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Development and validation of a model to categorize cardiovascular cause of death using health administrative data
Patel, S., Thompson, W., Sivaswamy, A., Khan, A., Ferreira-Legere, L., Lee, D. S., Abdel-Qadir, H., Jackevicius, C., Goodman, S., Farkouh, M. E., Tu, K., Kapral, M. K., Wijeysundera, H. C., Tam, D., Austin, P. C., Fang, J., Ko, D. T., & Udell, J. A. (2022). Development and validation of a model to categorize cardiovascular cause of death using health administrative data. American heart journal plus : cardiology research and practice, 22, Article 100207. https://doi.org/10.1016/j.ahjo.2022.100207
STUDY OBJECTIVE: Develop and evaluate a model that uses health administrative data to categorize cardiovascular (CV) cause of death (COD).
DESIGN: Population-based cohort.
SETTING: Ontario, Canada.
PARTICIPANTS: Decedents ≥ 40 years with known COD between 2008 and 2015 in the CANHEART cohort, split into derivation (2008 to 2012; n = 363,778) and validation (2013 to 2015; n = 239,672) cohorts.
MAIN OUTCOME MEASURES: Model performance. COD was categorized as CV or non-CV with ICD-10 codes as the gold standard. We developed a logistic regression model that uses routinely collected healthcare administrative to categorize CV versus non-CV COD. We assessed model discrimination and calibration in the validation cohort.
RESULTS: The strongest predictors for CV COD were history of stroke, history of myocardial infarction, history of heart failure, and CV hospitalization one month before death. In the validation cohort, the c-statistic was 0.80, the sensitivity 0.75 (95 % CI 0.74 to 0.75) and the specificity 0.71 (95 % CI 0.70 to 0.71). In the primary prevention validation sub-cohort, the c-statistic was 0.81, the sensitivity 0.71 (95 % CI 0.70 to 0.71) and the specificity 0.75 (95 % CI 0.75 to 0.75) while in the secondary prevention sub-cohort the c-statistic was 0.74, the sensitivity 0.81 (95 % CI 0.81 to 0.82) and the specificity 0.54 (95 % CI 0.53 to 0.54).
CONCLUSION: Modelling approaches using health administrative data show potential in categorizing CV COD, though further work is necessary before this approach is employed in clinical studies.