Vera E Valkhoff1, Preciosa M Coloma2, Gwen M C Masclee1, Rosa Gini3, Francesco Innocenti4, Francesco Lapi5, Mariam Molokhia6, Mees Mosseveld2, Malene Schou Nielsson7, Martijn Schuemie2, Frantz Thiessard8, Johan van der Lei2, Miriam C J M Sturkenboom9, Gianluca Trifirò10. 1. Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam The Netherlands. 2. Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands. 3. Agenzi Regionali di Sanità della Toscana, Via Pietro Dazzi 1 - 50141, Firenze, Italy. 4. Health Search, Italian College of General Practitioners, Via del Pignoncino, 9-11,50142, Florence, Italy. 5. Centre for Clinical Epidemiology, Jewish General Hospital, 3755 Côte-Sainte-Catherine Road, Montréal, QC H3T 1E2, Canada; Department of Preclinical and Clinical Pharmacology, University of Florence, Piazza di San Marco, 4, 50121 Firenze, Italy. 6. Department of Primary Care and Public Health Sciences, Kings College London, Division of Health and Social Care Research, 7th Floor, Capital House, 42 Weston Street, London SE1 3QD, United Kingdom. 7. Department of Clinical Epidemiology, Aarhus University Hospital, Olof Palmes Allé 43-45 DK-8200 Aarhus N, Denmark. 8. LESIM, ISPED, Universite Bordeaux 2', 146 Rue Léo Saignat, 33076 Bordeaux, France. 9. Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands. 10. Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Clinical and Experimental Medicine and Pharmacology, Via Consolare Pompea, 1, Messina, University of Messina, Italy. Electronic address: g.trifiro@erasmusmc.nl.
Abstract
OBJECTIVE: To evaluate the accuracy of disease codes and free text in identifying upper gastrointestinal bleeding (UGIB) from electronic health-care records (EHRs). STUDY DESIGN AND SETTING: We conducted a validation study in four European electronic health-care record (EHR) databases such as Integrated Primary Care Information (IPCI), Health Search/CSD Patient Database (HSD), ARS, and Aarhus, in which we identified UGIB cases using free text or disease codes: (1) International Classification of Disease (ICD)-9 (HSD, ARS); (2) ICD-10 (Aarhus); and (3) International Classification of Primary Care (ICPC) (IPCI). From each database, we randomly selected and manually reviewed 200 cases to calculate positive predictive values (PPVs). We employed different case definitions to assess the effect of outcome misclassification on estimation of risk of drug-related UGIB. RESULTS: PPV was 22% [95% confidence interval (CI): 16, 28] and 21% (95% CI: 16, 28) in IPCI for free text and ICPC codes, respectively. PPV was 91% (95% CI: 86, 95) for ICD-9 codes and 47% (95% CI: 35, 59) for free text in HSD. PPV for ICD-9 codes in ARS was 72% (95% CI: 65, 78) and 77% (95% CI: 69, 83) for ICD-10 codes (Aarhus). More specific definitions did not have significant impact on risk estimation of drug-related UGIB, except for wider CIs. CONCLUSIONS: ICD-9-CM and ICD-10 disease codes have good PPV in identifying UGIB from EHR; less granular terminology (ICPC) may require additional strategies. Use of more specific UGIB definitions affects precision, but not magnitude, of risk estimates.
OBJECTIVE: To evaluate the accuracy of disease codes and free text in identifying upper gastrointestinal bleeding (UGIB) from electronic health-care records (EHRs). STUDY DESIGN AND SETTING: We conducted a validation study in four European electronic health-care record (EHR) databases such as Integrated Primary Care Information (IPCI), Health Search/CSD Patient Database (HSD), ARS, and Aarhus, in which we identified UGIB cases using free text or disease codes: (1) International Classification of Disease (ICD)-9 (HSD, ARS); (2) ICD-10 (Aarhus); and (3) International Classification of Primary Care (ICPC) (IPCI). From each database, we randomly selected and manually reviewed 200 cases to calculate positive predictive values (PPVs). We employed different case definitions to assess the effect of outcome misclassification on estimation of risk of drug-related UGIB. RESULTS: PPV was 22% [95% confidence interval (CI): 16, 28] and 21% (95% CI: 16, 28) in IPCI for free text and ICPC codes, respectively. PPV was 91% (95% CI: 86, 95) for ICD-9 codes and 47% (95% CI: 35, 59) for free text in HSD. PPV for ICD-9 codes in ARS was 72% (95% CI: 65, 78) and 77% (95% CI: 69, 83) for ICD-10 codes (Aarhus). More specific definitions did not have significant impact on risk estimation of drug-related UGIB, except for wider CIs. CONCLUSIONS: ICD-9-CM and ICD-10 disease codes have good PPV in identifying UGIB from EHR; less granular terminology (ICPC) may require additional strategies. Use of more specific UGIB definitions affects precision, but not magnitude, of risk estimates.
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