The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Effectiveness and safety of opicapone in Parkinson’s disease patients with motor fluctuations: the OPTIPARK open-label study

Background The efficacy and safety of opicapone, a once-daily catechol-O-methyltransferase inhibitor, have been established in two large randomized, placebo-controlled, multinational pivotal trials. Still, clinical evidence from routine practice is needed to complement the data from the pivotal trials. Methods OPTIPARK (NCT02847442) was a prospective, open-label, single-arm trial conducted in Germany and the UK under clinical practice conditions. Patients with Parkinson’s disease and motor fluctuations were treated with opicapone 50 mg for 3 (Germany) or 6 (UK) months in addition to their current levodopa and other antiparkinsonian treatments. The primary endpoint was the Clinician’s Global Impression of Change (CGI-C) after 3 months. Secondary assessments included Patient Global Impressions of Change (PGI-C), the Unified Parkinson’s Disease Rating Scale (UPDRS), Parkinson’s Disease Questionnaire (PDQ-8), and the Non-Motor Symptoms Scale (NMSS). Safety assessments included evaluation of treatment-emergent adverse events (TEAEs) and serious adverse events (SAEs). Results Of the 506 patients enrolled, 495 (97.8%) took at least one dose of opicapone. Of these, 393 (79.4%) patients completed 3 months of treatment. Overall, 71.3 and 76.9% of patients experienced any improvement on CGI-C and PGI-C after 3 months, respectively (full analysis set). At 6 months, for UK subgroup only (n = 95), 85.3% of patients were judged by investigators as improved since commencing treatment. UPDRS scores at 3 months showed statistically significant improvements in activities of daily living during OFF (mean ± SD change from baseline: − 3.0 ± 4.6, p < 0.0001) and motor scores during ON (− 4.6 ± 8.1, p < 0.0001). The mean ± SD improvements of − 3.4 ± 12.8 points for PDQ-8 and -6.8 ± 19.7 points for NMSS were statistically significant versus baseline (both p < 0.0001). Most of TEAEs (94.8% of events) were of mild or moderate intensity. TEAEs considered to be at least possibly related to opicapone were reported for 45.1% of patients, with dyskinesia (11.5%) and dry mouth (6.5%) being the most frequently reported. Serious TEAEs considered at least possibly related to opicapone were reported for 1.4% of patients. Conclusions Opicapone 50 mg was effective and generally well-tolerated in PD patients with motor fluctuations treated in clinical practice. Trial registration Registered in July 2016 at clinicaltrials.gov (NCT02847442)

Evaluation of machine learning methods for covariate data imputation in pharmacometrics

Abstract Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, such as population pharmacokinetic and pharmacodynamic analyses. To this end, various statistical methods have been widely adopted. Here, we introduce two machine‐learning (ML) methods capable of imputing missing covariate data in a pharmacometric setting. Based on a previously published pharmacometric analysis, we simulated multiple missing data scenarios. We compared the performance of four established statistical methods, listwise deletion, mean imputation, standard multiple imputation (hereafter “Norm”), and predictive mean matching (PMM) and two ML based methods, random forest (RF) and artificial neural networks (ANNs), to handle missing covariate data in a statistically plausible manner. The investigated ML‐based methods can be used to impute missing covariate data in a pharmacometric setting. Both traditional imputation approaches and ML‐based methods perform well in the scenarios studied, with some restrictions for individual methods. The three methods exhibiting the best performance in terms of least bias for the investigated scenarios are the statistical method PMM and the two ML‐based methods RF and ANN. ML‐based approaches had comparable good results to the best performing established method PMM. Furthermore, ML methods provide added flexibility when encountering more complex nonlinear relationships, especially when associated parameters are suitably tuned to enhance predictive performance

Efficacy of TNF-Alpha Inhibitors to Control Inflammation and Prevent Secondary Complications in Non-Infectious Uveitis: A Real-Life Experience from Switzerland.

PURPOSE To evaluate the efficacy of systemic tumor necrosis factor-alpha inhibitors (TNFi) in the treatment of non-infectious uveitis (NIU). METHODS This Swiss multicenter retrospective cohort study included patients with NIU requiring TNFi during the period from 2001 to 2018. Risk factors for the occurrence of new complications were identified using Cox regression analysis and hazard ratios (HR). RESULTS Seventy-one patients (126 eyes; mean age 40.6 ± 14.4 years, mean duration of uveitis 46.0 ± 61.8 months) were followed for 40.2 ± 17.3 months after addition of TNFi. Under TNFi, visual acuity improved from 0.2 ± 0.3 to 0.1 ± 0.3 logMAR (p  0.05). In 80.2% of eyes, complications were present at baseline with epiretinal gliosis (39.7%), cataract (41.3%) and macular edema (ME; 27.8%) being the most common. New complications under TNFi were encountered in 49.2% of eyes, also including recurrence (5 eyes) or new onset of ME (14 eyes). The need for switching of TNFi was associated with further complications (HR 3.78, p = 0.012). CONCLUSION Although the efficacy and tolerability of TNFi in a real-life setting are favorable, treatment is often initiated late, i.e., after many eyes have already developed complications. Even with TNFi, new complications, particularly ME, cannot be completely avoided. Further research is needed to assess the impact of earlier initiation of TNFi therapy

Long-term Efficacy of TNF-alpha Inhibitors on Persistent Uveitic Macular Edema: A Swiss Multicenter Cohort Study.

PURPOSE To assess the efficacy of tumor necrosis factor-alpha inhibitors (TNFi) on uveitic macular edema (ME) unresponsive to conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs). METHODS This multicenter retrospective study included patients with uveitic ME persisting despite csDMARDs. The effect of an additional TNFi on central retinal thickness (CRT), best corrected visual acuity (BCVA) and corticosteroid need was evaluated. RESULTS Thirty-five eyes (26 patients, mean age 42.9 ± 15.2 years) were included. CRT decreased from 425 ± 137 µm to 294 ± 66 µm (p < .001) and 280 ± 48 µm (p < .001) at 1 and 4 years of follow-up, respectively. BCVA improved from 0.28 ± 0.22 to 0.21 ± 0.48 (1 year, p = .013) and 0.08 ± 0.13 logMAR (4 years, p = .002). The proportion of patients requiring systemic corticosteroids decreased from 88.5% to 34.8% (1 year) and 15.4% (4 years). CONCLUSION The addition of a TNFi resulted in an improvement of CRT and BCVA for up to 4 years in uveitic ME but rescue treatments were needed for some patients