Development and validation of a LASSO-logistic regression model for predicting subtherapeutic infliximab trough concentrations in patients with Crohn's disease.

Crohn disease (CD) patients face a clinically significant high risk of abdominal surgery. This study aims to develop a predictive model for estimating abdominal surgery in CD patients with Crohn Disease Activity Index (CDAI) 0-1. CD patients treated at the Second Xiangya Hospital of Central South University between 2016 and 2022 were retrospectively enrolled. Using a fixed random seed, the full cohort was randomly split into a training set and a validation set at a 5:5 ratio. Final predictors were selected using multivariable backward stepwise Cox regression, and hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated for surgery-associated factors. In addition, 8 machine learning survival prediction models were introduced, including least absolute shrinkage and selection operator (LASSO)-Cox regression, random survival forest (RSF), gradient boosting machine (GBM), CoxBoost, survival support vector machine (SurvivalSVM), extreme gradient boosting (XGBoost), supervised principal component regression (SuperPC), and partial least squares Cox regression (PLSR-Cox). The model with the best concordance index (C-index) performance in the validation set was chosen as the final decision-support model. Model discrimination was evaluated using the C-index and time-dependent ROC curves with corresponding area under the curve (AUCs), while calibration was assessed using calibration curves. Clinical net benefit was quantified using decision curve analysis (DCA). Shapley additive explanations (SHAP) was applied to interpret the contribution of model features to prediction, improving model explainability. Kaplan-Meier curves were used to describe cumulative surgery-free probability, and the Log-Rank test was used to compare differences across predicted risk strata and between biologic-exposed and non-exposed cohorts. A total of 615 patients were included in the study, comprising 307 patients in the training set and 308 patients in the validation set. Multivariable backward stepwise Cox regression identified 4 key variables significantly associated with abdominal surgery risk in CD patients with CDAI 0-1, including C-reactive protein (CRP, HR=1.07, 95% CI 1.01 to 1.14, P=0.025), albumin (ALB, HR=0.69, 95% CI 0.46 to 1.04, P=0.075), fibrinogen (Fg, HR=0.65, 95% CI 0.51 to 0.84, P<0.001), and Montreal B behavior classification (HR=2.26, 95% CI 1.23 to 4.17, P=0.009). Among 9 candidate predictive models, CoxBoost achieved the best performance in the validation set, yielding a C-index of 0.746 (95% CI 0.683 to 0.809). Time-dependent ROC analysis demonstrated that in the training set, the 1-, 3-, and 5-year AUCs were 0.778, 0.749, and 0.772, respectively, while in the validation set, the corresponding AUCs were 0.761, 0.797, and 0.751. Calibration curves showed good agreement between predicted and observed surgery risk, with Brier scores <0.25 at all evaluated time points. DCA showed that CoxBoost provided clinical net benefit within a meaningful probability range. SHAP analysis result indicates that Montreal B subtype is the primary factor influencing the modeled predictive outcomes. Risk stratification generated by the CoxBoost model revealed that the cumulative surgery-free probability differed significantly among low-, moderate-, and high-risk groups (Log-Rank P<0.001). In the moderate-to-high-risk group, exposure to biologic therapy was associated with a significant reduction in abdominal surgery risk (HR=0.54, 95% CI 0.33 to 0.89, P=0.014). In contrast, no significant effect of biologic therapy was observed in the low-risk group (HR=1.01, 95% CI 0.51 to 2.00, P=0.970). The CoxBoost model developed in this study effectively predicts abdominal surgery risk in CD patients with CDAI 0-1, and supports clinical decision-making regarding biologic therapy, providing evidence for personalized treatment planning.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

