2023 Cardiac Society of Australia and New Zealand Expert Position Statement on Catheter and Surgical Ablation for Atrial Fibrillation.

Catheter ablation for atrial fibrillation (AF) has increased exponentially in many developed countries, including Australia and New Zealand. This Expert Position Statement on Catheter and Surgical Ablation for Atrial Fibrillation from the Cardiac Society of Australia and New Zealand (CSANZ) recognises healthcare factors, expertise and expenditure relevant to the Australian and New Zealand healthcare environments including considerations of potential implications for First Nations Peoples. The statement is cognisant of international advice but tailored to local conditions and populations, and is intended to be used by electrophysiologists, cardiologists and general physicians across all disciplines caring for patients with AF. They are also intended to provide guidance to healthcare facilities seeking to establish or maintain catheter ablation for AF.

Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called "update problem," which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.

Pericardial Effusion Detection on Post-Mortem Computed Tomography Images Using Convolutional Neural Networks.

Pericardial effusion can be a sign of significant underlying diease and, in some cases, may lead to death. Post-mortem computed tomography (PMCT) is a well-established tool to assist death investigation processes in the forensic setting. In practice, the scarcity of well-trained radiologists is a challenge in processing raw whole-body PMCT images for pericardial effusion detection. In this work, we propose a Pericardial Effusion Automatic Detection (PEAD) framework to automatically process raw whole-body PMCT images to filter out the irrelevant images with heart organ absent and focus on pericardial effusion detection. In PEAD, the standard convolutional neural network architectures of VGG and ResNet are carefully modified to fit the specific characteristics of PMCT images. The experimental results prove the effectiveness of the proposed framework and modified models. The modified VGG and ResNet models achieved superior detection accuracy than the standard architecture with reduced processing speed.

Inflammation risk before cardiac surgery and the treatment effect of intraoperative dexamethasone.

Patients who exhibit high systemic inflammation after cardiac surgery may benefit most from pre-emptive anti-inflammatory treatments. In this secondary analysis (n = 813) of the randomised, double-blind Intraoperative High-Dose Dexamethasone for Cardiac Surgery trial, we set out to develop an inflammation risk prediction model and assess whether patients at higher risk benefit from a single intraoperative dose of dexamethasone (1 mg/kg). Inflammation risk before surgery was quantified from a linear regression model developed in the placebo arm, relating preoperatively available covariates to peak postoperative C-reactive protein. The primary endpoint was the interaction between inflammation risk and the peak postoperative C-reactive protein reduction associated with dexamethasone treatment. The impact of dexamethasone on the main clinical outcome (a composite of death, myocardial infarction, stroke, renal failure, or respiratory failure within 30 days) was also explored in relation to inflammation risk. Preoperatively available covariates explained a minority of peak postoperative C-reactive protein variation and were not suitable for clinical application (R2 = 0.058, P = 0.012); C-reactive protein before surgery (excluded above 10 mg/L) was the most predictive covariate (P < 0.001). The anti-inflammatory effect of dexamethasone increased as the inflammation risk increased (-0.689 mg/L per unit predicted peak C-reactive protein, P = 0.002 for interaction). No treatment-effect heterogeneity was detected for the main clinical outcome (P = 0.167 for interaction). Overall, risk predictions from a model of inflammation after cardiac surgery were associated with the degree of peak postoperative C-reactive protein reduction derived from dexamethasone treatment. Future work should explore the impact of this phenomenon on clinical outcomes in larger surgical populations.

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images.

BACKGROUND AND OBJECTIVE: High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. METHODS: Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. RESULTS: Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. CONCLUSIONS: Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

Lessons Learned from Natural Disasters around Digital Health Technologies and Delivering Quality Healthcare.

As climate change drives increased intensity, duration and severity of weather-related events that can lead to natural disasters and mass casualties, innovative approaches are needed to develop climate-resilient healthcare systems that can deliver safe, quality healthcare under non-optimal conditions, especially in remote or underserved areas. Digital health technologies are touted as a potential contributor to healthcare climate change adaptation and mitigation, through improved access to healthcare, reduced inefficiencies, reduced costs, and increased portability of patient information. Under normal operating conditions, these systems are employed to deliver personalised healthcare and better patient and consumer involvement in their health and well-being. During the COVID-19 pandemic, digital health technologies were rapidly implemented on a mass scale in many settings to deliver healthcare in compliance with public health interventions, including lockdowns. However, the resilience and effectiveness of digital health technologies in the face of the increasing frequency and severity of natural disasters remain to be determined. In this review, using the mixed-methods review methodology, we seek to map what is known about digital health resilience in the context of natural disasters using case studies to demonstrate what works and what does not and to propose future directions to build climate-resilient digital health interventions.

