ANZACS-QI Heart Failure Registry: a new approach using age-stratified sampling of hospital discharges to guide quality improvement (ANZACS-QI 79).

Heart failure is a major healthcare problem in New Zealand. The Acute Decompensated Heart Failure (ADHF) Registry was introduced in 2015, and has identified the need for quality improvement strategies to improve care of patients hospitalised with heart failure. In this paper, we describe the implementation of the revised ANZACS-QI Heart Failure Registry, which has a primary aim to support evidence-based management of and quality improvement measures for patients who are hospitalised with heart failure in New Zealand. Taking the learnings from the initial experience with the ADHF Registry, the revised ANZACS-QI Heart Failure Registry i) utilises age-stratified sampling of hospital discharge coding to identify a representative heart failure cohort, ii) utilises existing ANZACS-QI infrastructure for data-linkage to reduce the burden of manual data entry, iii) receives governance from the Heart Failure Working Group, and iv) focusses on established quality improvement indicators for heart failure management.

Impact of Different Diagnostic Measures on Drug Class Association with Dementia Progression Risk: A Longitudinal Prospective Cohort Study.

Background: The Clinical Dementia Rating Scale Sum of Boxes (CDRSOB) score is known to be highly indicative of cognitive-functional status and is regularly employed for clinical and research purposes. Objective: Our aim is to determine whether CDRSOB is consistent with clinical diagnosis in evaluating drug class associations with risk of progression to mild cognitive impairment (MCI) and dementia. Methods: We employed weighted Cox regression analysis on longitudinal NACC data, to identify drug classes associated with disease progression risk, using clinical diagnosis and CDRSOB as the outcome. Results: Aspirin (antiplatelet/NSAID), angiotensin II inhibitors (antihypertensive), and Parkinson's disease medications were significantly associated with reduced risk of progression to MCI/dementia and Alzheimer's disease medications were associated with increased MCI-to-Dementia progression risk with both clinical diagnosis and CDRSOB as the outcome. However, certain drug classes/subcategories, like anxiolytics, antiadrenergics, calcium (Ca2+) channel blockers, and diuretics (antihypertensives) were associated with reduced risk of disease progression, and SSRIs (antidepressant) were associated with increased progression risk only with CDRSOB. Additionally, metformin (antidiabetic medication) was associated with reduced MCI-to-Dementia progression risk only with clinical diagnosis as the outcome. Conclusions: Although the magnitude and direction of the effect were primarily similar for both diagnostic outcomes, we demonstrate that choice of diagnostic measure can influence the significance of risk/protection attributed to drug classes and consequently the conclusion of findings. A consensus must be reached within the research community with respect to the most accurate diagnostic outcome to identify risk and improve reproducibility.

2023 position statement on improving management for patients with heart failure in Aotearoa New Zealand.

Heart failure affects 1-3% of the population and remains a major public health problem, with high rates of hospitalisation and mortality. Health inequities in the incidence of heart failure have widened over the last 13 years in Aotearoa New Zealand. Urgent action is required to address the inequitable burden of heart failure among Maori and Pasifika. Regional and international heart failure guidelines now provide clear and consistent guidance on the contemporary approach to management for patients with heart failure. The purpose of this position statement is to ensure that all people in Aotearoa New Zealand have access to optimal healthcare delivery and pharmacotherapy for contemporary management of heart failure. Three main areas are addressed, including: 1) access to evidence-based pharmacotherapy for patients with heart failure, 2) the importance of early initiation and titration of pharmacotherapy, and 3) the workforce required to ensure timely delivery of heart failure therapies. Implementation of evidence-based healthcare will ensure all patients with heart failure in Aotearoa New Zealand have opportunity for substantial improvement in health.

The impact of hearing impairment and hearing aid use on progression to mild cognitive impairment in cognitively healthy adults: An observational cohort study.

INTRODUCTION: We assessed the association of self-reported hearing impairment and hearing aid use with cognitive decline and progression to mild cognitive impairment (MCI). METHODS: We used a large referral-based cohort of 4358 participants obtained from the National Alzheimer's Coordinating Center. The standard covariate-adjusted Cox proportional hazards model, the marginal structural Cox model with inverse probability weighting, standardized Kaplan-Meier curves, and linear mixed-effects models were applied to test the hypotheses. RESULTS: Hearing impairment was associated with increased risk of MCI (standardized hazard ratio [HR] 2.58, 95% confidence interval [CI: 1.73 to 3.84], P = .004) and an accelerated rate of cognitive decline (P < .001). Hearing aid users were less likely to develop MCI than hearing-impaired individuals who did not use a hearing aid (HR 0.47, 95% CI [0.29 to 0.74], P = .001). No difference in risk of MCI was observed between individuals with normal hearing and hearing-impaired adults using hearing aids (HR 0.86, 95% CI [0.56 to 1.34], P = .51). DISCUSSION: Use of hearing aids may help mitigate cognitive decline associated with hearing loss.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Shaping a data-driven era in dementia care pathway through computational neurology approaches.

BACKGROUND: Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. MAIN BODY: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations. CONCLUSION: The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Multi-time-point data preparation robustly reveals MCI and dementia risk factors.

INTRODUCTION: Conflicting results on dementia risk factors have been reported across studies. We hypothesize that variation in data preparation methods may partially contribute to this issue. METHODS: We propose a comprehensive data preparation approach comparing individuals with stable diagnosis over time to those who progress to mild cognitive impairment (MCI)/dementia. This was compared to the often-used "baseline" analysis. Multivariate logistic regression was used to evaluate both methods. RESULTS: The results obtained from sensitivity analyses were consistent with those from our multi-time-point data preparation approach, exhibiting its robustness. Compared to analysis using only baseline data, the number of significant risk factors identified in progression analyses was substantially lower. Additionally, we found that moderate depression increased healthy-to-MCI/dementia risk, while hypertension reduced MCI-to-dementia risk. DISCUSSION: Overall, multi-time-point-based data preparation approaches may pave the way for a better understanding of dementia risk factors, and address some of the reproducibility issues in the field.