Development and validation of a model to categorize cardiovascular cause of death using health administrative data
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...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-10-01
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| Series: | American Heart Journal Plus |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666602222001240 |
| _version_ | 1828132391504314368 |
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| author | Sagar Patel Wade Thompson Atul Sivaswamy Anam Khan Laura Ferreira-Legere Douglas S. Lee Husam Abdel-Qadir Cynthia Jackevicius Shaun Goodman Michael E. Farkouh Karen Tu Moira K. Kapral Harindra C. Wijeysundera Derrick Tam Peter C. Austin Jiming Fang Dennis T. Ko Jacob A. Udell |
| author_facet | Sagar Patel Wade Thompson Atul Sivaswamy Anam Khan Laura Ferreira-Legere Douglas S. Lee Husam Abdel-Qadir Cynthia Jackevicius Shaun Goodman Michael E. Farkouh Karen Tu Moira K. Kapral Harindra C. Wijeysundera Derrick Tam Peter C. Austin Jiming Fang Dennis T. Ko Jacob A. Udell |
| author_sort | Sagar Patel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T17:03:58Z |
| format | Article |
| id | doaj.art-c8549fa2b49642e4b8513b817a2728d4 |
| institution | Directory Open Access Journal |
| issn | 2666-6022 |
| language | English |
| last_indexed | 2024-04-11T17:03:58Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | American Heart Journal Plus |
| spelling | doaj.art-c8549fa2b49642e4b8513b817a2728d42022-12-22T04:13:06ZengElsevierAmerican Heart Journal Plus2666-60222022-10-0122100207Development and validation of a model to categorize cardiovascular cause of death using health administrative dataSagar Patel0Wade Thompson1Atul Sivaswamy2Anam Khan3Laura Ferreira-Legere4Douglas S. Lee5Husam Abdel-Qadir6Cynthia Jackevicius7Shaun Goodman8Michael E. Farkouh9Karen Tu10Moira K. Kapral11Harindra C. Wijeysundera12Derrick Tam13Peter C. Austin14Jiming Fang15Dennis T. Ko16Jacob A. Udell17Faculty of Medicine, University of Toronto, Toronto, CanadaWomen's College Research Institute, Toronto, Canada; ICES, Toronto, Canada; Research Unit of General Practice, University of Southern Denmark, Odense, Denmark; Department of Anesthesiology, Pharmacology, and Therapeutics, University of British Columbia, CanadaICES, Toronto, CanadaICES, Toronto, CanadaICES, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Canada; Cardiovascular Division, Department of Medicine, Women's College Hospital, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; Western University of Health Sciences, Pomona, CA, United States of AmericaWestern University of Health Sciences, Pomona, CA, United States of America; Division of Cardiology, St. Michael's Hospital, Toronto, Canada; Canadian VIGOUR Centre, University of Alberta, Edmonton, CanadaPeter Munk Cardiac Centre, University Health Network, Toronto, Canada; Heart and Stroke/Richard Lewar Centre of Excellence, University of Toronto, Toronto, CanadaInstitute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; North York General Hospital, Department of Family and Community Medicine, University of Toronto, Toronto, Canada; Toronto Western Hospital Family Health Team, University Health Network, Toronto, CanadaICES, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, CanadaDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, CanadaICES, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, CanadaICES, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Canada; Cardiovascular Division, Department of Medicine, Women's College Hospital, Toronto, Canada; Corresponding author at: Cardiovascular Division, Peter Munk Cardiac Centre, Toronto General Hospital and Women's College Hospital, University of Toronto, 76 Grenville Street, Toronto, ON M5S 1B1, Canada.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.http://www.sciencedirect.com/science/article/pii/S2666602222001240Healthcare outcome assessmentCohort studiesDatabases |
| spellingShingle | Sagar Patel Wade Thompson Atul Sivaswamy Anam Khan Laura Ferreira-Legere Douglas S. Lee Husam Abdel-Qadir Cynthia Jackevicius Shaun Goodman Michael E. Farkouh Karen Tu Moira K. Kapral Harindra C. Wijeysundera Derrick Tam Peter C. Austin Jiming Fang Dennis T. Ko Jacob A. Udell Development and validation of a model to categorize cardiovascular cause of death using health administrative data American Heart Journal Plus Healthcare outcome assessment Cohort studies Databases |
| title | Development and validation of a model to categorize cardiovascular cause of death using health administrative data |
| title_full | Development and validation of a model to categorize cardiovascular cause of death using health administrative data |
| title_fullStr | Development and validation of a model to categorize cardiovascular cause of death using health administrative data |
| title_full_unstemmed | Development and validation of a model to categorize cardiovascular cause of death using health administrative data |
| title_short | Development and validation of a model to categorize cardiovascular cause of death using health administrative data |
| title_sort | development and validation of a model to categorize cardiovascular cause of death using health administrative data |
| topic | Healthcare outcome assessment Cohort studies Databases |
| url | http://www.sciencedirect.com/science/article/pii/S2666602222001240 |
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