Using linked electronic data to validate algorithms for health outcomes in administrative databases

A strategy for assessment and validation of major bleeding cases in a primary health care database in Spain

PURPOSE

This study aims to validate major bleeding (MB) cases within a cohort of new users of direct oral anticoagulants (DOACs) in Electronic health records (EHRs) from primary care in Spain (BIFAP), introducing more efficient techniques and automating the process of validation in the pharmacoepidemiologic research with EHR data as much as possible.

METHODS

Registered bleedings were identified in a cohort of new users of DOACs in BIFAP using ICPC 2 and ICD 9 codes; we ascertained these bleedings as MB through a validation strategy based on the MB definition from the International Society on Thrombosis and Hemostasis, which used hospitalization and critical localization as proxies. We assessed hospitalization with hospital discharge information (only available for some years and regions) and a free text-based algorithm created to identify hospitalization in EHR's clinical notes. Incidence rates (IR) of MB were evaluated by bleeding type.

RESULTS

The study cohort included 104 614 patients, with 274521.5 p-y of follow up. There were 6143 registered bleedings during the study period (519 intracranial bleeding - ICB, 4606 gastrointestinal bleeding - GIB, 1018 extracranial bleeding - ECB), from which 1679 were confirmed as MB (416 ICB, 1086 GIB, and 177 ECB). The free text-based semi-automatic algorithm had moderate recall (0.59), but high specificity (0.99), and precision (0.94).

CONCLUSION

The combination of hospitalization and critical localization is a valid approach to validate MB in EHRs with incomplete information. The use of more automatic methods for case validation instead of manual review of clinical notes is favored.

Identifying diabetes cases from administrative data: a population-based validation study

Background

Health care data allow for the study and surveillance of chronic diseases such as diabetes. The objective of this study was to identify and validate optimal algorithms for diabetes cases within health care administrative databases for different research purposes, populations, and data sources.

Methods

We linked health care administrative databases from Ontario, Canada to a reference standard of primary care electronic medical records (EMRs). We then identified and calculated the performance characteristics of multiple adult diabetes case definitions, using combinations of data sources and time windows.

Results

The best algorithm to identify diabetes cases was the presence at any time of one hospitalization or physician claim for diabetes AND either one prescription for an anti-diabetic medication or one physician claim with a diabetes-specific fee code [sensitivity 84.2%, specificity 99.2%, positive predictive value (PPV) 92.5%]. Use of physician claims alone performed almost as well: three physician claims for diabetes within one year was highly specific (sensitivity 79.9%, specificity 99.1%, PPV 91.4%) and one physician claim at any time was highly sensitive (sensitivity 93.6%, specificity 91.9%, PPV 58.5%).

Conclusions

This study identifies validated algorithms to capture diabetes cases within health care administrative databases for a range of purposes, populations and data availability. These findings are useful to study trends and outcomes of diabetes using routinely-collected health care data.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3148-0) contains supplementary material, which is available to authorized users.