Background: Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH. Methods: Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model’s performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform. Results: A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705–0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model’s variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients. Conclusion: We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.

Background: A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients. Methods: As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA). Results: Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838–0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems. Conclusions: We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757–0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792–0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.

Spontaneous Intracerebral hemorrhage (sICH) is a disease with high mortality and disability. Non-contrast computed tomography (NCCT) is the most commonly used imaging method in the diagnosis and treatment of sICH. This study aimed to develop a clinically useful prediction model for the short-term prognosis of sICH patients based on NCCT features using a machine learning model. We retrospectively collected data from sICH patients from four centers in China between January 2021 and June 2024, used data from three centers as training cohort to build the model, and another single center data for external validation. The NCCT imaging features were combined with the basic clinical characteristics of sICH patients as training features for machine learning. We developed and verified the effectiveness of five models: support vector machine (SVM), logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost) and Light Gradient Boosting Machine (LightGBM). The clinical feature set, NCCT imaging feature set and fusion feature set were modeled separately and externally validated. The performance of machine learning models with different features was comprehensively evaluated using ROC curves, accuracy and other related indicators. The SHapley Additive exPlanations (SHAP) diagram was used to illustrate the importance of variables in the model, and the Sequential Forward Selection (SFS) was used to screen out the core features. Finally, a convenient and practical prognosis prediction platform was built based on the core features. This study is registered with ClinicalTrials.gov (NCT06535438). A total of 1091 sICH patients from three centers were included as the training cohort, and 102 patients from a single center were included as the external validation cohort. The LightGBM model showed the best performance in predicting the short-term prognosis of sICH patients, with an area under the receiver operating characteristic curve (AUROC) of 0.813 ± 0.012. The clinical feature cohort model (AUC: 0.822, 95% CI (0.763-0.881)), the NCCT imaging feature model (AUC: 0.770, 95% CI (0.704-0.835)) and the fusion model (AUC: 0.852, 95% CI (0.797-0.906)) were developed respectively. The external validation cohort were the clinical feature model (AUC: 0.792, 95% CI (0.689-0.894)), the NCCT imaging feature model (AUC: 0.746, 95% CI (0.637-0.855)), and the fusion feature (AUC: 0.796, 95% CI (0.694-0.897). Finally, the core factors obtained through screening, including Glasgow Coma Scale (GCS) score at admission, intraventricular hemorrhage (IVH), National Institutes of Health Stroke Scale (NIHSS) score at admission, hematoma volume, mean CT value, and black hole sign were incorporated into the model to generate a publicly accessible online platform (https://surge-ustc.shinyapps.io/multi_para_sih_prognosis/). The prediction model based on NCCT features established by the LightGBM model has a reliable predictive effect on the short-term prognosis of sICH patients and is of great clinical convenience and practicality. Funding provided by National Natural Science Foundation of China (82427808, 82171382, U23A20424, 82090034 and 82371201), the Anhui Province Clinical Medical Research Transformation Special Project (202204295107020006 and 202204295107020028) and Research Fund of Anhui Institute of translational medicine (2022zhyx-B11).

Objective: Traumatic subdural effusion (TSE) is a common complication of traumatic brain injury (TBI). This study aimed to determine the risk factors associated with subdural effusion and to propose a nomogram to predict the risk of TSE in patients with mild TBI.Methods: We retrospectively analyzed 120 patients with mild TBI between January 2015 and December 2020 at the Third People's Hospital of Hefei. The risk factors of TSE were selected using univariate and multivariable logistic regression analysis. A nomogram was developed to predict the incidence of TSE. Receiver operating characteristics and calibration plots were used to evaluate the discrimination and fitting performance.Results: Of the 120 patients, 32 developed subdural effusion after mild TBI. Univariate analysis showed that gender, age, history of hypertension, traumatic subarachnoid hemorrhage, subdural hematoma, basilar skull fracture, and cerebral contusion were varied significantly between groups (p < 0.05). Logistic multivariate regression analysis showed that the gender, age, history of hypertension, and basilar skull fracture were independent risk factors for TSE. Based on these results, a nomogram model was developed. The C-index of the nomogram was 0.78 (95% CI: 0.70–0.87). The nomogram had an area under the receiver operating characteristic curve of 0.78 (95% CI: 0.70–0.87). The calibration plot demonstrated the goodness of fit between the nomogram predictions and actual observations.Conclusion: Gender, age, history of hypertension, and basilar skull fracture can be used in a nomogram to predict subdural effusion after mild TBI.

This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms. Data were collected from two centers and categorized into development and validation cohorts. Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method. Five machine learning algorithms, logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM), were utilized to construct the predictive models. Model performance was evaluated through nested cross-validation and area under the curve (AUC) metrics, supplemented by interpretations using Shapley Additive explanations (SHAP) and line charts of AUC values. Models were subjected to external validation using an independent validation group, facilitating the early identification and management of pulmonary embolism risks in tuberculosis patients. Data from 694 patients were used for model development, and 236 patients from the validation group met the enrollment criteria. The optimal subset of variables identified included D-dimer, smoking status, dyspnea, age, sex, diabetes, platelet count, cough, fibrinogen, hemoglobin, hemoptysis, hypertension, chronic obstructive pulmonary disease (COPD), and chest pain. The RF model outperformed others, achieving an AUC of 0.839 (95% CI 0.780–0.899) and maintaining the highest average performance in external fivefold cross-validation (AUC: 0.906 ± 0.041). The RF model demonstrates high and consistent effectiveness in predicting pulmonary embolism risk in tuberculosis patients.