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Objective: To develop an artificial intelligence deep neural network (AI-DNN) algorithm to analyze 12-lead electrocardiogram (ECG) for detection of acute pulmonary embolism (PE) and PE categories. Patients and Methods: A cohort of patients seen between January 1, 1999, and December 31, 2020, from across the Mayo Clinic Enterprise with computed tomography pulmonary angiogram (CTPA) and ECG performed ±6 hours was identified. Natural language processing algorithms were applied to radiology reports to determine the diagnosis of acute PE, acute right ventricular strain pulmonary embolism (RVSPE), saddle pulmonary embolism (SADPE), or no PE. Diagnostic performance parameters of the AI-DNN reported were area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: A cohort of patients with CTPA report and ECG consisted of 79,894 patients including 7423 (9.3%) with acute PE, among whom 1138 patients had RVSPE or SADPE. Artificial intelligence deep neural network predicted acute PE with a modest accuracy of AUROC of 0.69 (95% CI, 0.68-0.71), sensitivity of 63.5%, specificity of 64.7%, PPV of 15.6%, and NPV of 94.5%. The AI-DNN prediction using the same algorithm for RVSPE or SADPE was higher (AUROC, 0.84; 95% CI, 0.81-0.86) with a sensitivity of 80.8%, specificity of 64.7.8%, PPV of 3.5%, and NPV of 99.5%. Conclusion: An AI-based analysis of 12-lead ECG shows modest detection power for acute PE in patients who underwent CTPA, with higher accuracy for high-risk PE. Moreover, with the high NPV, it has the clinical potential to exclude high-risk PE quickly and correctly.

Objective: To evaluate the effect of smart device-based telerehabilitation on Vo2peak in patients after myocardial infarction. Patients and Methods: This was a pilot, single-center, randomized, cross-over study with a 3-month intervention. One month after myocardial infarction, patients had cardiopulmonary exercise testing and a 6-minute walking test (6MWT) and were randomly assigned 1:1. In the intervention group, patients received a smartwatch to track the recommended number of steps, which was individualized and derived from the 6MWT. A study nurse telemonitored adherence to the recommended number of steps a day. In the control group, 150 minutes a week of moderate-intensity physical activity was recommended. After 3 months study arms were crossed over, and study procedures were repeated after 3 months. Results: Between June 1, 2019, and February 28, 2023, 64 patients were randomized, of which 61 (aged 51±10 years, 10% women) completed the study. Overall, the smart device-based telerehabilitation led to 2.31 mL/kg/min (95% CI, 1.25-3.37; P

The architectural heritage of a particular place, in most cases, is characterised by vernacular and unique constructions that have been adapted to local climate conditions. For this purpose, specific materials and construction methods have traditionally been used that, in addition to the durability of the construction, also allow for the consideration of the energy efficiency of the building itself. The present intersection of climate change and architecture has led to new exposure to the external agents for which constructions were designed, forcing, in most cases, a review of building envelopes and very costly proposals. From the point of view of efficiency, intervention strategies with passive measures are proposed that not only improve the energy performance and maintenance of buildings themselves, but also lower the overall energy consumption. Using a heritage case study of the city of Seville, the Moroccan Pavilion, at Expo 92, this work includes an analysis and proposal of effective action through a methodological study of energy efficiency. The problem of high energy consumption during the summer months in Seville is tested in the Pavilion. The results indicate an urgent need for renovation, and among different options, new intervention measures are recommended as an alternative to consumption based on knowledge and tradition; moreover, passive construction elements are proposed in accordance with the climatic reality of the environment for optimal conservation in new climate scenarios.

Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF

Background A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small‐vessel disease, implicating vascular disease in the development of depression. Cross‐sectional studies demonstrate a link between endothelial dysfunction and MDD, but the prospective association between peripheral endothelial dysfunction (PED) and an incident diagnosis of MDD is unknown. Methods and Results Patients undergoing a baseline assessment of cardiovascular risk were evaluated for PED using reactive hyperemia–peripheral arterial tonometry (≤1.8 consistent with PED). Patient medical records were reviewed to identify those who underwent a formal clinical evaluation of MDD after the index PED evaluation. The frequency of PED was compared in those with and without MDD. Logistic regression analyses were performed to assess the association between baseline PED and incident MDD. Between January 2006 and December 2020, 1614 patients underwent testing for PED. Four hundred eighty‐four (30.1%) patients underwent a formal evaluation for MDD after (0–15 years) the index procedure (mean±SD age, 52.8±13.8 years; 65.2% women). Of these, 157 (32.4%) had PED and 108 (31.0%) were diagnosed with MDD. Individuals with MDD had a higher frequency of PED (40.2% versus 30.2%; P=0.034) compared with those without MDD. In multivariable analyses, PED was significantly associated with MDD (odds ratio, 2.3 [95% CI, 1.4–3.8]; P

Purpose: To report 2 cases of presumed retinal hamartoma (RH) in pediatric patients with genetically-confirmed familial adenomatous polyposis (FAP), both evaluated by optical coherence tomography (OCT) and one evaluated with optical coherence tomography angiography (OCTA). Observations: A six-year-old girl presented with occasional blurry vision in the left eye. OCT showed a foveal hyperreflective lesion with disruption of photoreceptors and retinal pigment epithelium (RPE). A nine-year-old female with a past medical history of FAP presented with progressively decreasing vision and floaters in the right eye for the past 6 months. OCT showed a well-demarcated hyperreflective ovoid lesion in the fovea. OCTA revealed no flow signal within the lesion, as well as a second smaller hyperreflective lesion temporal to the fovea. Both patients were diagnosed with presumed retinal hamartoma in the setting of FAP. Conclusions and Importance: Presumed RH can occur in genetically-confirmed, pediatric FAP. On OCTA imaging, these lesions show no intrinsic vascularity. Evaluation with OCT and knowledge of foveal changes in these patients can help identify underlying systemic disease.

