Developing a clinical prediction rule for repeated consultations with functional somatic symptoms in primary care: a cohort study.

OBJECTIVES: Patients who present in primary care with chronic functional somatic symptoms (FSS) have reduced quality of life and increased health care costs. Recognising these early is a challenge. The aim is to develop and internally validate a clinical prediction rule for repeated consultations with FSS. DESIGN AND SETTING: Records from the longitudinal population-based ('Lifelines') cohort study were linked to electronic health records from general practitioners (GPs). PARTICIPANTS: We included patients consulting a GP with FSS within 1 year after baseline assessment in the Lifelines cohort. OUTCOME MEASURES: The outcome is repeated consultations with FSS, defined as ≥3 extra consultations for FSS within 1 year after the first consultation. Multivariable logistic regression, with bootstrapping for internal validation, was used to develop a risk prediction model from 14 literature-based predictors. Model discrimination, calibration and diagnostic accuracy were assessed. RESULTS: 18 810 participants were identified by database linkage, of whom 2650 consulted a GP with FSS and 297 (11%) had ≥3 extra consultations. In the final multivariable model, older age, female sex, lack of healthy activity, presence of generalised anxiety disorder and higher number of GP consultations in the last year predicted repeated consultations. Discrimination after internal validation was 0.64 with a calibration slope of 0.95. The positive predictive value of patients with high scores on the model was 0.37 (0.29-0.47). CONCLUSIONS: Several theoretically suggested predisposing and precipitating predictors, including neuroticism and stressful life events, surprisingly failed to contribute to our final model. Moreover, this model mostly included general predictors of increased risk of repeated consultations among patients with FSS. The model discrimination and positive predictive values were insufficient and preclude clinical implementation.

Similar association between objective and subjective symptoms in functional and organic tremor.

BACKGROUND: A previous study reported a dramatic mismatch in objectively detected and self-reported tremor duration in patients with functional tremor. As these findings have an important and widespread impact in both clinical care and research, we conducted a validation study with a longer study duration and a larger sample of patients. METHODS: Fourteen patients with functional tremor and 19 with organic tremor completed a 30 day study period. Objective tremor duration was recorded using a wrist-worn accelerometer. Simultaneously, participants completed a web-based diary five times a day, each time rating their symptom burden since the previous diary entry. RESULTS: Patients with functional tremor had shorter objective tremor duration compared to patients with organic tremor (21.6% vs 30.7%, P = 0.034). A post-hoc analysis revealed the difference in objective duration was mainly due to patients with essential tremor (37.2%). Subjective symptom burden was not significantly different between functional and organic tremors (38.7 vs 28.7 on a 0-100 VAS scale, P = 0.138). Finally, a mixed model analysis did not reveal significant differences in the association between subjective and objective tremor symptoms (P = 0.168). CONCLUSIONS: patients with functional tremor do have an objectively detectable, persistent tremor during daily life activities. Furthermore, they have a similar symptom burden and a similar association between subjective and objective tremor symptoms as patients with organic tremor.

Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care: A Proof-of-Principle Study.

BACKGROUND: Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. OBJECTIVE: This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. METHODS: We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher's tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). RESULTS: An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. CONCLUSIONS: Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.

Cohort Profile Update: the TRacking Adolescents' Individual Lives Survey (TRAILS).

TRAILS consists of a population cohort (N=2230) and a clinical cohort (N=543), both of which were followed from about age 11 years onwards. To date, the population cohort has been assessed five times over a period of 11 years, with retention rates ranging between 80% and 96%. The clinical cohort has been assessed four times over a period of 8 years, with retention rates ranging between 77% and 85%. Since the IJE published a cohort profile on the TRAILS in 2008, the participants have matured from adolescents into young adults. The focus shifted from parents and school to entry into the labour market and family formation, including offspring. Furthermore, psychiatric diagnostic interviews were administered, the database was linked to a Psychiatric Case Registry, and the availability of genome-wide SNP variations opened the door to genome-wide association studies regarding a wide range of (endo)phenotypes. With some delay, TRAILS data are available to researchers outside the TRAILS consortium without costs; access can be obtained by submitting a publication proposal (see www.trails.nl).