Key result
Gradient boosted machines best predict post-cardiac surgery LOS with an R^2 of 0.42.
Why the study?
The study sought to develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors.
Can machine learning models using only preoperative factors accurately predict resource utilization and clinical outcomes following cardiac operations?
Observational
Yes
Can machine learning models using only preoperative factors accurately predict resource utilization and clinical outcomes following cardiac operations?
Machine learning models, particularly gradient boosted machines, can predict resource utilization and clinical outcomes following cardiac surgery using only preoperative factors, which may help guide case scheduling.
May support preoperative ML for cardiac surgery planning; hypothesis-generating and requires prospective validation before adoption.
Objective: We sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors. Methods: All patients undergoing coronary artery bypass grafting and/or valve operations were identified in the 2015-2021 University of California Cardiac Surgery Consortium repository. The primary end point of the study was length of stay (LOS). Secondary endpoints included 30-day mortality, acute kidney injury, reoperation, postoperative blood transfusion and duration of intensive care unit admission (ICU LOS). Linear regression, gradient boosted machines, random forest, extreme gradient boosting predictive models were developed. The coefficient of determination and area under the receiver operating characteristic (AUC) were used to compare models. Important predictors of increased resource use were identified using SHapley summary plots. Results: Compared with all other modeling strategies, gradient boosted machines demonstrated the greatest performance in the prediction of LOS (coefficient of determination, 0.42), ICU LOS (coefficient of determination, 0.23) and 30-day mortality (AUC, 0.69). Advancing age, reduced hematocrit, and multiple-valve procedures were associated with increased LOS and ICU LOS. Furthermore, the gradient boosted machine model best predicted acute kidney injury (AUC, 0.76), whereas random forest exhibited greatest discrimination in the prediction of postoperative transfusion (AUC, 0.73). We observed no difference in performance between modeling strategies for reoperation (AUC, 0.80). Conclusions: Our findings affirm the utility of machine learning in the estimation of resource use and clinical outcomes following cardiac operations. We identified several risk factors associated with increased resource use, which may be used to guide case scheduling in times of limited hospital capacity.
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Verma et al. (2022) conducted an observational in Patients undergoing coronary artery bypass grafting and/or valve operations. Machine learning models (gradient boosted machines, random forest, extreme gradient boosting) vs. Linear regression and other modeling strategies was evaluated on Length of stay (LOS) (R^2 0.42). Gradient boosted machines demonstrated the greatest performance in predicting length of stay (R^2 0.42), ICU length of stay (R^2 0.23), and 30-day mortality (AUC 0.69) following cardiac operations.
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