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Coloma PM, Valkhoff VE, Mazzaglia G, et al. Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries. BMJ Open. 2013;3(6)doi: 10.1136/bmjopen-2013-002862.
Coloma, P. M., Valkhoff, V. E., Mazzaglia, G., Nielsson, M. S., Pedersen, L., Molokhia, M., Mosseveld, M., Morabito, P., Schuemie, M. J., van der Lei, J., Sturkenboom, M., Trifirò, G. (2013). Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries. BMJ open, 3(6), . https://doi.org/10.1136/bmjopen-2013-002862
Coloma, Preciosa M, et al. "Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries." BMJ open vol. 3,6 (2013). doi: https://doi.org/10.1136/bmjopen-2013-002862
Coloma PM, Valkhoff VE, Mazzaglia G, Nielsson MS, Pedersen L, Molokhia M, Mosseveld M, Morabito P, Schuemie MJ, van der Lei J, Sturkenboom M, Trifirò G. Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries. BMJ Open. 2013 Jun 20;3(6). doi: 10.1136/bmjopen-2013-002862. PMID: 23794587; PMCID: PMC3686251.
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BMJ Open. 2013 Jun 20;3(6). doi: 10.1136/bmjopen-2013-002862.

Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries.

BMJ open

Preciosa M Coloma, Vera E Valkhoff, Giampiero Mazzaglia, Malene Schou Nielsson, Lars Pedersen, Mariam Molokhia, Mees Mosseveld, Paolo Morabito, Martijn J Schuemie, Johan van der Lei, Miriam Sturkenboom, Gianluca Trifirò,

Affiliations

  1. Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands.

PMID: 23794587 PMCID: PMC3686251 DOI: 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: Epidemiology; Statistics & Research Methods

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