The cost-effectiveness of semaglutide in reducing cardiovascular risk among people with overweight and obesity and existing cardiovascular disease, but without diabetes.

BACKGROUND AND AIMS: The Semaglutide Effects on Cardiovascular Outcomes in People with Overweight or Obesity (SELECT) trial demonstrated significant reductions in cardiovascular outcomes in people with cardiovascular disease (CVD) and overweight or obesity (but without diabetes). However, the cost of the medication has raised concerns about its financial viability and accessibility within healthcare systems. This study explored whether use of semaglutide for the secondary prevention of CVD in overweight or obesity is cost-effective from the Australian healthcare perspective. METHODS: A Markov model was developed based on the SELECT trial to model the clinical outcomes and costs of a hypothetical population treated with semaglutide versus placebo, in addition to standard care, and followed up over 20 years. With each annual cycle, subjects were at risk of having non-fatal CVD events or dying. Model inputs were derived from SELECT and published literature. Costs were obtained from Australian sources. All outcomes were discounted by 5% annually. The main outcome of interest was the incremental cost-effectiveness ratio (ICER) in terms of cost per year of life saved (YoLS) and cost per quality-adjusted life year (QALY) gained. RESULTS: With an annual estimated cost of semaglutide of A${\$}$4175, the model resulted in ICERs of A${\$}$99 853 (US${\$}$143 504; £40 873) per YoLS and A${\$}$96 055 (US${\$}$138 046; £39 318) per QALY gained. CONCLUSIONS: Assuming a willingness-to-pay threshold of A${\$}$50 000, semaglutide is not considered cost-effective at the current price. A price of ≤ A${\$}$2000 per year or more targeted use in high-risk patients would be needed for it to be considered cost-effective in the Australian setting.

A Causal Model for Primary Prevention of Cardiovascular Disease: The Health Economic Model for the Primary Prevention of Cardiovascular Disease.

OBJECTIVES: Our objective was to design and develop an open-source model capable of simulating interventions for primary prevention of cardiovascular disease (CVD) that incorporated the cumulative effects of risk factors (eg, cholesterol years or blood-pressure years) to enhance health economic modeling in settings which clinical trials are not possible. METHODS: We reviewed the literature to design the model structure by selecting the most important causal risk factors for CVD-low-density lipoprotein-cholesterol (LDL-C), systolic blood pressure (SBP), smoking, diabetes, and lipoprotein (a) (Lp(a))-and most common CVDs-myocardial infarction and stroke. The epidemiological basis of the model involves the simulation of risk factor trajectories, which are used to modify CVD risk via causal effect estimates derived from Mendelian randomization. LDL-C, SBP, Lp(a), and smoking all have cumulative impacts on CVD risk, which were incorporated into the health economic model. The data for the model were primarily sourced from the UK Biobank study. We calibrated the model using clinical trial data and validated the model against the observed UK Biobank data. Finally, we performed an example health economic analysis to demonstrate the utility of the model. The model is open source. RESULTS: The model performed well in all validation tests. It was able to produce interpretable and plausible (consistent with expectations of the existing literature) results from an example health economic analysis. CONCLUSIONS: We have constructed an open-source health economic model capable of incorporating the cumulative effect of LDL-C (ie, cholesterol years), SBP (SBP-years), Lp(a), and smoking on lifetime CVD risk.

