Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries
Open Access
- 7 June 2013
- Vol. 3 (6), e002862
- https://doi.org/10.1136/bmjopen-2013-002862
Abstract
Objective To evaluate positive predictive value (PPV) of different disease codes and free text in identifying acute myocardial infarction (AMI) from electronic healthcare records (EHRs). Design Validation study of cases of AMI identified from general practitioner records and hospital discharge diagnoses using free text and codes from the International Classification of Primary Care (ICPC), International Classification of Diseases 9th revision-clinical modification (ICD9-CM) and ICD-10th revision (ICD-10). Setting Population-based databases comprising routinely collected data from primary care in Italy and the Netherlands and from secondary care in Denmark from 1996 to 2009. Participants A total of 4 034 232 individuals with 22 428 883 person-years of follow-up contributed to the data, from which 42 774 potential AMI cases were identified. A random sample of 800 cases was subsequently obtained for validation. Main outcome measures PPVs were calculated overall and for each code/free text. ‘Best-case scenario’ and ‘worst-case scenario’ PPVs were calculated, the latter taking into account non-retrievable/non-assessable cases. We further assessed the effects of AMI misclassification on estimates of risk during drug exposure. Results Records of 748 cases (93.5% of sample) were retrieved. ICD-10 codes had a ‘best-case scenario’ PPV of 100% while ICD9-CM codes had a PPV of 96.6% (95% CI 93.2% to 99.9%). ICPC codes had a ‘best-case scenario’ PPV of 75% (95% CI 67.4% to 82.6%) and free text had PPV ranging from 20% to 60%. Corresponding PPVs in the ‘worst-case scenario’ all decreased. Use of codes with lower PPV generally resulted in small changes in AMI risk during drug exposure, but codes with higher PPV resulted in attenuation of risk for positive associations. Conclusions ICD9-CM and ICD-10 codes have good PPV in identifying AMI from EHRs; strategies are necessary to further optimise utility of ICPC codes and free-text search. Use of specific AMI disease codes in estimation of risk during drug exposure may lead to small but significant changes and at the expense of decreased precision.Keywords
This publication has 34 references indexed in Scilit:
- Epidemiology of gout and hyperuricaemia in Italy during the years 2005–2009: a nationwide population-based studyAnnals Of The Rheumatic Diseases, 2012
- Using text‐mining techniques in electronic patient records to identify ADRs from medicine useBritish Journal of Clinical Pharmacology, 2012
- Electronic healthcare databases for active drug safety surveillance: is there enough leverage?Pharmacoepidemiology and Drug Safety, 2012
- The predictive value of ICD-10 diagnostic coding used to assess Charlson comorbidity index conditions in the population-based Danish National Registry of PatientsBMC Medical Research Methodology, 2011
- Existing data sources for clinical epidemiology: Aarhus University Prescription DatabaseClinical Epidemiology, 2010
- Cardiovascular risk factor assessment after pre-eclampsia in primary careBMC Family Practice, 2009
- The Cardiovascular Research NetworkCirculation: Cardiovascular Quality and Outcomes, 2008
- Understanding secondary databases: a commentary on “Sources of bias for health state characteristics in secondary databases”Journal of Clinical Epidemiology, 2007
- The impact of the ESC/ACC redefinition of myocardial infarction and new sensitive troponin assays on the frequency of acute myocardial infarctionAmerican Heart Journal, 2006
- Accuracy of medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital recordsAmerican Heart Journal, 2004