Fitness for purpose of routinely recorded health data to identify patients with complex diseases: The case of Sjögren's syndrome. Issue 4 (8th September 2020) - Wiegersma, Sytske; Flinterman, Linda E.; Seghieri, Chiara; Baldini, Chiara; Paget, John; Barrio Cortés, Jaime; Verheij, Robert A.
Abstract: Background: This study is part of the EU‐funded project HarmonicSS, aimed at improving the treatment and diagnosis of primary Sjögren's syndrome (pSS). pSS is an underdiagnosed, long‐term autoimmune disease that affects particularly salivary and lachrymal glands. Objectives: We assessed the usability of routinely recorded primary care and hospital claims data for the identification and validation of patients with complex diseases such as pSS. Methods: pSS patients were identified in primary care by translating the formal inclusion and exclusion criteria for pSS into a patient selection algorithm using data from Nivel Primary Care Database (PCD), covering 10% of the Dutch population between 2006 and 2017. As part of a validation exercise, the pSS patients found by the algorithm were compared to Diagnosis Related Groups (DRG) recorded in the national hospital insurance claims database (DIS) between 2013 and 2017. Results: International Classification of Primary Care (ICPC) coded general practitioner (GP) contacts combined with the mention of "Sjögren" in the disease episode titles, were found to best translate the formal classification criteria to a selection algorithm for pSS. A total of 1462 possible pSS patients were identified in primary care (mean prevalence 0.7‰, against 0.61‰ reported globally). The DIS contained 208 545 patients with a Sjögren related DRG or ICD10 code (prevalence 2017: 2.73‰). A total of 2 577 577 patients from Nivel PCD were linked to the DIS database. A total of 716 of the linked pSS patients (55.3%) were confirmed based on the DIS. Conclusion: Our study finds that GP electronic health records (EHRs) lack the granular information needed to apply the formal diagnostic criteria for pSS. The developed algorithm resulted in a patient selection that approximates the expected prevalence and characteristics, although only slightly over half of the patients were confirmed using the DIS. Without more detailed diagnostic information, the fitness for purpose of routine EHR data for patient identification and validation could not be determined.