Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm

Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conduc...

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Veröffentlicht in:BMC medical informatics and decision making Jg. 22; H. 1; S. 137 - 10
Hauptverfasser: Petrosyan, Yelena, Mesana, Thierry G., Sun, Louise Y.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 18.05.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Acute Kidney Injury - diagnosis
Acute Kidney Injury - epidemiology
Acute Kidney Injury - etiology
Acute renal failure
Adult
Algorithms
Artificial intelligence
Calibration
Cardiac surgery-associated acute kidney injury
Cardiac Surgical Procedures - adverse effects
Cardiovascular disease
Care and treatment
Classification
Computer applications
Coronary artery bypass
Data mining
Datasets
Diagnosis
Feature selection
Generalized linear models
Goodness of fit
Health aspects
Health Informatics
Health risk assessment
Health risks
Heart
Heart surgery
Humans
Information Systems and Communication Service
Kidneys
Learning algorithms
Machine Learning
Management of Computing and Information Systems
Mathematical models
Medicine
Medicine & Public Health
Methods
Model accuracy
Mortality
Patient outcomes
Patients
Predictive modeling
Random Forests
Regression models
Retrospective Studies
Risk
Risk Assessment
Risk Factors
Risk groups
Statistical analysis
Statistical methods
Statistical models
Statistical tests
Surgery
Canada
United States > US
Cardiac surgery-associated acute kidney injury
Random Forests
Predictive modeling
Data mining
Machine Learning
ISSN:1472-6947, 1472-6947
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Zusammenfassung:Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer–Lemeshow goodness-of-fit test) in the validation datasets. Results Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer–Lemeshow p  = 0.804, and AUC = 0.74, Hosmer–Lemeshow p  = 0.347, respectively). Conclusions We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies.
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ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-022-01859-w