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

Katharina Platzbecker; Annemarie Voss; Jonas Reinold; Anne Elbrecht; Wolfgang Biewener; Daniel Prieto-Alhambra; Annika M Jödicke; Tania Schink

doi:10.2147/CLEP.S380314

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

Clin Epidemiol. 2022 Nov 10:14:1351-1361. doi: 10.2147/CLEP.S380314. eCollection 2022.

Authors

Katharina Platzbecker¹, Annemarie Voss¹, Jonas Reinold¹, Anne Elbrecht², Wolfgang Biewener², Daniel Prieto-Alhambra^{3

4}, Annika M Jödicke³, Tania Schink¹

Affiliations

¹ Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
² Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
³ Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
⁴ Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

Abstract

Purpose: Validation of outcomes allows measurement of and correction for potential misclassification and targeted adjustment of algorithms for case definition. The purpose of our study was to validate algorithms for identifying cases of acute myocardial infarction (AMI), stroke, and cardiovascular (CV) death using patient profiles, ie, chronological tabular summaries of relevant available information on a patient, extracted from pseudonymized German claims data.

Patients and methods: Based on the German Pharmacoepidemiological Research Database (GePaRD), 250 cases were randomly selected (50% males) for each outcome between 2016 and 2017 based on the inclusion criteria age ≥50 years and continuous insurance ≥1 year and applying the following algorithms: hospitalization with a main diagnosis of AMI (ICD-10-GM codes I21.- and I22.-) or stroke (I63, I61, I64) or death with a hospitalization in the 60 days before with a main diagnosis of CV disease. Patient profiles were built including (i) age and sex, (ii) hospitalizations incl. diagnoses, procedures, discharge reasons, (iii) outpatient diagnoses incl. diagnostic certainty, physician specialty, (iv) outpatient encounters, and (v) outpatient dispensings. Using adjudication criteria based on clinical guidelines and risk factors, two trained physicians independently classified cases as "certain", "probable", "unlikely" or "not assessable". Positive predictive values (PPVs) were calculated as percentage of confirmed cases among all assessable cases.

Results: For AMI, the overall PPV was 97.6% [95% confidence interval 94.8-99.1]. The PPV for any stroke was 94.8% [91.3-97.2] and higher for ischemic (98.3% [95.0-99.6]) than for hemorrhagic stroke (86.5% [76.5-93.3]). The PPV for CV death was 79.9% [74.4-84.4]. It increased to 91.7% [87.2-95.0] after excluding 32 cases with data insufficient for a decision.

Conclusion: Algorithms based on hospital diagnoses can identify AMI, stroke, and CV death from German claims data with high PPV. This was the first study to show that German claims data contain information suitable for outcome validation.

Keywords: algorithm validation; claims data; patient profiles; positive predictive value.

Grants and funding

This study was funded by UCB Biopharma UCL in the context of a post-authorization safety study (PASS) requested by the European Medicines Agency. The study was performed in line with the ENCePP Code of Conduct. The authors had complete autonomy in the process of establishing the protocol, carrying out the analyses, and interpreting the results and retained the full right to publish the results without limitation.