2026-11-02

Background: Mortality in sepsis-associated liver injury (SALI) remains high, and early risk stratification is difficult. Existing prognostic models commonly focus on a single endpoint, usually 28-day mortality, and rarely undergo independent external validation. Methods: We developed machine-learning models using clinical features recorded during the first 24 h to predict all-cause in-hospital mortality within 7, 14, and 28 days. In MIMIC-IV (v3.1), 1,550 adults meeting the prespecified SALI criteria were allocated to a training cohort (n = 1,119) and a held-out internal validation cohort (n = 431). An independent Chinese cohort from Xiaogan Central Hospital (n = 229; 28-day in-hospital mortality, 45.9%) was used for external validation. One hundred candidate pipelines were evaluated using stratified 10-fold cross-validation. SHAP was used to describe feature attribution across prediction horizons. Results: AUROCs in the held-out internal validation cohort were 0.840, 0.841, and 0.850 for 7-, 14-, and 28-day mortality, respectively; corresponding external AUROCs were 0.720, 0.717, and 0.763. The external 28-day model outperformed modified SOFA (0.763 vs. 0.673; P = 0.011) and MELD-Na (0.763 vs. 0.636; P = 0.002). A parsimonious logistic-regression sensitivity model achieved an external 28-day AUROC of 0.762, not significantly different from the full model (P = 0.970). The 14-day model was miscalibrated externally; recalibration improved the calibration slope from 0.563 to 0.970 but did not materially improve the Brier score. SHAP rankings varied across horizons and were interpreted descriptively rather than causally. Conclusion: The models showed moderate transportability to one independent retrospective cohort. Multicentre prospective validation is needed before clinical implementation.

Persistent fever after initiating intravenous antibiotics is common in hospitalized patients with cancer and often leads to additional imaging and antimicrobial escalation. Early identification of patients unlikely to remain febrile could provide additional information during clinical reassessment. We developed and externally validated machine learning models to predict persistent fever 48-72 h after antibiotic initiation using electronic health records from two academic hospitals. Adult cancer patients with fever within 24-48 h of treatment were included. Models incorporated demographics, comorbidities, laboratory results, vital signs, and temperature patterns. The best-performing model achieved an AUROC of 0.82 (95% CI 0.79-0.84) internally and 0.78 (95% CI 0.77-0.79) externally. Temperature-related variables were the dominant predictors. Decision curve analysis demonstrated positive net benefit across much of the evaluated threshold range. Retrospective assessment suggested that approximately one quarter of antibiotic escalations and chest CT scans occurred in patients correctly predicted not to remain febrile, highlighting the potential contextual value of fever predictions. These findings indicate that short-term fever persistence can be predicted with clinically meaningful accuracy using routine data. However, clinical use should be limited to an adjunctive role rather than standalone rule-out decision-making, and prospective studies are required to evaluate safety, clinician uptake, and real-world impact.

Background: Elderly patients with sepsis-associated acute kidney injury (SA-AKI) have high short-term mortality; early identification of high-risk individuals is critical for personalized intervention. Traditional clinical scoring systems relying on linear assumptions and conventional machine learning models often lack interpretability, hindering translation to clinical practice. This study aimed to develop and externally validate an interpretable prognostic model for 28-day in-hospital mortality in elderly SA-AKI using routinely available clinical variables. Methods: Clinical data of 11,468 elderly SA-AKI patients were retrospectively extracted from the MIMIC-IV database and divided into training and internal test sets at a 7:3 ratio using stratified random sampling. A dual feature selection strategy combining LASSO regression and random forest was applied. Five machine learning models-decision tree, logistic regression, random forest, LightGBM, and XGBoost-were constructed in parallel. Model discrimination and calibration were evaluated by AUC, Brier score, accuracy, sensitivity, specificity, and F1 score. External independent validation was performed using a clinical cohort of 257 patients from the General Hospital of Ningxia Medical University. The SHAP method was used for global and individual-level interpretability of the optimal model. Results: Dual feature selection identified 10 independent predictors: age, weight, red blood cell distribution width, arterial partial pressure of oxygen, blood lactate, prothrombin time, activated partial thromboplastin time, blood urea nitrogen, serum creatinine, and respiratory rate. Among the five models, XGBoost demonstrated the best performance, with training AUC 0.9898 (accuracy 0.9472) and external validation AUC 0.9156 (accuracy 0.8560), indicating strong generalizability. SHAP global importance ranking showed age as the primary driver, followed by serum creatinine and arterial partial pressure of oxygen. Each core feature exhibited a nonlinear association with 28-day mortality risk, with clear model-derived risk inflection points rather than clinical intervention thresholds. Conclusion: The XGBoost-based interpretable model exhibits excellent discriminative performance and robust external generalizability for predicting 28-day mortality in elderly SA-AKI. The SHAP attribution framework renders model decisions transparent, providing quantitative evidence-based support for early risk stratification and hypothesis generation for individualized organ-protective strategies in the ICU.

