Improving the Identification of Out-of-Hospital Sudden Cardiac Deaths in a General Practice Research Database.

BACKGROUND: The ascertainment of sudden cardiac death (SCD) in electronic health databases is challenging.

OBJECTIVES: Our objective was to evaluate the applicability of the validated computer definition of SCD developed by Chung et al. in a retrospective study of SCD and domperidone exposure in the Clinical Practice Research Datalink (CPRD).

METHODS: We assessed out-of-hospital SCD by applying the validated computer definition and linking data with Hospital Episode Statistics and death certificates. We developed a separate algorithm to identify end-of-life care in noninstitutionalized patients and excluded associated deaths from the analysis to address their misclassification as SCD.

RESULTS: Of the 681,104 patients in the study cohort, 3444 were initially classified as out-of-hospital SCD. Next, 163 deaths were identified as expected deaths by our algorithm for end-of-life home care. After review of patient profiles, 162 were classified as expected deaths because of evidence that the patient received palliative or end-of-life care, but one was a false negative. The exclusion of such cases appreciably changed the odds ratio for current exposure to domperidone compared with non-use of study medications from 2.09 (95 % confidence interval [CI] 1.16-3.74) to 1.71 (95 % CI 0.92-3.18). A similar effect on the odds ratio was observed for current exposure to metoclopramide but not to proton pump inhibitors.

CONCLUSIONS: Our algorithm to identify end-of-life care at home in the CPRD performed well, with only one false negative. The exclusion of misclassified cases of SCD reduced the magnitude of the odds ratios for SCD associated with domperidone and metoclopramide exposure by controlling protopathic bias.