This study aimed to identify the predictive factors for prolonged length of stay (LOS) in elderly type 2 diabetes mellitus (T2DM) patients suffering from cerebral infarction (CI) and construct a predictive model to effectively utilize hospital resources. Clinical data were retrospectively collected from T2DM patients suffering from CI aged ≥65 years who were admitted to five tertiary hospitals in Southwest China. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were conducted to identify the independent predictors of prolonged LOS. A nomogram was constructed to visualize the model. The discrimination, calibration, and clinical practicality of the model were evaluated according to the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). A total of 13,361 patients were included, comprising 6,023, 2,582, and 4,756 patients in the training, internal validation, and external validation sets, respectively. The results revealed that the ACCI score, OP, PI, analgesics use, antibiotics use, psychotropic drug use, insurance type, and ALB were independent predictors for prolonged LOS. The eight-predictor LASSO logistic regression displayed high prediction ability, with an AUROC of 0.725 (95% confidence interval [CI]: 0.710-0.739), a sensitivity of 0.662 (95% CI: 0.639-0.686), and a specificity of 0.675 (95% CI: 0.661-0.689). The calibration curve (bootstraps = 1,000) showed good calibration. In addition, the DCA and CIC also indicated good clinical practicality. An operation interface on a web page (https://xxmyyz.shinyapps.io/prolonged_los1/) was also established to facilitate clinical use. The developed model can predict the risk of prolonged LOS in elderly T2DM patients diagnosed with CI, enabling clinicians to optimize bed management.

PurposeThis study aims to assess the diagnostic accuracy of cellular analysis of bronchoalveolar lavage fluid (BALF) in distinguishing between pulmonary infectious and non-infectious diseases in patients with pulmonary shadows. Additionally, it will develop and validate a novel scoring system based on a nomogram for the purpose of differential diagnosis.MethodsA retrospective analysis was conducted involving data from 222 patients with pulmonary shadows, whose etiological factors were determined at our institution. The cohort was randomly allocated into a training set comprising 155 patients and a validation set of 67 patients, (ratio of 7:3), the least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The receiver operating characteristic curve (ROC) and calibration curve were utilized to assess the prediction accuracy of the model. Decision curve analysis (DCA) and clinical impact curve (CIC) were employed to evaluate the clinical applicability of the model. Moreover, model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the risk factors.ResultsAmong the relevant predictors, the percentage of neutrophils in BALF (BALF NP) exhibited the most substantial differentiation, as evidenced by the largest area under the ROC curve (AUC = 0.783, 95% CI: 0.713–0.854). A BALF NP threshold of ≥16% yielded a sensitivity of 72%, specificity of 70%, a positive likelihood ratio of 2.07, and a negative likelihood ratio of 0.38. LASSO and multivariate regression analyses indicated that BALF NP (p < 0.001, OR = 1.04, 95% CI: 1.02–1.06) and procalcitonin (p < 0.021, OR = 52.60, 95% CI: 1.83–1510.06) serve as independent predictors of pulmonary infection. The AUCs for the training and validation sets were determined to be 0.853 (95% CI: 0.806–0.918) and 0.801 (95% CI: 0.697–0.904), respectively, with calibration curves demonstrating strong concordance. The DCA and CIC analyses indicated that the nomogram model possesses commendable clinical applicability. In models comparison, ROC analyses revealed that the nomogram exhibited superior discriminatory accuracy compared to alternative models, with DCA further identifying the nomogram as offering the highest net benefits across a broad spectrum of threshold probabilities.ConclusionBALF NP ≥16% serves as an effective discriminator between pulmonary infectious and non-infectious diseases in patients with pulmonary shadows. We have developed a nomogram model incorporating BALF NP and procalcitonin (PCT), which has proven to be a valuable tool for predicting the risk of pulmonary infections. This model holds significant potential to assist clinicians in making informed treatment decisions.

To develop and validate a predictive model for assessing the risk of short-term mortality in patients with invasive fungal diseases (IFDs) following cardiac surgery. This retrospective study analyzed clinical data from patients diagnosed with postoperative IFDs in the cardiac surgical intensive care unit (ICU) of Qilu Hospital of Shandong University (QLH), between January 2020 and December 2023. A total of 98 patients were included and divided into a non-survival group (n = 42) and a survival group (n = 56) based on 28-day mortality. Demographic, clinical, and postoperative parameters were collected. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for variable selection, and selected variables were then entered into multivariate logistic regression to identify independent risk factors. A nomogram was developed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC). Multivariate logistic regression, following variable selection by LASSO, identified a history of smoking, an elevated SOFA score, mean arterial pressure (MAP) below 70 mmHg, and tachyarrhythmia as independent risk factors for short-term mortality in this cohort (p < 0.05). The prediction model demonstrated excellent discrimination, with an area under the ROC curve (AUC) of 0.886 (95% CI: 0.816-0.957). The calibration curve showed good agreement between predicted and observed outcomes, with a mean absolute error of 0.023. Decision curve analysis indicated a net clinical benefit across a threshold probability range of 0.1 to 0.87. The clinical impact curve confirmed a high concordance between predicted mortality and actual outcomes. A history of smoking, an elevated SOFA score, MAP below 70 mmHg, and tachyarrhythmia independently predict short-term mortality in patients with IFDs after cardiac surgery. Therefore, the nomogram constructed from these factors provides an accurate and clinically applicable tool for risk stratification.

