Purpose: We aimed to develop a reliable identification algorithm combining diagnostic codes with several treatment factors for inpatients with acute ischemic stroke (AIS) to conduct pharmacoepidemiological studies using the administrative database MID-NET® in Japan.
Methods: We validated 11 identification algorithms based on 56 different diagnostic codes (International Classification of Diseases, Tenth Revision; ICD-10) using Diagnosis Procedure Combination (DPC) data combined with information on AIS therapeutic procedures added as "AND" condition or "OR" condition. The target population for this study was 366 randomly selected hospitalized patients with possible cases of AIS, defined as relevant ICD-10 codes and diagnostic imaging and prescription or surgical procedure, in three institutions between April 1, 2015 and March 31, 2017. We determined the positive predictive values (PPVs) of these identification algorithms based on comparisons with a gold standard consisting of chart reviews by experienced specialist physicians. Additionally, the sensitivities of them among 166 patients with the possible cases of AIS at a single institution were evaluated.
Results: The PPVs were 0.618 (95% confidence interval [CI]: 0.566-0.667) to 0.909 (95% CI: 0.708-0.989) and progressively increased with adding or limiting information on AIS therapeutic procedures as "AND" condition in the identification algorithms. The PPVs for identification algorithms based on diagnostic codes I63.x were >0.8. However, the sensitivities progressively decreased to a maximum of ~0.2 after adding information on AIS therapeutic procedures as "AND" condition.
Conclusions: The identification algorithms based on the combination of appropriate ICD-10 diagnostic codes in DPC data and other AIS treatment factors may be useful to studies for AIS at a national level using MID-NET®.
Keywords: ICD-10; acute ischemic stroke; algorithms; database; positive predictive value; sensitivity; validation studies.
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