Using clinical registries, administrative data and electronic medical records to improve medication safety and effectiveness in dementia

Cross-Regional Data Initiative for the Assessment and Development of Treatment for Neurological and Mental Disorders

Purpose

To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN).

Methods

An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives.

Results

The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK.

Conclusion

The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.

Using administrative healthcare data to evaluate drug repurposing opportunities for cancer: the possibility of using beta-blockers to treat breast cancer

Introduction: Cancer registries and hospital electronic medical records are commonly used to investigate drug repurposing candidates for cancer. However, administrative data are often more accessible than data from cancer registries and medical records. Therefore, we evaluated if administrative data could be used to evaluate drug repurposing for cancer by conducting an example study on the association between beta-blocker use and breast cancer mortality. Methods: A retrospective cohort study of women aged ≥50 years with incident breast cancer was conducted using a linked dataset with statewide hospital admission data and nationwide medication claims data. Women receiving beta blockers and first-line anti-hypertensives prior to and at diagnosis were compared. Breast cancer molecular subtypes and metastasis status were inferred by algorithms from commonly prescribed breast cancer antineoplastics and hospitalization diagnosis codes, respectively. Subdistribution hazard ratios (sHR) and corresponding 95% confidence intervals (CIs) for breast cancer mortality were estimated using Fine and Gray's competing risk models adjusted for age, Charlson comorbidity index, congestive heart failure, myocardial infraction, molecular subtype, presence of metastasis at diagnosis, and breast cancer surgery. Results: 2,758 women were hospitalized for incident breast cancer. 604 received beta-blockers and 1,387 received first-line antihypertensives. In total, 154 breast cancer deaths were identified over a median follow-up time of 2.7 years. We found no significant association between use of any beta-blocker and breast-cancer mortality (sHR 0.86, 95%CI 0.58-1.28), or when stratified by beta-blocker type (non-selective, sHR 0.42, 95%CI 0.14-1.25; selective, sHR 0.95, 95%CI 0.63-1.43). Results were not significant when stratified by molecular subtypes (e.g., triple negative breast cancer (TNBC), any beta blocker, sHR 0.16, 95%CI 0.02-1.51). Discussion: It is possible to use administrative data to explore drug repurposing opportunities. Although non-significant, an indication of an association was found for the TNBC subtype, which aligns with previous studies using registry data. Future studies with larger sample size, longer follow-up are required to confirm the association, and linkage to clinical data sources are required to validate our methodologies.

Patterns of Antipsychotic Dispensation to Long-Term Care Residents

OBJECTIVES

To describe dispensing patterns of antipsychotic medications to long-term care (LTC) residents and assess factors associated with continuation of an antipsychotic after a fall-related hospitalization.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

Nova Scotia Seniors Pharmacare Program (NSSPP) beneficiaries age 66 years and older who resided in LTC and received at least 1 dispensation of an antipsychotic within the study period of April 1, 2009, to March 31, 2017.

METHODS

Linkage of administrative claims data from the NSSPP and the Canadian Institute of Health Information Discharge Abstract Database identified LTC residents with an antipsychotic dispensation and from the subgroup of those dispensed antipsychotic medications who experienced a fall-related hospitalization. Antipsychotic dispensing patterns were reported with counts and means. Predictors of continuation of an antipsychotic after a fall-related hospitalization (sex, length of stay, days supplied, age, year of admission, rural/urban) were reported and analyzed with multiple logistic regression.

RESULTS

There were 19,164 unique NSSPP beneficiaries who were dispensed at least 1 prescription for an antipsychotic medication. Of those who received at least 1 antipsychotic dispensation 90% (n = 17,201) resided in LTC. A mean of 40% (n = 2637) of LTC residents received at least 1 antipsychotic dispensation in each year. Risperidone and quetiapine were dispensed most frequently. Of the 544 beneficiaries residing in LTC who survived a fall-related hospitalization, 439 (80.7%) continued an antipsychotic after hospital discharge. Female sex [OR 1.7, 95% CI (1.013‒2.943)], age 66‒69 [OR 4.587, 95% CI (1.4‒20.8)], 75-79 [OR 2.8, 95% CI (1.3‒6.3)], and 80‒84 years [OR 3.1, 95% CI (1.6‒6.4)] (compared with age 90+ years) were associated with increased risk of antipsychotic continuation.

CONCLUSIONS AND IMPLICATIONS

With 90% of antipsychotic dispensations in Nova Scotia being to LTC residents and 40% of LTC residents being dispensed antipsychotics in any year there is a need to address this level of antipsychotic dispensation to older adults.

Drug repurposing using real-world data

The use of real-world data in drug repurposing has emerged due to well-established advantages of drug repurposing in supplementing de novo drug discovery and incentives in incorporating real-world evidence in regulatory approvals. We conducted a scoping review to characterize repurposing studies using real-world data and discuss their potential challenges and solutions. A total of 250 studies met the inclusion criteria, of which 36 were original studies on hypothesis generation, 101 on hypothesis validation, and seven on safety assessment. Key challenges that should be addressed for future progress in using real-world data for repurposing include isolated data sources with poor clinical granularity, false-positive signals from data mining, the sensitivity of hypothesis validation to bias and confounding, and the lack of clear regulatory guidance.