Association of Drug-Disease Interactions with Mortality or Readmission in Hospitalised Middle-Aged and Older Adults: A Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVE: Multimorbidity is common in hospitalised adults who are at increased risk of inappropriate prescribing including drug-disease interactions. These interactions occur when a medicine being used to treat one condition exacerbates a concurrent medical condition and may lead to adverse health outcomes. The aim of this review was to examine the association between drug-disease interactions and the risk of mortality and readmission in hospitalised middle-aged and older adults. METHODS: A systematic review was conducted on drug-disease interactions in hospitalised middle-aged (45-64 years) and older adults (≥65 years). The study protocol was prospectively registered with PROSPERO (Registration Number: CRD42022341998). Drug-disease interactions were defined as a medicine being used to treat one condition with the potential to exacerbate a concurrent medical condition or that were inappropriate based on a comorbid medical condition. Both observational and interventional studies were included. The outcomes of interest were mortality and readmissions. The databases searched included MEDLINE, CINAHL, EMBASE, Web of Science, SCOPUS and the Cochrane Library from inception to 12 July, 2022. A meta-analysis was performed to pool risk estimates using the random-effects model. RESULTS: A total of 563 studies were identified and four met the inclusion criteria. All were observational studies in older adults, with no studies identified in middle-aged adults. Most of the studies were at risk of bias because of an inadequate adjustment for covariates and a lack of clarity around individuals lost to follow-up. There were various definitions of drug-disease interactions within these four studies. Two studies assessed drugs that were contraindicated based on renal function, one assessed an individual drug-disease combination, and one was based on the clinical judgement of a pharmacist. There were two studies that showed an association between drug-disease interactions and the outcomes of interest. One reported that the use of diltiazem in patients with heart failure was associated with an increased risk of readmissions. The second reported that the use of medicines contraindicated according to renal function were associated with increased risk of all-cause mortality and a composite of mortality and readmission. Three of the studies (total study population = 5705) were amenable to a meta-analysis, which showed no significant association between drug-disease interactions and readmissions (odds ratio = 1.0, 95% confidence interval 0.80-1.38). CONCLUSIONS: Few studies were identified examining the risk of drug-disease interactions and mortality and readmission in hospitalised adults. Most of the identified studies were at risk of bias. There is no universal accepted definition of drug-disease interactions in the literature. Further studies are needed to develop a standardised and accepted definition of these interactions to guide further research in this area.

Prevalence and Predictors of Emergency Medical Service Use in Patients Undergoing Primary Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction.

AIM: We aim to describe prevalence of Emergency Medical Service (EMS) use, investigate factors predictive of EMS use, and determine if EMS use predicts treatment delay and mortality in our ST-elevation myocardial infarction (STEMI) cohort. METHOD: We prospectively collected data on 5,602 patients presenting with STEMI for primary percutaneous coronary intervention (PCI) transported to PCI-capable hospitals in Victoria, Australia, from 2013-2018 who were entered into the Victorian Cardiac Outcomes Registry (VCOR). We linked this dataset to the Ambulance Victoria and National Death Index (NDI) datasets. We excluded late presentation, thrombolysed, and in-hospital STEMI, as well as patients presenting with cardiogenic shock and out-of-hospital cardiac arrest. RESULTS: In total, 74% of patients undergoing primary PCI for STEMI used EMS. Older age, female gender, higher socioeconomic status, and a history of prior ischaemic heart disease were independent predictors of using EMS. EMS use was associated with shorter adjusted door-to-balloon (53 vs 72 minutes, p<0.001) and symptom-to-balloon (183 vs 212 minutes, p<0.001) times. Mode of transport was not predictive of 30-day or 12-month mortality. CONCLUSIONS: EMS use in Victoria is relatively high compared with internationally reported data. EMS use reduces treatment delay. Predictors of EMS use in our cohort are consistent with those prevalent in prior literature. Understanding the patients who are less likely to use EMS might inform more targeted education campaigns in the future.

SGLT2 Inhibitor-Associated Ketoacidosis vs Type 1 Diabetes-Associated Ketoacidosis.

This cohort study examines the natural history and response to treatment of sodium glucose cotransporter 2 (SGLT2) inhibitor­associated ketoacidosis compared with that of type 1 diabetes­associated ketoacidosis.

Infectious and sepsis presentations to, and hospital admissions from emergency departments in Victoria, Australia.

