Validation Of Prediction Model Research Articles

Novel CT Image-Based Intracerebral Bleeding Risk Score for Patients With Acute Ischemic Stroke Undergoing Thrombolysis.

Symptomatic intracerebral hemorrhage (sICH) after intravenous recombinant tissue plasminogen activator in patients with acute ischemic stroke (AIS) remains a feared yet unpredictable complication. We aimed to develop and validate a new predictive model incorporating clinical variables and noncontrast head computed tomography imaging features to predict sICH in patients with AIS receiving intravenous recombinant tissue plasminogen activator. The predictive model was derived from 808 patients with AIS in the derivation cohort in Southeast China, based on multivariable logistic regression analysis. External validation was conducted in a validation cohort from Central China. Discrimination, calibration, and clinical usefulness of the predictive model were assessed. We observed 32 sICH events among 808 patients with AIS in the derivation cohort, and 21 sICH events out of 612 participants in the validation cohort. The variables in the predictive model included cerebral small vessel disease burden and early infarct signs on head computed tomography scan, atrial fibrillation, age, systolic blood pressure, and initial National Institutes of Health Stroke Scale score. The fitted model showed promising discrimination (optimism-corrected C statistic of 0.80) and acceptable calibration (Hosmer and Lemeshow goodness of fit P=0.816) in the derivation cohort. External validation showed similar discrimination (C statistic 0.82 [95% CI, 0.72-0.91]) and calibration (Hosmer and Lemeshow goodness of fit P=0.866). Our internally and externally validated prediction model for sICH in patients with AIS who received intravenous thrombolysis may facilitate individualized prediction for intracerebral bleeding risk after intravenous thrombolysis for acute ischemic stroke.

Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism

BackgroundPulmonary embolism (PE) is a common and potentially fatal condition. Timely and accurate risk assessment in patients with acute deep vein thrombosis (DVT) is crucial. This study aims to develop a deep learning-based, precise, and efficient PE risk prediction model (PE-Mind) to overcome the limitations of current clinical tools and provide a more targeted risk evaluation solution.MethodsWe analyzed clinical data from patients by first simplifying and organizing the collected features. From these, 37 key clinical features were selected based on their importance. These features were categorized and analyzed to identify potential relationships. Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. To validate its effectiveness, we compared this model with five commonly used prediction models.ResultsPE-Mind demonstrated the highest accuracy and reliability, achieving 0.7826 accuracy and an area under the receiver operating characteristic curve of 0.8641 on the prospective test set, surpassing other models. Based on this, we have also developed a Web server, PulmoRiskAI, for real-time clinician operation.ConclusionThe PE-Mind model improves prediction accuracy and reliability for assessing PE risk in acute DVT patients. Its convolutional architecture and residual modules substantially enhance predictive performance.

Clinical validation and optimization of machine learning models for early prediction of sepsis

IntroductionSepsis is a global health threat that has a high incidence and mortality rate. Early prediction of sepsis onset can drive effective interventions and improve patients’ outcome.MethodsData were collected retrospectively from a cohort of 2,329 adult patients with positive bacteria cultures from a tertiary hospital in China between October 1, 2019 and September 30, 2020. Thirty six clinical features were selected as inputs for the models. We trained models in predicting sepsis by machine learning (ML) methods, including logistic regression, decision tree, random forest (RF), multi-layer perceptron, and light gradient boosting. We evaluated the performance of the five ML models and the evaluation metrics were: area under the ROC curve (AUC), accuracy, F1-score, sensitivity and specificity. The data of another cohort of 2,286 patients between October 1, 2020 and April 1, 2022 were used to validate the performance of the model performing best in the in the internal validation set. Shapley additive explanations (SHAP) method was applied to evaluate feature importance and explain the predictions of this model.ResultsOf the five machine learning models developed, the RF model demonstrated the best performance in terms of AUC (0.818), F1 value (0.38), and sensitivity (0.746). The RF model also has a comparable AUC (0.771) in the external validation set. The SHAP method identified procalcitonin, albumin, prothrombin time, and sex as the important variables contributing to the prediction of sepsis.DiscussionThe RF model we developed showed the greatest potential for early prediction of sepsis in admitted patients, which could aid clinicians in their decision-making process. Our findings also suggested that male patients with bacterial infections and high procalcitonin levels, lower albumin levels, or prolonged prothrombin times were more likely to develop sepsis.

Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis.

Renal replacement therapy (RRT) plays a crucial role in managing acute pancreatitis (AP). This study aimed to develop and evaluate predictive models for determining the need for RRT among patients with AP in the intensive care unit (ICU). A retrospective selection of patients with AP was made from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version V2.0). The cohort was randomly divided into a training set (447 patients) and a validation set (150 patients). The least absolute shrinkage and selection operator (LASSO) regression cross-validation method was utilized to identify key features for model construction. Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. The mean age of the cohort was 59.17years, with an average Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 17.55. Acute kidney injury (AKI) was observed in 52.43% of patients with AP, and 9.05% required RRT. After feature selection, four of 41 clinical factors were ultimately chosen for use in model construction. The Lasso-Logistic Regression (Lasso-LR) model showed a high discriminative ability to predict RRT risk in patients with AP, with an area under the receiver operating characteristic (AUROC) of 0.955 (95% CI 0.924-0.987) in the training set. In the validation set, it maintained its discriminative performance, achieving an AUROC of 0.985 (95% CI 0.970-1.000). Calibration curves indicated an excellent fit in both sets (Brier scores: 0.039 and 0.032, respectively), suggesting high consistency. Decision curve analysis (DCA) highlighted the Lasso-LR model's significant clinical utility in predicting RRT likelihood in patients with AP. Developed via the LASSO regression cross-validation method, the Lasso-LR model significantly excels in predicting the requirement for RRT in patients with AP, demonstrating its potential for clinical application.

Development and validation of non-invasive prediction models for assessing kidney histopathological activity index in lupus nephritis.

To develop and validate prediction models for estimating the activity index (AI) of kidney histopathology in lupus nephritis (LN) using clinical and laboratory data. This study used single-center data from patients with kidney biopsy-confirmed LN between January 2012 and March 2022. The development and validation datasets were temporally cut. We discriminated AI > 10 and ≤ 10 as high and mild/moderate activity status, respectively. We constructed four models for AI: Model 1 included demographic information; Model 2 additionally incorporated data of systemic conditions; Model 3 further included kidney-specific conditions; and Model 4 included all the aforementioned predictors. Logistic regression was employed in Models 1 to 3, while Model 4 utilized least absolute shrinkage and selection operator for predictor selection and model building. Internal validation was performed using 1000 bootstrap resampling, while external validation was performed in the temporal validation dataset. Both calibration and discrimination metrics were evaluated. There were 160 patients in the development dataset and 70 patients in the validation dataset. In the temporal validation, all the models achieved acceptable calibration and excellent discrimination. Model 2 which contained relatively fewer predictors achieved the highest area under the receiver operator characteristic curve of 0.86 (95% confidence interval 0.76 to 0.94). Our Model 2 incorporating demographic and systemic indicators exhibited good performance in estimating the AI of LN. We thus provide a simple yet effective algorithm to predict AI in patients with LN, potentially aiding clinicians in non-invasively assessing disease activity and guiding treatment decisions. Key Points • We developed a prediction model (Model 2) incorporating demographic and systemic indicators to predict AI in patients with LN. • The prediction model can aid clinicians in noninvasively assessing disease activity and guiding treatment decisions.

Development and validation of clinical prediction model for functional independence measure following stroke rehabilitation.

