Journal of the American College of Cardiology · 2022
Risk Stratification for Congenital Heart Surgery for ICD-10 Administrative Data (RACHS-2).
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Allen Philip, Zafar Farhan, Mi Junhui, Crook Sarah, Woo Joyce, Jayaram Natalie, Bryant Roosevelt, Karamlou Tara, Tweddell James, Dragan Kacie, Cook Stephen, Hannan Edward L, Newburger Jane W, Bacha Emile A, Vincent Robert, Nguyen Khanh, Walsh-Spoonhower Kathleen, Mosca Ralph, Devejian Neil, Kamenir Steven A, Alfieris George M, Swartz Michael F, Meyer David, Paul Erin A, Billings John, Anderson Brett R, New York State CHS-COLOUR
Аннотация
BACKGROUND: As the cardiac community strives to improve outcomes, accurate methods of risk stratification are imperative. Since adoption of International Classification of Disease-10th Revision (ICD-10) in 2015, there is no published method for congenital heart surgery risk stratification for administrative data.
OBJECTIVES: This study sought to develop an empirically derived, publicly available Risk Stratification for Congenital Heart Surgery (RACHS-2) tool for ICD-10 administrative data.
METHODS: The RACHS-2 stratification system was iteratively and empirically refined in a training dataset of Pediatric Health Information Systems claims to optimize sensitivity and specificity compared with corresponding locally held Society of Thoracic Surgeons-Congenital Heart Surgery (STS-CHS) clinical registry data. The tool was validated in a second administrative data source: New York State Medicaid claims. Logistic regression was used to compare the ability of RACHS-2 in administrative data to predict operative mortality vs STAT Mortality Categories in registry data.
RESULTS: The RACHS-2 system captured 99.6% of total congenital heart surgery registry cases, with 1.0% false positives. RACHS-2 predicted operative mortality in both training and validation administrative datasets similarly to STAT Mortality Categories in registry data. C-statistics for models for operative mortality in training and validation administrative datasets-adjusted for RACHS-2-were 0.76 and 0.84 (95% CI: 0.72-0.80 and 0.80-0.89); C-statistics for models for operative mortality-adjusted for STAT Mortality Categories-in corresponding clinical registry data were 0.75 and 0.84 (95% CI: 0.71-0.79 and 0.79-0.89).
CONCLUSIONS: RACHS-2 is a risk stratification system for pediatric cardiac surgery for ICD-10 administrative data, validated in 2 administrative-registry-linked datasets. Statistical code is publicly available upon request.
OBJECTIVES: This study sought to develop an empirically derived, publicly available Risk Stratification for Congenital Heart Surgery (RACHS-2) tool for ICD-10 administrative data.
METHODS: The RACHS-2 stratification system was iteratively and empirically refined in a training dataset of Pediatric Health Information Systems claims to optimize sensitivity and specificity compared with corresponding locally held Society of Thoracic Surgeons-Congenital Heart Surgery (STS-CHS) clinical registry data. The tool was validated in a second administrative data source: New York State Medicaid claims. Logistic regression was used to compare the ability of RACHS-2 in administrative data to predict operative mortality vs STAT Mortality Categories in registry data.
RESULTS: The RACHS-2 system captured 99.6% of total congenital heart surgery registry cases, with 1.0% false positives. RACHS-2 predicted operative mortality in both training and validation administrative datasets similarly to STAT Mortality Categories in registry data. C-statistics for models for operative mortality in training and validation administrative datasets-adjusted for RACHS-2-were 0.76 and 0.84 (95% CI: 0.72-0.80 and 0.80-0.89); C-statistics for models for operative mortality-adjusted for STAT Mortality Categories-in corresponding clinical registry data were 0.75 and 0.84 (95% CI: 0.71-0.79 and 0.79-0.89).
CONCLUSIONS: RACHS-2 is a risk stratification system for pediatric cardiac surgery for ICD-10 administrative data, validated in 2 administrative-registry-linked datasets. Statistical code is publicly available upon request.