Introduction: Hepatocellular carcinoma (HCC) exhibits substantial molecular heterogeneity, complicating treatment selection and clinical management. Objectives: We developed an artificial intelligence-based histopathological framework to predict HCC biological characteristics from routine hematoxylin and eosin (H&E)-stained slides. Methods: Using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, transcriptomic phenotypes associated with angiogenesis, stemness, and autophagy were identified through gene set analysis, differential expression analysis, and consensus clustering, and evaluated for overall survival and drug sensitivity. Whole-slide images were processed into 224 × 224-pixel tumor tiles for phenotype prediction using feature extraction (UNI, ResNet50, CTrans, GigaPath, and Virchow2) combined with classic multiple instance learning and clustering-constrained attention-based multiple instance learning.. Models were externally validated at Renmin Hospital of Wuhan University and Tongji Hospital. Interpretability was assessed using attention heatmaps, and predictions were evaluated against molecular markers and tumor differentiation grades. Results: Unsupervised clustering identified two subgroups per phenotype, with significant difference in survival (all p < 0.004) and sensitivities to sorafenib or transcatheter arterial chemoembolization (all p < 0.0001). The tumor detection model achieved an area under the receiver operating characteristic curve (AUC) of 97.53%. The phenotype prediction models achieved internal optimal AUCs of 85.51%-91.05% and accuracies of 79.63%-88.89%. External optimal AUCs were 70.94%, 88.64%, and 91.17% for angiogenesis, stemness, and autophagy, with accuracies of 62.00%, 78.00%, and 84.00%, respectively. Attention heatmaps revealed distinct morphological features among subgroups, including neovascularization, increased nuclear-to-cytoplasmic ratios, and cytoplasmic vacuolization. Model predictions showed concordance with CD31/CD105 (κ = 0.398-0.432), CD133/LGR5 (κ = 0.428-0.629), Beclin-1/p62-LC3B (κ = 0.592-0.762) (all p < 0.05), and tumor differentiation grade (κ = - 0.167 to - 0.175, p < 0.05). Conclusion: This proof-of-concept AI framework predicted transcriptome-defined HCC phenotypes from routine histology, with predictions associated with prognosis and therapeutic response. It may provide a cost-effective approach for assessing HCC heterogeneity and complement molecular profiling.

We established and validated perineural invasion density (PNID) as a superior prognostic marker and developed an MRI-based AI model for its preoperative prediction. In this multicenter study of 1091 PCa patients undergoing radical prostatectomy, we evaluated PNID's association with biochemical recurrence (BCR) and radiographic progression-free survival (rPFS). We also analyzed concurrent β-blocker or antimuscarinic use with adjuvant ADT. A deep learning model was developed in a primary cohort (n = 523) and validated in two external cohorts (n = 317, 251). High-PNID was an independent prognostic factor for BCR (HR 4.06, 95% CI: 2.75-5.97, P < 0.001) and rPFS (HR 4.61, 95% CI: 2.97-7.16, P < 0.001). Notably, concurrent β-blocker use was significantly associated with favorable BCR outcomes exclusively in the High-PNID subgroup (HR 0.32, 95% CI: 0.12-0.84, P = 0.021), with no benefit in the Non-High PNID population or with antimuscarinics. The AI model showed excellent performance predicting High-PNID, with AUCs of 0.917 (training), 0.842 (internal validation), 0.889 and 0.915 (external validations). Model-identified High-PNID patients had significantly worse outcomes. PNID surpasses traditional PNI in prognostic accuracy and identifies a high-risk phenotype. High-PNID may define a subgroup benefiting from adjuvant β-blocker therapy, providing an exploratory clinical rationale for targeting the nerve-tumor microenvironment in stratified subpopulations. Hence, this model may be a reliable, non-invasive tool for preoperative identification of this high-risk subgroup, aiding clinicians in personalized risk stratification and treatment planning.