A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis.

This study presents an ontology that scopes the digital health ecosystem from a consumer-centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer-user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge," whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer-centered perspective and the method we used in deriving the ontology - formalizing the results of a systematic review based on data-driven analysis methods.

iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.

Methylomic and transcriptomic characterization of postoperative systemic inflammatory dysregulation.

In this study, we define and validate a state of postoperative systemic inflammatory dysregulation (PSID) based on postoperative phenotypic extremes of plasma C-reactive protein concentration following major abdominal surgery. PSID manifested clinically with significantly higher rates of sepsis, complications, longer hospital stays and poorer short, and long-term outcomes. We hypothesized that PSID will be associated with, and potentially predicted by, altered patterns of genome-wide peripheral blood mononuclear cell differential DNA methylation and gene expression. We identified altered DNA methylation and differential gene expression in specific immune and metabolic pathways during PSID. Our findings suggest that dysregulation results in, or from, dramatic changes in differential DNA methylation and highlights potential targets for early detection and treatment. The combination of altered DNA methylation and gene expression suggests that dysregulation is mediated at multiple levels within specific gene sets and hence, nonspecific anti-inflammatory treatments such as corticosteroids alone are unlikely to represent an effective therapeutic strategy.

Understanding digital health ecosystem from Australian citizens' perspective: A scoping review.

BACKGROUND: Digital health (DH) and the benefits of related services are fairly well understood. However, it still is critical to map the digital health care landscape including the key elements that define it as an ecosystem. Particularly, knowing the perspectives of citizens on this digital transformation is an important angle to capture. In this review we aim to analyze the relevant studies to identify how DH is understood and experienced by Australian citizens and what they may require from DH platforms. MATERIALS AND METHODS: A scoping literature review was conducted across several electronic databases (ACM Digital Library, OVID, PubMed, Scopus, IEEE, Science Direct, SAGE), as well as grey literature. Additionally, citation mining was conducted to identify further relevant studies. Identified studies were subjected to eligibility criteria and the final set of articles was independently reviewed, analyzed, discussed and interpreted by three reviewers. RESULTS: Of 3811 articles, 98 articles met the inclusion criteria with research-based articles-as opposed to review articles or white papers- comprising the largest proportion (72%) of the selected literature. The qualitative analysis of the literature revealed five key elements that capture the essence of the digital health ecosystem interventions from the viewpoint of the Australian citizens. The identified elements were "consumer/user", "health care", "technology", "use and usability", "data and information". These elements were further found to be associated with 127 subcategories. CONCLUSIONS: This study is the first of its kind to analyze and synthesize the relevant literature on DH ecosystems from the citizens' perspective. Through the lens of two research questions, this study defines the key components that were found crucial to understanding citizens' experiences with DH. This understanding lays a strong foundation for designing and fostering DH ecosystem. The results provide a solid ground for empirical testing.

DNA methylation patterns from peripheral blood separate coronary artery disease patients with and without heart failure.

AIMS: Natriuretic peptides are useful for diagnosis and prognostication of heart failure of any cause. Now, research aims to discover novel biomarkers that will more specifically define the heart failure phenotype. DNA methylation plays a critical role in the development of cardiovascular disease with the potential to predict fundamental pathogenic processes. There is a lack of data relating DNA methylation in heart failure that specifically focuses on patients with severe multi-vessel coronary artery disease. To begin to address this, we conducted a pilot study uniquely exploring the utility of powerful whole-genome methyl-binding domain-capture sequencing in a cohort of cardiac surgery patients, matched for the severity of their coronary artery disease, aiming to identify candidate peripheral blood DNA methylation markers of ischaemic cardiomyopathy and heart failure. METHODS AND RESULTS: We recruited a cohort of 20 male patients presenting for coronary artery bypass graft surgery with phenotypic extremes of heart failure but who otherwise share a similar coronary ischaemic burden, age, sex, and ethnicity. Methylation profiling in patient blood samples was performed using methyl-binding domain-capture sequencing. Differentially methylated regions were validated using targeted bisulfite sequencing. Gene set enrichment analysis was performed to identify differences in methylation at or near gene promoters in certain known Reactome pathways. We detected 567 188 methylation peaks of which our general linear model identified 68 significantly differentially methylated regions in heart failure with a false discovery rate <0.05. Of these regions, 48 occurred within gene bodies and 25 were located near enhancer elements, some within coding genes and some in non-coding genes. Gene set enrichment analyses identified 103 significantly enriched gene sets (false discovery rate <0.05) in heart failure. Validation analysis of regions with the strongest differential methylation data was performed for two genes: HDAC9 and the uncharacterized miRNA gene MIR3675. Genes of particular interest as novel candidate markers of the heart failure phenotype with reduced methylation were HDAC9, JARID2, and GREM1 and with increased methylation PDSS2. CONCLUSIONS: We demonstrate the utility of methyl-binding domain-capture sequencing to evaluate peripheral blood DNA methylation markers in a cohort of cardiac surgical patients with severe multi-vessel coronary artery disease and phenotypic extremes of heart failure. The differential methylation status of specific coding genes identified are candidates for larger longitudinal studies. We have further demonstrated the value and feasibility of examining DNA methylation during the perioperative period to highlight biological pathways and processes contributing to complex phenotypes.