To develop and internally validate a clinical prediction model that includes balance ability and nutritional indices for the motor-functional independence measure (M-FIM) at 90 days post-stroke stroke. This retrospective, single-center study included 566 patients with stroke undergoing rehabilitation at our rehabilitation hospital. The primary outcome was the M-FIM score of >61 at 3 months post-strokes onset. Stepwise conditional forward selection was first used to identify predictors for the achievement of M-FIM>61 at 90 days post-stroke, from 25 potential predictors at admission. The selected predictors were dichotomized with cut-off values to establish scoring systems, resulting in the B-ADL model, which includes postural balance (B), albumin level, age, arm function (A), days since stroke onset (D), and level of activities of daily living (ADL) (L). For internal validation, we corrected the optimism of the area under the curve of receiver operating characteristic curve (AUROC) induced by overfitting the original data using the bootstrap validation method. Calibration capacity was assessed using a calibration plot. We developed a clinical model to predict the M-FIM at 90 days post-stroke onset. The AUROC of the B-ADL model was 0.92 (sensitivity, 93.7%; specificity, 89.7%). The B-ADL model showed high accuracy with an AUROC of 0.970 in the internal validation. The scoring system in the validation cohort had a cut-off value of 5.5/12 points to predict the achievement of M-FIM>61 (AUROC: 0.950; 95% CI 0.930-0.970). The B-ADL model accurately predicted M-FIM >61 at 90 days post-stroke on the day of admission to the recovery rehabilitation ward. The B-ADL model is useful for optimizing rehabilitation programs and resource allocation, allowing for targeted interventions after stroke.

Development and Validation of Prediction Models for Perioperative Opioid Requirements: Integrating Machine-Learning Approach with Conventional Regression Method

Development and Validation of Prediction Models for Perioperative Opioid Requirements: Integrating Machine-Learning Approach with Conventional Regression Method

Development and validation of the Crohn's disease-intestinal failure-wean (CDIF-Wean) Score to predict outcomes of intestinal rehabilitation.

Enteral autonomy, a key outcome of intestinal rehabilitation in patients with intestinal failure (IF), is challenging to predict due to disease complexity and heterogeneity. The aim of this cohort study is to develop and validate a multivariate model to predict enteral autonomy in patients with IF caused by Crohn's disease (CDIF), and to derive an outcome-based severity classification for CDIF. The CDIF-Wean Score was constructed and internally validated in a cohort of 182 patients with CDIF from a tertiary IF unit. We performed stepwise backward selection to include relevant and significant clinical variables in a binomial regression with inverted probability of censoring weighting. The Score was externally validated in a separate cohort of 107 patients with CDIF from an independent tertiary IF unit. A severity classification, based on the CDIF-Wean Score, was evaluated with cumulative incidence curves for enteral autonomy and death during home parenteral support (HPS). In the CDIF-Wean Score, age, HPS duration, chronicity of Crohn's disease, intestinal anatomy, and eligibility and type of reconstructive surgery was predictive of enteral autonomy. The Score performed well in discrimination and calibration, with 0.84 and 0.84 area under the receiver operating characteristic curve, and 0.13 and 0.16 Brier scores in internal and external validation, respectively. The CDIF severity classification was significantly associated with both short- and long-term prognosis, where mild patients had a 7.5, 5.8 and 5.2-fold higher probability of enteral autonomy than severe patients at 1, 5 and 10 years (p<0.0001). The CDIF-Wean Score is the first validated prediction model for IF outcomes, and demonstrates accuracy, robustness and generalisability in the prognostication of CDIF patients.

Validation of the Barcelona Magnetic Resonance Imaging Predictive Model for Significant Prostate Cancer Detection in Men Undergoing Mapping per 0.5 Mm-Core Targeted Biopsies of Suspicious Lesions and Perilesional Areas

Validation of the Barcelona Magnetic Resonance Imaging Predictive Model for Significant Prostate Cancer Detection in Men Undergoing Mapping per 0.5 Mm-Core Targeted Biopsies of Suspicious Lesions and Perilesional Areas

Development and Validation of Machine Learning Models for Risk Prediction of Postpartum Stress Urinary Incontinence: A Prospective Observational Study.

