The Cost Effectiveness of Levodopa-Carbidopa Intestinal Gel in the Treatment of Advanced Parkinson’s Disease in England

Background: Parkinson’s disease is a progressive neurodegenerative disease, which significantly impacts patients’ quality of life and is associated with high treatment and direct healthcare costs. In England, levodopa/carbidopa intestinal gel (LCIG) is indicated for the treatment of levodopa-responsive advanced Parkinson’s disease with troublesome motor fluctuations when available combinations of medicinal products are unsatisfactory. Objective: We aimed to determine the cost effectiveness of LCIG compared to the standard of care for patients with advanced Parkinson’s disease in England, using real-world data. Methods: A Markov model was adapted from previous published studies, using the perspective of the English National Health System and Personal and Social Services to evaluate the cost effectiveness of LCIG compared to standard of care in patients with advanced Parkinson’s disease over a 20-year time horizon. The model comprised 25 health states, defined by a combination of the Hoehn and Yahr scale, and waking time spent in OFF-time. The base case considered an initial cohort of patients with an Hoehn and Yahr score of ≥ 3, and > 4 h OFF-time. Standard of care comprised standard oral therapies, and a proportion of patients were assumed to be treated with subcutaneous apomorphine infusion or injection in addition to oral therapies. Efficacy inputs were based on LCIG clinical trials where possible. Resource use and utility values were based on results of a large-scale observational study, and costs were derived from the latest published UK data, valued at 2017 prices. The EuroQol five-dimensions-3-level (EQ-5D-3L) instrument was used to measure utilities. Costs and quality-adjusted life-years were discounted at 3.5%. Both deterministic and probabilistic sensitivity analyses were conducted. Results: Total costs and quality-adjusted life-years gained for LCIG vs standard of care were £586,832 vs £554,022, and 2.82 vs 1.43, respectively. The incremental cost-effectiveness ratio for LCIG compared to standard of care was £23,649/quality-adjusted life-year. Results were sensitive to the healthcare resource utilisation based on real-world data, and long-term efficacy of LCIG. Conclusions: The base-case incremental cost-effectiveness ratio was estimated to be within the acceptable thresholds for cost effectiveness considered for England

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use