues for the selection of subsequent treatment strategies. Most current predictive models for pCR focus primarily on pre-treatment factors, neglecting the dynamic systemic changes that occur during neoadjuvant chemoradiotherapy, and are constrained by low accuracy and lack of integrity. PURPOSE: This study devised a novel predictor of pCR using dynamic alterations in systemic inflammation-nutritional marker indexes (SINI) during neoadjuvant therapy and developed a machine-learning model to predict pCR. METHODS: Two cohorts of patients with LARC from center one from 2012 to 2017 and from center two from 2020 to 2023 were integrated for analysis. This study compared dynamic changes in blood indexes before and after neoadjuvant therapy and surgical operation. A least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to mitigate collinearity and identify key indexes, constructing the SINI. Univariate and multiple logistic regression analyses were used to identify the independent risk factors associated with pCR. Additionally, 10 machine learning algorithms were employed to develop predictive models to assess risk. The hyperparameters of the machine learning models were optimized using a random search and 10-fold cross-validation. The models were assessed by examining various metrics, including the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal and external validation cohorts. Additionally, Shapley's additive explanations (SHAP) were employed to interpret the machine learning models. RESULTS: The study cohort comprised 677 patients from the center one and 224 patients from the center two. Six key indexes were identified, and a predictive index, SINI, was constructed. Univariate and multiple logistic regression analyses revealed that SINI, clinical T-stage, clinical N-stage, tumor size, and the distance from the anal verge were independent risk factors for pCR in patients with LARC following NCRT. The mean AUC value of the extreme gradient boosting (XGB) model in the 10-fold cross-validation of the training set was 0.877. The XGB model demonstrated superior performance in the internal and external validation sets. Specifically, in the internal test set, the XGB model achieved an AUC of 0.86, AUPRC of 0.707, accuracy of 0.82, and precision of 0.80. In the external validation set, the XGB model exhibited an AUC of 0.83, AUPRC of 0.702, accuracy of 0.81, and precision of 0.81. Additionally, the predictions generated by the XGB model were analyzed using SHAP. CONCLUSION: This study involved developing and validating an XGB model using SINI to predict pCR in patients with LARC. Besides, a SINI-based machine learning model shows promise in accurately predicting pCR following NCRT in patients with resectable LARC, offering valuable insights for personalized treatment approaches.

Predicting nosocomial pneumonia risk in level-1 trauma patients: An external validation study using the trauma quality improvement program.

BACKGROUND: Early identification of patients at risk of nosocomial pneumonia enables the opportunity for preventative measures, which may improve survival and reduce costs. Therefore, this study aimed to externally validate an existing prediction model (issued by Croce et al.) to predict nosocomial pneumonia in patients admitted to US level-1 trauma centers. METHODS: A retrospective cohort study including patients admitted to level-1 trauma centers and registered in the TQIP, a US nationwide trauma registry, admitted between 2013-2015 and 2017-2019. The main outcome was total nosocomial pneumonia for the first period and ventilator-associated pneumonia (VAP) for the second. Model discrimination and calibration were assessed before and after recalibration. RESULTS: The study comprised 902,231 trauma patients (N2013-2015 â€‹= â€‹180,601; N2017-2019 â€‹= â€‹721,630), with a median age of 52 in both periods, 64-65 â€‹% male, and approximately 90 â€‹% sustaining blunt traumatic injury. The median Injury Severity Scores were 13 (2013-2015) versus 9 (2017-2019); median Glasgow Coma Scale scores were 15. Nosocomial pneumonia incidence was 4.4 â€‹%, VAP incidence was 0.7 â€‹%. The original model demonstrated good to excellent discrimination for both periods (c-statistic2013-2015 0.84, 95%CI 0.83-0.84; c-statistic2017-2019 0.92, 95%CI 0.91-0.92). After recalibration, discriminatory capacity and calibration for the lower predicted probabilities improved. CONCLUSIONS: The Croce model can identify patients admitted to US level-1 trauma centers at risk of total nosocomial pneumonia and VAP. Implementing (modified) Croce models in route trauma clinical practice could guide judicious use of preventative measures and prescription of additional non-invasive preventative measures (e.g., increased monitoring, pulmonary physiotherapy) to decrease the occurrence of nosocomial pneumonia in at-risk patients.

Multi-center external validation of an automated method segmenting and differentiating atypical lipomatous tumors from lipomas using radiomics and deep-learning on MRI.

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.

External validation of an artificial intelligence multi-label deep learning model capable of ankle fracture classification.

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.

External Validation of Serologic Scores for the Detection of Liver Steatosis Among People With HIV.

