Temporarily implanted nitinol device versus prostatic urethral lift for minimally invasive surgical treatment of benign prostatic hyperplasia with lower urinary tract symptoms: a matching-adjusted indirect comparison.

INTRODUCTION: To evaluate the safety and efficacy of the temporarily implanted nitinol device (iTind) versus prostatic urethral lift (PUL) for minimally invasive surgical treatment of lower urinary tract symptoms secondary to benign prostatic hyperplasia in a matching-adjusted indirect comparison (MAIC). MATERIALS AND METHODS: Seven clinical trials were identified via a systematic literature review. Individual patient data from iTind trials and aggregated data from PUL trials were used in the MAIC. Safety and efficacy outcomes at 12 months post-treatment were compared between the adjusted iTind population and the pooled PUL population. RESULTS: iTind patients were significantly less likely than PUL patients to experience treatment-related adverse events within 3 months (25.0% vs. 79.8%; p < 0.001), including dysuria (17.8% vs. 34.7%; p = 0.001), hematuria (12.0% vs. 25.9%; p = 0.002), and pain (9.5% vs. 18.7%; p = 0.023). Rates of treatment-related adverse events from 3 to 12 months were also significantly lower among iTind than PUL patients (2.6% vs. 24.4%; p < 0.001). iTind and PUL efficacy outcomes were statistically equivalent on changes from baseline to 12 months on the International Prostate Symptom Score, quality of life, Qmax, post-void residual volume, and the Sexual Health Inventory for Men (all p > 0.05). CONCLUSIONS: This MAIC found superior safety and reduced risks of early and later treatment-related adverse events with iTind versus PUL. The 12-month efficacy was equivalent on subjective and objective urinary and sexual health metrics. This study finds that the iTind temporary device provides equivalent efficacy with lower adverse event risks versus the PUL permanent implants for patients with benign prostatic hyperplasia with lower urinary tract symptoms.

Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.