OBJECTIVE: To investigate the frequency and outcomes of adult infectious and sepsis presentations to, and hospital admissions from, Emergency Departments (EDs) in Victoria, Australia. METHODS: Retrospective cohort study using the Victorian Emergency Minimum Dataset and Victorian Admitted Episodes Dataset. We included adults (age ≥ 18 years) presenting to an ED, or admitted to hospital from ED in Victoria between July 2017 and June 2018. One-year mortality was analysed until June 2019 using the Victorian Death Index, and ICD-10 coding was used to identify cases. RESULTS: Among 1.28 million ED presentations over 1 year, 12.00% and 0.45% were coded with infectious and sepsis diagnoses, respectively. Despite having lower triage categories, patients with infections were more likely to be admitted to hospital (50.4% vs 44.9%), but not directly to ICU (0.8%). Patients coded with sepsis were assigned higher triage categories and required hospital admission much more frequently (96.4% vs 44.9%), including to ICU (15.9% vs 0.8%). Patients presenting with infections and sepsis had increased risk of 1-year mortality (adjusted hazard ratio 1.44 and 4.13, respectively). Of the 648 280 hospital admissions from the ED, infection and sepsis were coded in 23.69% and 2.66%, respectively, and the adjusted odds ratio for 1-year mortality were 1.64 and 4.79, respectively. CONCLUSIONS: Infections and sepsis are common causes of presentation to, and admission from the ED in Victoria. Such patients experience higher mortality than non-infectious patients, even after adjusting for age. There is a need to identify modifiable factors contributing to these outcomes.

Digital Health for Myocardial Infarction: Research Topics and Trends.

We aimed to map the topics and trends of research on digital health for myocardial infarction over the past ten years. This can inform future research directions and newly emerging topics for myocardial infarction care, diagnosis and monitoring. The Web of Science database was searched for papers related to digital health for myocardial infarction. 1,344 retrieved records were used for visualisation through bibliometrics and co-occurrence network analysis of keywords. Our mapping revealed several emerging topics in recent years, including artificial intelligence and deep learning. Higher emphasis on automated and artificially intelligent digital health systems in recent years can inform future clinical practice and research directions for myocardial infarction.

Lipid-Lowering Strategies for Primary Prevention of Coronary Heart Disease in the UK: A Cost-Effectiveness Analysis.

AIM: We aimed to assess the cost effectiveness of four different lipid-lowering strategies for primary prevention of coronary heart disease initiated at ages 30, 40, 50, and 60 years from the UK National Health Service perspective. METHODS: We developed a microsimulation model comparing the initiation of a lipid-lowering strategy to current standard of care (control). We included 458,692 participants of the UK Biobank study. The four lipid-lowering strategies were: (1) low/moderate-intensity statins; (2) high-intensity statins; (3) low/moderate-intensity statins and ezetimibe; and (4) inclisiran. The main outcome was the incremental cost-effectiveness ratio for each lipid-lowering strategy compared to the control, with 3.5% annual discounting using 2021 GBP (£); incremental cost-effectiveness ratios were compared to the UK willingness-to-pay threshold of £20,000-£30,000 per quality-adjusted life-year. RESULTS: The most effective intervention, low/moderate-intensity statins and ezetimibe, was projected to lead to a gain in quality-adjusted life-years of 0.067 per person initiated at 30 and 0.026 at age 60 years. Initiating therapy at 40 years of age was the most cost effective for all lipid-lowering strategies, with incremental cost-effectiveness ratios of £2553 (95% uncertainty interval: 1270, 3969), £4511 (3138, 6401), £11,107 (8655, 14,508), and £1,406,296 (1,121,775, 1,796,281) per quality-adjusted life-year gained for strategies 1-4, respectively. Incremental cost-effectiveness ratios were lower for male individuals (vs female individuals) and for people with higher (vs lower) low-density lipoprotein-cholesterol. For example, low/moderate-intensity statin use initiated from age 40 years had an incremental cost-effectiveness ratio of £5891 (3822, 9348), £2174 (772, 4216), and was dominant (i.e. cost saving; -2,760, 350) in female individuals with a low-density lipoprotein-cholesterol of ≥ 3.0, ≥ 4.0 and ≥ 5.0 mmol/L, respectively. Inclisiran was not cost effective in any sub-group at its current price. CONCLUSIONS: Low-density lipoprotein-cholesterol lowering from early ages is a more cost-effective strategy than late intervention and cost effectiveness increased with the increasing lifetime risk of coronary heart disease.