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.

External validation of hemoglobin and neutrophil levels as predictors of the effectiveness of ipilimumab plus nivolumab for treating renal cell carcinoma.

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.

Early Warning Models Using Machine Learning to Predict Sepsis-Associated Chronic Critical Illness: A Study Based on the Medical Information Mart for Intensive Care Database.

Background Patients with chronic critical illness (CCI) experience poor prognoses and incur high medical costs. However, there is currently limited clinical awareness of sepsis-associated CCI, resulting in insufficient vigilance. Therefore, it is necessary to build a machine learning model that can predict whether sepsis patients will develop CCI. Methods Clinical data on 19,077 sepsis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed. Predictive factors were identified using the Student's t-test, Mann-Whitney U test, or χ 2 test. Six machine learning classification models, namely, the logistic regression, support vector machine, decision tree, random forest, extreme gradient enhancement, and artificial neural network, were established. The optimal model was selected on the basis of its performance. Calibration curves were used to evaluate the accuracy of model classification, while the external validation dataset was used to evaluate the performance of the model. Results Thirty-seven characteristics, such as elevated alanine aminotransferase, rapid heart rate, and high Logistic Organ Dysfunction System scores, were identified as risk factors for developing CCI. The area under the receiver operating characteristic curve (AUROC) values for all models were above 0.73 on the internal test set. Among them, the extreme gradient enhancement model exhibited superior performance (F1 score = 0.91, AUROC = 0.91, Brier score = 0.052). It also exhibited stable prediction performance on the external validation set (AUROC = 0.72). Conclusion A machine learning model was established to predict whether sepsis patients will develop CCI. It can provide useful predictive information for clinical decision-making.

Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis.

Background: Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes. Objectives: To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data. Design: Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis. Participants: Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies). Predictors: Maternal clinical characteristics, biochemical and ultrasound markers. Primary outcomes: fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight. Analysis: First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model. Results: Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g). Limitations: We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data. Future work: International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation. Conclusion: The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management. Study registration: This study is registered as PROSPERO CRD42019135045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.

One in ten babies is born small for their age. A third of such small babies are considered to be 'growth-restricted' as they have complications such as dying in the womb (stillbirth) or after birth (newborn death), cerebral palsy, or needing long stays in hospital. When growth restriction is suspected in fetuses, they are closely monitored and often delivered early to avoid complications. Hence, it is important that we identify growth-restricted babies early to plan care. Our goal was to provide personalised and accurate estimates of the mother's chances of having a growth-restricted baby and predict the baby's weight if delivered at various time points in pregnancy. To do so, first we tested how accurate existing risk calculators ('prediction models') were in predicting growth restriction and birthweight. We then developed new risk-calculators and studied their clinical and economic benefits. We did so by accessing the data from individual pregnant women and their babies in our large database library (International Prediction of Pregnancy Complications). Published risk-calculators had various definitions of growth restriction and none predicted the chances of having a growth-restricted baby using our definition. One predicted baby's birthweight. This risk-calculator performed well, but underpredicted the birthweight by up to 143 g. We developed two new risk-calculators to predict growth-restricted babies (International Prediction of Pregnancy Complications-fetal growth restriction) and birthweight (International Prediction of Pregnancy Complications-birthweight). Both calculators accurately predicted the chances of the baby being born with growth restriction, and its birthweight. The birthweight was underpredicted by <9.7 g. The calculators performed well in both mothers predicted to be low and high risk. Further research is needed to determine the impact of using these calculators in practice, and challenges to implementing them in practice. Both International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight risk calculators will inform healthcare professionals and empower parents make informed decisions on monitoring and timing of delivery.

Single-center external validation and reconstruction of multiple predictive models for skip lateral lymph node metastasis in papillary thyroid carcinoma.

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.

Development and external validation of clinical predictive model for stress urinary incontinence in Chinese women : a multicenter retrospective study.

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.

Serum ganciclovir drug exposure in children receiving standard ganciclovir dosing.

