Validation of a claims-based algorithm identifying eligible study subjects in the ADAPTABLE pragmatic clinical trial.

Accepted

Journal Article

Objective:Validate an algorithm that uses administrative claims data to identify eligible study subjects for the ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness) pragmatic clinical trial (PCT). Materials and methods:This study used medical records from a random sample of patients identified as eligible for the ADAPTABLE trial. The inclusion criteria for ADAPTABLE were a history of acute myocardial infarction (AMI) or percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), or other coronary artery disease (CAD), plus at least one of several risk-enrichment factors. Exclusion criteria included a history of bleeding disorders or aspirin allergy. Using a claims-based algorithm, based on International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and 10th Edition (ICD-10) codes and Current Procedural Terminology (CPT) codes, we identified patients eligible for the PCT. The primary outcome was the positive predictive value (PPV) of the identification algorithm: the proportion of sampled patients whose medical records confirmed their ADAPTABLE study eligibility. Exact 95% confidence limits for binomial random variables were calculated for the PPV estimates. Results:Of the 185 patients whose medical records were reviewed, 168 (90.8%; 95% Confidence Interval: 85.7%, 94.6%) were confirmed study eligible. This proportion did not differ between patients identified with codes for AMI and patients identified with codes for PCI or CABG. Conclusion:The estimated PPV was similar to those in claims-based identification of drug safety surveillance events, indicating that administrative claims data can accurately identify study-eligible subjects for pragmatic clinical trials.

Full Text

Duke Authors

Cited Authors

  • Fishman, E; Barron, J; Dinh, J; Jones, WS; Marshall, A; Merkh, R; Robertson, H; Haynes, K

Published Date

  • December 2018

Published In

Volume / Issue

  • 12 /

Start / End Page

  • 154 - 160

PubMed ID

  • 30480162

Pubmed Central ID

  • 30480162

Electronic International Standard Serial Number (EISSN)

  • 2451-8654

International Standard Serial Number (ISSN)

  • 2451-8654

Digital Object Identifier (DOI)

  • 10.1016/j.conctc.2018.11.001

Language

  • eng