Background:. The umbilicus significantly contributes to abdominal aesthetics, with its reconstruction often necessary after certain procedures like umbilical herniorrhaphy, laparotomies, and abdominoplasty. Neoumbilicoplasty techniques have evolved, addressing various issues through approaches like skin and cartilage grafts, and local flaps. We are reporting our technique for neoumbilicoplasty. Methods:. This study describes a novel neoumbilicoplasty technique implemented in 90 patients (88 women, two men) who underwent lipoabdominoplasty between February 2021 and June 2023. Exclusion criteria included procedures unrelated to neoumbilicoplasty. Surgical steps involved precise marking, dissection, and suturing to create a natural umbilical hood. Patient satisfaction was measured using the Global Aesthetic Improvement Scale. Results:. The mean age was 37.7 years, with pre- and postoperative anatomics of 24.9 kg per m² and 24.2 kg per m², respectively. The average surgery duration was 84 minutes. No major complications occurred, but minor complications included dehiscence (6%), granuloma (5%), superficial infection (2%), bruising (1%), seroma (1%), and flattening (8%). Most complications were resolved with minor interventions. Patient satisfaction was high, with 96% of patients and the surgeon expressing significant satisfaction. Conclusions:. House-roof neoumbilicoplasty is an innovative technique designed to effectively restore abdominal aesthetics through straightforward steps and timing, combined with high-definition lipoabdominoplasty.

Purpose: To report 2 cases of enlarged foveal avascular zone (FAZ) on optical coherence angiography (OCTA) imaging in pediatric patients with cutis marmorata telangiectatica congenita (CMTC). Observations: A 10-week-old female and a 3-year-old male diagnosed with CMTC presented for retinal examination. Both had peripheral avascularity on fluorescein angiography (FA) and enlargement of the FAZ on OCTA in both eyes. Conclusions and Importance: Pediatric patients with CMTC should undergo ocular evaluation with not only FA, but also OCTA to more robustly evaluate the affected retina in this rare disease.

Purpose: To report 2 cases of atypically located, non-nasal colobomas in the pediatric population. Observations: A 3-week-old female neonate with no known past ocular or medical history was diagnosed with temporal iris and chorioretinal coloboma with tractional membranes upon examination under anesthesia and imaging. A 9-year-old female with a history of bilateral sensorineural hearing loss and left mild hydronephrosis presented with a temporal chorioretinal coloboma associated with retinal detachment. Conclusions and importance: Very few cases of atypically located, non-nasal pediatric colobomas have been reported, and they lack a clear cause or mechanism of formation. Continued documentation of their occurrence and research into their formation at a molecular and embryological level are warranted to better understand their pathogenesis.

Abstract Background Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification. Methods We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants. Results In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees. Conclusions Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.

Abstract Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645–1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298–1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.

Determining the type of kidney stones allows urologists to prescribe a treatment to avoid the recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature, it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculus types acquired with an endoscope during standard ureteroscopies. This contribution details the construction of an in-vivo dataset of endoscopic images with four of the most recurrent classes in clinical practice. It also describes the design, implementation, and results of the classifiers (shallow machine learning and deep learning-based methods) of kidney stones. Even if the best results were obtained by the InceptionV3 architecture (weighted accuracy, precision, recall and F1-Score of $97\pm 03\%, 97\pm 03\%, 98\pm 04\%$ , and $98\pm 03\%$ , respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted accuracy, precision, recall and F1-Score values of $96\pm 14\%$ ).

Objective: To develop and validate a machine learning model that predicts the most successful antihypertensive treatment for an individual. Patients and Methods: The causal, deep neural network-based model was trained on data from 16,917 newly diagnosed hypertensive patients attending Mayo Clinic’s primary care practices from January 1, 2005, to December 31, 2021. Eligibility criteria included a diagnosis of primary hypertension, blood pressure and creatinine measurements before antihypertensive treatment, treatment within 9 months of diagnosis, and at least 1 year of follow up. The primary outcome was model performance in predicting the likelihood of a successful antihypertensive treatment 1 year from the start of treatment. Treatment success was defined as achieving blood pressure control with no moderate or severe adverse effects. Model validation and guideline agreement was assessed on 1000 patients. Results: In the training set of 16,917 participants (60.8±14.7 years; 8344 [49.3%] women), 33.8% achieved blood pressure control without moderate or severe adverse effects for at least a year with initial treatment. The most common treatment was angiotensin-converting enzyme inhibitor (39.1% average success), and the most successful was angiotensin-converting enzyme inhibitor-thiazide combination (44.4% average success). Our custom-built causal, deep neural network-based model exhibited the highest accuracy in predicting individualized treatment success with a precision of 51.7%, recall of 44.4%, and F1 score of 47.8%. Compared with actual physician practice on the validation set (77.9% agreement), the algorithm aligned with the Eighth Joint National Committee hypertension guidelines 95.7% of the time. Conclusion: A machine learning algorithm can accurately predict the likelihood of antihypertensive treatment success and help personalize hypertension management.

