Dataset Information

Development and Validation of an Agency for Healthcare Research and Quality Indicator for Mortality After Congenital Heart Surgery Harmonized With Risk Adjustment for Congenital Heart Surgery (RACHS-1) Methodology.

ABSTRACT:

Background

The National Quality Forum previously approved a quality indicator for mortality after congenital heart surgery developed by the Agency for Healthcare Research and Quality (AHRQ). Several parameters of the validated Risk Adjustment for Congenital Heart Surgery (RACHS-1) method were included, but others differed. As part of the National Quality Forum endorsement maintenance process, developers were asked to harmonize the 2 methodologies.

Methods and results

Parameters that were identical between the 2 methods were retained. AHRQ's Healthcare Cost and Utilization Project State Inpatient Databases (SID) 2008 were used to select optimal parameters where differences existed, with a goal to maximize model performance and face validity. Inclusion criteria were not changed and included all discharges for patients <18 years with International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes for congenital heart surgery or nonspecific heart surgery combined with congenital heart disease diagnosis codes. The final model includes procedure risk group, age (0-28 days, 29-90 days, 91-364 days, 1-17 years), low birth weight (500-2499 g), other congenital anomalies (Clinical Classifications Software 217, except for 758.xx), multiple procedures, and transfer-in status. Among 17 945 eligible cases in the SID 2008, the c statistic for model performance was 0.82. In the SID 2013 validation data set, the c statistic was 0.82. Risk-adjusted mortality rates by center ranged from 0.9% to 4.1% (5th-95th percentile).

Conclusions

Congenital heart surgery programs can now obtain national benchmarking reports by applying AHRQ Quality Indicator software to hospital administrative data, based on the harmonized RACHS-1 method, with high discrimination and face validity.

SUBMITTER: Jenkins KJ

PROVIDER: S-EPMC4889177 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of an Agency for Healthcare Research and Quality Indicator for Mortality After Congenital Heart Surgery Harmonized With Risk Adjustment for Congenital Heart Surgery (RACHS-1) Methodology.

Jenkins Kathy J KJ Koch Kupiec Jennifer J Owens Pamela L PL Romano Patrick S PS Geppert Jeffrey J JJ Gauvreau Kimberlee K

Journal of the American Heart Association 20160520 5

<h4>Background</h4>The National Quality Forum previously approved a quality indicator for mortality after congenital heart surgery developed by the Agency for Healthcare Research and Quality (AHRQ). Several parameters of the validated Risk Adjustment for Congenital Heart Surgery (RACHS-1) method were included, but others differed. As part of the National Quality Forum endorsement maintenance process, developers were asked to harmonize the 2 methodologies.<h4>Methods and results</h4>Parameters th ...[more]

PMID: 27207997

Similar Datasets

Project description:BackgroundQ-methodology is an approach to studying complex issues of human 'subjectivity'. Although this approach was developed in the early twentieth century, the value of Q-methodology in healthcare was not recognised until relatively recently. The aim of this review was to scope the empirical healthcare literature to examine the extent to which Q-methodology has been utilised in healthcare over time, including how it has been used and for what purposes.MethodsA search of three electronic databases (Scopus, EBSCO-CINAHL Complete, Medline) was conducted. No date restriction was applied. A title and abstract review, followed by a full-text review, was conducted by a team of five reviewers. Included articles were English-language, peer-reviewed journal articles that used Q-methodology (both Q-sorting and inverted factor analysis) in healthcare settings. The following data items were extracted into a purpose-designed Excel spreadsheet: study details (e.g., setting, country, year), reasons for using Q-methodology, healthcare topic area, participants (type and number), materials (e.g., ranking anchors and Q-set), methods (e.g., development of the Q-set, analysis), study results, and study implications. Data synthesis was descriptive in nature and involved frequency counting, open coding and the organisation by data items.ResultsOf the 2,302 articles identified by the search, 289 studies were included in this review. We found evidence of increased use of Q-methodology in healthcare, particularly over the last 5 years. However, this research remains diffuse, spread across a large number of journals and topic areas. In a number of studies, we identified limitations in the reporting of methods, such as insufficient information on how authors derived their Q-set, what types of analyses they performed, and the amount of variance explained.ConclusionsAlthough Q-methodology is increasingly being adopted in healthcare research, it still appears to be relatively novel. This review highlight commonalities in how the method has been used, areas of application, and the potential value of the approach. To facilitate reporting of Q-methodological studies, we present a checklist of details that should be included for publication.

Project description:BackgroundOptimal strategies to improve national congenital heart surgery outcomes and reduce variability across hospitals remain unclear. Many policy and quality improvement efforts have focused primarily on higher-risk patients and mortality alone. Improving our understanding of both morbidity and mortality and current variation across the spectrum of complexity would better inform future efforts.MethodsHospitals participating in the Society of Thoracic Surgeons Congenital Heart Surgery Database (2014-2017) were included. Case mix-adjusted operative mortality, major complications, and postoperative length of stay were evaluated using Bayesian models. Hospital variation was quantified by the interdecile ratio (IDR, upper versus lower 10%) and 95% credible intervals (CrIs). Stratified analyses were performed by risk group (Society of Thoracic Surgeons-European Association for Cardiothoracic Surgery [STAT] category) and simulations evaluated the potential impact of reductions in variation.ResultsA total of 102 hospitals (n=84 407) were included, representing ≈85% of US congenital heart programs. STAT category 1 to 3 (lower risk) operations comprised 74% of cases. All outcomes varied significantly across hospitals: adjusted mortality by 3-fold (upper versus lower decile 5.0% versus 1.6%, IDR 3.1 [95% CrI 2.5-3.7]), mean length of stay by 1.8-fold (19.2 versus 10.5 days, IDR 1.8 [95% CrI 1.8-1.9]), and major complications by >3-fold (23.5% versus 7.0%, IDR 3.4 [95% CrI 3.0-3.8]). The degree of variation was similar or greater for low- versus high-risk cases across outcomes, eg, ≈3-fold mortality variation across hospitals for STAT 1 to 3 (IDR 3.0 [95% CrI 2.1-4.2]) and STAT 4 or 5 (IDR 3.1 [95% CrI 2.4-3.9]) cases. High-volume hospitals had less variability across outcomes and risk categories. Simulations suggested potential reductions in deaths (n=282), major complications (n=1539), and length of stay (101 183 days) over the 4-year study period if all hospitals were to perform at the current median or better, with 37% to 60% of the improvement related to the STAT 1 to 3 (lower risk) group across outcomes.ConclusionsWe demonstrate significant hospital variation in morbidity and mortality after congenital heart surgery. Contrary to traditional thinking, a substantial portion of potential improvements that could be realized on a national scale were related to variability among lower-risk cases. These findings suggest modifications to our current approaches to optimize care and outcomes in this population are needed.

Dataset Information

Development and Validation of an Agency for Healthcare Research and Quality Indicator for Mortality After Congenital Heart Surgery Harmonized With Risk Adjustment for Congenital Heart Surgery (RACHS-1) Methodology.

Background

Methods and results

Conclusions

Publications

Development and Validation of an Agency for Healthcare Research and Quality Indicator for Mortality After Congenital Heart Surgery Harmonized With Risk Adjustment for Congenital Heart Surgery (RACHS-1) Methodology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets