How Well Do Various Health Outcome Definitions Identify Appropriate Cases in Observational Studies?
Observational data can be useful for drug safety research, but accurate measurement of adverse health outcomes is paramount. Best practices for identifying important health outcomes of interest (HOI) are needed.
To evaluate the extent to which health outcome definitions commonly used in observational database studies identify cases that are consistent with expert panel assessment of the underlying data.
Competing HOI definitions were used to identify potential cases of acute liver injury (ALI; n = 208), acute kidney injury (AKI; n = 200), and myocardial infarction (MI; n = 204) in the Truven MarketScan Lab Database (MSLR). Panelists reviewed patient-level data and answered questions about whether they believed the case actually reflected the HOI and their certainty of case classification on a 10-point scale (1 = unlikely to 10 = likely). Each patient was reviewed independently by two panelists. Case disagreements were resolved through consensus meetings. Positive predictive value (PPV) was calculated as the number of cases deemed to be true over the total number of sampled cases. Kappa statistics assessed inter-rater agreement.
PPV ranged from 0 to 52 % across ALI definitions, 12 to 82 % across AKI definitions, and 1 to 56 % across MI definitions. Certainty scores on the 10-point scale paralleled the PPV, with a range of mean values from 1.7 to 4.8 across ALI definitions, 3.1 to 6.0 across AKI definitions, and 2.8 to 5.7 across MI definitions. Inter-rater agreement was low to moderate (Kappa range 0.0–0.6).
Existing HOI definitions had relatively low PPV based on expert panel review. Experts commonly disagreed on case classification. Additional work is needed to refine HOI case definitions if observational data are to be reliably used for health outcome assessment.
- 2.Observational Medical Outcomes Partnership (2010) [cited 2012 October 8]. http://omop.org/HOI.
- 3.Ryan PB, Madigan D. Observational Medical Outcomes Partnership (OMOP) methods evaluation. OMOP 2011 Symposium; 2011 January 11; Washington, DC.Google Scholar
- 4.Racoosin JA, Ryan PB. Implications of health outcomes of interest definitions: acute liver injury case study. OMOP 2011 Symposium; 2011 January 11; Washington, DC.Google Scholar
- 10.Observational Medical Outcomes Partnership. OMOP Collaborator—Thonmson Reuters. [March 7, 2012]. http://omop.org/CDMvocabV4.
- 11.Ryan PB. Lessons for building a risk identification and analysis system. Observational Medical Outcomes Partnership Symposium, June 28, 2012. Bethesda, MD. http://omop.org/2012OMOPmeeting.
- 13.Vessey MP. Learning how to control biases in studies to identify adverse effects of drugs. JLL Bulletin: Commentaries on the history of treatment evaluation. 2006. Accessed 26 June 2013. http://www.jameslindlibrary.org/illustrating/articles/learning-how-to-control-biases-in-studies-to-identify-adverse-ef--2.