Background Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting during office work, this environment may serve as a potentially feasible setting for interventions to reduce sedentary behavior. Methods and Results A randomized cross‐over clinical trial was conducted at an employee wellness center. Four office settings were evaluated on 4 consecutive days: stationary or sitting station on day 1 (referent), and 3 subsequent active workstations (standing, walking, or stepper) in randomized order. Neurocognitive function (Selective Attention, Grammatical Reasoning, Odd One Out, Object Reasoning, Visuospatial Intelligence, Limited‐Hold Memory, Paired Associates Learning, and Digit Span) and fine motor skills (typing speed and accuracy) were tested using validated tools. Average scores were compared among stations using linear regression with generalized estimating equations to adjust standard errors. Bonferroni method adjusted for multiple comparisons. Healthy subjects were enrolled (n=44), 28 (64%) women, mean±SD age 35±11 years, weight 75.5±17.1 kg, height 168.5±10.0 cm, and body mass index 26.5±5.2 kg/m2. When comparing active stations to sitting, neurocognitive test either improved or remained unchanged, while typing speed decreased without affecting typing errors. Overall results improved after day 1, suggesting habituation. We observed no major differences across active stations, except decrease in average typing speed 42.5 versus 39.7 words per minute with standing versus stepping (P=0.003). Conclusions Active workstations improved cognitive performance, suggesting that these workstations can help decrease sedentary time without work performance impairment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT06240286.

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer‐assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. Methods and Results Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle–brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all‐cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow‐up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78–3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49–2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43–22.39]) at 5 years. Conclusions An artificial intelligence–enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Background Although several studies have documented the impact of the COVID-19 pandemic on mental health, the long-term effects remain unclear. Aims To examine longitudinal changes in mental health before and during the consecutive COVID-19 waves in a well-established probability sample. Method An online survey was completed by the participants of the COVID-19 add-on study at four time points: pre-COVID-19 period (2014–2015, n = 1823), first COVID-19 wave (April to May 2020, n = 788), second COVID-19 wave (August to October 2020, n = 532) and third COVID-19 wave (March to April 2021, n = 383). Data were collected via a set of validated instruments, and analysed with latent growth models. Results During the pandemic, we observed a significant increase in stress levels (standardised β = 0.473, P < 0.001) and depressive symptoms (standardised β = 1.284, P < 0.001). The rate of increase in depressive symptoms (std. covariance = 0.784, P = 0.014), but not in stress levels (std. covariance = 0.057, P = 0.743), was associated with the pre-pandemic mental health status of the participants. Further analysis showed that secondary stressors played a predominant role in the increase in mental health difficulties. The main secondary stressors were loneliness, negative emotionality associated with the perception of COVID-19 disease, lack of resilience, female gender and younger age. Conclusions The surge in stress levels and depressive symptoms persisted across all three consecutive COVID-19 waves. This persistence is attributable to the effects of secondary stressors, and particularly to the status of mental health before the COVID-19 pandemic. Our findings reveal mechanisms underlying the surge in mental health difficulties during the COVID-19 waves, with direct implications for strategies promoting mental health during pandemics.

Summary: Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods: Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023. Findings: ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14–2.71), 4.23 (3.74–4.78), and 11.75 (10.2–13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age. Interpretation: ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired. Funding: Anumana.

Abstract Background Healthcare systems may be resilient and adaptive, but they are not fit for purpose in their current state. Increasing threats to health system sustainability have underscored the need to move towards a learning health system in which research and data are used routinely in clinical practice to facilitate system improvement. This study aimed to establish which elements of the learning health system were being realised within a university-based general practice and determine acceptability from staff to embrace further the transition towards a learning health system. Methods Semi-structured interviews were conducted with practice staff, including clinical and administrative staff, to determine the current state of the learning health system in the practice. An embedded researcher was placed within the general practice on a part-time basis to investigate the learning health system model. Interviews were transcribed and thematically analysed based on the National Academy of Medicine’s framework of learning health systems. Results In total, 32 (91%) practice staff were interviewed, comprising general practitioners (n = 15), nurses (n = 3), administrative staff (n = 13), and a psychologist (n = 1). Participants indicated that the practice was operating with several characteristics of a learning health system (e.g., emphasising science and informatics; focusing on patient-clinician partnerships; applying incentives; supporting a continuous learning culture; and establishing structures and governance for learning). These measures were supported by the university-based setting, and resultant culture of learning. Nevertheless, there were areas of the practice where the learning health system could be strengthened, specifically relating to the use of patient data and informatics. Staff generally expressed willingness to engage with the process of strengthening the learning health system within their practice. Conclusion Although the idea of a learning health system has been gaining traction in recent years, there are comparatively few empirical studies presented in the literature. This research presents a case study of a general practice that is operating as a learning health system and highlights the utility of using the learning health system framework.

Abstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ( $$p\le 5 \times 10^{-8}$$ p ≤ 5 × 10 - 8 ), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.

Abstract Dysregulated gene expression programs and redox and metabolic adaptations allow cancer cells to survive under high oxidative burden. These mechanisms also represent therapeutic vulnerabilities. Using triple-negative breast cancer (TNBC) as a model, we show that compared to normal human breast epithelial cells, the TNBC cells, MDA-MB-231 and MDA-MB-468 that harbor constitutively active STAT3 also express higher glucose-6-phosphate dehydrogenase (G6PD), thioredoxin reductase (TrxR)1, NADPH, and GSH levels for survival. Present studies discover that the natural product, R001, targets these adaptation mechanisms. Treatment of TNBC cells with R001 inhibited constitutively active STAT3, STAT3-regulated gene expression, and the functions of G6PD and TrxR1. Consequently, in the TNBC, but not normal cells, R001 suppressed GSH levels, but raised NADPH levels, reflective of a loss of mitochondrial respiration and which led to reactive oxygen species (ROS) induction, all of which led to loss of viable cells and inhibition of anchorage-dependent and independent growth. R001 treatment further led to early pyroptosis and late DNA damage, cell cycle arrest, and apoptosis only in the TNBC cells. Oral administration of 5 mg/kg R001 inhibited MDA-MB-468 xenografts growth in mice, with reduced pY705-STAT3, G6PD, TrxR1, and GSH levels. R001 serves as a therapeutic entity that targets the vulnerabilities of TNBC cells to inhibit tumor growth in vivo.

Objective: To characterize the utility of an existing electrocardiogram (ECG)-artificial intelligence (AI) algorithm of left ventricular dysfunction (LVD) in immune-mediated necrotizing myopathy (IMNM). Patients and Methods: A retrospective cohort observational study was conducted within our tertiary-care neuromuscular clinic for patients with IMNM meeting European Neuromuscular Centre diagnostic criteria (January 1, 2000, to December 31, 2020). A validated AI algorithm using 12-lead standard ECGs to detect LVD was applied. The output was presented as a percent probability of LVD. Electrocardiograms before and while on immunotherapy were reviewed. The LVD-predicted probability scores were compared with echocardiograms, immunotherapy treatment response, and mortality. Results: The ECG-AI algorithm had acceptable accuracy in LVD prediction in 74% (68 of 89) of patients with IMNM with available echocardiograms (discrimination threshold, 0.74; 95% CI, 0.6-0.87). This translates into a sensitivity of 80.0% and specificity of 62.8% to detect LVD. Best cutoff probability prediction was 7 times more likely to have LVD (odds ratio, 6.75; 95% CI, 2.11-21.51; P=.001). Early detection occurred in 18% (16 of 89) of patients who initially had normal echocardiograms and were without cardiorespiratory symptoms, of which 6 subsequently advanced to LVD cardiorespiratory failure. The LVD probability scores improved for patients on immunotherapy (median slope, −3.96; R = −0.12; P=.002). Mortality risk was 7 times greater with abnormal LVD probability scores (hazard ratio, 7.33; 95% CI, 1.63-32.88; P=.009). Conclusion: In IMNM, an AI-ECG algorithm assists detection of LVD, enhancing the decision to advance to echocardiogram testing, while also informing on mortality risk, which is important in the decision of immunotherapy escalation and monitoring.

Abstract Objective To compare colorectal cancer (CRC) incidences in carriers of pathogenic variants of the MMR genes in the PLSD and IMRC cohorts, of which only the former included mandatory colonoscopy surveillance for all participants. Methods CRC incidences were calculated in an intervention group comprising a cohort of confirmed carriers of pathogenic or likely pathogenic variants in mismatch repair genes (path_MMR) followed prospectively by the Prospective Lynch Syndrome Database (PLSD). All had colonoscopy surveillance, with polypectomy when polyps were identified. Comparison was made with a retrospective cohort reported by the International Mismatch Repair Consortium (IMRC). This comprised confirmed and inferred path_MMR carriers who were first- or second-degree relatives of Lynch syndrome probands. Results In the PLSD, 8,153 subjects had follow-up colonoscopy surveillance for a total of 67,604 years and 578 carriers had CRC diagnosed. Average cumulative incidences of CRC in path_MLH1 carriers at 70 years of age were 52% in males and 41% in females; for path_MSH2 50% and 39%; for path_MSH6 13% and 17% and for path_PMS2 11% and 8%. In contrast, in the IMRC cohort, corresponding cumulative incidences were 40% and 27%; 34% and 23%; 16% and 8% and 7% and 6%. Comparing just the European carriers in the two series gave similar findings. Numbers in the PLSD series did not allow comparisons of carriers from other continents separately. Cumulative incidences at 25 years were < 1% in all retrospective groups. Conclusions Prospectively observed CRC incidences (PLSD) in path_MLH1 and path_MSH2 carriers undergoing colonoscopy surveillance and polypectomy were higher than in the retrospective (IMRC) series, and were not reduced in path_MSH6 carriers. These findings were the opposite to those expected. CRC point incidence before 50 years of age was reduced in path_PMS2 carriers subjected to colonoscopy, but not significantly so.

