Machine learning approaches for risk assessment of peripherally inserted Central catheter-related vein thrombosis in hospitalized patients with cancer. (September 2019)

• Record Type: Journal Article
• Authors: Liu, Shanshan; Zhang, Fengyi; Xie, Lingling; Wang, Ying; Xiang, Qiufen; Yue, Zhiying; Feng, Yue; Yang, Yanmeng; Li, Junying; Luo, Li; Yu, Chunhua
• Is Part Of: International journal of medical informatics. Volume 129(2019)
• Page Start: 175
• Page End: 183
• Publication Date: 2019-09
• Subjects: Peripherally inserted central catheter-related vein thrombosis -- Machine learning approaches -- Assessment -- Seeley; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.06.001
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: This study acted as pioneer in using machine learning approaches to make effective assessments of peripherally inserted central venous catheter (PICC)-related thrombosis, and validated the feasibility of that. To the best of our knowledge, this study is the first one that take Genotype into consideration for PICC-related thrombosis risk assessment. The risk factors of PICC-related thrombosis in cancer patients were analyzed, and comparative analysis were conducted with other related research. Abstract: Objective: The aim of this study was to conduct an effective assessment of peripherally inserted central venous catheter (PICC)-related thrombosis based on machine learning (ML) techniques considering genotype. Design: We conducted a prospective cohort study of 348 cancer patients with PICCs who were admitted to the Department of Oncology of West China Hospital, over a 1-year period, between February 1, 2016, and February 31, 2017. We obtained the clinical attributes, onset, duration, and outcome of thrombosis from electronic health records. We assigned all patients to either the training or testing set, and used four models for comparison with the currently used criteria. Results: ML methods showed good efficiency in PICC-related thrombosis risk assessment (with areas under the curve of 0.7733, 0.7869, 0.7833, and 0.7717 respectively) and outperform the currently used criteria (Seeley), which did not identify any positive case. Conclusions: Our research confirmed that ML approaches are powerful tools to identify cancer patients with a high risk of PICC-related thrombosis, which outperform the currently used criteria (Seeley). Moreover, our research also offers some indications on the predictors and risk factors of PICC-related thrombosis. From our research, more-precise assessments can be performed in cancer patients with PICCs to help decide the prophylaxis and effectively lower the incidence of PICC-related thrombosis.