Commentary: Is machine learning needed to understand that heart failure is a risk in adult congenital cardiac surgery?

Central MessageIt is no surprise that heart failure is a risk for morbidity and mortality after redo ACHD surgery. In this study, machine learning is an optional way to show that.See Article page XXX. It is no surprise that heart failure is a risk for morbidity and mortality after redo ACHD surgery. In this study, machine learning is an optional way to show that. See Article page XXX. Heart failure is a well-known risk factor for morbidity and mortality after cardiac surgery in adults with acquired heart disease and is itself associated with hospitalization rates and death in adults with congenital heart disease (ACHD). It is not surprising, therefore, that the current study in the Journal by Griffeth and colleagues1Griffeth E.M. Stephens E.H. Dearani J.A. Shreve J.T. O’Sullivan D. Egbe A.C. et al.Impact of heart failure on reoperation in adult congenital heart disease: an innovative machine learning model.J Thorac Cardiovasc Surg. September 28, 2023; ([Epub ahead of print])Abstract Full Text Full Text PDF Scopus (1) Google Scholar finds that heart failure (ejection fraction) is a risk factor for a composite outcome of perioperative morbidity and mortality after redo cardiac surgery in ACHD. The authors also found that unadjusted long-term survival was less in patients with heart failure (although the Cox regression results did not support this association). The implication is that we should attempt to intervene on repairable problems in ACHD before the onset of significant heart failure, especially when the intervention can either lessen the clinical degree of heart failure or, better yet, prevent or reverse some of the intrinsic myocardial pathophysiology underlying it. Precision timing of intervention is the challenge, however. This study did not attempt to solve that problem. The authors make explicit note of using an “innovative machine learning model.” The technique, gradient boosting for regression, was actually formulated in the 1980s and the explanatory Shapley statistics described 70 years ago.2Valiant L. A theory of the learnable.Comm ACM. 1984; 27: 1134-1142Crossref Scopus (3397) Google Scholar,3Shapley L.S. A value for n-Person games.in: Contributions to the Theory of Games (AM-28). Volume II. Princeton University Press, 1953: 307-318Google Scholar It is used extensively, although not so often in cardiac surgical research. As a simple example, the procedure begins with a “weak” model (such as a constant equal to the mean value of the observed outcomes), then calculates the residuals (difference between the predicted and actual values), constructs a somewhat-better heuristic model of the residuals (often in the form of a “tree”), then adds some proportion (the “learning rate”) of that model to the original model. As a result, the updated model will have smaller residuals. One continues with these iterations until the mean square of the residuals is less than some threshold. Unlike conventional statistical models, the technique does not specify any a priori functional form to the relationship. It can work with empty cells, outliers, and high cardinality categorical values.4Masui T. All you need to know about gradient boosting algorithm—part 1. Regression. Towards Data Science.https://towardsdatascience.com/all-you-need-to-know-about-gradient-boosting-algorithm-part-1-regression-2520a34a502Date accessed: January 20, 2022Google Scholar,5Friedman J.H. Greedy function approximation: a gradient boosting machine.Ann Stat. 2001; 29: 1189-1232Crossref Scopus (14807) Google Scholar The classic Shapley value is calculated as the average contribution of a covariate (“feature”) value to the prediction in all models comprising all possible subsets (“coalitions”) of the feature set. It tells one what the relative importance of each of the features is.6Lundberg S.M. Lee S.-I. A unified approach to interpreting model predictions.in: Advances in Neural Information Processing Systems. 30. Curran Associates, Inc, 2017: 4765-4774Google Scholar Those to whom this method is new should be careful in applying it casually. Multiple problems may arise from the selection of features, choice of the so-called “hyperparameters,” and feature description (such as branching tree structure), especially when applied to small datasets.7Kumar I.E. Venkatasubramanian S. Scheidegger C. Friedler S.A. Problems with Shapley-value-based explanations as feature importance measures.Proceedings of the 37th International Conference on Machine Learning, PMLR. 2020; 119: 5491-5500Google Scholar,8Boehmke B. Greenwell B. Gradient Boosting. Hands-On Machine Learning With R. Chapman & Hall, 2019: 221-245Google Scholar In the present paper, the authors performed both conventional regression and gradient boosting and compared results. The comparison was somewhat clouded by juggling covariates around the various constructed models but, in the end, the discriminatory powers of the 2 calculated models, as assessed by the C-statistic, were not significantly different. Although the study may not have “sold” us on the use of this machine-learning technique, it increased our awareness of its availability and its potentially huge advantages in analyzing large cohort populations with large numbers of features having complex interactions and whose models are anticipated to have complex functional forms. The author reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest. Impact of heart failure on reoperation in adult congenital heart disease: An innovative machine learning modelThe Journal of Thoracic and Cardiovascular SurgeryPreviewThe study objectives were to evaluate the association between preoperative heart failure and reoperative cardiac surgical outcomes in adult congenital heart disease and to develop a risk model for postoperative morbidity/mortality. Full-Text PDF