Obesity remains a major public health problem, affecting almost half of adults in the United States. Increased risk of cardiovascular disease (CVD) and CVD mortality are major obesity‐related complications, and management guidelines now recommend weight loss as a key strategy for the primary prevention of CVD in patients with overweight or obesity. The recently demonstrated efficacy of some pharmacologic therapies for chronic weight management may encourage health care professionals to recognize obesity as a treatable serious chronic disease and motivate patients to re‐engage with weight loss when previous attempts have been ineffective or unsustainable. This review article summarizes the benefits and challenges associated with lifestyle changes, bariatric surgery, and historical pharmacologic interventions in the treatment of obesity, and focuses on the current evidence for the efficacy and safety of the newer glucagon‐like peptide‐1 receptor agonist medications in the management of obesity and potential reduction of CVD risk. We conclude that the available evidence demonstrates glucagon‐like peptide‐1 receptor agonists should be strongly considered in clinical practice for the treatment of obesity and reduction of CVD risk in people with type 2 diabetes. If ongoing research proves glucagon‐like peptide‐1 receptor agonists to be effective in reducing the risk of CVD onset in patients with obesity, irrespective of type 2 diabetes status, it will herald a new treatment paradigm in this setting, and now is the time for health care professionals to better recognize the benefits of these agents.

The application of Laser-Induced Breakdown Spectroscopy (LIBS) is presented for the direct elemental analysis of hydrocarbon-rich solids. In recent years, LIBS has become a powerful tool for obtaining elemental information and mapping analysis of different petroleum-rich samples with minimal to no sample preparation and without the need to separate the organic matter from the inorganic matter. By selecting the most intense and representative lines, the element distribution in a 2D map can be accessed in less than ten hours. For this reason, two types of hydrocarbon-rich solids were chosen for examination, i.e., core and shale. Nineteen elements were identified in the samples, and 2D mapping for Ca, Mg, Fe, Ti, Ni, C, H, K, O, and S is presented here. A detailed distribution of the elements, and the main components of the hydrocarbons present in these samples, were determined using LIBS. The H/C molar ratio was determined by building H and C calibration curves using data obtained from classical elemental analysis via combustion. These calibration curves contained a high degree of linearity (R2 > 0.98) with the limits of detection for C (193 nm), C (247 nm), and H (656 nm) of 848 mg kg−1, 353 mg kg−1, and 3.5 mg kg−1, respectively. By combining all of this information, LIBS allowed us to determine how these elements were spatially distributed, which elements were dominant in a given sample, and how much hydrocarbon was present, as well as providing a quantitative determination of the H/C molar ratio, and its correlation with the source of origin.

Background: The figure of Luis Simarro Lacabra is not well-known outside Spain, but within the country became a character of special scientific and public importance. Luis Simarro, a person of a marked progressist spirit, not only was involved from his youth for in political matters, showing great interest in the modernization of a nation anchored in traditionalism, but also transferred this way of thinking to his professional work as a doctor, neurologist, histologist, psychiatrist, psychologist, and teacher. Methods: The authors gathered biographic story of Simarro and summarized his work in microscopic work in neuroscience and the development of experimental psychology. Results: Simarro would become, for his own merit, one of the great founders of Spanish neuroscience, to which he made great contributions together with Santiago Ramón y Cajal. He was also the first professor of experimental psychology at Spanish University and developed an interesting political and institutional work. In fact, his lack of international recognition is mainly due to the fact that Luis Simarro, despite his great talent, was not especially prone to publishing the scientific works. Therefore, his neurological findings, sometimes, were to be unjustly attributed to other colleagues. Conclusion: In this article, we have provided a brief profile of the figure of Simarro with the intention of projecting the importance of his figure of it beyond Spanish borders.

Background There is a scarcity of validated rapid dietary screening tools for patient use in the clinical setting to improve health and reduce cardiovascular risk. The Healthy Eating Index (HEI) 2015 measures compliance with the 2015 to 2020 Dietary Guidelines for Americans but requires completion of an extensive diet assessment to compute, which is time consuming and impractical. The authors hypothesize that a 19‐item dietary survey assessing consumption of common food groups known to affect health will be correlated with the HEI‐2015 assessed by a validated food frequency questionnaire and can be further reduced without affecting validity. Methods and Results A 19‐item Eating Assessment Tool (EAT) of common food groups was created through literature review and expert consensus. A cross‐sectional survey was then conducted in adult participants from a preventive cardiology clinic or cardiac rehabilitation and in healthy volunteers (n=661, mean age, 36 years; 76% women). Participants completed an online 156‐item food frequency questionnaire, which was used to calculate the HEI score using standard methods. The association between each EAT question and HEI group was analyzed by Kruskal‐Wallis test. Linear regression models were subsequently used to identify univariable and multivariable predictors for HEI score for further reduction in the number of items. The final 9‐item model of Mini‐EAT was validated by 5‐fold cross validation. The 19‐item EAT had a strong correlation with the HEI score (r=0.73) and was subsequently reduced to the 9 items independently predictive of the HEI score: fruits, vegetables, whole grains, refined grains, fish or seafood, legumes/nuts/seeds, low‐fat dairy, high‐fat dairy, and sweets consumption, without affecting the predictive ability of the tool (r=0.71). Conclusions Mini‐EAT is a 9‐item validated brief dietary screener that correlates well with a comprehensive food frequency questionnaire. Future studies to test the Mini‐EAT's validity in diverse populations and for development of clinical decision support systems to capture changes over time are needed.

