Survival of medical treatment success in primary open-angle glaucoma and ocular hypertension.

BACKGROUND/AIMS: Topical agents to lower intraocular pressure (IOP) are the most common initial therapeutic measure in glaucoma prevention. This study aims to assess treatment success duration among patients initiating or intensifying topical glaucoma medication. METHODS: Medical records (2013‒2018) for adults initiating/intensifying topical glaucoma medication were extracted from five secondary-care and tertiary-care UK ophthalmology centres. Main study outcomes were time from treatment initiation/intensification to treatment failure (<20% IOP reduction or IOP >21 mm Hg at consecutive clinic visits, or intensification of glaucoma treatment) and time from treatment change to subsequent treatment intensification. RESULTS: Study eyes (n=6587) underwent treatment intensification 0-to-1 glaucoma drop (5358 events), 1-to-2 drops (1469 events) and 2-to-3 drops (857 events) during the observation period. Median time to treatment failure was 1.60 (95% CI 1.57 to 1.65), 1.00 (95% CI 0.94 to 1.07) and 0.92 (95% CI 0.81 to 1.02) years following escalation 0-to-1, 1-to-2 and 2-to-3 drops, respectively. Median time to treatment intensification (non-IOP-based criterion) was 4.68 (95% CI 4.50 to 5.08) years for treatment initiators, 3.83 (95% CI 3.36 to 4.08) years on escalation 1-to-2 drops and 4.35 (95% CI 3.82 to 4.88) years on escalation 2-to-3 drops. On multivariable regression, significant risk factors for both treatment failure and intensification were lower baseline visual field mean deviation, primary open-angle glaucoma and lower eyedrop count in the fellow eye; lower baseline IOP was associated with treatment failure, higher baseline IOP with treatment intensification. CONCLUSION: Large-scale survival analyses provide the expected duration of treatment success from topical glaucoma medication.

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study.

BACKGROUND: Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. OBJECTIVE: The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. METHODS: An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. RESULTS: The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). CONCLUSIONS: The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.