Long-term results of carbidopa/levodopa enteral suspension across the day in advanced Parkinson’s disease: Post-hoc analyses from a large 54-week trial

Introduction: Carbidopa/levodopa enteral suspension (CLES) previously demonstrated reduction in total daily OFF from baseline by over 4 hours in advanced Parkinson’s disease patients across 54 weeks. Evidence on CLES’s long-term effectiveness on patterns of motor-symptom control throughout the day remains limited. Methods: We present post-hoc analyses of a large, open-label study of CLES monotherapy (N = 289). Diary data recorded patients’ motor states at 30-minute intervals over 3 days at baseline and weeks 4, 12, 24, 36, and 54. Adjusted generalized linear mixed models assessed changes from baseline at each timepoint for four outcome measures: time to ON without troublesome dyskinesia (ON-woTD) after waking, motor-symptom control as measured by motor states’ durations throughout the day, number of motor-state transitions, and presence of extreme fluctuations (OFF to ON with TD). Results: Patients demonstrated short-term (wk4) and sustained (wk54) improvement in all outcomes compared to baseline. At weeks 4 and 54, patients were more likely to reach ON-woTD over the course of their day (HR: 1.86 and 2.51, both P < 0.0001). Across 4-hour intervals throughout the day, patients also experienced increases in ON-woTD (wk4: 58–65 min; wk54: 60–78 min; all P < 0.0001) and reductions in OFF (wk4: 50–61 min; wk54: 56–68 min; all P < 0.0001). At weeks 4 and 54, patients’ motor-state transitions were reduced by about half (IRR: 0.53 and 0.49, both P < 0.0001), and fewer patients experienced extreme fluctuations (OR: 0.22 and 0.15, both P < 0.0001). Conclusion: CLES monotherapy was associated with significant long-term reductions in motor-state fluctuations, faster time to ON-woTD upon awakening, and increased symptom control throughout the day

Comparative Effectiveness of Device-Aided Therapies on Quality of Life and Off-Time in Advanced Parkinson’s Disease : A Systematic Review and Bayesian Network Meta-analysis

Introduction: Research comparing levodopa/carbidopa intestinal gel (LCIG), deep brain stimulation (DBS), and continuous subcutaneous apomorphine infusion (CSAI) for advanced Parkinson’s disease (PD) is lacking. This network meta-analysis (NMA) assessed the comparative effectiveness of LCIG, DBS, CSAI and best medical therapy (BMT) in reducing off-time and improving quality of life (QoL) in patients with advanced PD. Methods: A systematic literature review was conducted for randomized controlled trials (RCTs), observational and interventional studies from January 2003 to September 2019. Data extracted at baseline and 6 months were off-time, as reported by diary or Unified Parkinson’s Disease Rating Scale Part IV item 39, and QoL, as reported by Parkinson’s Disease Questionnaire (PDQ-39/PDQ-8). Bayesian NMA was performed to estimate pooled treatment effect sizes and to rank treatments in order of effectiveness. Results: A total of 22 studies fulfilled the inclusion criteria (n = 2063 patients): four RCTs, and 16 single-armed, one 2-armed and one 3-armed prospective studies. Baseline mean age was between 55.5–70.9 years, duration of PD was 9.1–15.3 years, off-time ranged from 5.4 to 8.7 h/day in 9 studies, and PDQ scores ranged from 28.8 to 67.0 in 19 studies. Levodopa/carbidopa intestinal gel and DBS demonstrated significantly greater improvement in off-time and QoL at 6 months compared with CSAI and BMT (p < 0.05). There was no significant difference in the effects of LCIG and DBS, but DBS was ranked first for reduction in off-time, and LCIG was ranked first for improvement in QoL. Conclusions: This NMA found that LCIG and DBS were associated with superior improvement in off-time and PD-related QoL compared with CSAI and BMT at 6 months after treatment initiation. This comparative effectiveness research may assist providers, patients, and caregivers in the selection of the optimal device-aided therapy

Does the 5–2-1 criteria identify patients with advanced Parkinson's disease? Real-world screening accuracy and burden of 5–2-1-positive patients in 7 countries

