Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices.

OBJECTIVE: The present study aimed to early identify patients with persistent somatic symptoms (PSS) in primary care by exploring routine care data-based approaches. DESIGN/SETTING: A cohort study based on routine primary care data from 76 general practices in the Netherlands was executed for predictive modelling. PARTICIPANTS: Inclusion of 94 440 adult patients was based on: at least 7-year general practice enrolment, having more than one symptom/disease registration and >10 consultations. METHODS: Cases were selected based on the first PSS registration in 2017-2018. Candidate predictors were selected 2-5 years prior to PSS and categorised into data-driven approaches: symptoms/diseases, medications, referrals, sequential patterns and changing lab results; and theory-driven approaches: constructed factors based on literature and terminology in free text. Of these, 12 candidate predictor categories were formed and used to develop prediction models by cross-validated least absolute shrinkage and selection operator regression on 80% of the dataset. Derived models were internally validated on the remaining 20% of the dataset. RESULTS: All models had comparable predictive values (area under the receiver operating characteristic curves=0.70 to 0.72). Predictors are related to genital complaints, specific symptoms (eg, digestive, fatigue and mood), healthcare utilisation, and number of complaints. Most fruitful predictor categories are literature-based and medications. Predictors often had overlapping constructs, such as digestive symptoms (symptom/disease codes) and drugs for anti-constipation (medication codes), indicating that registration is inconsistent between general practitioners (GPs). CONCLUSIONS: The findings indicate low to moderate diagnostic accuracy for early identification of PSS based on routine primary care data. Nonetheless, simple clinical decision rules based on structured symptom/disease or medication codes could possibly be an efficient way to support GPs in identifying patients at risk of PSS. A full data-based prediction currently appears to be hampered by inconsistent and missing registrations. Future research on predictive modelling of PSS using routine care data should focus on data enrichment or free-text mining to overcome inconsistent registrations and improve predictive accuracy.

Predictors of Persistent Somatic Symptoms in the General Population: A Systematic Review of Cohort Studies.

OBJECTIVE: Up to 10% of the general population experiences persistent somatic symptoms (PSS). Numerous studies in a variety of health domains are dedicated to identifying factors that are associated with PSS onset. The present study aimed to provide an overview of predictors for PSS onset in the general population and the related health domains. METHODS: A systematic search was performed identifying longitudinal cohort studies that examined factors associated with PSS onset in the general population. Included studies measured potential predictors before PSS onset and were categorized according to the dynamic biopsychosocial model. Four levels of evidence were discerned for predictors, based on the number of studies and percentage of consistent findings. RESULTS: In the 154 articles eligible for analysis, 27 PSS subtypes were studied, with primary focus on fibromyalgia (25.0%) and irritable bowel syndrome (23.3%). Of the >250 predictors of PSS onset, 46 were investigated more than once and showed consistent results. Strong evidence identifies biological (e.g., infections, body weight-related metrics), psychological (e.g., sleep problems, psychopathology), interpersonal (life events, childhood/interpersonal stress), contextual (employment), and health behavioral (health care utilization) predictors. CONCLUSIONS: The results provide strong evidence for factors from all dynamic biopsychosocial domains, although interpersonal and health behavioral factors are relatively under investigated. Thus, evidence suggests that reduction of predictors of PSS onset to a specific factor/domain may be too restrictive. There is no evidence that this differs per PSS subtype. Exploring all domains and measuring common factors across subtypes are essential to improve the clinical course of PSS.

Fatigue after neurosurgery in patients with a brain tumor: The role of autonomic dysregulation and disturbed sleep.

Background Fatigue is prevalent in patients with a brain tumor and high levels of fatigue persist after neurosurgical tumor resection. The underlying mechanisms are insufficiently understood and this study examines the role of autonomic nervous system dysregulation and objective sleep characteristics in fatigue among post-surgical patients. Methods Patients undergoing craniotomy (N = 52; age 52.1 ± 15.0 years; 44% women) were evaluated at 3 months after surgery (median = 86 days). Fatigue was measured using the Multidimensional Fatigue Inventory. Autonomic nervous system indices were based on 24-h heart rate variability (HRV) analysis. Sleep parameters were measured using actigraphy: total sleep duration, efficiency, onset latency and wake after sleep onset (WASO). Data analyses of this cross-sectional study included correlation and multiple regression analysis. Results Fatigue scores were significantly elevated in tumor resection patients compared to healthy reference norms (p's < 0.05) with no differences between patients with glioma (N = 32) versus meningioma (N = 20). Associations between HRV indices and fatigue were non-significant (r values <0.16, p values > 0.25). Sleep duration was associated with physical fatigue (r = 0.35, p = 0.02), whereas WASO was associated with mental fatigue levels (r = 0.40, p = 0.006). Disturbed sleep measures were associated with HRV indices of reduced parasympathetic nervous system activity in glioma patients but not in meningioma patients. Conclusions Multiple nocturnal awakenings may result in mental fatigue and longer sleep time was associated with physical fatigue, which may reflect compensatory sleep patterns. Future intervention studies addressing sleep quality may be beneficial in treating fatigue in patients following neurosurgery for tumor resection.

The general practitioners perspective regarding registration of persistent somatic symptoms in primary care: a survey.

BACKGROUND: Persistent somatic symptoms (PSS) are common in primary care and often accompanied by an increasing disease burden for both the patient and healthcare. In medical practice, PSS is historically considered a diagnosis by exclusion or primarily seen as psychological. Besides, registration of PSS in electronic health records (EHR) is unambiguous and possibly does not reflect classification adequately. The present study explores how general practitioners (GPs) currently register PSS, and their view regarding the need for improvements in classification, registration, and consultations. METHOD: Dutch GPs were invited by email to participate in a national cross-sectional online survey. The survey addressed ICPC-codes used by GPs to register PSS, PSS-related terminology added to free text areas, usage of PSS-related syndrome codes, and GPs' need for improvement of PSS classification, registration and care. RESULTS: GPs (n = 259) were most likely to use codes specific to the symptom presented (89.3%). PSS-related terminology in free-text areas was used sparsely. PSS-related syndrome codes were reportedly used by 91.5% of GPs, but this was primarily the case for the code for irritable bowel syndrome. The ambiguous registration of PSS is reported as problematic by 47.9% of GPs. Over 56.7% of GPs reported needing additional training, tools or other support for PSS classification and consultation. GPs also reported needing other referral options and better guidelines. CONCLUSIONS: Registration of PSS in primary care is currently ambiguous. Approximately half of GPs felt a need for more options for registration of PSS and reported a need for further support. In order to improve classification, registration and care for patients with PSS, there is a need for a more appropriate coding scheme and additional training.

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 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.