Societal health and economic burden of cardiovascular diseases in the population with type 2 diabetes in Qatar. A 10-year forecasting model.

AIMS: To predict the future health and economic burden of cardiovascular disease (CVD) in type 2 diabetes (T2D) in Qatar. MATERIALS AND METHODS: A dynamic multistate model was designed to simulate the progression of fatal and non-fatal CVD events among people with T2D in Qatar aged 40-79 years. First CVD events [i.e. myocardial infarction (MI) and stroke] were calculated via the 2013 Pooled Cohort Equation, while recurrent CVD events were sourced from the REACH registry. Key model outcomes were fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years, total direct medical costs and total productivity loss costs. Utility and cost model inputs were drawn from published sources. The model adopted a Qatari societal perspective. Sensitivity analyses were performed to test the robustness of estimates. RESULTS: Over 10 years among people with T2D, model estimates 108 195 [95% uncertainty interval (UI) 104 249-112 172] non-fatal MIs, 62 366 (95% UI 60 283-65 520) non-fatal strokes and 14 612 (95% UI 14 472-14 744) CVD deaths. The T2D population accrued 4 786 605 (95% UI 4 743 454, 4 858 705) total years of life lived and 3 781 833 (95% UI 3 724 718-3 830 669) total quality-adjusted life years. Direct costs accounted for 57.85% of the total costs, with a projection of QAR41.60 billion (US$11.40 billion) [95% UI 7.53-147.40 billion (US$2.06-40.38 billion)], while the total indirect costs were expected to exceed QAR30.31 billion (US$8.30 billion) [95% UI 1.07-162.60 billion (US$292.05 million-44.55 billion)]. CONCLUSIONS: The findings suggest a significant economic and health burden of CVD among people with T2D in Qatar and highlight the need for more enhanced preventive strategies targeting this population group.

Digital Health Secondary Prevention Using Co-Design Procedures: Focus Group Study With Health Care Providers and Patients With Myocardial Infarction.

BACKGROUND: Myocardial infarction (MI) is a debilitating condition and a leading cause of morbidity and mortality worldwide. Digital health is a promising approach for delivering secondary prevention to support patients with a history of MI and for reducing risk factors that can lead to a future event. However, its potential can only be fulfilled when the technology meets the needs of the end users who will be interacting with this secondary prevention. OBJECTIVE: We aimed to gauge the opinions of patients with a history of MI and health professionals concerning the functions, features, and characteristics of a digital health solution to support post-MI care. METHODS: Our approach aligned with the gold standard participatory co-design procedures enabling progressive refinement of feedback via exploratory, confirmatory, and prototype-assisted feedback from participants. Patients with a history of MI and health professionals from Australia attended focus groups over a videoconference system. We engaged with 38 participants across 3 rounds of focus groups using an iterative co-design approach. Round 1 included 8 participants (4 patients and 4 health professionals), round 2 included 24 participants (11 patients and 13 health professionals), and round 3 included 22 participants (14 patients and 8 health professionals). RESULTS: Participants highlighted the potential of digital health in addressing the unmet needs of post-MI care. Both patients with a history of MI and health professionals agreed that mental health is a key concern in post-MI care that requires further support. Participants agreed that family members can be used to support postdischarge care and require support from the health care team. Participants agreed that incorporating simple games with a points system can increase long-term engagement. However, patients with a history of MI emphasized a lack of support from their health care team, family, and community more strongly than health professionals. They also expressed some openness to using artificial intelligence, whereas health professionals expressed that users should not be aware of artificial intelligence use. CONCLUSIONS: These results provide valuable insights into the development of digital health secondary preventions aimed at supporting patients with a history of MI. Future research can implement a pilot study in the population with MI to trial these recommendations in a real-world setting.

Sociocultural and Demographic Factors Predict Readmissions for General Surgery Patients.

