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      Cumulative In-Hospital Costs Associated With Single-Ventricle Palliation

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          Abstract

          Background

          In the SVR (Single Ventricle Reconstruction) Trial, 1-year survival in recipients of right ventricle to pulmonary artery shunts (RVPAS) was superior to that in those receiving modified Blalock-Taussig-Thomas shunts (MBTTS), but not in subsequent follow-up. Cost analysis is an expedient means of evaluating value and morbidity.

          Objectives

          The purpose of this study was to evaluate differences in cumulative hospital costs between RVPAS and MBTTS.

          Methods

          Clinical data from SVR and costs from Pediatric Health Information Systems database were combined. Cumulative hospital costs and cost-per-day-alive were compared serially at 1, 3, and 5 years between RVPAS and MBTTS. Potential associations between patient-level factors and cost were explored with multivariable models.

          Results

          In total, 303 participants (55% of the SVR cohort) from 9 of 15 sites were studied (48% MBTTS). Observed total costs at 1 year were lower for MBTTS ($701,260 ± 442,081) than those for RVPAS ($804,062 ± 615,068), a difference that was not statistically significant ( P = 0.10). Total costs were also not significantly different at 3 and 5 years ( P = 0.21 and 0.32). Similarly, cost-per-day-alive did not differ significantly for either group at 1, 3, and 5 years (all P > 0.05). In analyses of transplant-free survivors, total costs and cost-per-day-alive were higher for RVPAS at 1 year ( P = 0.05 for both) but not at 3 and 5 years ( P > 0.05 for all). In multivariable models, aortic atresia and prematurity were associated with increased cost-per-day-alive across follow-up ( P < 0.05).

          Conclusions

          Total costs do not differ significantly between MBTTS and RVPAS. The magnitude of longitudinal costs underscores the importance of efforts to improve outcomes in this vulnerable population.

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          Most cited references59

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          What Is Value in Health Care?

          New England Journal of Medicine, 363(26), 2477-2481
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            Generalized modeling approaches to risk adjustment of skewed outcomes data.

            There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., ordinary least square (OLS) on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized Gamma (GGM) distribution, which includes several of the standard alternatives as special cases-OLS with a normal error, OLS for the log-normal, the standard Gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust alternative estimator to the standard alternatives. An example using inpatient expenditures is also analyzed.
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              An empirically based tool for analyzing mortality associated with congenital heart surgery.

              Analysis of congenital heart surgery results requires a reliable method of estimating the risk of adverse outcomes. Two major systems in current use are based on projections of risk or complexity that were predominantly subjectively derived. Our goal was to create an objective, empirically based index that can be used to identify the statistically estimated risk of in-hospital mortality by procedure and to group procedures into risk categories. Mortality risk was estimated for 148 types of operative procedures using data from 77,294 operations entered into the European Association for Cardiothoracic Surgery (EACTS) Congenital Heart Surgery Database (33,360 operations) and the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (43,934 patients) between 2002 and 2007. Procedure-specific mortality rate estimates were calculated using a Bayesian model that adjusted for small denominators. Each procedure was assigned a numeric score (the STS-EACTS Congenital Heart Surgery Mortality Score [2009]) ranging from 0.1 to 5.0 based on the estimated mortality rate. Procedures were also sorted by increasing risk and grouped into 5 categories (the STS-EACTS Congenital Heart Surgery Mortality Categories [2009]) that were chosen to be optimal with respect to minimizing within-category variation and maximizing between-category variation. Model performance was subsequently assessed in an independent validation sample (n = 27,700) and compared with 2 existing methods: Risk Adjustment for Congenital Heart Surgery (RACHS-1) categories and Aristotle Basis Complexity scores. Estimated mortality rates ranged across procedure types from 0.3% (atrial septal defect repair with patch) to 29.8% (truncus plus interrupted aortic arch repair). The proposed STS-EACTS score and STS-EACTS categories demonstrated good discrimination for predicting mortality in the validation sample (C-index = 0.784 and 0.773, respectively). For procedures with more than 40 occurrences, the Pearson correlation coefficient between a procedure's STS-EACTS score and its actual mortality rate in the validation sample was 0.80. In the subset of procedures for which RACHS-1 and Aristotle Basic Complexity scores are defined, discrimination was highest for the STS-EACTS score (C-index = 0.787), followed by STS-EACTS categories (C-index = 0.778), RACHS-1 categories (C-index = 0.745), and Aristotle Basic Complexity scores (C-index = 0.687). When patient covariates were added to each model, the C-index improved: STS-EACTS score (C-index = 0.816), STS-EACTS categories (C-index = 0.812), RACHS-1 categories (C-index = 0.802), and Aristotle Basic Complexity scores (C-index = 0.795). The proposed risk scores and categories have a high degree of discrimination for predicting mortality and represent an improvement over existing consensus-based methods. Risk models incorporating these measures may be used to compare mortality outcomes across institutions with differing case mixes.
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                Author and article information

