Excess Deaths Associated with Rheumatic Heart Disease, Australia, 2013-2017.

During 2013-2017, the mortality rate ratio for rheumatic heart disease among Indigenous versus non-Indigenous persons in Australia was 15.9, reflecting health inequity. Using excess mortality methods, we found that deaths associated with rheumatic heart disease among Indigenous Australians were probably substantially undercounted, affecting accuracy of calculations based solely on Australian Bureau of Statistics data.

Long-Term Pattern and Associated Costs of Re-hospitalisations in Patients After Index Atrial Fibrillation Admission in Western Australia, 2011-2017.

AIMS: This study aimed to determine total and cardiovascular-specific re-hospitalisation patterns and associated costs within 2 years of index atrial fibrillation (AF) admission in Western Australia (WA). METHOD: Patients aged 25-94 years, surviving an index (first-in-period) AF hospitalisation (principal diagnosis) from 2011 to 2015 were identified from WA-linked administrative data and followed for 2 years. Person-level hospitalisation costs ($ Australian dollar) were computed using the Australian Refined Diagnosis Related Groups and presented as median with first and third quartile costs. RESULTS: The cohort comprised 17,080 patients, 59.0% men, mean age 69.6±13.3 (standard deviation) years, and 59.0% had a CHA2DS2-VA (one point for congestive heart failure, hypertension, diabetes mellitus, vascular disease or age 65-74 years; two points for prior stroke/transient ischaemic attack or age ≥75 years) score of 2 or more. Within 2 years, 13,776 patients (80.6%) were readmitted with median of 2 (1-4) readmissions. Among total all-cause readmissions (n=54,240), 40.1% were emergent and 36.6% were cardiovascular-related, led by AF (19.5%), coronary events (5.8%), and heart failure (4.2%). The median index AF admission cost was $3,264 ($2,899-$7,649) while cardiovascular readmission costs were higher, particularly stroke ($10,732 [$4,179-23,390]), AF ablation ($7,884 [$5,283-$8,878]), and heart failure ($6,759 [$6,081-$13,146]). Average readmission costs over 2 years per person increased by $4,746 (95% confidence interval [CI] $4,459-$5,033) per unit increase in baseline CHA2DS2-VA score. The average 2-year hospitalisation costs per patient, including index admission, was $27,820 (95% CI $27,308-$28,333) and total WA costs were $475.2 million between 2011 and 2017. CONCLUSIONS: Patients after index AF hospitalisation have a high risk of cardiovascular and other readmissions with considerable healthcare cost implications. Readmission costs increased progressively with baseline CHA2DS2-VA score. Better integrated management of AF and coexistent comorbidities is likely key to reducing readmissions and associated costs.

Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts.

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.

Is it advisable to perform radiosurgery for EGFR-TKI-controlled brain metastases? A retrospective study of the role of radiosurgery in lung cancer treatment.

PURPOSE: Given the availability of TKIs with high central nervous system efficacy, the question arises as to whether upfront SRS provides additional clinical benefits. The goal of this study was to characterize the clinical outcomes of SRS as salvage therapy for TKI-uncontrolled BMs. METHODS: This retrospective study included EGFR-mutant NSCLC patients presenting BMs at the time of primary tumor diagnosis. BMs were categorized into three subgroups, referred to as "Nature of TKI-treated BMs", "TKI-controlled brain metastases ± SRS", and "SRS salvage therapy". The first subgroup analysis characterized the effects of TKIs on tumor behavior. In the second subgroup, we compared outcomes of TKI-controlled BMs treated with TKI alone versus those treated with combined TKI-SRS therapy. The third subgroup characterized the outcomes of TKI-uncontrolled BMs treated with SRS as salvage therapy Clinical outcomes include local and distant tumor control. RESULTS: This study included 106 patients with a total of 683 BMs. TKI treatment achieved control in 63% of local tumors at 24 months. Among the TKI-controlled BMs, local tumor control was significantly higher in the combined TKI-SRS group (93%) than in the TKI-alone group (65%) at 24 months (p < 0.001). No differences were observed between the two groups in terms of distant tumor control (p = 0.832). In dealing with TKI-uncontrolled BMs, salvage SRS achieved local tumor control in 58% of BMs at 24 months. CONCLUSIONS: While upfront TKI alone proved highly effective in BM control, this study also demonstrated the outcomes of SRS when implemented concurrently with TKI or as salvage therapy for TKI-uncontrolled BMs. This study also presents a strategy of the precise timing and targeting of SRS to lesions in progression.

