Validity of an algorithm to identify cardiovascular deaths from administrative health records: a multi-database population-based cohort study

Lisa M Lix, Shamsia Sobhan, Audray St-Jean, Jean-Marc Daigle, Anat Fisher, Oriana H Y Yu, Sophie Dell'Aniello, Nianping Hu, Shawn C Bugden, Baiju R Shah, Paul E Ronksley, Silvia Alessi-Severini, Antonios Douros, Pierre Ernst, Kristian B Filion, Lisa M Lix, Shamsia Sobhan, Audray St-Jean, Jean-Marc Daigle, Anat Fisher, Oriana H Y Yu, Sophie Dell'Aniello, Nianping Hu, Shawn C Bugden, Baiju R Shah, Paul E Ronksley, Silvia Alessi-Severini, Antonios Douros, Pierre Ernst, Kristian B Filion

Abstract

Background: Cardiovascular death is a common outcome in population-based studies about new healthcare interventions or treatments, such as new prescription medications. Vital statistics registration systems are often the preferred source of information about cause-specific mortality because they capture verified information about the deceased, but they may not always be accessible for linkage with other sources of population-based data. We assessed the validity of an algorithm applied to administrative health records for identifying cardiovascular deaths in population-based data.

Methods: Administrative health records were from an existing multi-database cohort study about sodium-glucose cotransporter-2 (SGLT2) inhibitors, a new class of antidiabetic medications. Data were from 2013 to 2018 for five Canadian provinces (Alberta, British Columbia, Manitoba, Ontario, Quebec) and the United Kingdom (UK) Clinical Practice Research Datalink (CPRD). The cardiovascular mortality algorithm was based on in-hospital cardiovascular deaths identified from diagnosis codes and select out-of-hospital deaths. Sensitivity, specificity, and positive and negative predictive values (PPV, NPV) were calculated for the cardiovascular mortality algorithm using vital statistics registrations as the reference standard. Overall and stratified estimates and 95% confidence intervals (CIs) were computed; the latter were produced by site, location of death, sex, and age.

Results: The cohort included 20,607 individuals (58.3% male; 77.2% ≥70 years). When compared to vital statistics registrations, the cardiovascular mortality algorithm had overall sensitivity of 64.8% (95% CI 63.6, 66.0); site-specific estimates ranged from 54.8 to 87.3%. Overall specificity was 74.9% (95% CI 74.1, 75.6) and overall PPV was 54.5% (95% CI 53.7, 55.3), while site-specific PPV ranged from 33.9 to 72.8%. The cardiovascular mortality algorithm had sensitivity of 57.1% (95% CI 55.4, 58.8) for in-hospital deaths and 72.3% (95% CI 70.8, 73.9) for out-of-hospital deaths; specificity was 88.8% (95% CI 88.1, 89.5) for in-hospital deaths and 58.5% (95% CI 57.3, 59.7) for out-of-hospital deaths.

Conclusions: A cardiovascular mortality algorithm applied to administrative health records had moderate validity when compared to vital statistics data. Substantial variation existed across study sites representing different geographic locations and two healthcare systems. These variations may reflect different diagnostic coding practices and healthcare utilization patterns.

Keywords: Accuracy; Cause-specific mortality; Death certificates; Hospital records; Physician claims; Validation.

Conflict of interest statement

Dr. Alessi-Severini received research grants from Pfizer and Merck for studies not involving SGLT2 inhibitors or DPP-4 inhibitors. The remaining authors have no relevant conflicts of interest to disclose.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Study flow chart for development of the validation cohort. Legend: Initial study cohort was from an existing multi-database retrospective cohort study about the safety and effectiveness of sodium-glucose cotransporter-2 (SGLT2) inhibitors compared to dipeptidyl peptidase-4 (DPP4) inhibitors
Fig. 2
Fig. 2
Validity estimates (%) for the cardiovascular mortality algorithm, by location of death. Legend: Error bars = 95% confidence intervals, All = all sites, Can = all Canadian sites, PPV = positive predictive value, NPV = negative predictive value, AB = Alberta, BC = British Columbia, MB = Manitoba, ON = Ontario, QC = Quebec, CPRD = UK Clinical Practice Research Datalink

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