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

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.

Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data

“…Lix et al used a pre-existing cohort of patients receiving antidiabetic medication for their validation and opted for a much broader definition of CV death including, amongst others, death due to hypertensive disease. 16 The overall PPV was 54.5%, ranging from 34% to 73% for individual Canadian provinces. A meta-analysis by Singh et al included five studies, four of which reported PPVs for sudden cardiac death and one for AMI- and stroke-related death.…”

“…A study based on administrative health records from Canada reported an increase in the PPV if restricting the algorithm of CV death to inpatient deaths with a CV main discharge diagnosis. 16 This study is subject to limitations that need to be considered when interpreting the results. While claims data contain rich information on diagnoses, procedures, and health care received by patients, they lack clinical information like lab values and other diagnostic results.…”

“…A study based on administrative health records from Canada reported an increase in the PPV if restricting the algorithm of CV death to inpatient deaths with a CV main discharge diagnosis. 16 …”

Development and implementation of databases to track patient and safety outcomes

“…Using a large, linked, national health administrative database would allow investigating the incidence of events like opioid use after hospital discharge and the associated predictors on a national level [32]. In another study, a cardiovascular mortality prediction algorithm applied to administrative databases from Canada and UK had moderate validity when compared to vital statistics data [33]. Studies like these provide insights into current safety practices and healthcare utilization patterns outside of the USA.…”

Corrigendum

“…Update: Data on cardiovascular-related causes of death will primarily be captured using the following definition: (a) in-hospital (or emergency department) death with a cardiovascular disease diagnosis in the primary/most responsible diagnosis position or (b) out-of-hospital death (including death in an emergency department) without documentation of cancer in the 365 days before and including the date of death and without documentation of trauma in the 30 days before and including the date of death. 2 A death in the emergency department with a cardiovascular event as the primary diagnosis will be defined as "in-hospital" cardiovascular-related death, regardless of whether they had cancer or trauma code. Justification: We were recently notified of unanticipated delays in the linkage of the Office of the Register General deaths database at ICES, which will now take several more years to cover the entire trial period.…”