INTRODUCTION: Readmission is a poor outcome for both patients and healthcare systems. The association of certain sociocultural and demographic characteristics with likelihood of readmission is uncertain in general surgical patients. METHOD: A multi-centre retrospective cohort study of consecutive unique individuals who survived to discharge during general surgical admissions was conducted. Sociocultural and demographic variables were evaluated alongside clinical parameters (considered both as raw values and their proportion of change in the 1-2 days prior to admission) for their association with 7 and 30 days readmission using logistic regression. RESULTS: There were 12,701 individuals included, with 304 (2.4%) individuals readmitted within 7 days, and 921 (7.3%) readmitted within 30 days. When incorporating absolute values of clinical parameters in the model, age was the only variable significantly associated with 7-day readmission, and primary language and presence of religion were the only variables significantly associated with 30-day readmission. When incorporating change in clinical parameters between the 1-2 days prior to discharge, primary language and religion were predictive of 30-day readmission. When controlling for changes in clinical parameters, only higher comorbidity burden (represented by higher Charlson comorbidity index score) was associated with increased likelihood of 30-day readmission. CONCLUSIONS: Sociocultural and demographic patient factors such as primary language, presence of religion, age, and comorbidity burden predict the likelihood of 7 and 30-day hospital readmission after general surgery. These findings support early implementation a postoperative care model that integrates all biopsychosocial domains across multiple disciplines of healthcare.

The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery.

BACKGROUND: This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery. METHODS: Consecutive general surgery patients at two tertiary hospitals, over a 2-year period, were included. Observation and laboratory parameter data were stratified into training, testing and validation datasets. Random forest, XGBoost and logistic regression models were evaluated. Each ward round note time was taken as a different event. Primary outcome was classification accuracy of the algorithmic model able to predict discharge within the next 12 h on the validation data set. RESULTS: 42 572 ward round note timings were included from 8826 general surgery patients. Discharge occurred within 12 h for 8800 times (20.7%), and within 24 h for 9885 (23.2%). For predicting discharge within 12 h, model classification accuracies for derivation and validation data sets were: 0.84 and 0.85 random forest, 0.84 and 0.83 XGBoost, 0.80 and 0.81 logistic regression. For predicting discharge within 24 h, model classification accuracies for derivation and validation data sets were: 0.83 and 0.84 random forest, 0.82 and 0.81 XGBoost, 0.78 and 0.79 logistic regression. Algorithms generated a continuous number between 0 and 1 (or 0 and 100), representing readiness for discharge after general surgery. CONCLUSIONS: A derived artificial intelligence measure (the Adelaide Score) successfully predicts discharge within the next 12 and 24 h in general surgery patients. This may be useful for both treating teams and allied health staff within surgical systems.

Estimating the cost-effectiveness and return on investment of the Victorian Cardiac Outcomes Registry in Australia: a minimum threshold analysis.

OBJECTIVES: We sought to establish the minimum level of clinical benefit attributable to the Victorian Cardiac Outcomes Registry (VCOR) for the registry to be cost-effective. DESIGN: A modelled cost-effectiveness study of VCOR was conducted from the Australian healthcare system and societal perspectives. SETTING: Observed deaths and costs attributed to coronary heart disease (CHD) over a 5-year period (2014-2018) were compared with deaths and costs arising from a hypothetical situation which assumed that VCOR did not exist. Data from the Australian Bureau of Statistics and published sources were used to construct a decision analytic life table model to simulate the follow-up of Victorians aged ≥25 years for 5 years, or until death. The assumed contribution of VCOR to the proportional change in CHD mortality trend observed over the study period was varied to quantify the minimum level of clinical benefits required for the registry to be cost-effective. The marginal costs of VCOR operation and years of life saved (YoLS) were estimated. PRIMARY OUTCOME MEASURES: The return on investment (ROI) ratio and the incremental cost-effectiveness ratio (ICER). RESULTS: The minimum proportional change in CHD mortality attributed to VCOR required for the registry to be considered cost-effective was 0.125%. Assuming this clinical benefit, a net return of $A4.30 for every dollar invested in VCOR was estimated (ROI ratio over 5 years: 4.3 (95% CI 3.6 to 5.0)). The ICER estimated for VCOR was $A49 616 (95% CI $A42 228 to $A59 608) per YoLS. Sensitivity analyses found that the model was sensitive to the time horizon assumed and the extent of registry contribution to CHD mortality trends. CONCLUSIONS: VCOR is likely cost-effective and represents a sound investment for the Victorian healthcare system. Our evaluation highlights the value of clinical quality registries in Australia.

