Validity of an Algorithm to Identify Cardiovascular Deaths from Administrative Health Records: A Multi-Database Population-Based Cohort Study

Validity of an Algorithm to Identify Cardiovascular Deaths from Administrative Health Records: A Multi-Database Population-Based Cohort Study

Validity of an Algorithm to Identify Cardiovascular Deaths from Administrative Health Records: A Multi-Database Population-Based Cohort Study

Overview

What is the issue?
  • 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.
What is the aim of the study?
  • The aim of our study was to assess the validity of an algorithm applied to administrative health records in multiple jurisdictions for identifying cardiovascular deaths.
How was the study conducted?
  • We conducted a multi-database retrospective cohort study using administrative health records 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.

  • Overall and stratified estimates and 95% confidence intervals (CIs) were computed.

What did the study find?
  • 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.
Implications
  • 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, possibly reflecting different diagnostic coding practices and healthcare utilization patterns.
Key Messages
  • This variation across the sites suggests there are opportunities for methodological studies to address the bias associated with using a cardiovascular mortality algorithm derived from administrative health records.

Manuscripts

Lix LM, Sobhan S, St-Jean A, Daigle JM, Fisher A, Yu OHY, Dell’Aniello S, Hu N, Bugden SC, Shah BR, Ronksley PE, Alessi-Severini S, Douros A, Ernst P, Filion KB, for the Canadian Network for Observational Drug Effect Studies (CNODES) Investigators. Validity of an Algorithm to Identify Cardiovascular Deaths from Administrative Health Records: A Multi-Database Population-Based Cohort Study. BMC Health Serv Res. 2021 Jul 31;21(1):758.

Presentations

Project Team

Project Lead
Lisa Lix BSHEc, MSc, PhD, P Stat
Manitoba
Collaborator
Audray St-Jean MSc
Coordinating Center
Collaborator
Jean-Marc Daigle MSc
Quebec
Collaborator
Anat Fisher MD, PhD
British Columbia
Collaborator
Oriana Yu MD, MSc
CPRD
Collaborator
Sophie Dell'Aniello MSc
CPRD
Collaborator
Nianping Hu PhD
Saskatchewan
Collaborator
Baiju Shah MD, PhD
Ontario
Collaborator
Paul Ronksley PhD
Alberta
Collaborator
Antonios Douros MD, PhD
CPRD
Collaborator
Pierre Ernst MD, MSc, FRCPC
CPRD
Collaborator
Kristian Filion PhD
CPRD
Collaborator
Shamsia Sobhan
Manitoba