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Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data
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 (...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9661914/ https://www.ncbi.nlm.nih.gov/pubmed/36387925 http://dx.doi.org/10.2147/CLEP.S380314 |
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author | Platzbecker, Katharina Voss, Annemarie Reinold, Jonas Elbrecht, Anne Biewener, Wolfgang Prieto-Alhambra, Daniel Jödicke, Annika M Schink, Tania |
author_facet | Platzbecker, Katharina Voss, Annemarie Reinold, Jonas Elbrecht, Anne Biewener, Wolfgang Prieto-Alhambra, Daniel Jödicke, Annika M Schink, Tania |
author_sort | Platzbecker, Katharina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9661914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-96619142022-11-15 Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data Platzbecker, Katharina Voss, Annemarie Reinold, Jonas Elbrecht, Anne Biewener, Wolfgang Prieto-Alhambra, Daniel Jödicke, Annika M Schink, Tania Clin Epidemiol Original Research 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. Dove 2022-11-10 /pmc/articles/PMC9661914/ /pubmed/36387925 http://dx.doi.org/10.2147/CLEP.S380314 Text en © 2022 Platzbecker et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Platzbecker, Katharina Voss, Annemarie Reinold, Jonas Elbrecht, Anne Biewener, Wolfgang Prieto-Alhambra, Daniel Jödicke, Annika M Schink, Tania Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data |
title | Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data |
title_full | Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data |
title_fullStr | Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data |
title_full_unstemmed | Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data |
title_short | Validation of Algorithms to Identify Acute Myocardial Infarction, Stroke, and Cardiovascular Death in German Health Insurance Data |
title_sort | validation of algorithms to identify acute myocardial infarction, stroke, and cardiovascular death in german health insurance data |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9661914/ https://www.ncbi.nlm.nih.gov/pubmed/36387925 http://dx.doi.org/10.2147/CLEP.S380314 |
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