Background: The burden of Parkinson’s disease (PD) worsens with disease progression. However, the lack of objective and uniform disease classification challenges our understanding of the incremental burden in patients with advanced Parkinson’s disease (APD) and suboptimal medication control. The 5–2-1 criteria was proposed by clinical consensus to identify patients with advancing PD. Our objective was to evaluate the screening accuracy and incremental clinical burden, healthcare resource utilization (HCRU), and humanistic burden in PD patients meeting the 5–2-1 screening criteria. Methods: Data were drawn from the Adelphi Parkinson’s Disease Specific Program (DSP™), a multi-country point-in-time survey (2017–2020). People with PD who were naive to device-aided therapy and on oral PD therapy were included. Patients meeting the 5–2-1 screening criteria had one or more of the three clinical indicators of APD: (i) ≥5 doses of oral levodopa/day, OR (ii) “off” symptoms for ≥2 h of waking day, OR (iii) ≥1 h of troublesome dyskinesia. Clinician assessment of PD stage was used as the reference in this study. Clinical screening accuracy of the 5–2-1 criteria was assessed using area under the curve and multivariable logistic regression models. Incremental clinical, HCRU, and humanistic burden were assessed by known-group comparisons between 5 and 2-1-positive and negative patients. Results: From the analytic sample (n = 4714), 33% of patients met the 5–2-1 screening criteria. Among physician-classified APD patients, 78.6% were 5–2-1 positive. Concordance between clinician judgment and 5–2-1 screening criteria was > 75%. 5–2-1-positive patients were nearly 7-times more likely to be classified as APD by physician judgment. Compared with the 5–2-1-negative group, 5–2-1-positive patients had significantly higher clinical, HCRU, and humanistic burden across all measures. In particular, 5–2-1-positive patients had 3.8-times more falls, 3.6-times higher annual hospitalization rate, and 3.4-times greater dissatisfaction with PD treatment. 5–2-1-positive patients also had significantly lower quality of life and worse caregiver burden. Conclusions: 5–2-1 criteria demonstrated potential as a screening tool for identifying people with APD with considerable clinical, humanistic, and HCRU burden. The 5–2-1 screening criteria is an objective and reliable tool that may aid the timely identification and treatment optimization of patients inadequately controlled on oral PD medications

Psychometric Properties of Clinical Indicators for Identification and Management of Advanced Parkinson’s Disease : Real-World Evidence From G7 Countries

Introduction: Standardized and validated criteria to define advanced Parkinson’s disease (PD) or identify patient eligibility for device-aided therapy are needed. This study assessed the psychometric properties of clinical indicators of advanced PD and eligibility for device-aided therapy in a large population. Methods: This retrospective analysis of the Adelphi Parkinson’s Disease Specific Programme collected data from device-aided therapy-naïve people with PD in G7 countries. We assessed the presence of 15 clinical indicators of advancing PD and seven indicators of eligibility for device-aided therapy in patients classified with advanced PD or as eligible for device-aided therapy by the treating physician. Accuracy was assessed using area under the curve (AUC) and multivariable logistic regression models. Construct validity was examined via known-group comparisons of disease severity and burden among patients with and without each clinical indicator. Results: Of 4714 PD patients, 14.9% were classified with advanced PD and 17.5% as eligible for device-aided therapy by physician judgment. The presence of each clinical indicator was 1.9- to 7.3-fold more likely in patients classified with advanced PD. Similarly, the presence of device-aided therapy eligibility indicators was 1.8- to 5.5-fold more likely in patients considered eligible for device-aided therapy. All indicators demonstrated high clinical screening accuracy for identifying advanced PD (AUC range 0.84–0.89) and patients eligible for device-aided therapy (AUC range 0.73–0.80). The Unified Parkinson’s Disease Rating Scale (UPDRS) score, cognitive function, quality of life, and caregiver burden were significantly worse in indicator-positive patients. Conclusion: Specific clinical indicators of advanced PD and eligibility for device-aided therapy demonstrated excellent psychometric properties in a large sample, and thus may provide an objective and reliable approach for patient identification and treatment optimization

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

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

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