Code-Based Algorithms for Identifying Dementia in Electronic Health Records: Bridging the Gap Between Theory and Practice

Code-based algorithms are crucial tools in the detection of dementia using electronic health record data, with broad applications in medical research and healthcare. Vassilaki et al.'s study explores the efficacy of code-based algorithms in dementia detection using electronic health record data, achieving approximately 70% sensitivity and positive predictive value. Despite the promising results, the algorithms fail to detect around 30% of dementia cases, highlighting challenges in distinguishing cognitive decline factors. The study emphasizes the need for algorithmic improvements and further exploration across diverse healthcare systems and populations, serving as a critical step toward bridging gaps in dementia care and understanding.

Parkinson's Disease Case Ascertainment in the Sister Study: A Cohort for Environmental Health Research

BACKGROUND

Large prospective studies are essential for investigating the environmental causes of Parkinson's disease (PD), but PD diagnosis via clinical exams is often infeasible in such studies.

OBJECTIVE

To present case ascertainment strategy and data collection in a US cohort of women.

METHODS

In the Sister Study (n = 50,884, baseline ages 55.6±9.0), physician-made PD diagnoses were first reported by participants or their proxies. Cohort-wide follow-up surveys collected data on subsequent diagnoses, medication usage and PD-relevant motor and nonmotor symptoms. We contacted self-reported PD cases and their treating physicians to obtain relevant diagnostic and treatment history. Diagnostic adjudication was made via expert review of all available data, except nonmotor symptoms. We examined associations of nonmotor symptoms with incident PD, using multivariable logistic regression models and reported odds ratio (OR) and 95% confidence intervals (CI).

RESULTS

Of the 371 potential PD cases identified, 242 diagnoses were confirmed. Compared with unconfirmed cases, confirmed cases were more likely to report PD diagnosis from multiple sources, medication usage, and motor and nonmotor features consistently during the follow-up. PD polygenic risk score was associated with confirmed PD (ORinter-quartile range = 1.74, 95% CI: 1.45-2.10), but not with unconfirmed cases (corresponding OR = 1.05). Hyposmia, dream-enacting behaviors, constipation, depression, unexplained weight loss, dry eyes, dry mouth, and fatigue were significantly related to PD risk, with ORs from 1.71 to 4.88. Only one of the eight negative control symptoms was associated with incident PD.

CONCLUSION

Findings support our PD case ascertainment approach in this large cohort of women. PD prodromal presentation is likely beyond its well-documented profile.

Frailty Status and Cognitive Function Should Guide Prescribing in Long-term Care Facilities

Frailty, dementia and complex multimorbidity are highly prevalent among residents of long-term care facilities (LTCFs). Prescribing for residents of LTCFs is often informed by disease-specific clinical practice guidelines based on research conducted among younger and more robust adults. However, frailty and cognitive impairment may modify medication benefits and risks. Residents with frailty and advanced dementia may be at increased susceptibility to adverse drug events (ADEs) and often have a lower likelihood of achieving long-term therapeutic benefit from chronic preventative medications. For this reason, there is a strong rationale for deprescribing, particularlyamong residents with high medication burdens, swallowing difficulties or limited dexterity. Conversely, frailty and dementia have also been associated with under-prescribing of clinically indicated medications. Unnecessarily withholding treatment based on assumed risk may deprive vulnerable population groups from receiving evidence-based care. There is a need for specific evidence regarding medication benefits and risks in LTCF residents with frailty and dementia. Observational studies conducted using routinely collected health data may complement evidence from randomized controlled trials that often exclude people living with dementia, frailty and in LTCFs. Balancing over- and under-prescribing requires consideration of each resident's frailty and cognitive status, therapeutic goals, time-to-benefit, potential ADEs, and individual values or preferences. Incorporating frailty screening into medication review may also provide better alignment of medication regimens to changing goals of care. Timely identification of frail residents as part of treatment decision-making may assist with targeting interventions to minimize and monitor for ADEs. Shifting away from rigid application of conventional disease-specific clinical practice guidelines may provide an individualized and more holistic assessment of medication benefits and risks in the LTCF setting.

Application of Healthcare 'Big Data' in CNS Drug Research: The Example of the Neurological and mental health Global Epidemiology Network (NeuroGEN)

Neurological and psychiatric (mental health) disorders have a large impact on health burden globally. Cognitive disorders (including dementia) and stroke are leading causes of disability. Mental health disorders, including depression, contribute up to one-third of total years lived with disability. The Neurological and mental health Global Epidemiology Network (NeuroGEN) is an international multi-database network that harnesses administrative and electronic medical records from Australia, Asia, Europe and North America. Using these databases NeuroGEN will investigate medication use and health outcomes in neurological and mental health disorders. A key objective of NeuroGEN is to facilitate high-quality observational studies to address evidence-practice gaps where randomized controlled trials do not provide sufficient information on medication benefits and risks that is specific to vulnerable population groups. International multi-database research facilitates comparisons across geographical areas and jurisdictions, increases statistical power to investigate small subpopulations or rare outcomes, permits early post-approval assessment of safety and effectiveness, and increases generalisability of results. Through bringing together international researchers in pharmacoepidemiology, NeuroGEN has the potential to be paradigm-changing for observational research to inform evidence-based prescribing. The first focus of NeuroGEN will be to address evidence-gaps in the treatment of chronic comorbidities in people with dementia.