ObjectivesThis study developed a deep learning radiomics (DLR) model utilizing baseline computed tomography enterography (CTE) to non-invasively predict stratified healing in Crohn’s disease (CD) patients following infliximab (IFX) treatment.MethodsThe study included 246 CD patients diagnosed at three hospitals. From the first two hospitals, 202 patients were randomly divided into a training cohort (n = 141) and a testing cohort (n = 61) in a 7:3 ratio. The remaining 44 patients from the third hospital served as the validation cohort. Radiomics and deep learning features were extracted from both the active lesion wall and mesenteric adipose tissue. The most valuable features were selected using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was then employed to construct the radiomics, deep learning, and DLR models. Model performance was evaluated using receiver operating characteristic (ROC) curves.ResultsThe DLR model achieved an area under the ROC curve (AUC) of 0.948 (95% CI: 0.916–0.980), 0.889 (95% CI: 0.803–0.975), and 0.938 (95% CI: 0.868–1.000) in the training, testing, and validation cohorts, respectively in predicting mucosal healing (MH). Furthermore, the diagnostic performance of DLR model in predicting transmural healing (TH) was 0.856 (95% CI: 0.776–0.935).ConclusionsWe have developed a DLR model based on the radiomics and deep learning features of baseline CTE to predict stratified healing (MH and TH) in CD patients following IFX treatment with high accuracies in both testing and external cohorts.Critical relevance statementThe deep learning radiomics model developed in our study, along with the nomogram, can intuitively, accurately, and non-invasively predict stratified healing at baseline CT enterography.Key PointsEarly prediction of mucosal and transmural healing in Crohn’s Disease patients is beneficial for treatment planning.This model demonstrated excellent performance in predicting mucosal healing and had a diagnostic performance in predicting transmural healing of 0.856.CT enterography images of active lesion walls and mesenteric adipose tissue exhibit an association with stratified healing in Crohn’s disease patients.Graphical

Background Congenital ureteropelvic junction obstruction (UPJO) is a common cause of pediatric hydronephrosis, with postoperative urinary tract infection (UTI) affecting prognosis. This study aims to identify UTI risk factors and develop a machine learning-based predictive model for UPJO patients. Methods A retrospective analysis included 150 children with UPJO undergoing dismembered pyeloplasty (2014–2024). Clinical data were collected, and UTIs were classified as uncomplicated or complicated. The Least Absolute Shrinkage and Selection Operator (LASSO) regression selected key predictors, and the dataset was split (7: 3) for training and validation. Machine learning models (LASSO, random forest, support vector machine, gradient boosting, logistic regression) were compared using the receiver operating characteristic curve (ROC), calibration, and decision curve analysis (DCA). Statistical analyses were performed using Python, SPSS 27.0, and R 4.3.1 software. Results Among the 150 patients, 67 (44.67%) developed postoperative urinary tract infection (UTI), including 44 (29.33%) with simple UTI and 23 (15.33%) with complicated UTI. Multivariate logistic regression analysis identified comorbidities (OR = 3.22, P = 0.008), preoperative abnormal white blood cell (WBC) count (OR = 3.04, P = 0.018), preoperative UTI (OR = 3.93, P = 0.006), preoperative urine nitrite positivity (OR = 17.19, P &lt; 0.001), and presence of calculus (OR = 10.43, P = 0.015) as independent risk factors for postoperative UTI.Least Absolute Shrinkage and Selection Operator (LASSO) regression selected seven key variables: comorbidities, Double J stent indwelling time, presence of calculus, preoperative abnormal WBC count, preoperative UTI, preoperative urinary nitrite positivity, and intraoperative blood loss. The logistic regression-based predictive model demonstrated superior performance in the validation set (AUC = 0.81). A nomogram developed from this model exhibited excellent calibration and clinical net benefit. Conclusions This study successfully identified independent risk factors for postoperative UTI in children with UPJO and developed an efficient logistic regression-based predictive model and nomogram. These tools provide reliable evidence for early identification of high-risk patients and the formulation of personalized intervention strategies

