Association of semaglutide with reduced incidence and relapse of cannabis use disorder in real-world populations: a retrospective cohort study.

Cannabis is the most frequently used illicit drug in the United States with more than 45 million users of whom one-third suffer from a cannabis use disorder (CUD). Despite its high prevalence, there are currently no FDA-approved medications for CUD. Patients treated with semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA) approved for treating type 2 diabetes (T2D) and for weight management have reported reduced desire to drink and smoke. Preclinical studies have shown that semaglutide decreased nicotine and alcohol consumption. Preclinical and preliminary clinical evidence of semaglutide's potential beneficial effects on various substance use disorders led us to evaluate if it pertained to CUD. In this retrospective cohort study of electronic health records (EHRs) from the TriNetX Analytics Network, a global federated health research network of approximately 105.3 million patients from 61 large healthcare organizations in the US, we aimed to assess the associations of semaglutide with both incident and recurrent CUD diagnosis compared to non-GLP-1RA anti-obesity or anti-diabetes medications. Hazard ratio (HR) and 95% confidence intervals (CI) of incident and recurrent CUD were calculated for 12-month follow-up by comparing propensity-score matched patient cohorts. The study population included 85,223 patients with obesity who were prescribed semaglutide or non-GLP-1RA anti-obesity medications, with the findings replicated in 596,045 patients with T2D. In patients with obesity (mean age 51.3 years, 65.6% women), semaglutide compared with non-GLP-1RA anti-obesity medications was associated with lower risk for incident CUD in patients with no prior history CUD (HR: 0.56, 95% CI: 0.42-0.75), and recurrent CUD diagnosis in patients with a prior history CUD (HR: 0.62, 95% CI: 0.46-0.84). Consistent reductions were seen for patients stratified by gender, age group, race and in patients with and without T2D. Similar findings were replicated in the study population with T2D when comparing semaglutide with non-GLP-1RA anti-diabetes medications for incident CUD (HR: 0.40, 95% CI: 0.29-0.56) and recurrent CUD (HR: 0.66, 95% CI: 0.42-1.03). While these findings provide preliminary evidence of the potential benefit of semaglutide in CUD in real-world populations, further preclinical studies are warranted to understand the underlying mechanism and randomized clinical trials are needed to support its use clinically for CUD.

Association of Semaglutide With Tobacco Use Disorder in Patients With Type 2 Diabetes : Target Trial Emulation Using Real-World Data.

BACKGROUND: Reports of reduced desire to smoke in patients treated with semaglutide, a glucagon-like peptide receptor agonist (GLP-1RA) medication for type 2 diabetes mellitus (T2DM) and obesity, have raised interest about its potential benefit for tobacco use disorders (TUDs). OBJECTIVE: To examine the association of semaglutide with TUD-related health care measures in patients with comorbid T2DM and TUD. DESIGN: Emulation target trial based on a nationwide population-based database of patient electronic health records. SETTING: United States, 1 December 2017 to 31 March 2023. PARTICIPANTS: Seven target trials were emulated among eligible patients with comorbid T2DM and TUD by comparing the new use of semaglutide versus 7 other antidiabetes medications (insulins, metformin, dipeptidyl-peptidase-4 inhibitors, sodium-glucose cotransporter-2 inhibitors, sulfonylureas, thiazolidinediones, and other GLP-1RAs). MEASUREMENTS: The TUD-related health care measures (medical encounter for diagnosis of TUD, smoking cessation medication prescriptions, and smoking cessation counseling) that occurred within a 12-month follow-up were examined using Cox proportional hazards and Kaplan-Meier survival analyses. RESULTS: The study compared 222 942 new users of antidiabetes medications including 5967 of semaglutide. Semaglutide was associated with a significantly lower risk for medical encounters for TUD diagnosis compared with other antidiabetes medications, and was strongest compared with insulins (hazard ratio [HR], 0.68 [95% CI, 0.63 to 0.74]) and weakest but statistically significant compared with other GLP-1RAs (HR, 0.88 [CI, 0.81 to 0.96]). Semaglutide was associated with reduced smoking cessation medication prescriptions and counseling. Similar findings were observed in patients with and without a diagnosis of obesity. For most of the group comparisons, the differences occurred within 30 days of prescription initiation. LIMITATION: Documentation bias, residual confounding, missing data on current smoking behavior, body mass index, and medication adherence. CONCLUSION: Semaglutide was associated with lower risks for TUD-related health care measures in patients with comorbid T2DM and TUD compared with other antidiabetes medications including other GLP-1Ras, primarily within 30 days of prescription. These findings suggest the need for clinical trials to evaluate semaglutide's potential for TUD treatment. PRIMARY FUNDING SOURCE: National Institutes of Health.