Unplanned Readmissions and Long-Term Mortality Risk After Incident Heart Failure Hospitalisation in Western Australia, 2001-2015.

AIMS: To investigate the frequency and predictors of unplanned readmissions after incident heart failure (HF) hospitalisation and the association between readmissions and mortality over two years. METHODS: We performed a retrospective cohort study using Western Australian morbidity and mortality data to identify all patients, aged 25-94 years, who survived an incident (first-ever) HF hospitalisation (principal diagnosis) between 2001-2015. Ordinal logistic regression models determined the covariates independently associated with unplanned readmission(s). Cox proportional hazards models with time-varying exposures determined the hazard ratios (HR) of one or more readmissions for mortality over two years after incident HF. RESULTS: Of 18,693 patients, 53.4% male, mean age 74.4 (standard deviation [SD] 13.6) years, 61.3% experienced 32,431 unplanned readmissions (39.7% cardiovascular-related) within two years. Leading readmission causes were HF (19.1%), respiratory diseases (12.6%), and ischaemic heart disease (9.6%). All-cause death occurred in 27.2% of the cohort, and the multivariable-adjusted mortality HR of 1 (versus 0) readmission was 2.5 (95% confidence interval [CI], 2.3-2.7) increasing to 5.0 (95% CI, 4.7-5.4) for 2+ readmissions. The adjusted mortality HR of 1 and 2+ (versus 0) HF-specific readmission was 2.0 (95% CI, 1.8-2.1) and 3.6 (95% CI, 3.2-3.9), respectively. Coexistent cardiovascular and other comorbidities were independently associated with increased readmission and mortality risk. CONCLUSION: This study underlines the high burden of recurrent unplanned cardiovascular and other readmissions within two years after incident HF hospitalisation, and their additive adverse impact on mortality. Integrated multidisciplinary management of concomitant comorbidities, in addition to HF-targeted treatments, is necessary to improve long-term prognosis in HF patients.

The evidence that rheumatic heart disease control programs in Australia are making an impact.

OBJECTIVE: Rheumatic heart disease (RHD) comprises heart-valve damage caused by acute rheumatic fever (ARF). The Australian Government Rheumatic Fever Strategy funds RHD Control Programs to support detection and management of ARF and RHD. We assessed epidemiological changes during the years of RHD Control Program operation. METHODS: Linked RHD register, hospital and death data from four Australian jurisdictions were used to measure ARF/RHD outcomes between 2010 and 2017, including: 2-year progression to severe RHD/death; ARF recurrence; secondary prophylaxis delivery and earlier disease detection. RESULTS: Delivery of secondary prophylaxis improved from 53% median proportion of days covered (95%CI: 46-61%, 2010) to 70% (95%CI: 71-68%, 2017). Secondary prophylaxis adherence protected against progression to severe RHD/death (hazard ratio 0.2, 95% CI 0.1-0.8). Other measures of program effectiveness (ARF recurrences, progression to severe RHD/death) remained stable. ARF case numbers and concurrent ARF/RHD diagnoses increased. CONCLUSIONS: RHD Control Programs have contributed to major success in the management of ARF/RHD through increased delivery of secondary prevention yet ARF case numbers, not impacted by secondary prophylaxis and sensitive to increased awareness/surveillance, increased. IMPLICATIONS FOR PUBLIC HEALTH: RHD Control Programs have a major role in delivering cost-effective RHD prevention. Sustained investment is needed but with greatly strengthened primordial and primary prevention.

Rheumatic heart disease mortality in Indigenous and non-Indigenous Australians between 2010 and 2017.

