Natural history and burden of Huntington's disease in the UK: A population-based cohort study.

BACKGROUND: Huntington's disease (HD) is a rare neurodegenerative disease that presents with progressive psychological, cognitive and motor impairment. These diverse symptoms place a high burden on the patient, families and the healthcare systems they rely on. This study aimed to describe the epidemiology and clinical burden in individuals with HD compared with controls from the general population. METHODS: This cohort study utilised data from general practitioner medical records to estimate the prevalence and incidence of HD between January 2000 and December 2018. A cohort of incident HD cases were matched 1:3 to controls from the general population, in whom common clinical diagnoses, medications and healthcare interventions were compared at the time of first recorded diagnosis and at a time close to death. Incidence rates of common diagnoses and mortality were compared with matched controls in the time following HD diagnosis. RESULTS: Prevalence of HD increased between 2000 and 2018, whilst incidence remained stable. Prevalence of psychiatric diagnoses and symptomatic treatments were higher in HD cases than controls. A higher relative risk of psychotic disorders, depression, insomnia, dementia, weight loss, pneumonia and falls was observed in HD cases. Risk of death was >4 times higher in HD, with a median survival of ~12 years from first recorded diagnosis. CONCLUSIONS: This study demonstrates the significant and progressive clinical burden in individuals with HD up to 18 years after first recorded diagnosis.

Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis.

INTRODUCTION: During the COVID-19 pandemic, electronic health record (EHR) data has been used to investigate disease severity and risk factors for severe COVID-19 in people with multiple sclerosis (pwMS). Methodological challenges including sampling bias, and residual confounding should be considered when conducting EHR-based studies. We aimed to address these limitations related to the use of EHR data in order to identify risk factors, including the use of disease modifying therapies (DMTs), associated with hospitalization for COVID-19 amongst pwMS. METHODS: We performed a retrospective cohort study including a sample of 47,051 pwMS using a large US-based EHR and claims linked database. Follow-up started at the beginning of the pandemic, February 20th 2020, and continued until September 30th 2020. COVID-19 diagnosis was determined by the presence of ICD-10 diagnostic code for COVID-19, or a positive diagnostic laboratory test, or an ICD-10 diagnostic code for coronaviruses. We used Cox regression modeling to assess the impact of baseline demographics, MS disease history and pre-existing comorbidities on the risk of hospitalization for COVID-19. Then, we identified 5,169 pwMS using ocrelizumab (OCR) and 3,351 pwMS using dimethyl fumarate (DMF) at baseline, and evaluated the distribution of the identified COVID-19 risk factors between the two groups. Finally, we used Cox regression models, adjusted for the identified confounders, to estimate the risk of hospitalization for COVID-19 in pwMS treated with OCR compared to DMF. RESULTS: Among the pwMS cohort, we identified 799 COVID-19 cases (1.7%) which resulted in 182 hospitalizations for COVID-19 (0.4%). Population differences between the pwMS and COVID-19 cohorts were observed. Statistical modeling identified older age, male gender, African-American race, walking with assistance, non-ambulatory status, severe relapse requiring hospitalization in year prior to baseline, and specific comorbidities to be associated with a higher risk of COVID-19 related-hospitalization. Comparing the COVID-19 risk factors between OCR users and DMF users, MS characteristics including ambulatory status and MS subtype were highly imbalanced, likely arising from key differences in the labelled indications for these therapies. Compared to DMF use, in unadjusted (HR 1.58, 95% CI 0.73 - 3.44), adjusted (HR 1.28, 95% CI 0.58 - 2.83), propensity score weighted (HR 1.25, 95% CI 0.56 - 2.80), and doubly robust models (HR 1.29, 95% CI 0.57 - 2.89), no significantly increased risk of hospitalization for COVID-19 was associated with OCR use. CONCLUSION: We observed significant population differences when comparing all pwMS to COVID-19 cases, as well as significant differences in key confounders between OCR and DMF treated patients. In unadjusted analyses we did not observe a statistically significant higher risk of COVID-19 hospitalization in pwMS treated with OCR compared to DMF, with further attenuation of risk when adjusting for the key confounders. This study re-emphasises the importance to appropriately consider both sampling and confounding bias in EHR-based MS research.

Machine Learning to Predict Tamoxifen Nonadherence Among US Commercially Insured Patients With Metastatic Breast Cancer.

