Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment - Li, Yue; Nie, Shengxiao; Wang, Lei; Li, Dongsheng; Ma, Shengmiao; Li, Ting; Sun, Hong (Frontiers in Public Health, 2025)
BackgroundMachine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. It also seeks to identify key predictive factors for PICC-RVT using these models.MethodsWe conducted a retrospective multi-center cohort study involving 5,272 patients who underwent PICC placement. After preprocessing patient data, the models were trained. Demographic, clinical pathology, and treatment data were analyzed to identify predictive factors. A variable analysis was then conducted to determine the most significant predictors of PICC-RVT. Model performance was evaluated using the Concordance Index (c-index) and the composite Brier score, and the Intraclass Correlation Coefficient (ICC) from cross-validation folds assessed model stability.ResultsDeep learning models generally outperformed traditional machine learning models in terms of predictive accuracy (mean c-index: 0.949 vs. 0.732; mean integrated Brier score: 0.046 vs. 0.093). Specifically, the DeepSurv model demonstrated exceptional precision in risk assessment (c-index: 0.95). Stability varied with the number of predictive factors, with Cox-Time showing the highest ICC (0.974) with 16 predictive factors, and DeepSurv the most stable with 26 predictive factors (ICC: 0.983). Key predictors across models included albumin levels, prefill sealant type, and activated partial thromboplastin time.ConclusionMachine learning models that incorporate time-to-event data can effectively predict PICC-RVT risk. The DeepSurv model, in particular, shows excellent discriminative and calibration capabilities. Albumin levels, type of prefill sealant, and activated partial thromboplastin time are critical indicators for identifying and managing high-risk PICC-RVT patients.