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Predicting the 2-year course of persistent MUPS Nikki Claassen – van Dessel, MD PhD Dept of General Practice and Elderly Care Medicine VU University Medical Centre Amsterdam

MUPS? MUPS are very common MUPS are physical symptoms which persist for at least several weeks and for which no adequate explanation can be found after proper medical examination by a MD MUPS are very common 25-50% of symptoms presented to GP remain unexplained

Course of MUPS 50%↑, 10-30%↓ Persistent MUPS are associated with: Unnecessary diagnostic interventions and medication Great risk of functional impairment and psychological distress Doubling of medical care utilization Long term absence of work

Predicting MUPS Known predictors of the course of MUPS: Higher number of symptoms at baseline Longer duration of symptoms at baseline Female sex Medical characteristics? Psychological aspects? Cognitive aspects? Behavioural aspects?

Study objective To develop a prediction model for the course of MUPS in terms of symptom severity and physical/mental functioning

Methods Longitudinal prospective cohort study Multiple settings (primary care / specialized MUPS care) 325 MUPS patients, age: 18-70 years 2 year follow-up (4 follow-up moments)

Measures Outcomes: Potential predictors: Number and severity of symptoms (PHQ-15) Nunctional impairment (SF-36) Potential predictors: Patient characteritics Symptom characteristics Biological factors Psychological factors Behavioural factors Social factors Personality

Statistics Linear Mixed Models for all outcomes Backwards stepwise selection No imputation Explained variances Sensitivity analyses (for health care setting) Bootstrapping procedures We performed linear mixed model analyses using a backwards-stepwise procedure to predict symptom severity (PHQ-15 score), physical functioning (RAND 36 PCS score) and mental functioning (RAND 36 MCS score) over time. Within mixed model analysis an adjustment is made for the correlation between repeated observations within the subject, by modeling the variability among the subjects [50,51]. Predictors with a p≤0.05 in the multivariate analyses remained in the final models [48,52].   The quality of the final models was derived from the explained variance (R²). We decided not to use imputation techniques to handle missing data in outcomes or predictors, as altogether percentages of missing data were low.

Results – baseline data Text   Total group (N=325) Mean (SD) N (%) Age (range 19-70) 46.53 (12.3) Female gender 244 (75.1%) Nationality Dutch 279 (85.8%) Other 46 (14.2%) Number of somatic comorbidities 1.71 (1.60) Depressive symptoms (QIDS-SR, scale 0-27) 9.31 (4.9) Anxiety (BAI, scale 0-63) 12.45 (9.6) Symptom severity (PHQ-15, scale 0-30) 12.28 (5.3) Physical functioning (RAND 36 PCS, scale 0-100) 47.84 (19.8) Mental functioning (RAND 36 MCS, scale 0-100) 46.25 (17.8) Belangrijkste klacht 47% Bewegingsapparaat 17% Vermoeidheid 15% Neurologisch 12% Maag-darmstelsel

Results - identified predictors Unfavourable predictors Favourable predictors Physical comorbidities Limited alcohol use Severe symptoms at baseline Higher education Anxiety Better physical/mental function at baseline Catastrophising Extraversion Embarrassment Symptom focusing* Avoidance cognitions Damage cognitions* Avoidance behaviours Neuroticism

Quality of the models Explained interpersonal variance for all three models varied between 70.5 and 76.0% Performance of the models was comparable in primary and secondary/tertiary care

Conclusion The prediction models identified several relevant demographic, medical, psychological and behavioural predictors They can potentially be used in future classification systems External validation is needed prior to clinical implementation

Research team Nikki Claassen – van Dessel n.vandessel@vumc.nl Hans van der Wouden, PhD Jos Twisk, professor Joost Dekker, professor Henriette van der Horst, professor