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ORIGINAL RESEARCH article

Front. Public Health
Sec. Digital Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1445425
This article is part of the Research Topic Extracting Insights from Digital Public Health Data using Artificial Intelligence, Volume III View all 7 articles

Integrating Deep Learning in Public Health: A Novel Approach to PICC-RVT Risk Assessment

Provisionally accepted
Yue Li Yue Li 1Shengxiao Nie Shengxiao Nie 2*Lei Wang Lei Wang 2*Dongsheng Li Dongsheng Li 1*Shengmiao Ma Shengmiao Ma 3*Ting Li Ting Li 1*Hong Sun Hong Sun 4*
  • 1 Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
  • 2 Department of Nursing, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences,, Beijing, China
  • 3 School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing Municipality, China
  • 4 Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, beijing, China

The final, formatted version of the article will be published soon.

    Background: Machine 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.Objectives:This 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.Methods:We 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. Results: Deep 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. Conclusion: Machine 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.

    Keywords: artificial intelligence, machine learning, peripherally inserted central catheter, Time-to-event, Thrombosis

    Received: 07 Jun 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 Li, Nie, Wang, Li, Ma, Li and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Shengxiao Nie, Department of Nursing, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences,, Beijing, China
    Lei Wang, Department of Nursing, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences,, Beijing, China
    Dongsheng Li, Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
    Shengmiao Ma, School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100006, Beijing Municipality, China
    Ting Li, Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
    Hong Sun, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.