Pasquali, Sara K.

Background: Robust models exist to predict risk-adjusted mortality following congenital heart surgery. It is known that underlying diagnosis modifies predicted risk of operations, and institution-level outcomes vary based on their unique case mix. We therefore evaluated risk-adjusted outcomes for a subset of diagnosis-procedure (D-P) combinations within the Society of Thoracic Surgeons' (STS) congenital heart surgery database (CHSD). Methods: A clinician panel identified the most frequently encountered D-P combinations in CHSD between 07/2017 and 06/2021. Candidate modeling techniques were compared in a random 80% development cohort. Model performance was assessed using internal and cross-validation calibration, discrimination plots and optimism adjusted C-statistics. The final selected model was evaluated in the 20% validation sample. Results: The chosen subset of D-P combinations represented 45,384 of 87,589 (52%) unique episodes of surgical care in CHSD during this time-period. The development cohort (36,350 episodes) had 1.8% mortality (667 deaths). All candidate models had excellent discrimination with C-statistics ranging from 0.862 to 0.876. The model with the best combination of discrimination, calibration, and clinical face validity was a modified version of the current STS mortality risk model. This version included all current risk model variables with the addition of indicator variables for D-P combinations. In the 20% validation sample (9,034 episodes), this model was well calibrated (Figure) and had excellent discrimination (C = 0.879). Conclusion: We describe a cohort of D-P combinations widely encountered in the STS CHSD. Further, we refined the existing STS CHSD risk model for application to these D-P dyads to derive empirical risk-adjusted benchmark outcomes. This approach will benefit quality improvement efforts at an institutional level and may have implications for publicly reporting congenital heart surgery outcomes.

Care and outcomes for the more than 40,000 patients undergoing pediatric and congenital heart surgery in the United States annually are known to vary widely. While consensus recommendations have been published across numerous fields as one mechanism to promote a high level of care delivery across centers, it has been more than two decades since the last pediatric heart surgery recommendations were published in the United States. More recent guidance is lacking, and collaborative efforts involving the many disciplines engaged in caring for these children have not been undertaken to date. The present initiative brings together professional societies spanning numerous care domains and congenital cardiac surgeons, pediatric cardiologists, nursing, and other healthcare professionals from diverse programs around the country to develop consensus recommendations for United States centers. The focus of this initial work is on pediatric heart surgery, and it is recommended that future efforts focus in detail on the adult congenital population. We describe the background, rationale, and methodology related to this collaborative effort, and recommendations put forth for Essential Care Centers (essential services necessary for any program), and Comprehensive Care Centers (services to optimize comprehensive and high-complexity care), encompassing structure, process, and outcome metrics across 14 domains.

Objectives: STAT Mortality Categories (developed 2009) stratify congenital heart surgery procedures into groups of increasing mortality risk to characterize case mix of congenital heart surgery providers. This update of the STAT Mortality Score and Categories is empirically based for all procedures and reflects contemporary outcomes. Methods: Cardiovascular surgical operations in the Society of Thoracic Surgeons Congenital Heart Surgery Database (January 1, 2010 – June 30, 2017) were analyzed. In this STAT 2020 Update of the STAT Mortality Score and Categories, the risk associated with a specific combination of procedures was estimated under the assumption that risk is determined by the highest risk individual component procedure. Operations composed of multiple component procedures were eligible for unique STAT Scores when the statistically estimated mortality risk differed from that of the highest risk component procedure. Bayesian modeling accounted for small denominators. Risk estimates were rescaled to STAT 2020 Scores between 0.1 and 5.0. STAT 2020 Category assignment was designed to minimize within-category variation and maximize between-category variation. Results: Among 161,351 operations at 110 centers (19,090 distinct procedure combinations), 235 types of single or multiple component operations received unique STAT 2020 Scores. Assignment to Categories resulted in the following distribution: STAT 2020 Category 1 includes 59 procedure codes with model-based estimated mortality 0.2% to 1.3%; Category 2 includes 73 procedure codes with mortality estimates 1.4% to 2.9%; Category 3 includes 46 procedure codes with mortality estimates 3.0% to 6.8%; Category 4 includes 37 procedure codes with mortality estimates 6.9% to 13.0%; and Category 5 includes 17 procedure codes with mortality estimates 13.5% to 38.7%. The number of procedure codes with empirically derived Scores has grown by 58% (235 in STAT 2020 vs 148 in STAT 2009). Of the 148 procedure codes with empirically derived Scores in 2009, approximately one-half have changed STAT Category relative to 2009 metrics. The New STAT 2020 Scores and Categories demonstrated good discrimination for predicting mortality in an independent validation sample (July 1, 2017-June 30, 2019; sample size 46,933 operations at 108 centers) with C-statistic = 0.791 for STAT 2020 Score and 0.779 for STAT 2020 Category. Conclusions: The updated STAT metrics reflect contemporary practice and outcomes. New empirically based STAT 2020 Scores and Category designations are assigned to a larger set of procedure codes, while accounting for risk associated with multiple component operations. Updating STAT metrics based on contemporary outcomes facilitates accurate assessment of case mix.

Background: Nearly 90% of the children with heart disease in low- and middle-income countries (LMICs) cannot access cardiovascular (CV) services. Limitations include inadequate financial, human, and infrastructure resources. Nongovernmental organizations (NGOs) have played crucial roles in providing clinical services and infrastructure supports to LMICs CV programs; however, these outreach efforts are dispersed, inadequate, and lack coordination. Methods: A survey was sent to members of the World Society for Pediatric and Congenital Heart Society and PediHeart. Results: A clearinghouse was created to provide information on NGO structures, geographic reach, and scope of services. The survey identified 80 NGOs supporting CV programs in 92 LMICs. The largest outreach efforts were in South and Central America (42%), followed by Africa (18%), Europe (17%), Asia (17%), and Asia-Western Pacific (6%). Most NGOs (51%) supported two to five outreach missions per year. The majority (87%) of NGOs provided education, diagnostics, and surgical or catheter-based interventions. Working jointly with LMIC partners, 59% of the NGOs performed operations in children and infants; 41% performed nonbypass neonatal operations. Approximately a quarter (26%) reported that partner sites do not perform interventions in between missions. Conclusions: Disparity and inadequacy in pediatric CV services remain an important problem for LMICs. A global consensus and coordinated efforts are needed to guide strategies on the development of regional centers of excellence, a global outcome database, and a CV program registry. Future efforts should be held accountable for impacts such as growth in the number of independent LMIC programs as well as reduction in mortality and patient waiting lists.