Willeke M. Kitselaar (Netherlands)
Leiden University Medical Center
LUMC-Campus The Hague
Willeke Kitselaar is a PhD Candidate at Leiden University, the Netherlands, with a background in medical and neuropsychology, stress-related fundamental research. The focus of her PhD project is persistent somatic symptoms (PSS) or otherwise termed medically unexplained symptoms in primary care. The main goal of the project is to develop a tool for general practitioners which helps with the identification of PSS at an early stage. For this purpose, she mainly uses routine primary care data and takes a multidisciplinary approach, combining her background in psychology with knowledge from data science and population health management to apply theory- and data-driven methods for analyzing routine care data.

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IDENTIFYING PERSISTENT SOMATIC SYMPTOMS IN ELECTRONIC HEALTH RECORDS: EXPLORING MULTIPLE THEORY-DRIVEN METHODS OF IDENTIFICATION.

Session Name
0600 - INNOVATION AND FUTURE TRENDS II (ID 62)
Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 5
Presenter
Lecture Time
11:14 AM - 11:25 AM
Session Icon
Pre-Recorded with Live Q&A

Abstract

Abstract Body

Background and purpose: Persistent somatic symptoms (PSS) are highly prevalent in primary care and are a burden to both the patient and healthcare. Data-based identification of patients with PSS could foster various improvements in care. However, identification is currently hampered by unambiguous registration of PSS. The present study aims to explore different theory-driven methods for data-based identification of patients with PSS.

Methods: A cross-sectional study was performed on routine primary care data from 169,138 patients in the Netherlands. Identification of PSS was based on (A) PSS-related syndrome codes, (B) PSS-related terminology, (C) PSS-related symptom codes, and (D) 4-dimensional symptom questionnaire (4DSQ) scores. Sample size, demographics, chronic conditions, and health care utilization (HCU) as extracted via the four methods were explored. Sensitivity between methods was examined.

Results: The percentage of cases identified varied between 0.3% and 7.0% across the methods. Over 58.1% of cases had comorbid chronic physical condition(s) and over 33.8% had comorbid chronic mental condition(s). HCU was generally higher for cases selected by any method compared to the total cohort. HCU was relatively higher for method B compared to the other methods. In 26.7% of cases, cases were selected by multiple methods. Sensitivity between methods was generally low.

Conclusions: The different methods yielded different patient samples within our cohort. Therefore, for the most comprehensive data-based selection of PSS cases, a combination of methods A, C, and D would be recommended. Additionally, advanced (data-driven) methods are needed to create a more sensitive algorithm for identifying the full spectrum of PSS.

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