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Infection / Boosting Machine Learning Algorithms / Cross Infection · Journal article
Renal Failure · August 5, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a retrospective machine learning model developed to predict in-hospital infection risk in SLE patients using nine routinely available clinical variables. The Gradient Boosting model achieved an AUC of 0.858 on internal validation, but the study reports model development and performance metrics only; no external validation, clinical implementation, or impact on outcomes is demonstrated.
Retrospective cohort study with machine learning model development and internal cross-validation. Adult patients aged ≥18 years with systemic lupus erythematosus from three departments using a population-based electronic medical record database spanning 2000–2024. Among the cohort, 3,157 (40.3%) patients developed in-hospital infection after 72 h of hospitalization.. Intervention: Machine learning model using nine clinical variables: daily prednisone equivalent dose, albumin, hydroxychloroquine use, C-reactive protein, D-dimer, glucose, cystatin C, hemoglobin, and alpha1-globulin. Compared with: Ten different machine learning models were compared; no comparison with existing clinical prediction tools reported. n = 7,833. Chinese PLA General Hospital, Beijing, China; described as single-centre study from three departments.
Among 7,833 adult SLE patients, 3,157 (40.3%) developed in-hospital infection after 72 h of hospitalisation Gradient Boosting model demonstrated best performance with AUC of 0.858 on independent validation set 5-fold cross-validation yielded mean AUC of 0.855 ± 0.002; 10-fold cross-validation yielded mean AUC of 0.854 ± 0.008
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This model is not yet ready for clinical application. Whilst it demonstrates good discriminative performance in internal validation, external prospective validation in independent cohorts and demonstration of clinical utility (impact on clinical decisions, patient stratification, or outcomes) are required before adoption in practice.
A machine learning model development study using retrospective electronic health records with internal validation but no external prospective validation, no clinical outcome trial, and no demonstration of impact on clinical decision-making or patient outcomes.
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This model is not yet ready for clinical application. Whilst it demonstrates good discriminative performance in internal validation, external prospective validation in independent cohorts and demonstration of clinical utility (impact on clinical decisions, patient stratification, or outcomes) are required before adoption in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Infection is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), yet effective tools for early identification of high-risk patients are lacking. This study aimed to develop an explainable machine learning (ML) model to predict in-hospital infection risk among SLE patients. We analyzed adult patients (≥18 years) with SLE (n = 7,833) from three departments using a population-based electronic medical record database (2000-2024). Among them, 3,157 (40.3%) patients developed an infection after 72 h of hospitalization. An initial comprehensive variable pool of 108 candidate predictors was included, encompassing demographics, comprehensive laboratory parameters, clinical features, disease activity, and treatment exposures. Ten machine learning models were applied. Model performance was evaluated using six metrics. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Nine predictors were selected: daily prednisone equivalent dose, albumin, hydroxychloroquine use, C-reactive protein, D-dimer, glucose, cystatin C, hemoglobin, and alpha1-globulin. Among all models tested, the Gradient Boosting model demonstrated the best overall performance on the independent validation set, with an area under the curve (AUC) of 0.858, with its robustness confirmed by 5-fold and 10-fold cross-validation (mean AUCs of 0.855 ± 0.002 and 0.854 ± 0.008, respectively). SHAP analysis revealed that daily prednisone equivalent dose, albumin, and hydroxychloroquine use were the most influential factors. We developed and validated a high-performance, explainable ML model using nine routinely available clinical variables to accurately predict in-hospital infection risk in SLE patients. This tool provides transparent, individualized risk assessment and has the potential to guide personalized clinical stratification and early intervention, ultimately improving patient outcomes.
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