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Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification
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 P...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451292/ https://www.ncbi.nlm.nih.gov/pubmed/34535479 http://dx.doi.org/10.1136/bmjopen-2021-049907 |
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author | Kitselaar, Willeke M Numans, Mattijs E Sutch, Stephen P Faiq, Ammar Evers, Andrea WM van der Vaart, Rosalie |
author_facet | Kitselaar, Willeke M Numans, Mattijs E Sutch, Stephen P Faiq, Ammar Evers, Andrea WM van der Vaart, Rosalie |
author_sort | Kitselaar, Willeke M |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8451292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-84512922021-10-05 Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification Kitselaar, Willeke M Numans, Mattijs E Sutch, Stephen P Faiq, Ammar Evers, Andrea WM van der Vaart, Rosalie BMJ Open General practice / Family practice 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. BMJ Publishing Group 2021-09-17 /pmc/articles/PMC8451292/ /pubmed/34535479 http://dx.doi.org/10.1136/bmjopen-2021-049907 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | General practice / Family practice Kitselaar, Willeke M Numans, Mattijs E Sutch, Stephen P Faiq, Ammar Evers, Andrea WM van der Vaart, Rosalie Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
title | Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
title_full | Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
title_fullStr | Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
title_full_unstemmed | Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
title_short | Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
title_sort | identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification |
topic | General practice / Family practice |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451292/ https://www.ncbi.nlm.nih.gov/pubmed/34535479 http://dx.doi.org/10.1136/bmjopen-2021-049907 |
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