Published online Dec 24, 2022. doi: 10.5306/wjco.v13.i12.967
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
Urinary tract infection (UTI) is a common type of postoperative infection following cytoreductive surgery for ovarian cancer, which severely impacts the prognosis and quality of life of patients.
To develop a machine learning assistant model for the prevention and control of nosocomial infection.
A total of 674 elderly patients with ovarian cancer who were treated at the Department of Gynaecology at Jingzhou Central Hospital 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 machine learning-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.
A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier, support vector machine, extreme gradient boosting, and artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The random forest classifier model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy.
These findings demonstrate that the machine learning-based prediction model developed using the random forest classifier 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.
Core Tip: Using a machine learning-based algorithm, we developed a feasible and robust method to identify factors that are significant for predicting urinary tract infections. The random forest classifier was especially robust and can improve the prediction and early detection of urinary tract infections in patients with ovarian cancer. 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 urinary tract infections in clinical practice by incorporating the presentation of simple clinical data.