Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Difficult-to-treat primary immune thrombocytopenia in adults: Prevalence and burden. Results from the CARMEN-France registry.

The aim of this study was to assess the prevalence and the burden of difficult-to-treat primary ITP (pITP), defined by the need for another ITP treatment after romiplostim and eltrombopag. Adult patients were selected in the prospective, real-world CARMEN-France registry up to December 2021. Out of 821 adult patients with pITP, 29 had difficult-to-treat ITP (3.5%; 95% confidence interval [CI]: 2.3%-4.8% in total; 7.6%; 95% CI: 4.9%-10.2% of patients needing ≥2nd line treatment). The 3-year cumulative incidence of bleeding, infection and thrombosis was 100%, 24.1% and 13.8% respectively. The median cumulative duration of hospital stays was 31 days (median follow-up: 30.3 months).

Long-term sutimlimab improves quality of life for patients with cold agglutinin disease: CARDINAL 2-year follow-up.

Cold agglutinin disease (CAD) is a rare form of autoimmune hemolytic anemia with a substantial burden on patient's quality of life. CARDINAL was a 2-part, open-label, single-arm, multicenter phase 3 study evaluating the C1s inhibitor, sutimlimab, for treatment of CAD. Part A consisted of the pivotal study phase, with the part B extension phase assessing long-term safety and durability of response including patient-reported outcomes, which is the focus of this report. Altogether, 22 patients continued from part A to part B, majority female (68.2%) with a median age of 71.5 years (range, 55-85). Throughout treatment, score improvement on the Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue scale exceeded a predefined, group-level clinically important change of ≥5 points vs baseline, with a mean (standard error [SE]) change of 11.7 (3.7) points at week 135. The 12-Item Short Form Health Survey physical and mental component scores remained above baseline, with week 123 mean change (SE) exceeding clinically important changes of 3.9 for physical and 2.8 for mental component scores at 4.7 (2.8) and 3.8 (5.7) points, respectively. EuroQol Visual Analogue Scale, scoring patients' self-rated health, also remained above baseline with a change of 17.1 (5.6) points at week 135. Patient Global Impression of (fatigue) Severity improved vs baseline, corroborating FACIT-Fatigue scores. Patient Global Impression of Change indicated a reduction in perceived disease burden. Data from CARDINAL part B support sustained alleviation of CAD disease burden after long-term treatment with sutimlimab over 2 years, returning toward baseline upon treatment cessation. This trial was registered at www.clinicaltrials.gov as #NCT03347396.

Accelerating diagnosis of Parkinson's disease through risk prediction.

BACKGROUND: Characterization of prediagnostic Parkinson's Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. METHODS: We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. RESULTS: We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. CONCLUSIONS: Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Health Assessment Questionnaire-Disability Index (HAQ-DI) use in modelling disease progression in diffuse cutaneous systemic sclerosis: an analysis from the EUSTAR database.

BACKGROUND: Patients with diffuse cutaneous systemic sclerosis (dcSSc) have a poor prognosis. The importance of monitoring subjective measures of functioning and disability, such as the Health Assessment Questionnaire-Disability Index (HAQ-DI), is important as dcSSc is rated by patients as worse than diabetes or hemodialysis for quality of life impairment. This European Scleroderma Trials and Research (EUSTAR) database analysis was undertaken to examine the importance of impaired functionality in dcSSc prognosis. The primary objectives were to identify predictors of death and HAQ-DI score progression over 1 year. HAQ-DI score, major advanced organ involvement, and death rate were also used to develop a comprehensive model to predict lifetime dcSSc progression. METHODS: This was an observational, longitudinal study in patients with dcSSc registered in EUSTAR. Death and HAQ-DI scores were, respectively, analyzed by Cox regression and linear regression analyses in relation to baseline covariates. A microsimulation Markov model was developed to estimate/predict natural progression of dcSSc over a patient's lifetime. RESULTS: The analysis included dcSSc patients with (N = 690) and without (N = 4132) HAQ-DI score assessments from the EUSTAR database. Baseline HAQ-DI score, corticosteroid treatment, and major advanced organ involvement were predictive of death on multivariable analysis; a 1-point increase in baseline HAQ-DI score multiplied the risk of death by 2.7 (p <  0.001) and multiple advanced major organ involvement multiplied the risk of death by 2.8 (p <  0.05). Multivariable analysis showed that baseline modified Rodnan Skin Score (mRSS) and baseline HAQ-DI score were associated with HAQ-DI score progression at 1 year (p <  0.05), but there was no association between baseline organ involvement and HAQ-DI score progression at 1 year. HAQ-DI score, major advanced organ involvement, and death were successfully used to model long-term disease progression in dcSSc. CONCLUSIONS: HAQ-DI score and major advanced organ involvement were comparable predictors of mortality risk in dcSSc. Baseline mRSS and baseline HAQ-DI score were predictive of HAQ-DI score progression at 1 year, indicating a correlation between these endpoints in monitoring disease progression. It is hoped that this EUSTAR analysis may change physician perception about the importance of the HAQ-DI score in dcSSc.

Outcomes Associated with Dronedarone Use in Patients with Atrial Fibrillation.