Gastric Alimetry Expands Patient Phenotyping in Gastroduodenal Disorders Compared with Gastric Emptying Scintigraphy.

INTRODUCTION: Gastric emptying testing (GET) assesses gastric motility, however, is nonspecific and insensitive for neuromuscular disorders. Gastric Alimetry (GA) is a new medical device combining noninvasive gastric electrophysiological mapping and validated symptom profiling. This study assessed patient-specific phenotyping using GA compared with GET. METHODS: Patients with chronic gastroduodenal symptoms underwent simultaneous GET and GA, comprising a 30-minute baseline, 99m TC-labelled egg meal, and 4-hour postprandial recording. Results were referenced to normative ranges. Symptoms were profiled in the validated GA App and phenotyped using rule-based criteria based on their relationships to the meal and gastric activity: (i) sensorimotor, (ii) continuous, and (iii) other. RESULTS: Seventy-five patients were assessed, 77% female. Motility abnormality detection rates were as follows: GET 22.7% (14 delayed, 3 rapid), GA spectral analysis 33.3% (14 low rhythm stability/low amplitude, 5 high amplitude, and 6 abnormal frequency), and combined yield 42.7%. In patients with normal spectral analysis, GA symptom phenotypes included sensorimotor 17% (where symptoms strongly paired with gastric amplitude, median r = 0.61), continuous 30%, and other 53%. GA phenotypes showed superior correlations with Gastroparesis Cardinal Symptom Index, Patient Assessment of Upper Gastrointestinal Symptom Severity Index, and anxiety scales, whereas Rome IV Criteria did not correlate with psychometric scores ( P > 0.05). Delayed emptying was not predictive of specific GA phenotypes. DISCUSSION: GA improves patient phenotyping in chronic gastroduodenal disorders in the presence and absence of motility abnormalities with increased correlation with symptoms and psychometrics compared with gastric emptying status and Rome IV criteria. These findings have implications for the diagnostic profiling and personalized management of gastroduodenal disorders.

Geometric deep learning enables 3D kinematic profiling across species and environments.

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.

Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

Unplanned Rehospitalisation due to Medication Harm following an Acute Myocardial Infarction.

INTRODUCTION: The contribution of medication harm to rehospitalisation and adverse patient outcomes after an acute myocardial infarction (AMI) needs exploration. Rehospitalisation is costly to both patients and the healthcare facility. Following an AMI, patients are at risk of medication harm as they are often older and have multiple comorbidities and polypharmacy. This study aimed to quantify and evaluate medication harm causing unplanned rehospitalisation after an AMI. METHODS: This was a retrospective cohort study of patients discharged from a quaternary hospital post-AMI. All rehospitalisations within 18 months were identified using medical record review and coding data. The primary outcome measure was medication harm rehospitalisation. Preventability, causality, and severity assessments of medication harm were conducted. RESULTS: A total of 1,564 patients experienced an AMI, and 415 (26.5%) were rehospitalised. Eighty-nine patients (5.7% of total population; 6.0% of those discharged) experienced a total of 101 medication harm events. Those with medication harm were older (p = 0.007) and had higher rates of heart failure (p = 0.005), chronic kidney disease (p = 0.046), chronic obstructive pulmonary disease (p = 0.037), and a prior history of ischaemic heart disease (p = 0.005). Gastrointestinal bleeding, acute kidney injury, and hypotension were the most common medication harm events. Forty percent of events were avoidable, and 84% were classed as "serious." Furosemide, antiplatelets, and angiotensin-converting enzyme inhibitors were the most commonly implicated medications. The median time to medication harm rehospitalisation was 79 days (interquartile range: 16-200 days). CONCLUSION: Medication harm causes unplanned rehospitalisation in 5.7% of all AMI patients (1 in 17 patients; 6.0% of those discharged). The majority of harm was serious and occurred within the first 200 days of discharge. This study highlights that measures to attenuate the risk of medication harm rehospitalisation are essential, including post-discharge medication management.