Novel behaviour change frameworks for digital health interventions: A critical review.

Digital health interventions - interventions delivered over digital media to support the health of users - are becoming increasingly prevalent. Utilising an intervention development framework can increase the efficacy of digital interventions for health-related behaviours. This critical review aims to outline and review novel behaviour change frameworks that guide digital health intervention development. Our comprehensive search for preprints and publications used PubMed, PsycINFO, Scopus, Web of Science and the Open Science Framework repository. Articles were included if they: (1) were peer-reviewed; (2) proposed a behaviour change framework to guide digital health intervention development; (3) were written in English; (4) were published between 1/1/19 and 1/8/2021; and (5) were applicable to chronic diseases. Intervention development frameworks considered the user, intervention elements and theoretical foundations. However, the timing and policy of interventions are not consistently addressed across frameworks. Researchers should deeply consider the digital applicability of behaviour change frameworks to improve intervention success.

Artificial intelligence for secondary prevention of myocardial infarction: A qualitative study of patient and health professional perspectives.

BACKGROUND: Artificial intelligence (AI) has potential to improve self-management of several chronic conditions. However, the perspective of patients and healthcare professionals regarding AI-enabled health management programs, which are key to successful implementation, remains poorly understood. PURPOSE: To explore the opinions of people with a history of myocardial infarction (PHMI) and health professionals on the use of AI for secondary prevention of MI. PROCEDURE: Three rounds of focus groups were conducted via videoconferencing with 38 participants: 22 PHMI and 16 health professionals. FINDINGS: We identified 21 concepts stemming from participants' views, which we classified into five categories: Trust; Expected Functions; Adoption; Concerns; and Perceived Benefits. Trust covered the credibility of information and safety to believe health advice. Expected Functions covered tailored feedback and personalised advice. Adoption included usability features and overall interest in AI. Concerns originated from previous negative experience with AI. Perceived Benefits included the usefulness of AI to provide advice when regular contact with healthcare services is not feasible. Health professionals were more optimistic than PHMI about the usefulness of AI for improving health behaviour. CONCLUSIONS: Altogether, our findings provide key insights from end-users to improve the likelihood of successful implementation and adoption of AI-enabled systems in the context of MI, as an exemplar of broader applications in chronic disease management.

Projecting the Health and Economic Burden of Cardiovascular Disease Among People with Type 2 Diabetes, 2022-2031.

OBJECTIVE: The aim was to project the health and economic outcomes of cardiovascular disease (CVD) among people with type 2 diabetes from Australian public healthcare and societal perspectives over the next decade. METHODS: A dynamic multistate model with yearly cycles was developed to project cardiovascular events among Australians with type 2 diabetes aged 40-89 years from 2022 to 2031. CVD risk (myocardial infarction [MI] and stroke) in the type 2 diabetes population was estimated using the 2013 pooled cohort equation, and recurrent cardiovascular event rates in the type 2 diabetes with established CVD population were obtained from the global Reduction of Atherothrombosis for Continued Health (REACH) registry. Costs and utilities were derived from published sources. Outcomes included fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years (QALYs), total healthcare costs, and total productivity losses. The annual discount rate was 5%, applied to outcomes and costs. RESULTS: Between 2022 and 2031, a total of 83,618 non-fatal MIs (95% uncertainty interval [UI] 83,170-84,053) and 58,774 non-fatal strokes (95% UI 58,458-59,013) were projected. Total years of life lived and QALYs (discounted) were projected to be 9,549,487 (95% UI 9,416,423-9,654,043) and 6,632,897 (95% UI 5,065,606-7,591,679), respectively. Total healthcare costs and total lost productivity costs (discounted) were projected to be 9.59 billion Australian dollars (AU$) (95% UI 1.90-30.45 billion) and AU$9.07 billion (95% UI 663.53 million-33.19 billion), respectively. CONCLUSIONS: CVD in people with type 2 diabetes will substantially impact the Australian healthcare system and society over the next decade. Future work to investigate different strategies to optimize the control of risk factors for the prevention and treatment of CVD in type 2 diabetes in Australia is warranted.