This study aims to develop a postpartum stress urinary incontinence (PPSUI) risk prediction model based on an updated definition of PPSUI, using machine learning algorithms. The goal is to identify the best model for early clinical screening to improve screening accuracy and optimize clinical management strategies. This prospective study collected data from 1208 postpartum women, with the dataset randomly divided into training and testing sets (8:2). Five machine learning algorithms-logistic regression, decision trees, random forests, support vector machines (SVM), and eXtreme gradient boosting (XGBoost)-were used to construct the PPSUI risk prediction model. Model performance was evaluated using multiple metrics, and the best-performing model was selected and validated for generalizability with the testing set. The final model retained ten features: birth weight, weight gain during pregnancy, BMI before delivery, pre-pregnancy BMI, age of delivery, gestation, parity, pre-delivery uterine height, age of first delivery, and labor analgesia. Among the five algorithms, the random forest model performed best, with a test AUC of 0.995 (95% CI 0.990-0.999, P < 0.05), accuracy of 0.956, precision of 0.957, recall of 0.944, specificity of 0.966, and F1 score of 0.951. The model's high generalizability was confirmed with the testing set and further validated through bootstrapping and tenfold cross-validation. The random forest model shows strong clinical potential for PPSUI risk prediction and early screening. Future studies should expand the sample size and include multi-center data to further enhance the model's clinical applicability.

Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age

BackgroundLow-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A...

P-850. External Validation of Multiple Bacteremia Prediction Models in a Large US Emergency Department

Abstract Background We previously proposed bacteremia prediction models that might save a quarter of blood cultures (BCs) while missing few bacteremia patients. In this study, we aimed to validate these models in the United States.Table 1:Discrimination, calibration, and clinical usefulness upon external validation for five bacteremia prediction models.Basic variables were age, systolic blood pressure (mmHg), heart rate (beats/min), respiratory rate (breaths/min), temperature (Celsius) and Glasgow coma scale. The basic model was tested in 46236 patients, the model with CRP was tested in 8393 patients, the model with PCT and the only PCT model was tested in 12864 patients and the model with both CRP and PCT was tested in 2317 patients. A sensitivity of 95% means that out of 100 patients with bacteremia, the model detects 95 patients and misses 5 patients. CI = confidence interval, BCs = blood cultures, CRP = C-reactive protein, PCT = Procalcitonin. Methods We validated five models using retrospectively collected data. We included patients ≥ 18 years old with one or more BCs drawn during their visit to University of Michigan Hospital, Ann Arbor Emergency Department, from October 2013 until October 2023. The models used basic information such as age and vital signs, with or without the addition of C-reactive protein (CRP), procalcitonin (PCT) or both. We created cohorts based on availability of the biomarkers CRP and PCT. Missing data (except biomarkers) was statistically imputed. Discrimination was assessed with the C-statistic and calibration using calibration plots. Clinical usefulness was evaluated by sensitivity, specificity, and reduced BCs. Results We studied 46,608 patients, of whom 372 were excluded because of suspected endocarditis or indwelling vascular catheter. Median age was 61 years, 52% were male. 3816 patients had bacteremia (8.3%), while 2674 (5.8%) patients had contaminated BCs. PCT was known for 12,864 patients, CRP for 8,393 patients and 2,317 patients had both PCT and CRP biomarkers available. We observed substantial improved performance for models containing PCT (Table 1). The best performing model had a C-statistic of 0.80 (95% CI 0.79-0.82), and adequate calibration (intercept -0.20 (95% CI -0.27 to -0.14), slope 0.79 (95% CI 0.75-0.84)). All models somewhat underestimated the risk of bacteremia in the lower risk ranges, leading to more missed bacteremia patients compared to the European cohorts used to develop the models. At a minimum sensitivity of 95%, the models were able to reduce 13-30% of BCs. Conclusion Recently proposed prediction models have satisfactory discrimination and calibration for US patients. However, almost all models would have missed more bacteremic episodes compared to the European development cohorts, underlining the importance of validation in other geographical settings. Procalcitonin showed substantial predictive performance for bacteremia and should be considered for any bacteremia prediction model. Disclosures Laraine Washer, MD, Ferring Pharmaceuticals: Advisor/Consultant

