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Published on: May 15, 2020
Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of
Willeke M Kitselaar1,2, Mattijs E Numans2, Stephen P Sutch2,3
1Health, Medical and Neuropsychology, Leiden University Faculty of Social and Behavioural Sciences, Leiden, The Netherlands w.m.kitselaar@fsw.leidenuniv.nl.
Objective:
Persistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data.
Design:
A cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined.
Setting:
Coded electronic health record data were extracted from 76 general practices in the Netherlands.
Participants:
Patients who were registered for at least 1 year during 2014-2018, were included (n=169 138).
Outcome Measures:
Identification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic conditions and (3) healthcare utilisation (HCU) variables. Overlap between methods and practice specific differences were examined.
Results:
The percentage of cases identified varied between 0.3% and 7.0% across the methods. Over 58.1% of cases had chronic physical condition(s) and over 33.8% had chronic mental condition(s). HCU was generally higher for cases selected by any method compared with the total cohort. HCU was higher for method B compared with the other methods. In 26.7% of cases, cases were selected by multiple methods. Overlap between methods was low.
Conclusions:
Different methods yielded different patient samples which were general practice specific. Therefore, for the most comprehensive data-based selection of PSS cases, a combination of methods A, C and D would be recommended. Advanced (data-driven) methods are needed to create a more sensitive algorithm for identifying the full spectrum of PSS. For clinical purposes, method B could possibly support screening of patients who are currently missed in daily practice.
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