The antiarrhythmic drug dronedarone was designed to reduce the extra-cardiac adverse effects associated with amiodarone use in treatment of patients with atrial fibrillation / atrial flutter (AF/AFL). This epidemiological study used a retrospective cohort design to compare risk of cardiovascular-related hospitalizations and death in AF/AFL patients treated with dronedarone versus other antiarrhythmic drugs (AADs). AF/AFL patients with incident dronedarone fills were matched by propensity score (PS) to incident users of other AADs. The primary study outcome was hospitalization for cardiovascular (CV) causes within 24 months after the first study drug fill. A secondary composite outcome comprised hospitalization for CV causes or all-cause mortality during follow-up. In the AF/AFL patient cohort meeting eligibility criteria, 6,964 incident users of dronedarone and 25 607 incident users of other AADs were identified. The PS-matched cohort comprised 6,349 Dronedarone users (91.2% of all eligible) and 12,698 other AAD users. Dronedarone patients had a significantly lower risk of hospitalization for a CV event compared to Other AAD users (hazard ratio = 0.87; 95% confidence interval = 0.79 to 0.96). This was consistent with results for the composite outcome (hazard ratio=0.86; 95% confidence interval = 0.78 to 0.95). In conclusion, AF/AFL patients initiated on dronedarone versus other AADs had significantly lower risk of CV hospitalizations as well as the composite CV hospitalization / death from any cause.

Measuring treatment satisfaction in MS: Is the Treatment Satisfaction Questionnaire for Medication fit for purpose?

BACKGROUND: The Treatment Satisfaction Questionnaire for Medication (TSQM) was designed to assess patient treatment satisfaction in chronic diseases. Its performance has not been examined in multiple sclerosis (MS). The 14 items of the TSQM cover four domains: Effectiveness, Side Effects, Convenience, and Global Satisfaction. OBJECTIVE: To evaluate performance of the TSQM in patients with relapsing MS, using data collected from the TENERE study (NCT00883337), in which 324 patients received oral teriflunomide or subcutaneous interferon beta-1a for ⩾48 weeks. METHODS: Five measurement properties were examined using traditional psychometric methods: data completeness, scale-to-sample targeting, scaling assumptions, reliability (including test-retest), and construct validity (internal: item-level scaling success, confirmatory factor analysis, and exploratory factor analysis; external: convergence, discrimination, and group differences). RESULTS: There were few (<2%) missing item data; domain scores could be computed for all patients. Score distributions were skewed toward higher satisfaction; two domains had marked ceiling effects. Scaling assumptions were supported. Internal consistency reliability was high (Cronbach's α > 0.90). Internal validity tests supported item groupings. Correlations supported convergent and discriminant construct validity; hypothesis testing supported group differences validity. CONCLUSION: This investigation found the TSQM to be a useful tool, exhibiting good psychometric measurement properties in patients with relapsing MS in the TENERE study.

Multiple sclerosis relapses are associated with increased fatigue and reduced health-related quality of life - A post hoc analysis of the TEMSO and TOWER studies.

BACKGROUND: Two pivotal phase 3 teriflunomide studies provided data on relapses, fatigue, and health-related quality of life (HRQoL) in patients with relapsing forms of multiple sclerosis (MS). OBJECTIVES: Using pooled data from the TEMSO (NCT00134563) and TOWER (NCT00751881) studies, we investigated the association between relapse severity, and changes from baseline to Week 108 in fatigue and HRQoL outcomes. METHODS: Four definitions of relapse severity were applied in this analysis: sequelae post-relapse; relapse leading to hospitalization; relapse requiring intravenous corticosteroids; and intense relapse. We assessed the association between relapse severity and changes in Fatigue Impact Scale score (n=959), physical and mental health component summary scores from the Short Form (SF)-36 questionnaire (n=904), and SF-6D utility index scores (n=820). RESULTS: Irrespective of the definition of relapse severity applied, in patients experiencing severe relapse(s), fatigue was increased and HRQoL was decreased; these changes were statistically significant (p<0.0001), and were also clinically significant in many cases. The greatest worsening in fatigue and HRQoL was observed in patients with relapses leading to hospitalization. CONCLUSIONS: Given that severe relapses adversely affect patient-reported fatigue and HRQoL, prevention of severe relapses should be an important therapeutic aim in the treatment of patients with MS.

Teriflunomide reduces relapses with sequelae and relapses leading to hospitalizations: results from the TOWER study.

Teriflunomide is a once-daily oral immunomodulator approved for the treatment of relapsing-remitting multiple sclerosis. This post hoc analysis of the Phase III TOWER study evaluated the effects of teriflunomide treatment on five severe relapse outcomes: relapses with sequelae defined by an increase in Expanded Disability Status Scale (EDSS)/functional system (FS) score (sequelae-EDSS/FS) 30 days post relapse; relapses with sequelae defined by the investigator (sequelae-investigator); relapses leading to hospitalization; relapses treated with intravenous corticosteroids; and intense relapses using the definition of Panitch et al. from the EVIDENCE study based on specified increases in EDSS for severe relapses. Adjusted annualized rates for the five severe relapse outcomes were derived using a Poisson model with robust error variance, with treatment, baseline EDSS strata and region as covariates. Compared with placebo, teriflunomide significantly reduced annualized rates of relapses with sequelae-EDSS/FS [14 mg, 36.6 % (p = 0.0021); 7 mg, 31.3 % (p = 0.0104)] and sequelae-investigator [14 mg only, 53.5 % (p = 0.0004)], relapses leading to hospitalization [14 mg only, 33.6 % (p = 0.0155)], relapses requiring intravenous corticosteroids [14 mg, 35.7 % (p = 0.0002); 7 mg, 21.5 % (p = 0.0337)], and intense relapses [14 mg only, 52.5 % (p = 0.0015)]. Patients treated with teriflunomide 14 mg spent significantly fewer nights in hospital for relapse (p = 0.009) and had lower annualized rates of all hospitalizations (p = 0.030). Taken together, the positive effects of teriflunomide on severe relapses indicate that teriflunomide may reduce relapse-related healthcare costs.