ABSTRACT
Objective: Monoclonal antibody (Mab) treatments have significantly improved the quality and quantity of life, but they are some of the most expensive treatments, resulting in a degree of hesitancy to introduce new Mab agents. A system for estimating the effect of Mab drugs, in general, would optimally inform health strategy and fully realize how a single scientific discovery can deliver health benefits. We evaluated such a method with several well-established Mab regimens. Methods: We selected five different Mab regimens in oncology and rheumatology in England. We carried out two systematic literature reviews and meta-analyses to assess health outcomes (Health Assessment Questionnaire-Disability Index for rheumatoid arthritis; overall mortality for melanoma) from real-world data and compared them to the outcomes from randomized control trials (RCTs). We applied economic modeling to estimate the net monetary benefits for health outcomes for the estimated patient population size for each Mab regimen. Results: Meta-analyses of 27 eligible real-world data (RWD) sets and 26 randomized controlled trial (RCT) sets found close agreement between the observed and expected health outcomes. A Markov model showed the net positive monetary benefit in three Mab regimens and the negative benefit in two regimens. However, because of limited access to NHS data, the economic model made several assumptions about the number of treated patients and the cost of treatment to the NHS, the accuracy of which may affect the estimation of the net monetary benefit. Conclusion: RCT results reliably inform the real-world experience of Mab treatments. Calculation of the net monetary benefit by the algorithm described provides a valuable overall measure of the health impact, subject to the accuracy of data inputs. This study provides a compelling case for building a comprehensive, systematized, and accessible database and related analytics, on all Mab treatments within health services.
ABSTRACT
OBJECTIVE: To identify predictors of admission following emergency department (ED) attendances for gout flares and to describe barriers to optimal inpatient gout care. METHODS: ED attendances and hospital admissions with primary diagnoses of gout were analyzed at 2 UK-based hospitals between January 1, 2017, and December 31, 2020. Demographic and clinical predictors of ED disposition (admission or discharge) and reattendance for gout flares were identified using logistic regression and survival models, respectively. Case note reviews (n = 59), stakeholder meetings, and process mapping were performed to capture detailed information on gout management and to identify strategies to optimize care. RESULTS: Of 1220 emergency attendances for gout flares, 23.5% required hospitalization (median length of stay: 3.6 days). Recurrent attendances for flares occurred in 10.4% of patients during the study period. In multivariate logistic regression models, significant predictors of admission from ED were older age, overnight ED arrival time, higher serum urate (SU), higher C-reactive protein, and higher total white cell count at presentation. Detailed case note reviews showed that only 22.6% of patients with preexisting gout were receiving urate-lowering therapy (ULT) at presentation. Initial diagnostic uncertainty was common, yet rheumatology input and synovial aspirates were rarely obtained. By 6 months postdischarge, 43.6% were receiving ULT; however, few patients had treat-to-target dose optimization, and only 9.1% achieved SU levels ≤ 360 µmol/L. CONCLUSION: We identified multiple predictors of hospitalization for acute gout. Treat-to-target optimization of ULT following hospitalization remains inadequate and must be improved if admissions are to be prevented.