Radiomics Analysis of Fat Saturated T2-Weighted MRI Sequences for Prognostic Prediction to Soft-Tissue Sarcoma of the Extremities and Trunk Treated With Neoadjuvant Radiotherapy

ObjectivesDelays in cancer diagnosis for patients with non-specific symptoms (NSSs) lead to poorer outcomes. Rapid Diagnostic Clinics (RDCs) expedite care, but most NSS patients do not have cancer, highlighting the need for better risk stratification. This study aimed to develop biomarker-based clinical prediction scores to differentiate high-risk and low-risk NSS patients, enabling more targeted diagnostics.DesignRetrospective and prospective cohort study.SettingSecondary care RDC in London.ParticipantsAdult patients attending an RDC between December 2016 and September 2023 were included. External validation used data from another RDC.Outcome measuresThe primary outcome was a cancer diagnosis. Biomarker-based risk scores were developed using Latent Class Analysis (LCA) and Least Absolute Shrinkage and Selection Operator (LASSO). Model performance was assessed using logistic regression, receiver operating characteristic curves (AUROC) and decision curve analysis.ResultsAmong 5821 RDC patients, LCA identified high white cell count, low haemoglobin, low albumin, high serum lambda light chain, high neutrophil-to-lymphocyte ratio, high serum kappa light chain (SKLC), high erythrocyte sedimentation rate (ESR), high C-reactive protein (CRP) and high neutrophils as cancer risk markers. LASSO selected high platelets, ESR, CRP, SKLC, alkaline phosphatase and lactate dehydrogenase. Each one-point increase in score predicted higher odds of cancer (LCA: AOR 1.19, 95% CI 1.16 to 1.23; LASSO: AOR 1.29, 95% CI 1.25 to 1.34). Scores ≥2 predicted significantly higher cancer odds (LCA: AOR 3.79, 95% CI 2.91 to 4.95; LASSO: AOR 3.44, 95% CI 2.66 to 4.44). Discrimination was good (AUROC: LCA 0.74; LASSO 0.73). External validation in 573 patients confirmed predicted increases in cancer risk per one-point LASSO score rise (AOR 1.28, 95% CI 1.15 to 1.42), with a borderline increase for LCA (AOR 1.16, 95% CI 1.06 to 1.27).ConclusionBiomarker-based scores effectively identified NSS patients at higher cancer risk. LCA captured a broader biomarker range, offering higher sensitivity, while LASSO achieved higher specificity with fewer markers. These scores may also help detect severe benign conditions, improving RDC triage. Further validation is needed before broader clinical implementation.

BackgroundThe in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients.MethodsThis study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.ResultsA total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769–0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance.ConclusionsIn this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.

BACKGROUND: Recurrence of lumbar disc herniation (LDH) is an adverse event after percutaneous endoscopic transforaminal discectomy (PETD). Accurate prediction of the risk of recurrent LDH (rLDH) after surgery remains a major challenge for spine surgeons. OBJECTIVES: To develop and validate a prognostic model based on risk factors for rLDH after PETD. STUDY DESIGN: Retrospective study. SETTING: Inpatient surgery center. METHODS: Clinical data were retrospectively collected from 645 patients with LDH who underwent PETD at the Affiliated Hospital of Xuzhou Medical University from January 1, 2017 to January 1, 2021. Predictors significantly associated with rLBH were screened according to least absolute shrinkage and selection operator (LASSO) regression, and a prognostic model was established, followed by internal model validation using the enhanced bootstrap method. The performance of the model was assessed using receiver operating characteristic (ROC) curves and calibration curves. Finally, the clinical usefulness of the model was analyzed using decision curve analysis (DCA) and clinical impact curves (CICs). RESULTS: Among the 645 patients included in this study, 56 experienced recurrence of LDH after PETD (8.7%). Seven factors significantly associated with rLDH were selected by LASSO regression, including age, type of herniation, level of herniation, Modic changes, Pfirrmann classification, smoking, and history of high-intensity physical work. The bias-corrected curve of the model fit well with the apparent curve, and the area under the ROC curve was 0.822 (95% confidence interval, 0.76-0.88). The DCA and CIC confirmed that the prognostic model had good clinical utility. LIMITATIONS: This is a single-center study, and we used internal validation only. CONCLUSIONS: The prognostic model developed in this study had excellent comprehensive performance and could well predict the risk of rLDH after PETD. This model could be used to identify patients at high risk for rLDH at an early stage to individualize the patient’s treatment modality and postoperative rehabilitation plan. KEY WORDS: Recurrent lumbar disc herniation, prognostic model, percutaneous endoscopic transforaminal discectomy, individualized treatment

