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Ann Thorac Surg. 2015 Sep;100(3):1054-62. doi: 10.1016/j.athoracsur.2015.07.014. Epub 2015 Aug 3.

The Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model: Part 1-Statistical Methodology.

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Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina. Electronic address:
Johns Hopkins All Children's Heart Institute, Saint Petersburg, Tampa, and Orlando, Florida; Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland; Florida Hospital for Children, Orlando, Florida.
Department of Pediatrics and Communicable Diseases, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan.
Division of Cardiothoracic Surgery, Department of Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Division of Pediatric Cardiac Surgery, Benioff Children's Hospital, University of California, San Francisco, San Francisco, California.
Section of Congenital Cardiovascular Surgery, University of Illinois College of Medicine at Peoria, Children's Hospital of Illinois, Peoria, Illinois.
Institute for Health Care Research and Improvement, Baylor Health Care System, Dallas, Texas.
The Society of Thoracic Surgeons, Chicago, Illinois.
Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina.
Massachusetts General Hospital Department of Surgery and Center for Quality and Safety, and Harvard Medical School, Boston, Massachusetts.



This study's objective was to develop a risk model incorporating procedure type and patient factors to be used for case-mix adjustment in the analysis of hospital-specific operative mortality rates after congenital cardiac operations.


Included were patients of all ages undergoing cardiac operations, with or without cardiopulmonary bypass, at centers participating in The Society of Thoracic Surgeons Congenital Heart Surgery Database during January 1, 2010, to December 31, 2013. Excluded were isolated patent ductus arteriosus closures in patients weighing less than or equal to 2.5 kg, centers with more than 10% missing data, and patients with missing data for key variables. Data from the first 3.5 years were used for model development, and data from the last 0.5 year were used for assessing model discrimination and calibration. Potential risk factors were proposed based on expert consensus and selected after empirically comparing a variety of modeling options.


The study cohort included 52,224 patients from 86 centers with 1,931 deaths (3.7%). Covariates included in the model were primary procedure, age, weight, and 11 additional patient factors reflecting acuity status and comorbidities. The C statistic in the validation sample was 0.858. Plots of observed-vs-expected mortality rates revealed good calibration overall and within subgroups, except for a slight overestimation of risk in the highest decile of predicted risk. Removing patient preoperative factors from the model reduced the C statistic to 0.831 and affected the performance classification for 12 of 86 hospitals.


The risk model is well suited to adjust for case mix in the analysis and reporting of hospital-specific mortality for congenital heart operations. Inclusion of patient factors added useful discriminatory power and reduced bias in the calculation of hospital-specific mortality metrics.

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