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February 7, 2026Scientific Reports0 citationsOpen Access

Interpretable machine-learning risk prediction of unplanned extubation among cancer patients with peripherally inserted central catheters

HLHongmei LiuUniversity of Electronic Science and Technology of China RHRun HeUniversity of Electronic Science and Technology of China QLQiong LiuFirst Affiliated Hospital of University of South China

Key Points

This study analyzes the risk factors for unplanned extubation of peripherally inserted central catheters in cancer patients, focusing on quality of life.
Included 212 cancer patients undergoing PICC catheterization.
Categorized patients into PICC-UE group (n=23) and non-PICC-UE group (n=189).
Collected data on 30 potential risk factors, emphasizing HRQOL and self-management scores.
Applied four machine-learning algorithms with various encoding and data imbalance processing methods.
Unplanned extubation incidence was 10.8%, with significant HRQOL differences between groups (P < 0.001).
Weighted quality of life was identified as the most influential risk factor for PICC-UE based on SHAP values (1.02).
Upper arm circumference also significantly predicted PICC-UE (SHAP value = 0.22).

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.

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