ObjectiveUsing machine learning (ML) algorithms integrated with deep learning and radiomics technologies, we developed a nomogram model through an in-depth analysis and mining of clinical data and imaging features from patients with aneurysmal subarachnoid hemorrhage (aSAH). This model was aimed to predict the risk of developing chronic hydrocephalus in aSAH patients.MethodsThis study enrolled 410 patients diagnosed with subarachnoid hemorrhage (SAH) in the Neurosurgery Department of the Affiliated People’s Hospital of Jiangsu University between January 2020 and December 2023. Clinical and imaging characteristic data were collected from these patients. Using radiomic methods, we extracted features from the white matter surrounding the anterior horns of both lateral ventricles, ultimately selecting seven radiomic features to calculate the radiomics score. An automatic segmentation model based on the 3D-Unet architecture was specifically developed to measure hematoma volume. Initially, univariate analysis was conducted on all features, and the least absolute shrinkage and selection operator (LASSO) regression model was applied for feature selection using 10-fold cross-validation to optimize the penalty parameter. Key risk factors were identified, and various ML algorithms were used to construct and validate a predictive model, leading to the development of a clinical-radiological nomogram. To evaluate the model’s discriminative ability, we performed receiver operating characteristic (ROC) curve analysis and calculated the area under the curve (AUC). Additionally, the consistency between model predictions and actual outcomes was assessed using calibration curves. Further evaluation included plotting precision-recall (P-R) curves, decision curve analysis (DCA), and clinical impact curves (CIC) to demonstrate the net benefit of the model at various thresholds in the training and test sets, validating its clinical utility.ResultsA total of 180 patients were included, and a 3D-Unet automatic segmentation model was developed to accurately identify and quantify SAH volume. In the test set, the model achieved a Dice similarity coefficient (DSC) of 0.85 ± 0.04, an intersection over union (IoU) of 0.74 ± 0.06, a Hausdorff distance (HD) of 20.4 ± 12.3, and an average symmetric surface distance (ASSD) of 0.31 ± 0.23, demonstrating excellent performance in identifying SAHs. After screening features such as hematoma volume and radiomic score through univariate logistic regression (LR), 21 potential risk factors were identified. LASSO regression further refined these to nine key risk factors. Combining the results from both analyses, 6 independent predictive factors were determined: cerebrospinal fluid lactic acid level, sodium (Na), corpus callosum angle, interval to blood clearance, periventricular white matter changes, and hematoma volume. Among 8 ML models, the LR model showed the best performance, with AUC values of 0.884 [95% confidence interval (CI): 0.826–0.942] in the training cohort and 0.860 (95% CI: 0.758–0.962) in the test cohort. The calibration curve of the LR model showed a high agreement between predicted probabilities and observed outcomes. Additionally, DCA and CIC analyses demonstrated significant net benefits across different risk thresholds, confirming high consistency between predictions and actual outcomes.ConclusionThe developed 3D-Unet automatic segmentation model accurately identified hematomas and calculated their volume, addressing the challenge of quantitatively assessing SAH volume in clinical practice. Hematoma volume, a key risk factor, was integrated with clinical and radiological features from computed tomography (CT) scans using ML methods to construct a clinical-radiological nomogram. This nomogram effectively predicted the development of chronic hydrocephalus in patients with aSAH.