Objective: With the emergence of artificial intelligence (AI)-based health interventions, systemic racism remains a concern as these advancements are frequently developed without race-specific data analysis or validation. To evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-concept ancillary study for community-based screening. Methods: Enrollees completed cardiovascular testing including standard 12-lead ECG and a limited echocardiogram (TTE). All ECGs were analyzed using previously published institution-based AI algorithms. AI-ECG predictions were generated for age, sex, and decreased left ventricular ejection fraction (LVEF). Diagnostic accuracy of the AI-ECG for decreased LVEF and sex was quantified using area under the receiver operating characteristic curve (AUC). Correlation between actual age and AI-ECG predicted age was assessed using Pearson correlation coefficients. Results: Fifty-four participants completed both an ECG and TTE (mean age 55 years [range 31-87 years]; 66.7% female). All participants were in sinus rhythm, and the median LVEF of the cohort was 60-65%. The AI-ECG for decreased LVEF demonstrated excellent performance with an AUC of 0.892 (95% confidence interval [CI] 0.708-1); sensitivity=50% (95% CI 9.5-90.5%; n=1/2) and specificity=96% (95% CI 86.8-98.9%; n=49/51). The AI-ECG for participant sex demonstrated similar performance with AUC of 0.944 (95% CI 0.891-0.998); sensitivity=100% (95% CI 82.4-100.0%; n=18/18) and specificity=77.8% (95% CI 61.9-88.3%; n=28/36). The AI-ECG predicted mean age was 55 years (range 26.9-72.6 years) with a strong correlation to actual age (R=0.769; p

Abstract Aims/Hypothesis The need for understanding obesity as a chronic disease, its stigmatization, and the lack of actionability related to it demands a new approach. The adiposity‐based chronic disease (ABCD) model is based on adiposity amount, distribution, and function, with a three stage complication‐centric rather than a body mass index (BMI)‐centric approach. The prevalence rates and associated risk factors are presented. Methods In total, 2159 participants were randomly selected from Czechia. ABCD was established as BMI ≥ 25 kg/m2 or high body fat percent, or abdominal obesity and then categorized by their adiposity‐based complications: Stage 0: none; Stage 1: mild/moderate; Stage 2: severe. Results ABCD prevalence was 62.8%. Stage 0 was 2.3%; Stage 1 was 31.4%; Stage 2 was 29.1%. Comparing with other classifiers, participants in Stage 2 were more likely to have diabetes, hypertension, and metabolic syndrome than those with overweight, obesity, abdominal obesity, and increased fat mass. ABCD showed the highest sensitivity and specificity to detect participants with peripheral artery disease, increased intima media, and vascular disease. Conclusion/Interpretation The ABCD model provides a more sensitive approach that facilitates the early detection and stratification of participants at risk compared to traditional classifiers.

Abstract Background Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. Methods This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011–2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. Results Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all P trend

More than 500 million people worldwide live with cardiovascular disease (CVD). Health systems today face fundamental challenges in delivering optimal care due to ageing populations, healthcare workforce constraints, financing, availability and affordability of CVD medicine, and service delivery. Digital health technologies can help address these challenges. They may be a tool to reach Sustainable Development Goal 3.4 and reduce premature mortality from non-communicable diseases (NCDs) by a third by 2030. Yet, a range of fundamental barriers prevents implementation and access to such technologies. Health system governance, health provider, patient and technological factors can prevent or distort their implementation. World Heart Federation (WHF) roadmaps aim to identify essential roadblocks on the pathway to effective prevention, detection, and treatment of CVD. Further, they aim to provide actionable solutions and implementation frameworks for local adaptation. This WHF Roadmap for digital health in cardiology identifies barriers to implementing digital health technologies for CVD and provides recommendations for overcoming them.

BackgroundThe prevalence of obesity continues to increase in spite of substantial efforts towards its prevention, posing a major threat to health globally. Circadian disruption has been associated with a wide range of preclinical and clinical disorders, including obesity. However, whether rest-activity rhythm (RAR), an expression of the endogenous circadian rhythm, is associated with excess adiposity is poorly understood. Here we aimed to assess the association of RAR with general and abdominal obesity.MethodsNon-institutionalized adults aged ≥20 years participating in the US National Health and Nutrition Examination Survey (NHANES) 2011-2014 who wore accelerometers for at least four 24-hour periods were included (N=7,838). Amplitude, mesor, acrophase and pseudo-F statistic of RAR were estimated using extended cosinor model, and interdaily stability (IS) and intradaily variability (IV) were computed by nonparametric methods. We tested the association between rest-activity rhythm and general obesity defined by body mass index and abdominal obesity by waist circumference. Waist-to-height ratio, sagittal abdominal diameter, and total and trunk fat percentages measured by imaging methods were also analyzed.ResultsIn multivariable analysis, low amplitude (magnitude of the rhythm), mesor (rhythm-corrected average activity level), pseudo-F statistic (robustness of the rhythm), IS (day-to-day rhythm stability), or high IV (rhythm fragmentation) were independently associated with higher likelihood of general or abdominal obesity (all Ps

