Popular Snore Aids: Do They Work?

Objective The study goal was to critically evaluate 3 popular noninvasive treatments for snoring: an oral spray lubricant applied before bedtime, a nasal strip designed to maintain nasal valve patency, and a head-positioning pillow. Study design Prospective, randomized blinded clinical trial of 3 popular noninvasive snore aids using objective acoustic snoring analysis and subjective patient and bed-partner questionnaires in 40 snoring patients. A digital recorder allowed snoring analysis with data collected in the home environment over 1 week. Results There is neither objective nor subjective benefit to the use of tested popular noninvasive snore aids. Palatal snoring, palatal loudness, average loudness of snoring, averaged palatal flutter frequency, and respiratory disturbance index did not significantly change when comparing the 3 snoring aids with no treatment. Subjective comments and complications are reviewed as well. Conclusion This is the first prospective comparison trial of popular noninvasive snoring aids. There is no significant objective or subjective snoring improvement in the anti-snoring aids studied compared with the use of no aid. Significance Outcome studies aid in verifying or refuting claims made by popular noninvasive snore aids

Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Abstract Background Biological disease-modifying antirheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis. However, as bDMARDs may also lead to adverse events and are expensive, tapering them is of great clinical interest. Tapering according to disease activity-guided dose optimization (DGDO) does not seem to affect long term remission rates, but flares are frequent during this process. Our objective was to develop a model for the prediction of flares during bDMARD tapering using data from routine care and to evaluate its potential clinical impact. Methods We used a joint latent class model to repeatedly predict the probability of a flare occurring within the next 3 months. The model was developed using longitudinal data on disease activity (DAS28) and other routine care data from two clinics. Predictive accuracy was assessed in cross-validation and external validation was performed with data from the DRESS (Dose REduction Strategy of Subcutaneous tumor necrosis factor inhibitors) trial. Additionally, we simulated the reduction in number of flares and bDMARD dose when implementing the model as a decision aid during bDMARD tapering in the DRESS trial. Results Data from 279 bDMARD courses were used for model development. The final model included two latent DAS28-trajectories, bDMARD type and dose, disease duration, and seropositivity. The area under the curve of the final model was 0.76 (0.69–0.83) in cross-validation and 0.68 (0.62–0.73) in external validation. In simulation of prediction-aided decisions, the mean number of flares over 18 months decreased from 1.21 (0.99–1.43) to 0.75 (0.54–0.96). The reduction in he bDMARD dose was mostly maintained, increasing from 54 to 64% of full dose. Conclusions We developed a dynamic flare prediction model, exclusively based on data typically available in routine care. Our results show that using this model to aid decisions during bDMARD tapering may significantly reduce the number of flares while maintaining most of the bDMARD dose reduction. Trial registration The clinical impact of the prediction model is currently under investigation in the PATIO randomized controlled trial (Dutch Trial Register number NL9798). </jats:sec

Additional file 5 of Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Additional file 5: Supplementary Table S2. Simulation results for different cutoff points (baseline predictions included). 95% confidence intervals are presented between brackets. a. The mean difference in bDMARD dose divided by the mean number of flares compared with the DRESS [9] DGDO arm. The number therefore represents the increase in bDMARD dose that was needed to prevent a flare for this specific tapering strategy. b. The mean difference in the number of flares, divided by the mean difference in bDMARD dose, compared to routine care. The ratio thus represents the number of extra flares that occurred for each extra full dose of bDMARD that is tapered compred to routine care over 18 monhts using this specific tapering strategy. bDMARD: biological disease-modifying antirheumatic drug, DGDO: disease activity guided dose optimisation

Additional file 3 of Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Additional file 3: Supplementary Figure S3. AUC and calibration plot without baseline predictions. A. Receiver operator characteristic (ROC)-curve of external validation of the flare prediction model in DRESS data [9], where baseline predictions are removed. The rationale is that the prediction model cannot truly function as a ‘joint’ model at baseline, as no longitudinal data is available. B: Calibration plot in DRESS-data, excluding baseline predictions. Patients were grouped based on their predicted probability from lowest to highest predicted 3-monthly risk of flare (x-axis) using the median, 25th and 75th percentile. On the y-axis these groups are compared with the observed frequency of flare within 3 months. Perfectly calibrated predictions would be expected to be at the diagonal. AUC: Area Under the Curve

Additional file 2 of Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Additional file 2: Supplementary Figure S2. Calibration plot of flare prediction model including baseline predictions Calibration plot in external DRESS-data [9]. Patients were grouped based on their predicted probability from lowest to highest predicted 3-monthly risk of flare (x-axis) using the median, 25th and 75th percentile. On the y-axis these groups are compared with the observed frequency of flare within 3 months. Perfectly calibrated predictions would be expected to be at the diagonal

Additional file 6 of Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Additional file 6: Supplementary Table S3. Simulation results with and without baseline predictions. 95% confidence intervals are presented between brackets. The results from external validation in the DRESS trial [9] without baseline predictions, for the optimal cutoffpoint of 35% as determined in simulation (see Supplementary Table S2). The rationale for leaving out baseline predictions is that the prediction model cannot truly function as a ‘joint’ model at baseline, as no longitudinal data is available. a. The mean difference in bDMARD dose divided by the mean number of flares compared with the DRESS DGDO arm. The number therefore represents the increase in bDMARD dose that was needed to prevent a flare for this specific tapering strategy. b. The mean difference in the number of flares, divided by the mean difference in bDMARD dose, compared to routine care. The ratio thus represents the number of extra flares that occurred for each extra full dose of bDMARD that is tapered compred to routine care over 18 monhts using this specific tapering strategy. bDMARD: biological disease-modifying antirheumatic drug, DGDO: disease activity guided dose optimisation

Additional file 1 of Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Additional file 1: Supplementary Figure S1. Receiver operating characteristic (ROC) curve in external validation. ROC-curve of the model in external validation in data of the Dose Reduction Strategy of Subcutaneous TNF inhibitors (DRESS) trial [9]