Evidence map›Paper›PMID 41639249›Full record

ArticleScientific reports2026

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

Yanying Yang, Yuwei Yang, Qiong Liu, Chenxi Li, Yin Long, Xuanxuan Li, Hongmei Liu, Run He

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yanying Yang *School of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, People's Republic of China. 2729433618@qq.com.
Yuwei YangSchool of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, People's Republic of China.
Qiong Liu *School of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, People's Republic of China.
Chenxi Li *School of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, People's Republic of China.
Yin LongSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Xuanxuan LiSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Hongmei LiuSchool of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, People's Republic of China.
Run HeSchool of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Airway ExtubationCatheterization, Central VenousCatheterization, PeripheralMachine LearningNeoplasmsAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedPredictive Learning ModelsQuality of LifeRisk FactorsCancerMachine learningPeripherally inserted central catheter line insertionProlapseRisk

Identifiers

PMID41639249
PMCPMC12923833

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.