Resonators based on micro and nanoelectromechanical systems (MEMS/NEMS) are used in many applications, including biological and gas sensors, magnetic field sensors, RF switches, accelerometers, piezoelectric micro and nanogenerators, and viscosity sensors. The design of these resonators requires analytical models to predict their mechanical behavior and optimize the sensitivity and resolution. However, most of these models are only applied to resonators with rectangular and uniform cross-sections. In this paper, we present the analytical modeling to determine the first bending resonant frequency, out-of-plane deflections, and normal stresses of MEMS/NEMS-based multilayered resonators with variable cross-sections and multiple fixed supports. The proposed modeling is derived using the well-known Rayleigh and Macaulay methods, as well as the Euler-Bernoulli beam theory. This analytical modeling is applied to four multilayered resonators with different clamped supports and non-uniform cross-sections. The results of our analytical modeling agree well with respect to those of finite element method (FEM) models and experimental data reported in the literature. The proposed analytical modeling can be used to estimate the frequency shift of resonators due to variations of their geometric parameters, number of clamped-supports or mechanical properties of the materials. Furthermore, this modeling can be used to obtain optimal designs of resonators that ensure safe operations and enhanced performance for sensors and energy harvesters in telecommunications, automotive sector, aerospace industry, consumer electronics, non-destructive testing, and navigation.

Background: Miguel de Cervantes (1547–1617) was a Spanish writer, who has often been considered as the greatest writer in the Spanish language and one of the world's outstanding novelists. His novel Don Quixote, is a work often cited as both the first modern novel and one of the best works of world literature. The life of Cervantes has been full of fascinations, imaginations, attracting the attention from all walks of people, including psychiatrists. Methods: With career interest in psychopharmacology, the author in this review intends to focus on Cervantes's notions in his works on the use of psychotropic agents. The author also categorized psychotropic agents into four different scenarios of use – therapeutic remedies, toxic and poisonous agents (love philters, poisonous potions), antidotes as well as drugs of abuse (witches' ointments). Results: Cervantes' works were found that Cervantes made references to those preparations in Don Quixote, The Galatea, Journey to Parnassus, The Spanish English Lady, The Lawyer of Glass, The Jealous Old Man from Extremadura, The Dialogue of the Dogs, Pedro de Urdemalas and The Diversion. The main agents cited by Cervantes in the context analyzed included henbane, tobacco, rhubarb, rosemary, vervain, and in a masked way, opium. Cervantes did not identify the ingredients of other preparations with psychotropic properties, although, in the sense of the symptoms described by the author, they could be plants of the Solanaceae family, such as the henbane, nightshade, jimson weed, belladonna, or mandrake. Conclusion: Cervantes' texts, although by no means scientific treatises, give us with a correct description of the uses (and effects) of psychotropic substances in late Renaissance Spain, and explain how a group of drugs could have four archetypal qualities – remedy, poison, antidote, and drug of abuse.

Objective: To investigate the effect of replacing sitting with standing on cardiovascular risk factors tested in clinical trials. Methods: We searched databases from inception up to August 28, 2019, for studies examining the effect of replacing sitting with standing on fasting blood glucose, fasting insulin, and lipid levels; blood pressure; body fat mass; weight; and waist circumference in healthy adults. Differences in mean ± SD values were used for pooling the data and calculating the mean differences and CIs. Results: The search found 3507 abstracts. Nine clinical trials (8 randomized and 1 nonrandomized) with 877 (64.4% [n=565] women) participants met all inclusion criteria. The mean ± SD age was 45.34±5.41 years; mean follow-up was 3.81 months, and mean difference in standing time between the intervention and control groups was 1.33 hours per day. The follow-up fasting blood glucose and body fat mass values were slightly but significantly lower than baseline records in the intervention groups compared with control groups (−2.53; 95% CI, −4.27 to −0.79 mg/dL; and −0.75; 95% CI, −0.91 to −0.59 kg). The analysis for fasting insulin levels, lipid levels, blood pressure, weight, and waist circumference revealed no significant differences. Conclusion: Replacing sitting with standing can result in very small but statistically significant decreases in fasting blood glucose levels and body fat mass with no significant effect on lipid levels, blood pressure, weight, and waist circumference. Replacing sitting with standing can be used as an adjunctive intervention to decrease the burden of cardiovascular risk factors but cannot be used as an alternative to physical activity to decrease sedentary time.