OBJECTIVES: To generate contemporary age-specific mortality rates for Indigenous and non-Indigenous Australians aged <65 years who died from rheumatic heart disease (RHD) between 2013 and 2017, and to ascertain the underlying causes of death (COD) of a prevalent RHD cohort aged <65 years who died during the same period. METHODS: For this retrospective, cross-sectional epidemiological study, Australian RHD deaths for 2013-2017 were investigated by first, mortality rates generated using Australian Bureau of Statistics death registrations where RHD was a coded COD, and second COD analyses of death records for a prevalent RHD cohort identified from RHD register and hospitalisations. All analyses were undertaken by Indigenous status and age group (0-24, 25-44, 45-64 years). RESULTS: Age-specific RHD mortality rates per 100 000 were 0.32, 2.63 and 7.41 among Indigenous 0-24, 25-44 and 45-64 year olds, respectively, and the age-standardised mortality ratio (Indigenous vs non-Indigenous 0-64 year olds) was 14.0. Within the prevalent cohort who died (n=726), RHD was the underlying COD in 15.0% of all deaths, increasing to 24.6% when RHD was included as associated COD. However, other cardiovascular and non-cardiovascular conditions were the underlying COD in 34% and 43% respectively. CONCLUSION: Premature mortality in people with RHD aged <65 years has approximately halved in Australia since 1997-2005, most notably among younger Indigenous people. Mortality rates based solely on underlying COD potentially underestimates true RHD mortality burden. Further strategies are required to reduce the high Indigenous to non-Indigenous mortality rate disparity, in addition to optimising major comorbidities that contribute to non-RHD mortality.

Evidence-based medication adherence among seniors in the first year after heart failure hospitalisation and subsequent long-term outcomes: a restricted cubic spline analysis of adherence-outcome relationships.

PURPOSE: Non-adherence to heart failure (HF) medications is associated with poor outcomes. We used restricted cubic splines (RCS) to assess the continuous relationship between adherence to renin-angiotensin system inhibitors (RASI) and ß-blockers and long-term outcomes in senior HF patients. METHODS: We identified a population-based cohort of 4234 patients, aged 65-84 years, 56% male, who were hospitalised for HF in Western Australia between 2003 and 2008 and survived to 1-year post-discharge (landmark date). Adherence was calculated using the proportion of days covered (PDC) in the first year post-discharge. RCS Cox proportional-hazards models were applied to determine the relationship between adherence and all-cause death and death/HF readmission at 1 and 3 years after the landmark date. RESULTS: RCS analysis showed a curvilinear adherence-outcome relationship for both RASI and ß-blockers which was linear above PDC 60%. For each 10% increase in RASI and ß-blocker adherence above this level, the adjusted hazard ratio for 1-year all-cause death fell by an average of 6.6% and 4.8% respectively (trend p < 0.05) and risk of all-cause death/HF readmission fell by 5.4% and 5.8% respectively (trend p < 0.005). Linear reductions in adjusted risk for these outcomes at PDC ≥ 60% were also seen at 3 years after landmark date (all trend p < 0.05). CONCLUSION: RCS analysis showed that for RASI and ß-blockers, there was no upper adherence level (threshold) above 60% where risk reduction did not continue to occur. Therefore, interventions should maximise adherence to these disease-modifying HF pharmacotherapies to improve long-term outcomes after hospitalised HF.

Unplanned 30-day readmissions, comorbidity and impact on one-year mortality following incident heart failure hospitalisation in Western Australia, 2001-2015.

