Impact of heart failure on reoperation in adult congenital heart disease: An innovative machine learning model.

Publisher: Mosby Country of Publication: United States NLM ID: 0376343 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-685X (Electronic) Linking ISSN: 00225223 NLM ISO Abbreviation: J Thorac Cardiovasc Surg Subsets: MEDLINE

Publication: St. Louis, MO : Mosby
Original Publication: St. Louis.

Heart Defects, Congenital*/surgery ; Heart Defects, Congenital*/mortality ; Heart Defects, Congenital*/complications ; Machine Learning* ; Heart Failure*/mortality ; Heart Failure*/surgery ; Heart Failure*/physiopathology ; Reoperation*/statistics & numerical data ; Cardiac Surgical Procedures*/adverse effects ; Cardiac Surgical Procedures*/mortality ; Cardiac Surgical Procedures*/methods; Humans ; Female ; Male ; Retrospective Studies ; Adult ; Risk Factors ; Risk Assessment ; Middle Aged ; Postoperative Complications/mortality ; Treatment Outcome ; Time Factors

Objectives: The 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.
Methods: Single-institution retrospective cohort study of adult patients with congenital heart disease undergoing reoperative cardiac surgery between January 1, 2010, and March 30, 2022. Heart failure defined clinically as preoperative diuretic use and either New York Heart Association Class II to IV or systemic ventricular ejection fraction less than 40%. Composite outcome included operative mortality, mechanical circulatory support, dialysis, unplanned noncardiac reoperation, persistent neurologic deficit, and cardiac arrest. Multivariable logistic regression and machine learning analysis using gradient boosting technology were performed. Shapley statistics determined feature influence, or impact, on model output.
Results: Preoperative heart failure was present in 376 of 1011 patients (37%); those patients had longer postoperative length of stay (6 [5-8] vs 5 [4-7] days, P < .001), increased postoperative mechanical circulatory support (21/376 [6%] vs 16/635 [3%], P = .015), and decreased long-term survival (84% [80%-89%] vs 90% [86%-93%]) at 10 years (P = .002). A 7-feature machine learning risk model for the composite outcome achieved higher area under the curve (0.76) than logistic regression, and ejection fraction was most influential (highest mean |Shapley value|). Additional risk factors for the composite outcome included age, number of prior cardiopulmonary bypass operations, urgent/emergency procedure, and functionally univentricular physiology.
Conclusions: Heart failure is common among adult patients with congenital heart disease undergoing cardiac reoperation and associated with longer length of stay, increased postoperative mechanical circulatory support, and decreased long-term survival. Machine learning yields a novel 7-feature risk model for postoperative morbidity/mortality, in which ejection fraction was the most influential.
Competing Interests: Conflict of Interest Statement The authors 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.
(Copyright © 2023 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

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R38 HL150086 United States HL NHLBI NIH HHS

Keywords: machine learning; mechanical circulatory support; postoperative morbidity; survival

Date Created: 20230930 Date Completed: 20240515 Latest Revision: 20240517

20240517

PMC10972775

10.1016/j.jtcvs.2023.09.045

37776991