Commentary on “Validation of a Pretransplant Risk Prediction Model for Early Allograft Dysfunction After Living-donor Liver Transplantation”

Commentary on “Validation of a Pretransplant Risk Prediction Model for Early Allograft Dysfunction After Living-donor Liver Transplantation”

Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery

BackgroundClinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient’s own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.MethodsPatients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017–2021 were used to develop the model (randomly split: 80% training, 20% test). The primary outcome was a 30-day composite outcome including several postoperative major complications and mortality; secondary outcomes included common individual complications included in the primary composite outcome (sepsis, progressive renal insufficiency or renal failure, and mortality). Patients’ EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model performance.ResultsOverall, 66,262 patients (median [IQR] age 78 [(70.9–84.0], female 53.9%, White race 68.5%) received urgent or emergency non-cardiac surgery (25.7% orthopedic cases, 21.9% general surgery cases). AUCROC ranged from 0.655 (Decision Tree) – 0.804 (XGBoost) for the primary composite outcome. XGBoost AUCROC was 0.823, 0.781, and 0.839 in predicting outcomes of sepsis, progressive renal insufficiency or renal failure, and mortality, respectively.ConclusionsWe developed a model to accurately predict major postoperative complications in geriatric patients undergoing urgent or emergency surgery using the patient’s own existing EHR data. EHR implementation of this model could efficiently support clinicians’ surgical risk assessment and perioperative decision-making discussions in this vulnerable patient population.

Development and validation of a recurrence risk prediction model for elderly schizophrenia patients

ObjectiveTo construct a recurrence prediction model for Elderly Schizophrenia Patients (ESCZP) and validate the model’s spatial external applicability.MethodsThe modeling cohort consisted of 365 ESCZP cases from the Seventh People’s Hospital of Dalian, admitted between May 2022 and April 2024. Variables were selected using Lasso-Logistic regression to construct the recurrence prediction model, with a nomogram plotted using the “RMS” package in R 4.3.3 software. Model validation was performed using 1,000 bootstrap resamples. Spatial external validation was conducted using 172 cases ESCZP from the Fourth Affiliated Hospital of Qiqihar Medical College during the same period. The model’s discrimination, accuracy, and clinical utility were assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). The Hosmer-Lemeshow test was used to evaluate model fit.ResultsA total of 537 cases ESCZP were included based on inclusion and exclusion criteria, with 150 recurrences within two years and 387 non-recurrences. Lasso-Logistic regression analysis identified Medication status, Premorbid personality, Exercise frequency, Drug adverse reactions, Family care, Social support, and Life events as predictors of ESCZP recurrence. The AUC for the modeling cohort was 0.877 (95% CI: 0.837–0.917). For the external validation cohort, the AUC was 0.838 (95% CI: 0.776–0.899). Calibration curves indicated that the fit was close to the reference line, demonstrating high model stability. DCA results showed good net benefit at a threshold probability of 80%.ConclusionThe nomogram prediction model developed based on Lasso-Logistic regression shows potential in identifying the risk of recurrence in ESCZP. However, further validation and refinement are needed before it can be applied in routine clinical practice.

