Comparative Effectiveness of Carbidopa–Levodopa Enteral Suspension and Deep Brain Stimulation on Parkinson’s Disease-Related Pill Burden Reduction in Advanced Parkinson’s Disease: A Retrospective Real-World Cohort Study

Introduction: In advanced Parkinson’s disease (PD), a high pill burden is associated with poor compliance, reduced control of symptoms, and decreased quality of life. We assessed the impact of carbidopa–levodopa enteral suspension (CLES) and deep brain stimulation (DBS) on PD-related pill burden. Methods: A retrospective cohort analysis was conducted in the IBM MarketScan and Medicare Supplemental databases. Patients with advanced PD, taking only PD medications, and initiating CLES or DBS between 9 January 2015 and 31 July 2019 were identified. CLES patients were matched to DBS patients in a 1:3 ratio based on a propensity score to balance patient characteristics. Pill burden was measured as a 30-day average number of PD-related pills per day and was captured monthly. Pill-free status was evaluated as the percentage of patients receiving CLES or DBS monotherapy. Descriptive statistics were used to compare pill counts and assess the proportion of patients on monotherapy at 6 and 12 months after initiating CLES or DBS. Results: The cohorts included 34 CLES patients matched to 97 DBS patients. A significant reduction in PD-related pill burden was observed at 6 months after initiation of CLES or DBS (∆CLES: −5.62, p < 0.0001; ∆DBS: −1.48, p = 0.0022). PD-related pill burden reduction in CLES patients was significantly greater than in matched DBS patients at 6 months (∆: −4.14, p < 0.0001), which was sustained at 12 months after initiation. At 12 months, nearly three times more CLES patients were pill free than DBS patients (29.41% and 10.31%, respectively, p = 0.0123). Conclusions: Device-aided therapies such as CLES and DBS are effective in significantly reducing PD-related pill burden. Patients treated with CLES were more likely to achieve pill-free status than patients receiving DBS. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

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.Development and application of statistical models for medical scientific researc