PURPOSE: Adherence to tamoxifen citrate among women diagnosed with metastatic breast cancer can improve survival and minimize recurrence. This study aimed to use real-world data and machine learning (ML) methods to classify tamoxifen nonadherence. METHODS: A cohort of women diagnosed with metastatic breast cancer from 2012 to 2017 were identified from IBM MarketScan Commercial Claims and Encounters and Medicare claims databases. Patients with < 80% proportion of days coverage in the year following treatment initiation were classified as nonadherent. Training and internal validation cohorts were randomly generated (4:1 ratio). Clinical procedures, comorbidity, treatment, and health care encounter features in the year before tamoxifen initiation were used to train logistic regression, boosted logistic regression, random forest, and feedforward neural network models and were internally validated on the basis of area under receiver operating characteristic curve. The most predictive ML approach was evaluated to assess feature importance. RESULTS: A total of 3,022 patients were included with 40% classified as nonadherent. All models had moderate predictive accuracy. Logistic regression (area under receiver operating characteristic 0.64) was interpreted with 94% sensitivity (95% CI, 89 to 92) and 0.31 specificity (95% CI, 29 to 33). The model accurately classified adherence (negative predictive value 89%) but was nondiscriminate for nonadherence (positive predictive value 48%). Variable importance identified top predictive factors, including age ≥ 55 years and pretreatment procedures (lymphatic nuclear medicine, radiation oncology, and arterial surgery). CONCLUSION: ML using baseline administrative data predicts tamoxifen nonadherence. Screening at treatment initiation may support personalized care, improve health outcomes, and minimize cost. Baseline claims may not be sufficient to discriminate adherence. Further validation with enriched longitudinal data may improve model performance.

Huntington's Disease in Israel: A Population-Based Study Using 20 Years of Routinely-Collected Healthcare Data.

BACKGROUND: Huntington's disease (HD) is a rare, genetic, neurodegenerative disease. Obtaining population-level data on epidemiology and disease management is challenging. OBJECTIVE: To investigate the epidemiology, clinical manifestations, treatment, and healthcare utilization of patients with HD in Israel. METHODS: Retrospective population-based cohort study, including 20 years of routinely collected data from Maccabi Healthcare Services, an insurer and healthcare provider for one-quarter of the Israeli population. RESULTS: The study cohort included 109 adult patients (aged ≥18 years) diagnosed with HD, with mean age of 49.9 years and 56%females. The most common HD-related conditions were anxiety (40%), behavioral problems (34%), sleep disorders (21%), and falls (13%). Annual incidence rates for HD ranged from 0.17 to 1.34 per 100,000 from 2000 to 2018; the 2018 crude prevalence in adults was 4.36 per 100,000. Median survival from diagnosis was approximately 12 years (95%CI: 10.4-15.3). The most frequent symptomatic treatments were antidepressants (69%), antipsychotics (63%), and tetrabenazine (63%), the only drug approved for the treatment of HD chorea in Israel during the examined period. Patterns of healthcare utilization changed as disease duration increased, reflected by increased frequency of emergency department visits and home visits. CONCLUSION: This retrospective population-based study provides insights into the prevalence, incidence, clinical profile, survival, and resource utilization of patients with HD in ethnically diverse Israel. The findings in this study are generally consistent with the international literature and demonstrate the value of routinely collected healthcare data as a complementary resource in HD research.

Digital Tumor Board Solutions Have Significant Impact on Case Preparation.

PURPOSE: Multidisciplinary tumor boards (TBs) are the gold standard for decision-making in cancer care. Variability in preparation, conduction, and impact is widely reported. The benefit of digital technologies to support TBs is unknown. This study evaluated the impact of the NAVIFY Tumor Board solution (NTB) on TB preparation time across multiple user groups in 4 cancer categories: breast, GI, head and neck (ie, ear, nose, and throat, or ENT), and hematopathology. METHODS: This prospective study evaluated TB preparation time in multiple phases pre- and post-NTB implementation at an academic health care center. TB preparation times were recorded for multiple weeks using a digital time tracker. RESULTS: Preparation times for 59 breast, 61 GI, 36 ENT, and 71 hematopathology cancer TBs comparing a pre-NTB phase to 3 phases of NTB implementation were evaluated between February 2018 and July 2019. NTB resulted in significant reductions in overall preparation time (30%) across 3 TBs pre-NTB compared with the final post-NTB implementation phase. In the breast TB, NTB reduced overall preparation time by 28%, with a 76% decrease in standard deviation (SD). In the GI TB, a 23% reduction in average preparation time was observed for all users, with a 48% decrease in SD. In the ENT TB, a 33% reduction in average preparation time was observed for all users, with a 73% decrease in SD. The hematopathology TB, which was the cocreation partner and initial adopter of the solution, showed variable results. CONCLUSION: This study showed a significant impact of a digital solution on time preparation for TBs across multiple users and different TBs, reflecting the generalizability of the NTB. Adoption of such a solution could improve the efficiency of TBs and have a direct economic impact on hospitals.