Abstract
Unplanned extubation of peripherally inserted central catheters (PICC-UE) in patients with cancer has been linked to factors including women, diabetes, thrombosis history, valved catheter, double-lumen catheter, and self-management. However, the effect of patient quality of life has not been explored. Therefore, this study aimed to analyze PICC-UE risk factors using a machine-learning algorithm, focusing on the role of patient quality of life. A total of 212 cancer patients who underwent PICC catheterization were included in this study from February 2021 to June 2022. Patients were categorized into two groups based on PICC-UE occurrence: the PICC-UE group (n = 23) and the non-PICC-UE group (n = 189). Referring to previous reports and professional cognition, data of 30 potential risk factors within one week before extubation were collected, with a focus on incorporating health-related quality of life (HRQOL) and patient self-management scores. PICC-UE risk factors were examined using four machine-learning algorithms with three encoding methods and four data imbalance processing methods. Then, the key factors causing PICC-UE were interpreted using the SHapley Additive exPlanations (SHAP) tool. PICC-UE occurred in 23 of 212 patients (overall incidence: 10.8%). The HRQOL score, which has been underexplored in prior studies, demonstrated a statistically significant difference between the PICC-UE and non-PICC-UE groups (P < 0.001) and exhibited a strong association with patient self-management score, as evidenced by its concentration in the upper right quadrant of the planar scatter plot. As a novel derivative composite metric, weighted quality of life (WQOL), calculated as the product of HRQOL and self-management scores, was identified as the most influential risk factor for PICC-UE, surpassing both individual self-management and HRQOL scores (SHAP-value = 1.02 vs. 0.22 and 0.00). Furthermore, increased upper arm circumference was also found to be a significant predictor (SHAP value = 0.22). This study demonstrates the synergistic effect of patients’ quality of life and self-management capacity on the PICC-UE occurrence. The WQOL metric, which integrates both factors, serves as a significant predictor of PICC-UE occurrence and should therefore be considered an essential component in clinical assessment.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Funding
This research was financially supported by the Sichuan Medical Association of China (S18080). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
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Yanying Yang: data curation, formal analysis, Funding acquisition, methodology, writing - original draft; Yuwei Yang: data curation, formal analysis, writing - review; Qiong Liu: Conceptualization, writing - review; Chenxi Li: investigation; Yin Long: methodology; Xuanxuan Li: methodology; Hongmei Liu: methodology, Investigation; Run He: Investigation;
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The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Mianyang Central Hospital approved this study (protocol code: S2018085, date of approval: 2018). Written informed consent was obtained from all participating individuals.
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Yang, Y., Yang, Y., Liu, Q. et al. Interpretable machine-learning risk prediction of unplanned extubation among cancer patients with peripherally inserted central catheters. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37411-3
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DOI: https://doi.org/10.1038/s41598-026-37411-3
