ArticleBMC medical informatics and decision making2026
Machine learning prediction of PICC-associated thrombotic complications in critically ill patients.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPeripherally inserted central catheters (PICCs) are widely used vascular access devices in intensive care, yet thrombotic complications remain a significant clinical concern. Traditional risk assessment tools fail to capture the complex, non-linear interactions among thrombosis risk factors in critically ill patients.
methodsThis retrospective cohort study, conducted in adherence to TRIPOD + AI guidelines, developed and compared five machine learning models for predicting PICC-associated thrombotic complications using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. A total of 8,145 PICC insertions from 6,376 patients were analyzed, with 29 predictor variables spanning demographics, coagulation parameters, hematologic indices, renal function, vital signs, comorbidities, and treatment variables. Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance (16.88% event rate), applied exclusively to the training set after the train-test split. Hyperparameters were optimized via five-fold cross-validated grid search.
resultsRandom Forest achieved the best discriminative performance (AUC-ROC 0.809, 95% CI: 0.780-0.838, bootstrap n = 2,000), significantly outperforming all other models on DeLong's test (all P < 0.01); its calibration slope of 1.369 indicated mild underconfidence. Decision curve analysis confirmed net clinical benefit for the three tree-based ensembles across threshold probabilities of approximately 1-50%. Feature importance analysis identified prior thrombosis history as the dominant predictor, followed by coagulation parameters and hematologic indices. Sensitivity analyses using patient-clustered data splitting, multiple imputation (m = 5, Rubin's rules), first-PICC-only subsets, and a no-prior-thrombosis subgroup confirmed the robustness of the primary findings.
conclusionsThese findings support the development of machine learning-based clinical decision support tools for individualized thrombosis risk stratification in critical care settings. The completed TRIPOD + AI checklist is provided as Additional file 1.
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