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Prognostic Utility of a Novel Risk Prediction Model for 1-Year Mortality Following Surgery for Congenital or Acquired Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect and the leading cause of mortality from birth defects, affecting about 1% of all live births per year in the United States. With advances in medical therapies and surgical techniques, the survival outcomes of patients with CHD have improved dramatically over the years.

The past two decades have also seen the advent of several case-mix adjustment tools for analyzing operative mortality following pediatric and congenital heart surgery (CHS), including expert opinion-based tools and empirically derived tools (e.g., Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Congenital Heart Surgery [STAT] mortality categories and STS Congenital Heart Surgery Database episode-of-care mortality risk model).

Despite the use of increasingly comprehensive models for assessing operative mortality, there is a dearth of clinical prediction rules for mortality at one year following discharge from CHS. To aid prognostication, counseling of patients and families, and designing interval treatment regimens following surgery for congenital or acquired heart disease, Aditya Sengupta, MD, Meena Nathan, MD, MPH, and colleagues at Boston Children's Hospital studied data from 2011 to 2021 to develop a novel risk prediction model for one-year mortality that holistically accounts for clinical, anatomic, echocardiographic, and socioeconomic factors. These risk factors included age, prematurity, major non-cardiac anomalies, syndromes, or genetic abnormalities, the Childhood Opportunity Index (as a proxy for neighborhood socioeconomic status), STAT mortality category, major adverse postoperative complications, and pre-discharge residual lesion severity. Of 10,412 consecutive operations for congenital or acquired heart disease over the study period at the authors’ institution, 8,808 cases met entry criteria, including survival to discharge.

The group formulated a weighted risk score based on all the variables of interest to create the final risk prediction model. This prediction tool allows patients to be easily stratified into low, medium, high, and very high-risk groups at discharge and may assist clinicians in determining frequency of follow-up, tailoring post-surgical treatment strategies, and counseling families about future expectations upon discharge.

Dr. Sengupta will present this study and its implications for prognostication and follow-up for these children Sunday, May 7, at the American Association for Thoracic Surgery (AATS) 103rd Annual Meeting in Los Angeles.