Gender differences in cardiovascular disease risk: Adolescence to young adulthood.

BACKGROUND AND AIMS: Gender differences in cardiovascular disease (CVD) have been well documented but rarely for young adults and the extent to which gender related lifestyle differences may contribute to gender differences in CVD risk experienced by young adults have not been reported. METHODS AND RESULTS: Data are from a long-running cohort study, the Mater-University of Queensland Study of Pregnancy (MUSP). We track gender differences in CVD related behaviours at 21 and 30 years (consumption of a Western Diet/Health-Oriented Diet, cigarette smoking, vigorous physical exercise, heavy alcohol consumption). At 30 years we compare males and females for CVD risk, and the extent to which lifestyle behaviours at 21 and 30 years contribute to CVD risk. At both 21 and 30 years of age, males more frequently consume a Western Diet and less often a Health Oriented Diet. By contrast, males are also much more likely to report engaging in vigorous physical activity. On most CVD markers, males exhibit much higher levels of risk than do females at both 21 and 30 years. At 30 years of age males have about five times the odds of being at high risk of CVD. Some lifestyle behaviours contribute to this additional risk. CONCLUSION: Young adult males much more frequently engage in most CVD related risk behaviours and males have a higher level of CVD risk. Gender differences in CVD risk remain high even after adjustment for CVD lifestyles, though dietary factors independently contribute to CVD risk at 30 years.

Application of estimand framework to the design and analysis of multi-regional clinical trials.

With the increasing globalization of drug development and the publication of the International Council for Harmonisation (ICH) E17 guideline (ICH International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use 2017), multi-regional clinical trials (MRCTs) have become a preferred option to accelerate the availability of new medical products by design, execution and simultaneous submission under one protocol. MRCTs, with the participation of all major regions including countries from both developed and emerging markets, surely make new drug development more efficient. Even though the proposed estimand framework (ICH E9 (R1) (2019), came later in 2019 and was not mentioned in ICH E17, the application of the estimand framework has the potential to enhance the design, execution, and analysis in MRCTs. Defining an estimand within the regional context in MRCTs is an important issue that requires careful consideration. Given that consistency evaluation of treatment effects across regions is critical in MRCTs, the utilization of the estimand framework for regional consistency evaluation is also worth discussion. This paper aims to address these two questions. The five attributes of the estimand definition are discussed within a multi-regional context. It is imperative to thoroughly consider regional intrinsic/extrinsic factors when planning the estimand and estimation of MRCTs. A holistic approach is summarized to conduct consistency evaluation. When a regional inconsistency is observed, the possible reasons need to be further explored under five attributes of the estimand framework. Two real case studies are discussed to illustrate the application of the estimand framework in the consistency evaluation.

Health systems challenges, mitigation strategies and adaptations to maintain essential health services during the COVID-19 pandemic: learnings from the six geopolitical regions in Nigeria.