Obesity Is Associated with BRAFV600E-Mutated Thyroid Cancer.

Background: Thyroid cancer incidence has increased in many parts of the world since the 1980s, as has the prevalence of obesity. Evidence suggests that people with greater body size have higher thyroid cancer risk. However, it is unclear whether this association is causal or is driven by over-diagnosis of indolent cancers, because overweight/obese people use health services more frequently than those of normal weight, thus conferring greater opportunity for incidental diagnosis. Assessing whether obesity is associated with higher-risk thyroid cancers might help clarify this issue. Methods: We recruited 1013 people diagnosed with thyroid cancer between 2013 and 2016 and 1057 population controls, frequency matched by sex and age group. We used logistic regression to assess the association between body mass index (BMI) and overall thyroid cancer risk as well as by tumor BRAF mutational status as a marker of potentially higher-risk cancer. Results: Overall, obesity was associated with greater risk of thyroid cancer (odds ratio [OR] = 1.72; 95% confidence interval [CI 1.37-2.16] for obese vs. normal BMI). The association with obesity was significantly stronger for BRAF-mutation positive than BRAF-negative papillary thyroid cancers (PTCs; OR = 1.71 [CI 1.17-2.50] for BRAF-positive vs. BRAF-negative cancers). The increased risks associated with overweight/obesity did not vary by histological subtypes or presence/absence of adverse tumor histologic features. Conclusions: Greater risk of BRAF-mutated PTCs among those with high BMI suggests that the association may not merely reflect greater health care service use and indicates an independent relationship between obesity and clinically important thyroid cancer.

Understanding Pathways to the Diagnosis of Thyroid Cancer: Are There Ways We Can Reduce Over-Diagnosis?

BACKGROUND: The incidence of thyroid cancer has rapidly increased, and ecological evidence suggests this is due in some part to over-diagnosis. Understanding pathways to diagnosis could help determine whether unnecessary diagnosis can be avoided. METHODS: A population-based sample (n = 1007) of thyroid cancer patients diagnosed between July 2013 and August 2016 was recruited from Queensland, Australia (response rate 67%). Information from structured telephone interviews was used to describe diagnostic pathways for thyroid cancer, to investigate factors associated with diagnostic pathways, and to assess the most prevalent modes of diagnoses by which the lowest-risk, potentially over-diagnosed thyroid cancers (intrathyroidal microcarcinomas) are detected. RESULTS: Only 38% of participants presented with symptoms potentially related to thyroid cancer. Older age at diagnosis was associated with a lower prevalence of symptomatic diagnosis (prevalence ratio [PR] = 0.46 [confidence interval (CI) 0.31-0.68] for 70-79 vs. <30 years), as was frequent medical contact, while living in rural/regional areas was associated with a higher prevalence of symptomatic diagnosis (PR = 1.17 [CI 1.00-1.37] for rural/regional areas vs. major cities). Symptomatic diagnosis also occurred more for those whose tumors had adverse histopathological features (larger size, lymph node involvement, lymphovascular invasion). The likelihood of diagnosis of intrathyroidal microcarcinomas was greatest for those having surgical resection or monitoring for benign thyroid disease (PR = 3.87 [CI 2.81-5.32] and PR = 2.21 [CI 1.53-3.18], respectively). CONCLUSIONS: A minority of newly detected thyroid cancer cases were diagnosed because of symptoms. Access to medical care and factors related to cancer aggressiveness were associated with how diagnoses occurred. The likelihood of diagnosing the lowest-risk thyroid cancers was higher in situations related to management of other thyroid conditions. Adherence to thyroid management guidelines could reduce some thyroid cancer over-diagnosis, but ultimately better diagnostic tools are needed to differentiate between indolent cancers and those of clinical significance.

