Retrospective Cohort Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Dec 24, 2022; 13(12): 967-979
Published online Dec 24, 2022. doi: 10.5306/wjco.v13.i12.967
Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery
Jiao Ai, Yao Hu, Fang-Fang Zhou, Yi-Xiang Liao, Tao Yang
Jiao Ai, Fang-Fang Zhou, Yi-Xiang Liao, Tao Yang, Department of Urology, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434020, Hubei Province, China
Yao Hu, Department of Obstetrics and Gynaecology, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434020, Hubei Province, China
Author contributions: Ai J and Hu Y contributed equally to this work; Yang T designed the research study; Ai J, Hu Y, Zhou FF, and Liao YX performed the research; All authors have read and approved the final manuscript.
Institutional review board statement: This study was approved by the Institutional Review Committee of Jingzhou Central Hospital (JZ-2022014).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrolment.
Conflict-of-interest statement: All authors declare that there are no conflicts of interest.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement checklist of items, and the manuscript was prepared and revised according to the STROBE Statement checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tao Yang, MD, Surgical Oncologist, Department of Urology, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, No. 26 Chuyuan Road, Jingzhou 434020, Hubei Province, China. yangtaotao123321@163.com
Received: August 1, 2022
Peer-review started: August 1, 2022
First decision: November 11, 2022
Revised: November 17, 2022
Accepted: December 8, 2022
Article in press: December 8, 2022
Published online: December 24, 2022
Processing time: 139 Days and 13.6 Hours
ARTICLE HIGHLIGHTS
Research background

Nowadays, predictive models based on advanced algorithms have been gradually applied to the medical field, which also enables many diseases to be detected and diagnosed early. Among them, the machine learning (ML) algorithm relies on repeated iterative operations to accurately output the results. Therefore, it can improve the accuracy and robustness of prediction.

Research motivation

Given the superior ability of the ML-based algorithm to improve the accuracy of muscular invasion prediction, we applied the ML-assisted decision-support model to assess the risk of urinary tract infection (UTI) using clinical parameters and direct clinical decision-making prior to treatment decisions.

Research objectives

We developed an ML assistant model for the prevention and control of nosocomial infection.

Research methods

A total of 674 elderly patients with ovarian cancer treated between January 31, 2016 and January 31, 2022 and met the inclusion criteria of the study were selected as the research subjects. A retrospective analysis of the postoperative UTI and related factors was performed by reviewing the medical records. Five ML-assisted models were developed using two-step estimation methods from the candidate predictive variables. The robustness and clinical applicability of each model were assessed using the receiver operating characteristic curve, decision curve analysis and clinical impact curve.

Research results

A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier (RFC), support vector machine, extreme gradient boosting, artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The RFC model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy.

Research conclusions

These findings demonstrated that the ML-based prediction model developed using the RFC can be used to identify elderly patients with ovarian cancer who may have postoperative UTI. This can help with treatment decisions and enhance clinical outcomes.

Research perspectives

Using an ML-based algorithm, we developed a feasible and robust method to identify factors that are significant for predicting UTIs. The RFC, which can improve the prediction and early detection of UTIs in patients with ovarian cancer, was particularly robust. In addition, the five most crucial factors were age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia. Clinicians may find it extremely helpful to assess the individualised risk of UTI in clinical practice by incorporating the presentation of simple clinical data.