BACKGROUND: Readmissions within 30 days after heart failure (HF) hospitalisation is considered an important healthcare quality metric, but their impact on medium-term mortality is unclear within an Australian setting. We determined the frequency, risk predictors and relative mortality risk of 30-day unplanned readmission in patients following an incident HF hospitalisation. METHODS: From the Western Australian Hospitalisation Morbidity Data Collection we identified patients aged 25-94 years with an incident (first-ever) HF hospitalisation as a principal diagnosis between 2001 and 2015, and who survived to 30-days post discharge. Unplanned 30-day readmissions were categorised by principal diagnosis. Logistic and Cox regression analysis determined the independent predictors of unplanned readmissions in 30-day survivors and the multivariable-adjusted hazard ratio (HR) of readmission on mortality within the subsequent year. RESULTS: The cohort comprised 18,241 patients, mean age 74.3 ± 13.6 (SD) years, 53.5% males, and one-third had a modified Charlson Comorbidity Index score of ≥ 3. Among 30-day survivors, 15.5% experienced one or more unplanned 30-day readmission, of which 53.9% were due to cardiovascular causes; predominantly HF (31.4%). The unadjusted 1-year mortality was 15.9%, and the adjusted mortality HR in patients with 1 and ≥ 2 cardiovascular or non-cardiovascular readmissions (versus none) was 1.96 (95% confidence interval (CI) 1.80-2.14) and 3.04 (95% CI, 2.51-3.68) respectively. Coexistent comorbidities, including ischaemic heart disease/myocardial infarction, peripheral arterial disease, pneumonia, chronic kidney disease, and anaemia, were independent predictors of both 30-day unplanned readmission and 1-year mortality. CONCLUSION: Unplanned 30-day readmissions and medium-term mortality remain high among patients who survived to 30 days after incident HF hospitalisation. Any cardiovascular or non-cardiovascular readmission was associated with a two to three-fold higher adjusted HR for death over the following year, and various coexistent comorbidities were important associates of readmission and mortality risk. Our findings support the need to optimize multidisciplinary HF and multimorbidity management to potentially reduce repeat hospitalisation and improve survival.

Emergent readmission and long-term mortality risk after incident atrial fibrillation hospitalisation.

OBJECTIVE: To assess the frequency and predictors of unplanned readmissions after hospitalisation for incident atrial fibrillation (AF) and the association of readmissions with mortality over 2 years. METHODS: We performed a retrospective cohort study using Western Australian morbidity and mortality data to identify all patients, aged 25-94 years, who survived incident (first-ever) hospitalisation for AF (principal diagnosis), between 2001 and 2015. Ordinal logistic models determined the covariates independently associated with unplanned readmission(s), and Cox proportional hazards models with time-varying exposures determined the hazard ratios (HR) of one or more readmissions for mortality over 2 years after incident AF. RESULTS: Of 22 956 patients, 57.7% male, mean age 67.9 (SD 13.8) years, 44.0% experienced 22 053 unplanned readmissions within 2 years, 50.6% being cardiovascular-related. All-cause death occurred in 8.0% of the cohort, and the multivariable-adjusted mortality HR of 1 (vs 0) readmission was 2.9 (95% CI 2.6 to 3.3), increasing to 5.6 (95% CI 5.0 to 6.5) for 3+ readmissions. First emergent readmission for AF, stroke, heart failure or myocardial infarction was independently associated with an increased hazard for mortality. Coexistent cardiovascular and other comorbidities were independently associated with increased readmission and mortality risk, whereas AF ablation was associated with reduced risk. CONCLUSION: This study highlights the large burden of unplanned all-cause and cardiovascular-specific readmissions within 2 years after being hospitalised for incident AF and their associated adverse impact on mortality. Concomitant comorbidities are independently associated with unplanned hospitalisations and mortality, which supports integrated multidisciplinary management of comorbidities, along with AF-targeted treatments, to improve long-term outcomes in patients with AF.

Unplanned 30-day readmission, comorbidity, and impact on mortality after incident atrial fibrillation hospitalization in Western Australia, 2001-2015.

Background: The healthcare burden of atrial fibrillation (AF) is dominated by hospitalizations, but data on 30-day unplanned readmissions after AF hospitalization and impact on mortality are limited. Objective: To assess causes and trends of 30-day unplanned readmission in incident (first-ever) hospitalized AF patients, and the risk of readmission for subsequent all-cause mortality. Methods: Patients aged 25-94 years, with an incident AF hospitalization (principal diagnosis) between 2001 and 2015, and surviving 30 days post discharge, were identified from linked Western Australian hospitalization and mortality data. Unplanned 30-day readmissions were categorized by principal diagnosis. Multivariable logistic and Cox regression analyses determined the independent predictors of readmission and the hazard ratio (HR) with 95% confidence intervals (CI) of readmission for subsequent 1-year mortality. Results: Of 22,814 patients, 57.7% male, mean age 67.8 ± 13.8 (standard deviation) years, 9.5% experienced 1 or more 30-day unplanned readmissions, with standardized rates increasing 2.0% annually (95% CI, 1.0%-3.1%). Among all readmissions, 64.8% were cardiovascular-related, with AF (31.7%), coronary events (12.2%), and heart failure (8.5%) being the most frequent. In 30-day survivors, 4.3% died within 1 year. Patients with any cardiovascular or noncardiovascular readmission (vs none) had a multivariable-adjusted mortality HR of 2.12 (95% CI, 1.82-2.45). Coexistent comorbidities were independently associated with 30-day unplanned readmission and 1-year mortality. Conclusion: Following incident AF hospitalization, 30-day unplanned readmissions were common, mostly cardiovascular-related, but any readmission, regardless of cause, was associated with a 2-fold higher adjusted mortality risk. Our findings also support the importance of comorbidity optimization within an integrated care pathway to reduce adverse outcomes in AF patients.

Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation.

BACKGROUND: Appropriate anticoagulant therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Multilabel machine learning (ML) techniques may improve predictive performance and support decision-making for anticoagulant therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. METHODS: This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The outcomes were ischemic stroke (167), major bleeding (430) admissions, all-cause death (1912) and event-free survival (7387). Discrimination and calibration of ML models were compared with clinical risk scores by area under the curve (AUC). Risk stratification was assessed using net reclassification index (NRI). RESULTS: Multilabel gradient boosting classifier chain provided the best AUCs for stroke (0.685 95% CI 0.676, 0.694), major bleeding (0.709 95% CI 0.703, 0.716) and death (0.765 95% CI 0.763, 0.768) compared to multi-layer neural networks and classifier chain using support vector machine. It provided modest performance improvement for stroke compared to AUC of CHA2DS2-VASc (0.652, NRI = 3.2%, p-value = 0.1), but significantly improved major bleeding prediction compared to AUC of HAS-BLED (0.522, NRI = 22.8%, p-value < 0.05). It also achieved greater discriminant power for death compared with AUC of CHA2DS2-VASc (0.606, p-value < 0.05). ML models identified additional risk features such as hemoglobin level, renal function. CONCLUSIONS: Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.

Comparing self-reported and measured hypertension and hypercholesterolaemia at standard and more stringent diagnostic thresholds: the cross-sectional 2010-2015 Busselton Healthy Ageing study.

BACKGROUND: Population health behaviour and risk factor surveys most often rely on self-report but there is a lack of studies assessing the validity of self-report using Australian data. This study investigates the sensitivity, specificity and agreement of self-reported hypertension and hypercholesterolaemia with objective measures at standard and more stringent diagnostic thresholds; and factors associated with sensitivity and specificity of self-report at different thresholds. METHODS: This study was a secondary analysis of a representative community-based cross-sectional sample of 5,092 adults, aged 45-69 years, residing in Busselton, Western Australia, surveyed in 2010-2015. Participants completed a self-administered questionnaire. Blood pressure and serum cholesterol levels were measured. RESULTS: At currently accepted diagnostic thresholds, sensitivities of self-reported hypertension and hypercholesterolaemia were 58.5% and 39.6%, respectively and specificities were >90% for both. Agreement using Cohen's kappa coefficient was 0.562 and 0.223, respectively. At two higher diagnostic thresholds, sensitivities of self-reported hypertension and hypercholesterolaemia improved by an absolute 14-23% and 15-25%, respectively and specificities remained >85%. Agreement was substantial for hypertension (kappa = 0.682-0.717) and moderate for hypercholesterolaemia (kappa = 0.458-0.533). Variables that were independently associated with higher sensitivity and lower specificity of self-report were largely consistent across thresholds and included increasing age, body mass index, worse self-rated health, diabetes and family history of hypertension. CONCLUSIONS: Self-reported hypertension and hypercholesterolaemia often misclassify individuals' objective status and underestimate objective prevalences, at standard diagnostic thresholds, which has implications for surveillance studies that rely on self-reported data. Self-reports of hypertension, however, may be reasonable indicators of those with blood pressures ≥160/100 mmHg or those taking anti-hypertensive medications. Self-reported hypercholesterolaemia data should be used with caution at all thresholds.

Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

DXA-Derived vs Standard Anthropometric Measures for Predicting Cardiometabolic Risk in Middle-Aged Australian Men and Women.