Development and validation of a prediction model for chronic post-surgical pain risk: a single-center prospective study of video-assisted thoracoscopic lung cancer surgery

BackgroundChronic post-surgical pain (CPSP) is a common complication following video-assisted thoracoscopic surgery (VATS) that significantly impacts the quality of life of patients. Although multiple risk factors have been identified, no systematically validated prediction model exists to guide clinical decision-making.ObjectivesThis study aimed to develop and validate a risk prediction model for CPSP in patients undergoing VATS for lung cancer.MethodsThis prospective cohort study included clinical data from 400 patients with non-small cell lung cancer who underwent VATS from June 2022 to June 2023. Patients were randomly assigned to a training cohort and an internal test cohort and assessed for sleep quality, psychological status, and pain levels. A nomogram prediction model was established based on variables significantly associated with CPSP in the training cohort. The model was internally validated in the internal test cohort to evaluate its discrimination, calibration, and clinical utility.ResultsIndependent risk factors for CPSP included female gender, severe acute pain post-surgery, lymph node dissection, and cold pain sensation, while paravertebral nerve block was identified as a protective factor. The AUC values were 0.878 in training cohort and 0.805 in internal test cohort, respectively, indicating that the model performed well in identifying patients at risk for CPSP. The calibration curves in both cohorts showed a good fit, indicating that the model’s predictions were reliable. And the DCA curve showed that using our model to guide decisions resulted in a higher net benefit compared to a strategy of not screening or treating all patients.ConclusionAn effective risk prediction model for CPSP was successfully developed and validated in this study. This model can aid physicians in conducting more accurate assessments of CPSP risk in patients prior to surgery and in offering personalized strategies for managing CPSP.Clinical registration numberRegistration website: https://www.chictr.org.cn/. Registration date: 2022/5/21. Registration number: ChiCTR2200060196.

Long Term Performance of Phase 1 Jointed Concrete Pavement Test Sections at the MnROAD Facility

Since 1994, the MnROAD pavement test facility has conducted comprehensive measurements of the performance and response of a variety of jointed concrete pavement designs. The primary objective of each MnROAD test section is to provide data toward validation or improvement of pavement performance prediction models. This paper examines the long-term trends of several critical performance parameters, for each of the fourteen original jointed concrete pavement sections constructed during Phase 1 (1994-2007) of MnROAD. These parameters include slab cracking, transverse joint deflection and load transfer efficiency, and ride quality. Through continual monitoring of the long-term performance of MnROAD jointed concrete pavement sections, there now exists a useful database of information related to concrete pavement behavior, particularly in colder climates. Analysis of MnROAD Phase 1 data reaffirms that AASHTO-based design methods are conservative in their prediction of performance.

Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases

Hepatorenal syndrome (HRS) is a key contributor to poor prognosis in liver cirrhosis. This study aims to leverage the database to build a predictive model for early identification of high-risk patients. From two sizable public databases, we retrieved pertinent information about the cirrhosis patients’ therapies, comorbidities, laboratory results, and demographics. Patients from the eICU database served as a test set for external validation, while patients from the MIMIC database were divided into training and validation groups. Variables were screened using LASSO regression, Extreme Gradient Boosting (XG Boost), and Random Forest (RF). Core risk factors were determined from the intersection of the three methods. A predictive model was constructed using multivariable logistic regression and visualized via a nomogram. Model performance was assessed using ROC curves, decision curve analysis (DCA), clinical impact curves (CIC), and calibration curves. Eight critical variables associated with HRS were identified using machine learning methods. The final predictive model, based on five key variables—spontaneous bacterial peritonitis, red blood cell count, creatinine, activated partial thromboplastin time, and total bilirubin—showed excellent discrimination, with AUCs of 0.832 (95% CI 0.8069–0.8563) in the training set and 0.8415 (95% CI 0.8042–0.8789) in the validation set. The AUC in the external test set was 0.8212 (95% CI 0.7784–0.864). By integrating the MIMIC-IV database and machine learning algorithms, we developed an effective predictive model for HRS in liver cirrhosis patients, providing a robust tool for early clinical intervention.

