Background: Many patients with severe asthma are overweight or obese, often attributed to unintentional weight gain as a side effect of oral corticosteroids (OCSs). Anti-IL-5/5Ra biologics... Show moreBackground: Many patients with severe asthma are overweight or obese, often attributed to unintentional weight gain as a side effect of oral corticosteroids (OCSs). Anti-IL-5/5Ra biologics significantly reduce OCS use, but their long-term effects on weight are unknown.Objectives: To examine (1) weight change up to 2 years after anti-IL-5/5Ra initiation in subgroups on the basis of maintenance OCS use at start of treatment and (2) whether cumulative OCS exposure before or changes in OCS exposure during treatment are related to weight change.Methods: Real-world data on weight and cumulative OCS dose from adults included in the Dutch Registry of Adult Patients with Severe asthma for Optimal DIsease management before and at least 2 years after starting anti-IL-5/5Ra were analyzed using linear mixed models and linear regression analyses.Results: For the included 389 patients (55% female; mean body mass index, 28 +/- 5 kg/m(2); 58% maintenance OCS), mean weight decreased -0.27 kg/y (95% CI, -0.51 to -0.03; P = .03), with more weight loss in patients with maintenance OCS use than in those without maintenance OCS use (-0.87 kg/y [95% CI, -1.21 to -0.52; P < .001] vs +0.54 kg/y [0.26 to 0.82; P < .001]). Greater weight loss at 2 years was associated with higher cumulative OCS dose in the 2 years before anti-IL-5/5Ra initiation (beta = -0.24 kg/g; 95% CI, -0.38 to -0.10; P < .001) and, independently, greater reduction in cumulative OCS dose during follow-up (beta = 0.27 kg/g; 95% CI, 0.11 to 0.43; P < .001).Conclusions: Anti-IL-5/5Ra therapy is associated with long-term weight reduction, especially in patients with higher OCS exposure before treatment and those able to reduce OCS use during treatment. However, the effect is small and does not apply to all patients, and so additional interventions seem necessary if weight change is desired. Show less
Dijk, W.B. van; Fiolet, A.T.L.; Schuit, E.; Sammani, A.; Groenhof, T.K.J.; Graaf, R. van der; ... ; Mosterd, A. 2021
Objective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of... Show moreObjective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial.Study Design and Setting: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel.Results: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%).Conclusion: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information. (C) 2020 The Authors. Published by Elsevier Inc. Show less
PurposeSystems for magnetic resonance (MR-) guided radiotherapy enable daily MR imaging of cancer patients during treatment, which is of interest for treatment response monitoring and biomarker... Show morePurposeSystems for magnetic resonance (MR-) guided radiotherapy enable daily MR imaging of cancer patients during treatment, which is of interest for treatment response monitoring and biomarker discovery using quantitative MRI (qMRI). Here, the performance of a 1.5 T MR-linac regarding qMRI was assessed on phantoms. Additionally, we show the feasibility of qMRI in a prostate cancer patient on this system for the first time.Materials and methodsFour 1.5 T MR-linac systems from four institutes were included in this study. T1 and T2 relaxation times, and apparent diffusion coefficient (ADC) maps, as well as dynamic contrast enhanced (DCE) images were acquired. Bland–Altman statistics were used, and accuracy, repeatability, and reproducibility were determined.ResultsMedian accuracy for T1 ranged over the four systems from 2.7 to 14.3%, for T2 from 10.4 to 14.1%, and for ADC from 1.9 to 2.7%. For DCE images, the accuracy ranged from 12.8 to 35.8% for a gadolinium concentration of 0.5 mM and deteriorated for higher concentrations. Median short-term repeatability for T1 ranged from 0.6 to 5.1%, for T2 from 0.4 to 1.2%, and for ADC from 1.3 to 2.2%. DCE acquisitions showed a coefficient of variation of 0.1–0.6% in the signal intensity. Long-term repeatability was 1.8% for T1, 1.4% for T2, 1.7% for ADC, and 17.9% for DCE. Reproducibility was 11.2% for T1, 2.9% for T2, 2.2% for ADC, and 18.4% for DCE.ConclusionThese results indicate that qMRI on the Unity MR-linac is feasible, accurate, and repeatable which is promising for treatment response monitoring and treatment plan adaptation based on daily qMRI. Show less