It is not clear if dual-energy X-ray absorptiometry (DXA) adiposity measures are superior to standard anthropometric measures for predicting cardiometabolic (CM) risk factors in a middle-aged general population. In the Busselton Healthy Ageing Study, we assessed a range of standard anthropometric and DXA-derived adiposity measures to predict metabolic syndrome (MetS) and CM risk factors in 4831 "baby boomers" aged 45-69 yr. Anthropometric and whole body DXA (GE Lunar Prodigy) measures were collected. Cross-sectional relationships of overall adiposity (BMI; DXA fat mass index, body fat %), central adiposity (waist circumference (WC); DXA trunk fat, android fat, abdominal visceral adipose tissue (VAT)) and ratio index (waist-to-hip ratio; DXA trunk/legs fat, android/gynoid ratio, VAT/total fat) with MetS and its components (as both continuous and binary outcomes) were evaluated using linear and logistic regression adjusting for age and lifestyle factors. Youden's Index was used to determine the optimal cut-points for predicting MetS. In linear regression analyses, central adiposity measures showed stronger associations with MetS score and CM risk factors than overall adiposity measures and fat ratio index, and DXA-VAT provided stronger associations than WC. Logistic regression models showed similar findings. For MetS diagnosis present in 35.9% of males and 24.4% of females, the highest odds ratio (95% CI) per SD change was observed for DXA-VAT (males: 5.02 [4.28, 5.88]; females: 3.91 [3.40, 4.49]), which remained significant (all p < 0.001) after further adjustment for BMI (males: 3.27 [2.65, 4.02]; females: 3.37 [2.79, 4.06]) or WC (males: 2.46 [1.95, 3.10]; females: 2.75 [2.21, 3.43]). The optimal DXA-VAT mass cut-point for predicting MetS was 1608 grams in males and 893 grams in females. DXA-VAT was superior to standard anthropometric and other DXA-derived adiposity measures for prediction of cardiometabolic risk factors, and has clinical utility for identifying middle-aged individuals at increased risk of MetS.

APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies.

INTRODUCTION: The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. METHODS: We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species. RESULTS: A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. DISCUSSION: Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.

Validation of ICD-10-AM Coding for Myocardial Infarction Subtype in Hospitalisation Data.

BACKGROUND: International Classification of Disease (ICD) codes are central for identifying myocardial infarction (MI) in administrative hospitalisation data, however validation of MI subtype codes is limited. We measured the sensitivity and specificity of ICD-10-AM (Australian Modification) codes for ST-elevation MI (STEMI) and non-STEMI (NSTEMI). METHODS: A sample of MI admissions was obtained from a dataset containing all MI hospitalisations in Western Australia (WA) for 2003, 2008 and 2013. Clinical data were collected from hospital medical records (n=799 patients). Cases were classified by ICD-10-AM codes for STEMI, NSTEMI and unspecified MI, and compared to clinical classification from review of available electrocardiographs (ECGs) and cardiac biomarkers (n=660). Sensitivity and specificity for ICD-10-AM coding versus clinical classification was measured, stratified by calendar year of discharge. RESULTS: The majority of classifiable cases had MI recorded in the principal diagnosis field (STEMI n=293, 84.2%; NSTEMI n=202, 74.3%; unspecified MI n=20, 50.0%). Overall sensitivity of the ICD-10-AM STEMI code was 86.3% (95% CI 81.7-90.0%) and was higher when restricted to MI as a principal versus secondary diagnosis (88.8% vs 66.7%). Comparable values for NSTEMI were 66.7% (95% CI 61.5-71.6%), and 68.8% vs 61.4% respectively. Between 2003 and 2013, sensitivity for both MI subtypes increased: 80.2-89.5% for STEMI, and 51.2-73.8% for NSTEMI. Specificity was high for NSTEMI throughout (88.2% 95% CI 84.1-91.6%), although improving over time for STEMI (68.1-76.4%). CONCLUSIONS: The sensitivity and specificity of ICD-10-AM codes for MI subtypes in hospitalisation data are generally high, particularly for principal diagnosis cases. However, the temporal improvement in sensitivity in coding of MI subtypes, particularly NSTEMI, may necessitate modification to trend studies using administrative hospitalisation data.