BACKGROUND: The COVID-19 pandemic control strategies disrupted the smooth delivery of essential health services (EHS) globally. Limited evidence exists on the health systems lens approach to analyzing the challenges encountered in maintaining EHS during the COVID-19 pandemic. This study aimed to identify the health system challenges encountered and document the mitigation strategies and adaptations made across geopolitical zones (GPZs) in Nigeria. METHODS: The national qualitative survey of key actors across the six GPZs in Nigeria involved ten states and the Federal Capital Territory (FCT) which were selected based on resilience, COVID-19 burden and security considerations. A pre-tested key informant guide was used to collect data on service utilization, changes in service utilization, reasons for changes in primary health centres' (PHCs) service volumes, challenges experienced by health facilities in maintaining EHS, mitigation strategies implemented and adaptations to service delivery. Emerging sub-themes were categorized under the appropriate pillars of the health system. RESULTS: A total of 22 respondents were interviewed. The challenges experienced in maintaining EHS cut across the pillars of the health systems including: Human resources shortage, shortages in the supply of personal protective equipments, fear of contracting COVID-19 among health workers misconception, ignorance, socio-cultural issues, lockdown/transportation and lack of equipment/waiting area (. The mitigation strategies included improved political will to fund health service projects, leading to improved accessibility, affordability, and supply of consumables. The health workforce was motivated by employing, redeploying, training, and incentivizing. Service delivery was reorganized by rescheduling appointments and prioritizing some EHS such as maternal and childcare. Sustainable systems adaptations included IPC and telehealth infrastructure, training and capacity building, virtual meetings and community groups set up for sensitization and engagement. CONCLUSION: The mitigation strategies and adaptations implemented were important contributors to EHS recovery especially in the high resilience LGAs and have implications for future epidemic preparedness plans.

Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.

BACKGROUND: International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). OBJECTIVE: This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). METHODS: By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. RESULTS: Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. CONCLUSIONS: An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

Associations of semaglutide with first-time diagnosis of Alzheimer's disease in patients with type 2 diabetes: Target trial emulation using nationwide real-world data in the US.

INTRODUCTION: Emerging preclinical evidence suggests that semaglutide, a glucagon-like peptide receptor agonist (GLP-1RA) for type 2 diabetes mellitus (T2DM) and obesity, protects against neurodegeneration and neuroinflammation. However, real-world evidence for its ability to protect against Alzheimer's disease (AD) is lacking. METHODS: We conducted emulation target trials based on a nationwide database of electronic health records (EHRs) of 116 million US patients. Seven target trials were emulated among 1,094,761 eligible patients with T2DM who had no prior AD diagnosis by comparing semaglutide with seven other antidiabetic medications. First-ever diagnosis of AD occurred within a 3-year follow-up period and was examined using Cox proportional hazards and Kaplan-Meier survival analyses. RESULTS: Semaglutide was associated with significantly reduced risk for first-time AD diagnosis, most strongly compared with insulin (hazard ratio [HR], 0.33 [95% CI: 0.21 to 0.51]) and most weakly compared with other GLP-1RAs (HR, 0.59 [95% CI: 0.37 to 0.95]). Similar results were seen across obesity status, gender, and age groups. DISCUSSION: These findings support further studies to assess semaglutide's potential in preventing AD. HIGHLIGHTS: Semaglutide was associated with 40% to 70% reduced risks of first-time AD diagnosis in T2DM patients compared to other antidiabetic medications, including other GLP-1RAs. Semaglutide was associated with significantly lower AD-related medication prescriptions. Similar reductions were seen across obesity status, gender, and age groups. Our findings provide real-world evidence supporting the potential clinical benefits of semaglutide in mitigating AD initiation and development in patients with T2DM. These findings support further clinical trials to assess semaglutide's potential in delaying or preventing AD.

Distinct roles of delta- and theta-band neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