Body mass index and type 2 diabetes in Thai adults: defining risk thresholds and population impacts.

BACKGROUND: Body mass index (BMI) cut-off values (>25 and >30) that predict diabetes risk have been well validated in Caucasian populations but less so in Asian populations. We aimed to determine the BMI threshold associated with increased type 2 diabetes (T2DM) risk and to calculate the proportion of T2DM cases attributable to overweight and obesity in the Thai population. METHODS: Participants were those from the Thai Cohort Study who were diabetes-free in 2005 and were followed-up in 2009 and 2013 (n = 39,021). We used multivariable logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the BMI-T2DM association. We modelled non-linear associations using restricted cubic splines. We estimated population attributable fractions (PAF) and the number of T2DM incident cases attributed to overweight and obesity. We also calculated the impact of reducing the prevalence of overweight and obesity on T2DM incidence in the Thai population. RESULTS: Non-linear modelling indicated that the points of inflection where the BMI-T2DM association became statistically significant compared to a reference of 20.00 kg/m2 were 21.60 (OR = 1.27, 95% CI 1.00-1.61) and 20.03 (OR = 1.02, 95% CI 1.02-1.03) for men and women, respectively. Approximately two-thirds of T2DM cases in Thai adults could be attributed to overweight and obesity. Annually, if prevalent obesity was 5% lower, ~13,000 cases of T2DM might be prevented in the Thai population. CONCLUSIONS: A BMI cut-point of 22 kg/m2, one point lower than the current 23 kg/m2, would be justified for defining T2DM risk in Thai adults. Lowering obesity prevalence would greatly reduce T2DM incidence.

Low back pain and limitations of daily living in Asia: longitudinal findings in the Thai cohort study.

BACKGROUND: Low back pain (LBP) is a major cause of disability throughout the world. However, longitudinal evidence to relate low back pain and functional limitations is mostly confined to Western countries. In this study, we investigate the associations between low back pain and functional limitations in a prospective cohort of Thai adults. METHODS: We analysed information from the Thai Cohort Study of adult Open University adults which included 42,785 participants in both 2009 and 2013, with the majority aged 30 to 65 years and residing nationwide. We used multivariate logistic regression to explore the longitudinal associations between LBP in 2009 and 2013 ('never': no LBP in 2009 or 2013; 'reverting': LBP in 2009 but not in 2013; 'incident': no LBP in 2009 but LBP in 2013; and 'chronic': reporting LBP at both time points) and the outcome of functional limitations relating to Activities of Daily Living (ADL) in 2013. RESULTS: Low back pain was common with 30% of cohort members reporting low back pain in both 2009 and 2013 ('chronic LBP'). The 'chronic LBP' group was more likely than the 'never' back pain group to report functional limitations in 2013: adjusted odds ratios 1.60 [95% Confidence Interval: 1.38-1.85] for difficulties getting dressed; 1.98 [1.71-2.30] for walking; 2.02 [1.71-2.39] for climbing stairs; and 3.80 [3.38-4.27] for bending/kneeling. Those with 'incident LBP' or 'reverting LBP' both had increased odds of functional limitations in 2013 but the odds were not generally as high. CONCLUSIONS: Our nationwide data from Thailand suggests that LBP is a frequent public health problem among economically productive age groups with adverse effects on the activities of daily living. This study adds to the limited longitudinal evidence on the substantial impact of low back pain in Southeast Asia.

Incidence and risk factors for type 2 diabetes mellitus in transitional Thailand: results from the Thai cohort study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is increasingly prevalent in countries undergoing rapid development, including Thailand. We assessed T2DM incidence over an 8-year period in a nationwide cohort of Thai adults. METHODS: Thai Cohort Study participants were surveyed in 2005, 2009 and 2013. The analysed cohort members were aged (15-88), did not have diabetes in 2005 and were followed up by questionnaire in 2013 (n=39 507). T2DM was ascertained using self-report, which has been validated using physician interviews. We calculated the 8-year cumulative incidence of T2DM. Multivariable logistic regression assessed associations between potential risk factors and T2DM incidence. RESULTS: 8-year cumulative incidence of T2DM (2005 to 2013) was 177 per 10 000 (95% CI 164 to 190). Crude and age-standardised cumulative incidences of T2DM by sex were 249 per 10 000 (95% CI 226 to 272) and 222 per 10 000 (95% CI 219 to 225) for men; and 119 per 10 000 (95% CI 105 to 133) and 96 per 10 000 (95% CI 94 to 98) for women, respectively. T2DM increased significantly for both sexes with increasing age and body mass index (BMI) (p trend <0.001 for both). Residence in an urban area as a child associated with T2DM among men and women (OR=1.4, 95% CI 1.1 to 1.7 and OR=1.4, 95% CI 1.01 to 1.79); this was no longer statistically significant after adjusting for BMI. Among men, smoking (OR=1.7, 95% CI 1.3 to 2.2) and alcohol intake (OR=1.8, 95% CI 1.1 to 3.0) were associated with T2DM. CONCLUSIONS: This study found that the sociodemographic and lifestyle changes that have accompanied Thailand's economic development are associated with T2DM risk in a large cohort of Thai adults. Our findings highlight the need to address these transitions to prevent a further increase in the national incidence of T2DM, particularly among Thai men.