                Contributors
                @obyrne_md
                Journal
                JACC Adv
                JACC Adv
                JACC: Advances
                Elsevier
                2772-963X
                26 May 2022
                June 2022
                26 May 2022
                : 1
                : 2
                : 100029
                Affiliations
                [a ]Division of Cardiology, The Children's Hospital of Philadelphia and Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [b ]Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
                [c ]Leonard Davis Institute of Health Economics and Cardiovascular Outcomes, Quality, and Evaluative Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [d ]Division of Cardiology, Medical University of South Carolina, Charleston, South Carolina, USA
                [e ]Department of Biostatistics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
                [f ]Data Science and Biostatistics Unit, Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
                [g ]Division of Cardiology, New York-Presbyterian Morgan-Stanley Children’s Hospital/Columbia University Irving Medical Center, New York, New York, USA
                [h ]Department of Cardiology, Children’s Hospital Boston and Harvard University Medical School, Boston, Massachusetts, USA
                [i ]Children’s Healthcare of Atlanta, Sibley Heart Center and Emory University School of Medicine, Atlanta, Georgia, USA
                [j ]Sections of Critical Care and Cardiology, Texas Children’s Hospital and Baylor College of Medicine, Houston, Texas, USA
                [k ]Children’s Hospital of Wisconsin, Milwaukee, Wisconsin, USA
                [l ]Division of Cardiothoracic Surgery, Departments of Surgery and Pediatrics, Congenital Heart Center, University of Florida, Gainesville, Florida, USA
                [m ]Division of Pediatric Cardiology, Duke University School of Medicine, Durham, North Carolina, USA
                [n ]Division of Cardiology, Department of Pediatrics, Children’s Hospital of Los Angeles, Keck School of Medicine of University of Southern California, Los Angeles, California, USA
                [o ]Department of Pediatrics, Labatt Family Heart Centre, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
                [p ]Division of Cardiology, Primary Children’s Hospital and University of Utah School of Medicine, Salt Lake City, Utah, USA
                [q ]Division of Pediatric Cardiothoracic Surgery, C.S. Mott Children’s Hospital and University of Michigan School of Medicine, Ann Arbor, Michigan, USA
                [r ]The Heart Institute, Cincinnati Children’s Hospital Medical Center and Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio, USA
                [s ]Division of Cardiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
                Author notes
                [] Address for correspondence: Dr Michael L. O'Byrne, The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, Pennsylvania 19104, USA. obyrnem@ 123456chop.edu @obyrne_md
                Article
                S2772-963X(22)00030-8 100029
                10.1016/j.jacadv.2022.100029
                11198056
                38939312
                c82785b1-9f0d-474b-90dd-0b0c8aefd785
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 16 March 2022
                : 20 April 2022
                : 22 April 2022
                Categories
                Original Research
                Pediatric Cardiology

                economic analysis,hypoplastic left heart syndrome,outcomes research,pediatrics

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