P1051 Establishment and Validation of Prediction Models for Short-term and Long-term Effectiveness of Ustekinumab in Patients with Crohn’s Disease

Abstract Background Ustekinumab (UST), a novel biological agent for Crohn’s disease (CD), has shown efficacy in CD patients. Our objective was to establish clinical prediction models to accurately forecast the short- and long-term effectiveness of UST in individuals diagnosed with CD. Methods We established a derivation cohort comprising patients diagnosed with CD and treated with UST at the Sixth Affiliated Hospital of Sun Yat-sen University from May 2020 to July 2023. The patients were assigned in a 7:3 ratio at random to the training cohort and the validation cohort. Predictors were compressed through the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Logistic regression analysis was used to create the final prediction models. Results A total of 464 patients with CD were included in the statistical analysis, 171 of whom had complete endoscopic data. Four models were constructed: two to predict the probability of clinical or endoscopic remission at week 16 or 20 based on baseline data, and two to predict the probability of clinical or endoscopic remission at week 52 based on data obtained at week 16 or 20. The C-index was 0.714, 0.866, 0.814 and 0.841, respectively. Because of poor calibration, we constructed another model to predict the probability of endoscopic remission at week 52 based on baseline data (C-index:0.748). In the internal validation, the above models showed good performance. Conclusion We have constructed several models to predict the short- and long-term effectiveness of UST in patients with CD, promising to be a simple and practical clinical tool.

Development and validation of a hyperlipidemia risk prediction model for middle-aged and older adult Chinese using 2015 CHARLS data.

To develop and validate a predictive model for hyperlipidemia risk among middle-aged and older adult individuals in China, this study aims to offer an effective screening tool for identifying those at risk. In this study, we included 6,629 middle-aged and older adult individuals, aged 45 and above, who met the inclusion criteria from the 2015 China Health and Retirement Longitudinal Study (CHARLS) as our research subjects. Utilizing the LASSO regression and multivariate Logistic regression method, we analyzed the independent risk factors associated with hyperlipidemia among these subjects. Subsequently, we established a risk prediction model for hyperlipidemia in the middle-aged and older adult population using statistical software Stata 17.0. The prevalence rate of hyperlipidemia among the 6,629 middle-aged and older adult participants was 26.32% (1,745 out of 6,629). The LASSO regression and multivariate Logistic regression analysis all revealed that Body Mass Index (BMI), fasting blood glucose, serum uric acid, C-reactive protein, and white blood cell count were independent risk factors for hyperlipidemia in this demographic (with Odds Ratios (OR) greater than 1 and p-values less than 0.05). From these findings, a nomogram prediction model was constructed to estimate the risk of hyperlipidemia for middle-aged and older adult individuals. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) for the nomograms was 0.717 (95% Confidence Interval (CI): 0.703-0.731), indicating good discrimination. The Decision Curve Analysis (DCA) demonstrated that when the probability of hyperlipidemia in the middle-aged and older adult population falls between 0.11 and 0.61, the application of the nomogram yields the highest net benefit, suggesting that the nomogram model possesses good clinical applicability. The Spiegelhalter's z-statistic test confirmed that the predicted probabilities from the nomogram model are in good agreement with the observed frequencies of hyperlipidemia (p = 0.560). The Brier score for the nomogram model was 17.1%, which is below the threshold of 25%, indicating good calibration. To internally validate the nomogram model, we performed bootstrap resampling 500 times. The C-statistic for the nomogram model from this internal validation was 0.716, and the Brier score was 11.4%, suggesting that the model not only has good predictive efficiency but also good stability. The nomogram model, which incorporates the identified risk factors for hyperlipidemia in middle-aged and older adult individuals, has demonstrated good predictive efficiency and clinical applicability. It can serve as a valuable tool to assist healthcare professionals in screening for high-risk groups and implementing targeted preventive interventions. By doing so, it has the potential to significantly reduce the incidence of hyperlipidemia among this demographic.