Changing age-specific trends in incidence, comorbidities and mortality of hospitalised heart failure in Western Australia between 2001 and 2016.

BACKGROUND: Incident heart failure (HF) hospitalisation rates in most high-income countries are stable or declining. However, HF incidence may be increasing in younger people linked to changing risk factor profiles in the general population. We examined age and sex-specific patterns of incidence, comorbidities and mortality of hospitalised HF in Western Australia (WA) between 2001 and 2016. METHODS AND RESULTS: All WA residents aged 25-94 years, with an incident (first-ever) principal HF discharge diagnosis between 2001 and 2016 were included (n = 22,476). Poisson regression derived annual age and sex-standardised rates of incident HF and 1-year mortality overall, and by age groups (25-54, 55-74, 75-94), across the study period. Overall, the age and sex-standardised rates of incident HF increased marginally by 0.6% per year (95% confidence interval (CI), 0.3, 0.8) whereas incidence increased by 3.1% per year (95% CI, 2.2, 4.0) in the 25-54 year age-group (trend p < 0.0001). There was a high prevalence (≥15%) of obesity, diabetes mellitus, cardiomyopathy, hypertension, ischemic heart disease, atrial fibrillation, and chronic kidney disease in younger HF patients. Overall standardised 1-year mortality declined by -1.0% per year (95%CI, -0.4, -1.6), driven largely by the mortality decline in the 55-74 year age group. CONCLUSION: Incident HF hospitalisation rates have been rising in WA since 2006, notably in individuals under 55 years. The underlying reasons require further investigation, particularly the population-attributable risk related to increasing obesity and diabetes mellitus in the general population. Rising HF incidence along with declining mortality rates portends to an increasing HF burden in the community.

Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study.

BACKGROUND AND OBJECTIVE: Chronic medical conditions accumulate within individuals with age. However, knowledge concerning the trends, patterns and determinants of multimorbidity remains limited. This study assessed the prevalence and patterns of multimorbidity using extensive individual phenotyping in a general population of Australian middle-aged adults. METHODS: Participants (n = 5029, 55% female), born between 1946 and 1964 and attending the cross-sectional phase of the Busselton Healthy Ageing Study (BHAS) between 2010 and 2015, were studied. Prevalence of 21 chronic conditions was estimated using clinical measurement, validated instrument scores and/or self-reported doctor-diagnosis. Non-random patterns of multimorbidity were explored using observed/expected (O/E) prevalence ratios and latent class analysis (LCA). Variables associated with numbers of conditions and class of multimorbidity were investigated. RESULTS: The individual prevalence of 21 chronic conditions ranged from 2 to 54% and multimorbidity was common with 73% of the cohort having 2 or more chronic conditions. (mean ± SD 2.75 ± 1.84, median = 2.00, range 0-13). The prevalence of multimorbidity increased with age, obesity, physical inactivity, tobacco smoking and family history of asthma, diabetes, myocardial infarct or cancer. There were 13 pairs and 27 triplets of conditions identified with a prevalence > 1.5% and O/E > 1.5. Of the triplets, arthritis (> 50%), bowel disease (> 33%) and depression-anxiety (> 33%) were observed most commonly. LCA modelling identified 4 statistically and clinically distinct classes of multimorbidity labelled as: 1) "Healthy" (70%) with average of 1.95 conditions; 2) "Respiratory and Atopy" (11%, 3.65 conditions); 3) "Non-cardiometabolic" (14%, 4.77 conditions), and 4) "Cardiometabolic" (5%, 6.32 conditions). Predictors of multimorbidity class membership differed between classes and differed from predictors of number of co-occurring conditions. CONCLUSION: Multimorbidity is common among middle-aged adults from a general population. Some conditions associated with ageing such as arthritis, bowel disease and depression-anxiety co-occur in clinically distinct patterns and at higher prevalence than expected by chance. These findings may inform further studies into shared biological and environmental causes of co-occurring conditions of ageing. Recognition of distinct patterns of multimorbidity may aid in a holistic approach to care management in individuals presenting with multiple chronic conditions, while also guiding health resource allocation in ageing populations.