Early vs. Late Readmission following Percutaneous Coronary Intervention: Predictors and Impact on Long-Term Outcomes.

BACKGROUND: Readmissions within 1 year after percutaneous coronary intervention (PCI) are common (18.6-50.4% in international series) and a burden to patients and health services, however their long-term implications are not well characterised. We compared predictors of 30-day (early) and 31-day to 1-year (late) unplanned readmission and the impact of unplanned readmission on long-term clinical outcomes post-PCI. METHODS: Patients enrolled in the GenesisCare Cardiovascular Outcomes Registry (GCOR-PCI) from 2008 to 2020 were included in the study. Multivariate logistic regression analysis was performed to identify predictors of early and late unplanned readmission. A Cox proportion hazards regression model was used to explore the impact of any unplanned readmission during the first year post-PCI on the clinical outcomes at 3 years. Finally, patients with early and late unplanned readmission were compared to determine which group was at the highest risk of adverse long-term outcomes. RESULTS: The study comprised 16,911 consecutively enrolled patients who underwent PCI between 2009-2020. Of these, 1422 patients (8.5%) experienced unplanned readmission within 1-year post-PCI. Overall, the mean age was 68.9 ± 10.5 years, 76.4% were male and 45.9% presented with acute coronary syndromes. Predictors of unplanned readmission included increasing age, female gender, previous CABG, renal impairment and PCI for acute coronary syndromes. Unplanned readmission within 1 year of PCI was associated with an increased risk of MACE (adjusted HR 1.84 (1.42-2.37), p < 0.001) and death over a 3-year follow-up (adjusted HR 1.864 (1.34-2.59), p < 0.001) compared with those without readmission within 1-year post-PCI. Late compared with early unplanned readmission within the first year of PCI was more frequently associated with subsequent unplanned readmission, MACE and death between 1 and 3 years post-PCI. CONCLUSIONS: Unplanned readmissions in the first year following PCI, particularly those occurring more than 30 days after discharge, were associated with a significantly higher risk of adverse outcomes, such as MACE and death at 3 years. Strategies to identify patients at high risk of readmission and interventions to reduce their greater risk of adverse events should be implemented post-PCI.

Estimating the impact of Angelman syndrome on parental productivity in Australia using productivity-adjusted life years.

BACKGROUND: Angelman syndrome (AS) is a rare genetic condition characterized by global developmental delay, including severe intellectual disability. The parents of persons with AS experience increased stress, anxiety, and depression. This impacts parents' career choices and productivity. OBJECTIVE: To estimate, for the first time, the total productivity lost by the parents of persons with AS over a 10-year period in Australia and the corresponding cost to society. METHODS: A cost-of-illness model with simulated follow-up over a 10-year period was developed, with 2019 as the baseline year, facilitated by a Markov chain of life tables. The prevalence of persons with AS and their parents, the productivity-adjusted life years (PALYs) lost by parents, and the cost to society were estimated. Key data were obtained from a prospective cohort of AS families, peer-reviewed literature, and publicly available sources. RESULTS: The base-case productivity burden borne by the estimated 330 living parents of the 428 prevalent persons with AS totaled AUD$45.30 million, corresponding to a loss of 38.42% of PALYs per parent. CONCLUSIONS: Caring for a child with AS has a significant impact on the productivity of affected parents, with a large associated impact on the broader Australian economy.