The brain tracks and encodes multi-level speech features during spoken language processing. It is evident that this speech tracking is dominant at low frequencies (<8 Hz) including delta and theta bands. Recent research has demonstrated distinctions between delta- and theta-band tracking but has not elucidated how they differentially encode speech across linguistic levels. Here, we hypothesised that delta-band tracking encodes prediction errors (enhanced processing of unexpected features) while theta-band tracking encodes neural sharpening (enhanced processing of expected features) when people perceive speech with different linguistic contents. EEG responses were recorded when normal-hearing participants attended to continuous auditory stimuli that contained different phonological/morphological and semantic contents: (1) real-words, (2) pseudo-words and (3) time-reversed speech. We employed multivariate temporal response functions to measure EEG reconstruction accuracies in response to acoustic (spectrogram), phonetic and phonemic features with the partialling procedure that singles out unique contributions of individual features. We found higher delta-band accuracies for pseudo-words than real-words and time-reversed speech, especially during encoding of phonetic features. Notably, individual time-lag analyses showed that significantly higher accuracies for pseudo-words than real-words started at early processing stages for phonetic encoding (<100 ms post-feature) and later stages for acoustic and phonemic encoding (>200 and 400 ms post-feature, respectively). Theta-band accuracies, on the other hand, were higher when stimuli had richer linguistic content (real-words > pseudo-words > time-reversed speech). Such effects also started at early stages (<100 ms post-feature) during encoding of all individual features or when all features were combined. We argue these results indicate that delta-band tracking may play a role in predictive coding leading to greater tracking of pseudo-words due to the presence of unexpected/unpredicted semantic information, while theta-band tracking encodes sharpened signals caused by more expected phonological/morphological and semantic contents. Early presence of these effects reflects rapid computations of sharpening and prediction errors. Moreover, by measuring changes in EEG alpha power, we did not find evidence that the observed effects can be solitarily explained by attentional demands or listening efforts. Finally, we used directed information analyses to illustrate feedforward and feedback information transfers between prediction errors and sharpening across linguistic levels, showcasing how our results fit with the hierarchical Predictive Coding framework. Together, we suggest the distinct roles of delta and theta neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

Normative Values for Body Surface Gastric Mapping Evaluations of Gastric Motility Using Gastric Alimetry: Spectral Analysis.

INTRODUCTION: Body surface gastric mapping (BSGM) is a new noninvasive test of gastric function. BSGM offers several novel and improved biomarkers of gastric function capable of differentiating patients with overlapping symptom profiles. The aim of this study was to define normative reference intervals for BSGM spectral metrics in a population of healthy controls. METHODS: BSGM was performed in healthy controls using Gastric Alimetry (Alimetry, New Zealand) comprising a stretchable high-resolution array (8 × 8 electrodes; 196 cm 2 ), wearable Reader, and validated symptom-logging App. The evaluation encompassed a fasting baseline (30 minutes), 482 kCal meal, and 4-hour postprandial recording. Normative reference intervals were calculated for BSGM metrics including the Principal Gastric Frequency, Gastric Alimetry Rhythm Index (a measure of the concentration of power in the gastric frequency band over time), body mass index (BMI)-adjusted amplitude (µV), and fed:fasted amplitude ratio. Data were reported as median and reference interval (5th and/or 95th percentiles). RESULTS: A total of 110 subjects (55% female, median age 32 years [interquartile range 24-50], median BMI 23.8 kg/m 2 [interquartile range 21.4-26.9]) were included. The median Principal Gastric Frequency was 3.04 cycles per minute; reference interval: 2.65-3.35 cycles per minute. The median Gastric Alimetry Rhythm Index was 0.50; reference interval: ≥0.25. The median BMI-adjusted amplitude was 37.6 µV; reference interval: 20-70 µV. The median fed:fasted amplitude ratio was 1.85; reference interval ≥1.08. A higher BMI was associated with a shorter meal-response duration ( P = 0.014). DISCUSSION: This study provides normative reference intervals for BSGM spectral data to inform diagnostic interpretations of abnormal gastric function.

Rapid magnetically directed assembly of pre-patterned capillary-scale microvessels.

