Scholarlink

Background <p>Peripherally 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.</p> Methods <p>This 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.</p> Results <p>Random Forest achieved the best discriminative performance (AUC-ROC 0.809, 95% CI: 0.780–0.838, bootstrap <i>n</i> = 2,000), significantly outperforming all other models on DeLong’s test (all <i>P</i> &lt; 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.</p> Conclusions <p>These 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning prediction of PICC-associated thrombotic complications in critically ill patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

查看全文
  • Liyan Xia,
  • Hailan Jiang,
  • Yanfei Zhang,
  • Jiahui Guo,
  • Mengying Sun,
  • Haitao Wu,
  • Yang Cui,
  • Yongjie Wang

摘要

Background

Peripherally 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.

Methods

This 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.

Results

Random 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.

Conclusions

These 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.