TY - JOUR
T1 - Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury
T2 - A Systematic Review
AU - Wu, Chieh Chen
AU - Poly, Tahmina Nasrin
AU - Weng, Yung Ching
AU - Lin, Ming Chin
AU - Islam, Md Mohaimenul
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/8
Y1 - 2024/8
N2 - While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.
AB - While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.
KW - acute kidney injury
KW - algorithm bias
KW - kidney disease
KW - machine learning
KW - sepsis
UR - http://www.scopus.com/inward/record.url?scp=85200794546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85200794546&partnerID=8YFLogxK
U2 - 10.3390/diagnostics14151594
DO - 10.3390/diagnostics14151594
M3 - Review article
AN - SCOPUS:85200794546
SN - 2075-4418
VL - 14
JO - Diagnostics
JF - Diagnostics
IS - 15
M1 - 1594
ER -