Incidence and Predictors of Acute Kidney Injury Following Advanced Ovarian Cancer Cytoreduction at a Tertiary UK Centre: An Exploratory Analysis and Insights from Explainable Artificial Intelligence

TL;DR

This study explores the occurrence of acute kidney injury after ovarian cancer surgery and uses AI to identify risk factors and improve monitoring strategies.

Contribution

The study introduces AI-based insights for predicting acute kidney injury after ovarian cancer surgery, offering personalized risk profiles.

Findings

Acute kidney injury occurred in 6.72% of patients following advanced ovarian cancer surgery.

Younger age, lower comorbidity index, longer procedure duration, and greater surgical effort were predictive of AKI.

AI models provided individual risk profiles and highlighted the complexity of AKI prediction.

Abstract

Background/Objectives: The incidence of acute kidney injury (AKI) following advanced epithelial ovarian cancer (EOC) surgery has not been extensively studied. This study aimed to investigate the incidence of AKI and identify preoperative and intraoperative predictors in patients undergoing advanced EOC cytoreduction using both traditional statistics and Artificial Intelligence (AI) modelling. Methods: Retrospective data were collected for 134 patients with a suspected or confirmed diagnosis of advanced EOC (FIGO Stage III–IV) who underwent surgical cytoreduction between January 2021 and December 2022 at a UK tertiary referral centre. AKI was diagnosed according to the KDIGO criteria. Data on 22 patient variables were extracted, including age, Charlson Comorbidity Index (CCI), procedure length, surgical complexity, and length of hospital stay. Logistic regression analysis was used for feature selection to identify AKI predictors, and an extreme gradient boost (XGBoost) model was applied to all variables related to AKI events. Results: The incidence of postoperative AKI was 6.72% (n=9). Predictive factors for AKI included younger age (OR = 0.942, p=0.037), lower CCI (OR = 0.415, p=0.015), longer procedure duration (OR = 1.006, p=0.019), and greater surgical effort (OR = 1.427, p=0.007). Patients with perioperative AKI experienced a doubling in the length of hospital stay (p=0.008). Mortality rates were similar between patients with and without AKI. AI-driven algorithms highlighted the complexity of AKI prediction and provided individual risk profiles, enabling future stratification and prompting different frequencies of AKI monitoring following cytoreduction. Conclusions: Predicting AKI is a complex task. This study found a lower-than-expected incidence of AKI following advanced EOC cytoreductive surgery. AKI is linked to heightened surgical risk-taking, underscoring the need for improved guidelines focusing on postoperative monitoring for targeted patients. Artificial Intelligence offers the potential for personalized AKI prediction.

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