Key result
Claims data algorithms identify AMI, stroke, and CV death with up to ~98% PPV.
Why the study?
Validating outcomes allows measurement and correction of potential misclassification and targeted adjustment of algorithms for case definition in German claims data.
Can algorithms based on hospital diagnoses accurately identify AMI, stroke, and CV death in German claims data?
Observational (n=750)
Can algorithms based on hospital diagnoses accurately identify AMI, stroke, and CV death in German claims data?
Algorithms based on hospital diagnoses can identify AMI, stroke, and CV death from German claims data with high positive predictive values, validating their use in epidemiological research.
Supports algorithm validation for AMI, stroke, and CV death in German claims; extends evidence for observational research use.
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.
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Platzbecker et al. (2022) conducted an observational in Acute myocardial infarction, stroke, and cardiovascular death (n=750). Algorithms based on hospital diagnoses vs. Physician adjudication was evaluated on Positive predictive values (PPVs) for AMI, stroke, and CV death. Algorithms based on hospital diagnoses identified acute myocardial infarction, stroke, and cardiovascular death from German claims data with high positive predictive values of 97.6%, 94.8%, and 79.9%.
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