ObjectiveThis study aimed to develop and validate a nomogram to predict the risk of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritopreneal dialysis.MethodsA retrospective analysis was conducted on clinical data from 376 patients at Nanhai District People’s Hospital in Foshan City, Guangdong Province, between December 2017 and December 2024. The dataset was randomly divided into a training set (n = 244) and a validation set (n = 132). Risk factors for PDAP were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression, and a predictive nomogram was developed and validated using R4.1.3. The model’s performance was evaluated through receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, decision curve analysis (DCA), and clinical impact curves (CICs).ResultsEight potential predictors were selected by LASSO regression analysis. Multivariate logistic regression analysis confirmed that age, dialysis duration, albumin, hemoglobin, β2-microglobulin, Potassium and lymphocyte count were independent risk factors for PDAP occurrence (P = 0.001). The nomogram’s area under the curve (AUC) was 0.929 (95% CI: 0.896–0.962) in the training set and 0.905 (95% CI: 0.855–0.955) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated a good model fit (training set χ2 = 13.181, P = 0.106; validation set χ2 = 8.264, P = 0.408). Both DCA and CIC revealed that the nomogram model had good clinical utility in predicting PDAP.ConclusionThe proposed nomogram exhibited excellent predictive performance and clinical utility, providing a valuable tool for early identification and intervention in PDAP. Further external validation and prospective studies are recommended.

BackgroundPredicting rupture risk is important for aneurysm management. This research aimed to develop and validate a nomogram model to forecast the rupture risk of posterior communicating artery (PcomA) aneurysms.MethodsClinical, morphological, and hemodynamic parameters of 107 unruptured PcomA aneurysms and 225 ruptured PcomA aneurysms were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) analysis was applied to identify the optimal rupture risk factors, and a web-based dynamic nomogram was developed accordingly. The nomogram model was internally validated and externally validated independently. The receiver operating characteristic (ROC) curve was used to assess the discrimination of nomogram, and simultaneously the Hosmer–Lemeshow test and calibration plots were used to assess the calibration. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical utility of nomogram additionally.ResultsFour optimal rupture predictors of PcomA aneurysms were selected by LASSO and identified by multivariate logistic analysis, including hypertension, aspect ratio (AR), oscillatory shear index (OSI), and wall shear stress (WSS). A web-based dynamic nomogram was then developed. The area under the curve (AUC) in the training and external validation cohorts was 0.872 and 0.867, respectively. The Hosmer–Lemeshow p > 0.05 and calibration curves showed an appropriate fit. The results of DCA and CIC indicated that the net benefit rate of the nomogram model is higher than other models.ConclusionHypertension, high AR, high OSI, and low WSS were the most relevant risk factors for rupture of PcomA aneurysms. A web-based dynamic nomogram thus established demonstrated adequate discrimination and calibration after internal and external validation. We hope that this tool will provide guidance for the management of PcomA aneurysms.

To investigate the risk factors for neurogenic lower urinary tract dysfunction (NLUTD) in patients with acute ischemic stroke (AIS), and develop an internally validated predictive nomogram. The study aims to offer insights for preventing AIS-NLUTD. We conducted a retrospective study on AIS patients in a Shenzhen Hospital from June 2021 to February 2023, categorizing them into non-NLUTD and NLUTD groups. The bivariate analysis identified factors for AIS-NLUTD (p < 0.05), integrated into a least absolute shrinkage and selection operator (LASSO) regression model. Significant variables from LASSO were used in a multivariate logistic regression for the predictive model, resulting in a nomogram. Nomogram performance and clinical utility were evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC). Internal validation used 1000 bootstrap resamplings. A total of 373 participants were included in this study, with an NLUTD incidence rate of 17.7% (66/373). NIHSS score (OR = 1.254), pneumonia (OR = 6.631), GLU (OR = 1.240), HGB (OR = 0.970), and hCRP (OR = 1.021) were used to construct a predictive model for NLUTD in AIS patients. The model exhibited good performance (AUC = 0.899, calibration curve p = 0.953). Internal validation of the model demonstrated strong discrimination and calibration abilities (AUC = 0.898). Results from DCA and CIC curves indicated that the prediction model had high clinical utility. We developed a predictive model for AIS-NLUTD and created a nomogram with strong predictive capabilities, assisting healthcare professionals in evaluating NLUTD risk among AIS patients and facilitating early intervention.