Capillary scale vascularization is critical to the survival of engineered 3D tissues and remains an outstanding challenge for the field of tissue engineering. Current methods to generate micro-scale vasculature such as 3D printing, two photon hydrogel ablation, angiogenesis, and vasculogenic assembly face challenges in rapidly creating organized, highly vascularized tissues at capillary length-scales. Within metabolically demanding tissues, native capillary beds are highly organized and densely packed to achieve adequate delivery of nutrients and oxygen and efficient waste removal. Here, we adopt two existing techniques to fabricate lattices composed of sacrificial microfibers that can be efficiently and uniformly seeded with endothelial cells (ECs) by magnetizing both lattices and ECs. Ferromagnetic microparticles (FMPs) were incorporated into microfibers produced by solution electrowriting (SEW) and fiber electropulling (FEP). By loading ECs with superparamagnetic iron oxide nanoparticles (SPIONs), the cells could be seeded onto magnetized microfiber lattices. Following encapsulation in a hydrogel, the capillary templating lattice was selectively degraded by a bacterial lipase that does not impact mammalian cell viability or function. This work introduces a novel approach to rapidly producing organized capillary networks within metabolically demanding engineered tissue constructs which should have broad utility for the fields of tissue engineering and regenerative medicine.

Better cardiac care - the patient experience - a qualitative study.

BACKGROUND: In 2015, a Brisbane tertiary hospital's cardiac unit implemented a new model of multidisciplinary care (Better Cardiac Care (BCC)) for Aboriginal and Torres Strait Islander patients. Since then, clinical indicators for Aboriginal and Torres Strait Islander cardiac patients have improved, but the recipients' voices have not been heard. This research aimed to determine the acceptability and appropriateness, features of value, and opportunities for improvements in this model of care, from the perspective of patients and their family members. METHODS: This descriptive qualitative study employed a narrative methodology. BCC Health Workers contacted prospective participants; with consent, interested individuals were then contacted by the Aboriginal Research Officer (RO) who arranged yarning sessions and consent. Family members were also invited to share their stories of their loved ones' hospitalisation. Two researchers conducted the interviews, using a yarning approach. Inductive narrative analysis, informed by Aboriginal and Torres Strait Islander ways of Being, Knowing, and Doing, focused on enabling participants' stories to be heard and understood from their perspectives. RESULTS: Relationality was at the heart of the BCC model of care, particularly between patients and Aboriginal and Torres Strait Islander staff. The relationality included a responsibility for holistic care, extending beyond hospital discharge, although support and handover for family members required improvement. The Aboriginal and Torres Strait Islander staff understood the contextual and structural challenges faced by participants, including the disempowerment and racism experienced in healthcare. This understanding was shared with the BCC team who, in turn, protected, advocated for, and holistically supported participants through their cardiac health journeys. CONCLUSIONS: Empowering (and employing) Aboriginal and Torres Strait Islander staff, and relating to patients as people, enabled BCC to meet Aboriginal and Torres Strait Islander patient's needs and improve outcomes. The wider health system and health academia could benefit from exploring and valuing Aboriginal and Torres Strait Islander discourses of relationality.

Dietary patterns and young adult body mass change: A 9-year longitudinal study.

PURPOSE: While excessive weight gain is highest during young adulthood, the extent to which specific dietary patterns are associated with changes in measures of body mass in this course of life remains unknown. We aimed to examine the associations of dietary patterns at 21 years with changes in body weight and body mass index (BMI) between 21 and 30 years. METHODS: We used data on young adults from a long-running birth cohort in Australia. Western and prudent dietary patterns were identified applying principal component analysis to 33 food groups obtained by a food frequency questionnaire at 21 years. Body weight and height were measured at 21 and 30 years. Multivariable regression models, using generalized estimating equations, were adjusted for concurrent changes in sociodemographic and lifestyle variables in evaluating the effect of identified dietary patterns on changes in weight and BMI over time. RESULTS: In the fully adjusted model, young adults in the highest tertile of the Western pattern had a mean weight gain of 9.9 (95% CI 8.5, 11.3) kg compared to those in the lowest that had a mean weight gain of 7.1 (95% CI 5.6, 8.5) kg, P-for linear trend = 0.0015. The corresponding values for mean gains in BMI were 3.1 (95% CI 2.7, 3.6) kg/m2 for young adults in the highest tertile compared to 2.4 (95% CI 1.9, 2.9) kg/m2 for those in lowest, P-for linear trend = 0.0164. There was no evidence of a significant association between the prudent pattern and mean changes in each outcome over time in this study. CONCLUSIONS: The findings of the current study show that greater adherence to the Western diet at 21 years was positively associated with increases in body weight and BMI from 21 to 30 years of age, whereas the prudent diet had no significant association with these outcomes. The findings provide evidence that the adverse effects of the Western diet on weight gain in young adulthood could partly be prevented through optimising diet in the early course of life.

