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ObjectivesTo develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.DesignSecondary analysis of a randomised controlled trial.Setting59 general practices in The Netherlands.Participants202 adults, aged 50 years and older with a chronic vestibular syndrome who received either stand-alone VR (98) or blended VR (104). Stand-alone VR consisted of a 6-week, internet-based intervention with weekly online sessions and daily exercises. In blended VR, the same intervention was supplemented with physiotherapy support.Main outcome measuresSuccessful treatment was defined as: clinically relevant improvement of (1) vestibular symptoms (≥3 points improvement Vertigo Symptom Scale—Short Form); (2) vestibular-related disability (>11 points improvement Dizziness Handicap Inventory); and (3) both vestibular symptoms and vestibular-related disability. We assessed performance of the predictive models by applying calibration plots, Hosmer-Lemeshow statistics, area under the receiver operating characteristic curves (AUC) and applied internal validation.ResultsImprovement of vestibular symptoms, vestibular-related disability or both was seen in 121, 81 and 64 participants, respectively. We generated predictive models for each outcome, resulting in different predictors in the final models. Calibration for all models was adequate with non-significant Hosmer-Lemeshow statistics, but the discriminative ability of the final predictive models was poor (AUC 0.54 to 0.61). None of the identified models are therefore suitable for use in daily general practice to predict treatment success of online VR.ConclusionIt is difficult to predict treatment success of internet-based VR and it remains unclear who should be treated with stand-alone VR or blended VR. Because we were unable to develop a useful prediction model, the decision to offer stand-alone or blended VR should for now be based on availability, cost effectiveness and patient preference.Trial registration numberThe Netherlands Trial Register NTR5712.

IntroductionMajor depressive disorders (MDD), diabetes mellitus type 2 (DM2) and coronary heart disease (CHD) are leading contributors to the global burden of disease and often co-occur.ObjectivesTo evaluate the 2-year effectiveness of a stepped-care intervention to prevent MDD compared with usual care and to develop a prediction model for incident depression in patients with DM2 and/or CHD with subthreshold depression.MethodsData of 236 Dutch primary care patients with DM2/CHD with subthreshold depression (Patient Health Questionnaire 9 (PHQ-9) score ≥6, no current MDD according to the Mini International Neuropsychiatric Interview (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria)) who participated in the Step-Dep trial were used. A PHQ-9 score of ≥10 at minimally one measurement during follow-up (at 3, 6, 9, 12 and 24 months) was used to determine the cumulative incidence of MDD. Potential demographic and psychological predictors were measured at baseline via web-based self-reported questionnaires and evaluated using a multivariable logistic regression model. Model performance was assessed with the Hosmer-Lemeshow test, Nagelkerke’s R2explained variance and area under the receiver operating characteristic curve (AUC). Bootstrapping techniques were used to internally validate our model.Results192 patients (81%) were available at 2-year follow-up. The cumulative incidence of MDD was 97/192 (51%). There was no statistically significant overall treatment effect over 24 months of the intervention (OR 1.37; 95% CI 0.52 to 3.55). Baseline levels of anxiety, depression, the presence of >3 chronic diseases and stressful life events predicted the incidence of MDD (AUC 0.80, IQR 0.79–0.80; Nagelkerke’s R20.34, IQR 0.33–0.36).ConclusionA model with 4 factors predicted depression incidence during 2-year follow-up in patients with DM2/CHD accurately, based on the AUC. The Step-Dep intervention did not influence the incidence of MDD. Future depression prevention programmes should target patients with these 4 predictors present, and aim to reduce both anxiety and depressive symptoms.Trial registration numberNTR3715.

Objective To develop and internally validate a prediction model for tinnitus recovery following unilateral cochlear implantation. Design A cross-sectional retrospective study. Setting A questionnaire concerning tinnitus was sent to patients with bilateral severe to profound hearing loss, who underwent unilateral cochlear implantation at the University Medical Center Utrecht, the Netherlands, between 1 January 2006 and 31 December 2015. Participants Of 137 included patients, 87 patients experienced tinnitus preoperatively. Data of these 87 patients were used to develop the prediction model. Primary and secondary outcome measures The outcome of the prediction model was tinnitus recovery. Investigated predictors were: age, gender, duration of deafness, preoperative hearing performance, tinnitus duration, severity and localisation, follow-up duration, localisation of cochlear implant (CI) compared with tinnitus side, surgical approach, insertion depth of the electrode, CI brand and difference in hearing threshold following cochlear implantation. Multivariable backward logistic regression was performed. Missing data were handled using multiple imputation. The performance of the model was assessed by the calibrative and discriminative ability of the model. The prediction model was internally validated using bootstrapping techniques. Results The tinnitus recovery rate was 40%. A lower preoperative Consonant-Vowel-Consonant (CVC) score, unilateral localisation of tinnitus and larger deterioration of residual hearing at 250 Hz revealed to be relevant predictors for tinnitus recovery. The area under the receiver operating characteristics curve (AUC) of the initial model was 0.722 (IQR: 0.703–0.729). After internal validation of this prediction model, the AUC decreased to 0.696 (IQR: 0.667–0.700). Conclusion and relevance Lower preoperative CVC score, unilateral localisation of tinnitus and larger deterioration of residual hearing at 250 Hz were significant predictors for tinnitus recovery following unilateral cochlear implantation. The performance of the model developed in this retrospective study is promising. However, before clinical use of the model, the conduction of a larger prospective study is recommended.

Contains fulltext : 171230.pdf (Publisher’s version ) (Open Access) OBJECTIVE: To evaluate whether a model that was previously developed to predict 14-day mortality for nursing home residents with dementia and lower respiratory tract infection who received antibiotics could be applied to residents who were not treated with antibiotics. Specifically, in this same data set, to update the model using recalibration methods; and subsequently examine the historical, geographical, methodological and spectrum transportability through external validation of the updated model. DESIGN: 1 cohort study was used to develop the prediction model, and 4 cohort studies from 2 countries were used for the external validation of the model. SETTING: Nursing homes in the Netherlands and the USA. PARTICIPANTS: 157 untreated residents were included in the development of the model; 239 untreated residents were included in the external validation cohorts. OUTCOME: Model performance was evaluated by assessing discrimination: area under the receiver operating characteristic curves; and calibration: Hosmer and Lemeshow goodness-of-fit statistics and calibration graphs. Further, reclassification tables allowed for a comparison of patient classifications between models. RESULTS: The original prediction model applied to the untreated residents, who were sicker, showed excellent discrimination but poor calibration, underestimating mortality. Adjusting the intercept improved calibration. Recalibrating the slope did not substantially improve the performance of the model. Applying the updated model to the other 4 data sets resulted in acceptable discrimination. Calibration was inadequate only in one data set that differed substantially from the other data sets in case-mix. Adjusting the intercept for this population again improved calibration. CONCLUSIONS: The discriminative performance of the model seems robust for differences between settings. To improve calibration, we recommend adjusting the intercept when applying the model in settings where different mortality rates are expected. An impact study may evaluate the usefulness of the two prediction models for treated and untreated residents and whether it supports decision-making in clinical practice.