Intravenous ganciclovir (GCV) is used for the treatment of cytomegalovirus (CMV) infection in immunocompromised children. Although the therapeutic target for treatment is unclear, studies have shown a serum area under the concentration-time curve (AUC24h) ≥40 mg/L·h correlates with effective CMV prevention. This study aimed to externally validate existing GCV population pharmacokinetic (PopPK) models and develop a model if needed and evaluate the serum AUC24h achieved with standard GCV dosing and propose an optimized dosing strategy for immunocompromised children. Ganciclovir drug monitoring data from two pediatric hospitals were retrospectively collected, and published pediatric PopPK models were externally validated. The population AUC24h with standard GCV dosing (5 mg/kg twice daily) was calculated, and an optimized dosing strategy was determined using Monte Carlo simulations to achieve an AUC24h between 40 and 100 mg/L·h. Overall, 161 samples from 23 children with a median (range) age of 9.0 years (0.4-17.0) and weight of 28.2 kg (5.6-73.3) were analyzed. Transferability of published pediatric PopPK models was limited. Thus, a one-compartment model with first-order absorption and elimination with weight and serum creatinine as covariates was developed. The median (5th-95th percentiles) steady state AUC24h with standard dosing was 38.3 mg/L·h (24.8-329.2) with 13 children having an AUC24h <40 mg/L·h, particularly those aged <4 years (8/13). An optimized simulated GCV dosing regimen, ranging from 2 to 13 mg/kg twice daily for children with normal renal function, achieved 61%-78% probability of target attainment. Standard GCV dosing likely results in inadequate drug exposure in more than half of the children, particularly those aged <4 years. An optimized dosing regimen has been proposed for clinical validation.

Development and external validation of a novel modality for rapid recognition of aortic dissection based on peripheral pulse oximetry waveforms.

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.

Optimized personalized management approach for moderate/severe OHSS: development and prospective validation of an OHSS risk assessment index.

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.

External validation and comparison of four prediction scores for severe maternal morbidity.

BACKGROUND: Severe maternal morbidity (SMM) is increasing in the United States. Several tools and scores exist to stratify an individual's risk of SMM. OBJECTIVE: We sought to examine and compare the validity of four scoring systems for predicting SMM. STUDY DESIGN: This was a retrospective cohort study of all individuals in the Consortium on Safe Labor dataset, which was conducted from 2002 to 2008. Individuals were excluded if they had missing information on risk factors. SMM was defined based on the Centers for Disease Control and Prevention excluding blood transfusion. Blood transfusion was excluded due to concerns regarding the specificity of International Classification of Diseases codes for this indicator and its variable clinical significance. Risk scores were calculated for each participant using the Assessment of Perinatal Excellence (APEX), California Maternal Quality Care Collaborative (CMQCC), Obstetric Comorbidity Index (OB-CMI), and modified OB-CMI. We calculated the probability of SMM according to the risk scores. The discriminative performance of the prediction score was examined by the areas under receiver operating characteristic curves and their 95% confidence intervals (95% CI). The area under the curve for each score was compared using the bootstrap resampling. Calibration plots were developed for each score to examine the goodness-of-fit. The concordance probability method was used to define an optimal cutoff point for the best-performing score. RESULTS: Of 153, 463 individuals, 1115 (0.7%) had SMM. The CMQCC scoring system had a significantly higher area under the curve (95% CI) (0.78 [0.77-0.80]) compared to the APEX scoring system, OB-CMI, and modified OB-CMI scoring systems (0.75 [0.73-0.76], 0.67 [0.65-0.68], 0.66 [0.70-0.73]; P<.001). Calibration plots showed excellent concordance between the predicted and actual SMM for the APEX scoring system and OB-CMI (both Hosmer-Lemeshow test P values=1.00, suggesting goodness-of-fit). CONCLUSION: This study validated four risk-scoring systems to predict SMM. Both CMQCC and APEX scoring systems had good discrimination to predict SMM. The APEX score and the OB-CMI had goodness-of-fit. At ideal calculated cut-off points, the APEX score had the highest sensitivity of the four scores at 71%, indicating that better scoring systems are still needed for predicting SMM.

A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study developed and validated machine learning models to predict the risk of in-hospital mortality in ICU patients with AF. METHODS AND RESULTS: Medical Information Mart for Intensive Care (MIMIC)-IV dataset and eICU Collaborative Research Database (eICU-CRD) were analyzed. Among ten classifiers compared, adaptive boosting (AdaBoost) showed better performance in predicting all-cause mortality in AF patients. A compact model with 15 features was developed and validated. Both the all variable and compact models exhibited excellent performance with area under the receiver operating characteristic curves (AUCs) of 1(95%confidence interval [CI]: 1.0-1.0) in the training set. In the MIMIC-IV testing set, the AUCs of the all variable and compact models were 0.978 (95% CI: 0.973-0.982) and 0.977 (95% CI: 0.972-0.982), respectively. In the external validation set, the AUCs of all variable and compact models were 0.825 (95% CI: 0.815-0.834) and 0.807 (95% CI: 0.796-0.817), respectively. CONCLUSION: An AdaBoost-based predictive model was subjected to internal and external validation, highlighting its strong predictive capacity for assessing the risk of in-hospital mortality in ICU patients with AF.