A health advocacy intervention for adolescents with intellectual disability: a cluster randomized controlled trial.

AIM: Adolescents with intellectual disability experience poorer heath than their peers in the general population, partially due to communication barriers and knowledge gaps in their health history. This study aimed to test a health intervention package against usual care for a range of health promotion and disease detection outcomes. METHOD: A parallel-group cluster randomized controlled trial was conducted with Australian adolescents with intellectual disability living in the community. Randomization occurred at school level. The intervention package consisted of classroom-based health education, a hand-held personalized health record, and a health check. Evidence of health promotion, disease prevention, and case-finding activities were extracted from general practitioners' records for 12 months post-intervention. RESULTS: Clinical data was available for 435 of 592 (73.5%) participants from 85 schools. Adolescents allocated to receive the health intervention were more likely to have their vision (odds ratio [OR] 3.3; 95% confidence interval [CI] 1.8-6.1) and hearing (OR 2.7; 95% CI 1.0-7.3) tested, their blood pressure checked (OR 2.4; 95% CI 1.6-3.7), and weight recorded (OR 4.8; 95% CI 3.1-7.6). There was no difference between health intervention and usual care for identification of new diseases. INTERPRETATION: The school-based intervention package increased healthcare activity in adolescents with intellectual disability living in the community.

Impact of High-Sensitivity Troponin I Testing with Sex-Specific Cutoffs on the Diagnosis of Acute Myocardial Infarction.

BACKGROUND: High-sensitivity cardiac troponin I (hs-cTnI) assays show sex-dependent differences in the 99th percentile of healthy populations, with concentrations in women approximately 50% lower. The adoption of sex-specific cutoffs seems appropriate, although it is not yet clear what effect these will have on acute myocardial infarction (AMI) diagnosis and management. METHODS: We conducted a retrospective pre- and postchangeover analysis of troponin I testing in the 6 months before and after moving from the contemporary Abbott Architect TnI assay (cTnI) to hs-cTnI at 2 tertiary centers in Australia and New Zealand. The cTnI cutoff was 30 ng/L for both sexes, whereas a female-specific cutoff of 16 ng/L was adopted upon changeover to hsTnI. RESULTS: Changeover from the cTnI assay to the hs-cTnI assay increased the number of female patients with increased troponin I concentrations at both sites (from 29.7% to 34.9% and from 22.4% to 30.8%; P < 0.001). There was no statistically significant change in the number of men with increased concentrations in the same time period (P = 0.09). The increased percentage of women with increased troponin I was not associated with an increase in the number of women with AMI diagnoses at either center. Angiographic data available from 1 center showed no change in the percentage of angiograms performed in women. CONCLUSIONS: Although increasing the proportion of women with increased troponin I, adopting sex-specific cutoffs with the hs-cTnI assay did not lead to an increase in AMI diagnoses in females, or in the number of women undergoing angiography.

Coronary Vasospasm as an Unexpected Cause of Intraoperative Hemodynamic Instability and Cardiac Arrest.

We present a case of intraoperative coronary artery vasospasm during thoracic surgery causing profound hemodynamic instability. A 68-year-old man undergoing completion right pneumonectomy exhibited intraoperative widespread ST elevation with associated hypotension. Transesophageal echocardiography performed by the anesthetist revealed hypokinetic apical and lateral walls, prompting transportation to the catheterization laboratory, with angiography demonstrating widespread coronary artery spasm. Intracoronary nitroglycerin relieved the vasospasm; however, heparin administration caused significant postoperative bleeding, resulting in cardiac arrest requiring resuscitation and return to the operating room. He ultimately recovered and was discharged to a rehabilitation facility 3 weeks later.