Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep 16:22:100207.
doi: 10.1016/j.ahjo.2022.100207. eCollection 2022 Oct.

Development and validation of a model to categorize cardiovascular cause of death using health administrative data

Sagar Patel  1 , Wade Thompson  2   3   4   5 , Atul Sivaswamy  3 , Anam Khan  3 , Laura Ferreira-Legere  3 , Douglas S Lee  3   6   7   8 , Husam Abdel-Qadir  3   6   7   8   9 , Cynthia Jackevicius  3   6   10 , Shaun Goodman  10   11   12 , Michael E Farkouh  8   13 , Karen Tu  6   14   15 , Moira K Kapral  3   7 , Harindra C Wijeysundera  3   6   7   16 , Derrick Tam  7 , Peter C Austin  3   6 , Jiming Fang  3 , Dennis T Ko  3   6   7   16 , Jacob A Udell  3   6   7   8   9
Affiliations

Development and validation of a model to categorize cardiovascular cause of death using health administrative data

Sagar Patel et al. Am Heart J Plus. .

Abstract

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.

Keywords: Cohort studies; Databases; Healthcare outcome assessment.

PubMed Disclaimer

Conflict of interest statement

Dr. Udell is supported by a Heart and Stroke Foundation National New Investigator-Ontario Clinician Scientist Award; Ontario Ministry of Research, Innovation and Science Early Researcher Award; grants from 10.13039/100004325AstraZeneca, 10.13039/100009009Novartis, and 10.13039/100004339Sanofi. Dr. Udell reports receiving personal fees for consulting for or honoraria from Amgen, AstraZeneca, Boehringer-Ingelheim, Janssen, Merck, Novartis and Sanofi. Dr. J. Tu was supported by a Tier 1 Canada Research Chair in Health Services Research and an Eaton Scholar award from the 10.13039/501100009227Department of Medicine, University of Toronto. Dr. Austin is supported by a Mid-Career Investigator Award from the 10.13039/100004411Heart and Stroke Foundation. Dr. Lee is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto. Dr. Farkouh is the Peter Munk Chair in Multinational Clinical Trials at Peter Munk Cardiac Centre, University Health Network, University of Toronto. Dr. K Tu receives a Research Scholar Award from the 10.13039/501100008097Department of Family and Community Medicine, University of Toronto. Dr. Goodman receives research grant support (e.g., steering committee or data and safety monitoring committee) and/or speaker/consulting honoraria (e.g., advisory boards) from: 10.13039/100002429Amgen, Anthos Therapeutics, 10.13039/100004325AstraZeneca, 10.13039/100013711Bayer Canada, 10.13039/100001003Boehringer Ingelheim, 10.13039/100008021Bristol Myers Squibb, 10.13039/100008322CSL Behring, Daiichi-Sankyo/American Regent, 10.13039/100004312Eli Lilly and Company, Esperion, 10.13039/501100003122Ferring Pharmaceuticals, HLS Therapeutics, JAMP Pharma, 10.13039/100004334Merck, 10.13039/100009009Novartis, Novo Nordisk A/C, Pendopharm/Pharmascience, 10.13039/100004319Pfizer, 10.13039/100009857Regeneron, 10.13039/100004339Sanofi, 10.13039/501100011725Servier, Valeo Pharma; and salary support/honoraria from the Heart and Stroke Foundation of Ontario/University of Toronto (Polo) Chair, Canadian Heart Research Centre and MD Primer, Canadian VIGOUR Centre, Cleveland Clinic Coordinating Centre for Clinical Research, Duke Clinical Research Institute, New York University Clinical Coordinating Centre, PERFUSE Research Institute, TIMI Study Group (Brigham Health). Dr. Kapral holds the Lillian Love Chair in Women's Health at the University Health Network/University of Toronto.

Figures

Fig. 1
Fig. 1
Observed and predicted probability of cardiovascular death in the derivation cohort (2008 to 2012) across deciles of risk for cardiovascular-related death. Comparison of predicted deaths using our CV prediction model, to observed deaths captured using Registrar General of Ontario Vital Statistics Database.
Fig. 2
Fig. 2
Observed and predicted probability of cardiovascular death in the validation cohort (2013 to 2015) across deciles of predicted risk for cardiovascular-related death, for overall cohort and subgroups of primary prevention and secondary prevention. Comparison of predicted deaths using our CV prediction model, to observed deaths captured using Registrar General of Ontario Vital Statistics Database.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Siri M., DL C., Statistics CoN, Council NR, Education DoBaSSa . The National Academies Press; 2009. Vital Statistics: Summary of a Workshop. - PubMed
    1. Statistics NCfH . Centers for Disease Control and Prevention; 2020. NDI early release pilot program.https://www.cdc.gov/nchs/ndi/ndi_early_release.htm
    1. Brooke H.L., Talback M., Hornblad J., et al. The swedish cause of death register. Eur. J. Epidemiol. Sep 2017;32(9):765–773. doi: 10.1007/s10654-017-0316-1. - DOI - PMC - PubMed
    1. Hammar N., Alfredsson L., Rosen M., Spetz C.L., Kahan T., Ysberg A.S. A national record linkage to study acute myocardial infarction incidence and case fatality in Sweden. Int. J. Epidemiol. Oct 2001;30(Suppl 1):S30–S34. - PubMed
    1. Helweg-Larsen K. The Danish register of causes of death. Scand. J. Public Health. Jul 2011;39(7 Suppl):26–29. doi: 10.1177/1403494811399958. - DOI - PubMed

LinkOut - more resources