External validation of a novel nomogram for diagnosis of Protein Energy Wasting in adult hemodialysis patients.

Background: Protein Energy Wasting (PEW) has high incidence in adult hemodialysis patients and refers to a state of decreased protein and energy substance. It has been demonstrated that PEW highly affects the quality of survival and increases the risk of death. Nevertheless, its diagnostic criteria are complex in clinic. To simplify the diagnosis method of PEW in adult hemodialysis patients, we previously established a novel clinical prediction model that was well-validated internally using bootstrapping. In this multicenter cross-sectional study, we aimed to externally validate this nomogram in a new cohort of adult hemodialysis patients. Methods: The novel prediction model was built by combining four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and body mass index (BMI). We evaluated the performance of the new model using discrimination (Concordance Index), calibration plots, and Clinical Impact Curve to assess its predictive utility. Results: From September 1st, 2022 to August 31st, 2023, 1,158 patients were screened in five medical centers in Shanghai. 622 (53.7%) hemodialysis patients were included for analysis. The PEW predictive model was acceptable discrimination with the area under the curve of 0.777 (95% CI 0.741-0.814). Additionally, the model revealed well-fitted calibration curves. The McNemar test showed the novel model had similar diagnostic efficacy with the gold standard diagnostic method (p > 0.05). Conclusion: Our results from this cross-sectional external validation study further demonstrate that the novel model is a valid tool to identify PEW in adult hemodialysis patients effectively.

Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19.

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. GOV IDENTIFIER: NCT05663528.

External Validation of Stockholm3 in a Retrospective German Clinical Cohort.

BACKGROUND AND OBJECTIVE: Stockholm3 is a comprehensive blood test amalgamating protein biomarkers, genetic indicators, and clinical data to predict clinically significant prostate cancer risk (International Society of Urological Pathology grade ≥2 upon biopsy). Our study aims to externally validate Stockholm3 and compare its performance with the use of prostate-specific antigen (PSA) and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) for clinically significant prostate cancer detection. METHODS: We gathered data from men subjected to prostate biopsies at the Martini-Klinik, Germany, between 2014 and 2017. Participants were selected based on elevated PSA levels or suspicious digital rectal examinations, all undergoing a 10-12-core systematic biopsy without a magnetic resonance imaging-targeted biopsy. We assessed Stockholm3 and RPCRC performance for clinically significant prostate cancer detection. Furthermore, we compared the proportion of men recommended for biopsy and biopsy outcomes with Stockholm3 and RPCRC against PSA ≥3 ng/ml. KEY FINDINGS AND LIMITATIONS: Our study encompassed 405 biopsied men, with a median age of 66 yr (interquartile range [IQR]: 60-72), PSA levels at 7 ng/ml (IQR: 5.2-10.8), and Stockholm3 scores at 18 (IQR: 10-34). Among them, 128 men (31%) received clinically significant prostate cancer diagnoses. Employing the recommended Stockholm3 threshold (≥15) could have reduced unnecessary biopsies by 52%, while detecting 92% of clinically significant cases compared with using PSA ≥3 ng/ml as a biopsy criterion. Both Stockholm3 and RPCRC exhibited strong discrimination, with area under the curve values of 0.80 (95% confidence interval [CI]: 0.76-0.85) and 0.75 (95% CI: 0.70-0.80), respectively. Stockholm3 demonstrated good calibration, while RPCRC underestimated the risk compared with observed outcomes. Moreover, Stockholm3 yielded positive clinical net benefits, whereas RPCRC yielded negative net benefits for clinically relevant thresholds. CONCLUSIONS AND CLINICAL IMPLICATIONS: Stockholm3 utilization could detect 92% of clinically significant prostate cancer cases while simultaneously reducing unnecessary biopsies by 52%, compared with the PSA ≥3 ng/ml criterion, based on our analysis within a cohort of men who underwent systematic biopsies. PATIENT SUMMARY: In a German clinical cohort of 405 men, Stockholm3, a blood test for early prostate cancer detection, exhibited favorable clinical benefits. It identified a substantial number of clinically significant cases while reducing unnecessary biopsies by over half in men without the disease and those with clinically nonsignificant prostate cancer.

Development and validation of a new scoring system to discriminate between uncomplicated and complicated appendicitis.

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.

Prospective external validation of radiomics-based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non-small-cell lung cancer: A multi-institutional analysis.

BACKGROUND AND PURPOSE: This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT). MATERIALS AND METHODS: The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test. RESULTS: In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116). CONCLUSION: Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.