Dietary patterns explaining variations in blood biomarkers in young adults are associated with the 30-year predicted cardiovascular disease risks in midlife: A follow-up study.

BACKGROUND AND AIMS: To examine a combined effect of dietary intakes, blood lipid and insulin resistance in young adulthood on the risk of predicted CVD through midlife. METHODS AND RESULTS: Data of young adults from a birth cohort study in Australia were used. Reduced rank regression (RRR) and partial least squares (PLS) methods identified dietary patterns rich in meats, refined grains, processed and fried foods, and high-fat dairy and low in whole grains and low-fat dairy from dietary intakes obtained at 21-years, and blood lipids and measures of insulin resistance measured at 30-years of age. Using standard CVD risk factors measured at 30-years of age, the Framingham Heart Study risk-prediction algorithms were used to calculate the 30-year predicted Framingham CVD risk scores. The scores represent Hard CVD events; coronary death, myocardial infarction and stroke and Full CVD events; Hard CVD plus coronary insufficiency and angina pectoris, transient ischaemic attack, intermittent claudication, and congestive heart failure in midlife. Sex-specific upper quartiles of CVD risk scores were used to define high-risk groups. Modified Poisson regression models were used to estimate relative risks (RRs) with 95% CI. Greater adherence to the diet identified applying RRR in young adulthood was associated with higher risks of predicted Hard CVD (RR: 1.60; 1.14, 2.25) and Full CVD (RR: 1.46; 1.04, 2.05) events in midlife. The diet from PLS showed similar trend of association for the risk of predicted Hard CVD events (RR: 1.49; 1.03, 2.16) in adjusted models. CONCLUSION: Dietary patterns associated with variations in blood lipids and insulin resistance in young adulthood are associated with increased risks of predicted CVD events in midlife. The findings suggest that diet induced altered blood lipids and insulin resistance in the life course of young adulthood could increase the risks of CVD events in later life.

Estimand in benefit-risk assessment.

ICH E9(R1) introduces the estimand framework to strengthen dialogues between sponsors and regulators during drug development. A well-structured benefit-risk assessment (BRA) framework also intends to facilitate communication among stakeholders. However, the estimand in ICH E9(R1) is written mainly from the perspective of a single measure of treatment effect in clinical trials. There is lack of systematic discussion on estimand in the context of BRA. This paper initiates the BRA discussion under the estimand framework. By identifying two types of BRA approaches, we summarize and discuss completed clinical trials, using the estimand language for BRA. Benefits and challenges of using estimand for BRA are also discussed.

Application of estimand framework in ICH E9 (R1) to safety evaluation.

Defining the right question of interest is important to a clinical study. ICH E9 (R1) introduces the framework of an estimand and its five attributes, which provide a basis for connecting different components of a study with its clinical questions. Most of the applications of the estimand framework focus on efficacy instead of safety assessment. In this paper, we expand the estimand framework into the safety evaluation and compare/contrast the similarity and differences between safety and efficacy estimand. Furthermore, we present and discuss applications of a safety estimand to oncology trials and pooled data analyses. At last, we also discuss the potential usage of safety estimand to handle the impacts of COVID-19 pandemic on safety assessment.

Application of estimand framework in ICH E9 (R1) to vaccine trials.

Over the past decades, the primary interest in vaccine efficacy or immunogenicity evaluation mostly focuses on the biological effect of immunization in complying with the vaccination schedule in a targeted population. The safety questions, which are essential for vaccines as they are generally given to large healthy populations, need to be clearly defined to reflect the risk assessment of interest. ICH E9 (R1) provides a structured framework to clarify the clinical questions and formulate the treatment effect as an estimand. This paper applies the estimand framework to vaccine clinical trials on common clinical questions regarding efficacy, immunogenicity, and safety.