scholarly journals The Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model: Part 2—Clinical Application

2015 ◽  
Vol 100 (3) ◽  
pp. 1063-1070 ◽  
Author(s):  
Jeffrey P. Jacobs ◽  
Sean M. O’Brien ◽  
Sara K. Pasquali ◽  
J. William Gaynor ◽  
John E. Mayer ◽  
...  
2015 ◽  
Vol 100 (3) ◽  
pp. 1054-1062 ◽  
Author(s):  
Sean M. O’Brien ◽  
Jeffrey P. Jacobs ◽  
Sara K. Pasquali ◽  
J. William Gaynor ◽  
Tara Karamlou ◽  
...  

2019 ◽  
Vol 10 (4) ◽  
pp. 446-453 ◽  
Author(s):  
Devin M. Parker ◽  
Allen D. Everett ◽  
Meagan E. Stabler ◽  
JoAnna Leyenaar ◽  
Luca Vricella ◽  
...  

Background: Very little is known about clinical and biomarker predictors of readmissions following pediatric congenital heart surgery. The cardiac biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) can help predict readmission in adult populations, but the estimated utility in predicting risk of readmission or mortality after pediatric congenital heart surgery has not previously been studied. Our objective was to evaluate the association between pre- and postoperative serum biomarker levels and 30-day readmission or mortality for pediatric patients undergoing congenital heart surgery. Methods: We measured pre- and postoperative NT-proBNP levels in two prospective cohorts of 522 pediatric patients <18 years of age who underwent at least one congenital heart operation from 2010 to 2014. Blood samples were collected before and after surgery. We evaluated the association between pre- and postoperative NT-proBNP with readmission or mortality within 30 days of discharge, using multivariate logistic regression, adjusting for covariates based on the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Mortality Risk Model. Results: The Johns Hopkins Children's Center cohort and the Translational Research Investigating Biomarker Endpoints in Acute Kidney Injury (TRIBE-AKI) cohort demonstrate event rates of 12.9% and 9.4%, respectively, for the composite end point. After adjustment for covariates in the STS congenital risk model, we did not find an association between elevated levels of NT-proBNP and increased risk of readmission or mortality following congenital heart surgery for either cohort. Conclusions: In our two cohorts, preoperative and postoperative values of NT-proBNP were not significantly associated with readmission or mortality following pediatric congenital heart surgery. These findings will inform future studies evaluating multimarker risk assessment models in the pediatric population.


2015 ◽  
Vol 100 (5) ◽  
pp. 1728-1736 ◽  
Author(s):  
Stephanie M. Fuller ◽  
Xia He ◽  
Jeffrey P. Jacobs ◽  
Sara K. Pasquali ◽  
J. William Gaynor ◽  
...  

2009 ◽  
Vol 87 (2) ◽  
pp. 584-587 ◽  
Author(s):  
Victor T. Tsang ◽  
Katherine L. Brown ◽  
Mats Johanssen Synnergren ◽  
Nicholas Kang ◽  
Marc R. de Leval ◽  
...  

2021 ◽  
Vol 12 (2) ◽  
pp. 246-281 ◽  
Author(s):  
Marshall L. Jacobs ◽  
Jeffrey P. Jacobs ◽  
Dylan Thibault ◽  
Kevin D. Hill ◽  
Brett R. Anderson ◽  
...  

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.


Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S355-S356
Author(s):  
Prince J. Kannankeril ◽  
Andrew E. Radbill ◽  
Sara L. Van Driest ◽  
Andrew H. Smith ◽  
Frank A. Fish

2021 ◽  
Vol 12 (4) ◽  
pp. 461-462
Author(s):  
S. Ram Kumar

Measuring outcomes in pediatric cardiac care has been one of the more widespread, and at the same time controversial and often polarizing, quality improvement initiatives undertaken in the medical field. Risk models, such as the Society of Thoracic Surgeons Congenital Heart Surgery Risk Model, have been developed to account for comorbidities while predicting the expected mortality for a given surgical encounter. In this issue of the journal, Bertsimas and colleagues report on machine learning approaches to predict adverse outcomes in congenital heart surgery using the European Congenital Heart Surgeons Association’s congenital database. A head-to-head comparison of machine learning models and the currently available risk models utilizing the same data set are required to better understand the strengths and weaknesses of each of these approaches. Such a focused analysis will shed light on future approaches for risk modeling, which will undoubtedly continue to benefit from the guidance provided by expert clinical intuition.


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