Abstract Background The term arterial stiffness (ArSt) describes structural changes in arterial wall related to the loss of elasticity and is known as an independent predictor of cardiovascular diseases (CVD). The evidence relating to ArSt and triglycerides (TG) shows contradictory results. This paper means to survey the association between high TG and ArSt, utilizing the cardio-ankle vascular index (CAVI). Methods Subjects aged between 25 and 64 years from a random population-based sample were evaluated between 2013 and 2016. Data from questionnaires, blood pressure, anthropometric measures, and blood samples were collected and analyzed. CAVI was measured using VaSera VS-1500 N devise. Subjects with a history of CVD or chronic renal disease were excluded. Results One thousand nine hundred thirty-four participants, 44.7% of males, were included. The median age was 48 (Interquartile Range [IQR] 19) years, TG levels were 1.05 (0.793) mmol/L, and CAVI 7.24 (1.43) points. Prevalence of high CAVI was 10.0% (14.5% in males and 6.4% in females; P

Base isolation solutions are efficient alternatives for seismic protection of buildings and for enhancing resilient capacity. Currently, seismic isolation is focused principally on the critical infrastructure of public health, transportation, education, etc. Despite these considerations, the current worldwide implementation of this technology is still insufficient. A crucial step to be taken into the promotion of any earthquake-resistant construction technique is the development of design codes that, although being inspired in the major international regulations, account for the local seismic effects, among other factors. With the aim of assisting code developers, this work analyzes and compares the code requirements for seismic base isolation in Japan, China, Russia, Italy, USA, and Chile. Two prototype seismically isolated hospital buildings located in high and medium seismicity zones (Los Angeles and New Mexico, respectively) were analyzed and designed with the examined codes. It is concluded that there are high differences among some of their requirements even though the technology used is the same.

Background: Ambulatory overnight oximetry (OXI) has emerged as a cost-effective initial test for sleep disordered breathing. Obesity is closely associated with obstructive sleep apnea (OSA); however, whether body mass index (BMI) or waist-to-hip ratio (WHR) predicts abnormal overnight OXI remains unknown.Methods: We performed a retrospective cross-sectional study of 393 men seen in the Executive Health Program at Mayo Clinic in Rochester, Minnesota who underwent ambulatory overnight OXI ordered by preventive medicine physicians between January 1, 2004 through December 31, 2010. We compared participant/spouse-reported symptoms (sleepiness, snoring), physician indications for OXI (obesity, fatigue), Epworth Sleepiness Scale scores, anthropomorphic measurements (WHR, BMI), and comorbid medical conditions (hypertension, diabetes) with OXI results.Results: 295 of the 393 men who completed OXI had abnormal results. During multivariate analysis, the strongest independent predictor of abnormal OXI for men was WHR (≥1.0, OR = 5.59) followed by BMI (≥30.0 kg/m2, OR = 2.75), age (≥55 yrs, OR = 2.06), and the presence of snoring (OR = 1.91, P < 0.05 for all). A strong association was observed between WHR and abnormal OXI in obese (BMI ≥ 30.0 kg/m2, OR = 6.28) and non-obese (BMI < 29.9 kg/m2, OR = 6.42, P < 0.01 for both) men. Furthermore, 88 men with abnormal OXI underwent polysomnography with 91% being subsequently diagnosed with OSA.Conclusions: In ambulatory, predominantly middle-aged men undergoing preventive services evaluation many physician indications for OXI were not predictors of abnormal results; however, WHR strongly predicted abnormal OXI in obese and non-obese men. As such, we suggest middle-aged men who snore and have a WHR ≥1.0 should be directly referred to a sleep clinic for polysomnography.

BackgroundLeft ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested.ObjectiveTo analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%.Methodology/principal findingsThis is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction ConclusionThe AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.

Objective: To investigate the association of pet ownership, and specifically dog ownership, with cardiovascular diseases (CVD) risk factors and cardiovascular health (CVH) in the Kardiovize Brno 2030 study, a randomly selected prospective cohort in Central Europe. Patients and Methods: We included 1769 subjects (aged from 25 to 64 years; 44.3% males) with no history of CVD who were recruited from January 1, 2013, to December 19, 2014. We compared sociodemographic characteristics, CVD risk factors, CVH metrics (ie, body mass index, healthy diet, physical activity level, smoking status, blood pressure, fasting glucose, and total cholesterol), and score between pet owners and non-pet owners or dog owners and several other subgroups. Results: Approximately 42% of subjects owned any type of pet: 24.3% owned a dog and 17.9% owned another animal. Pet owners, and specifically dog owners, were more likely to report physical activity, diet, and blood glucose at ideal level, and smoking at poor level, which resulted in higher CVH score than non-pet owners (median, 10; interquartile range = 3 vs median, 9; interquartile range = 3; P=0.006). Compared with owners of other pets, dog owners were more likely to report physical activity and diet at ideal level. The comparison of dog owners with non-dog owners yielded similar results. After adjustment for covariates, dog owners exhibited higher CVH scores than non-pet owners (β=0.342; SE=0.122; P=0.005), other pet-owners (β=0.309; SE=0.151; P=0.041), and non-dog owners (β=0.341; SE=0.117; P=0.004). Conclusion: Except for smoking, dog owners were more likely to achieve recommended level of behavioral CVH metrics (physical activity and diet) than non-dog owners, which translated into better CVH.

Background: Cardiac rehabilitation (CR) is a clinically-effective but complex model of care. The purpose of this study was to characterize the nature of CR programs around the world, in relation to guideline recommendations, and compare this by World Health Organization (WHO) region. Methods: In this cross-sectional study, a piloted survey was administered online to CR programs globally. Cardiac associations and local champions facilitated program identification. Quality (benchmark of ≥75% of programs in a given country meeting each of 20 indicators) was ranked. Results were compared by WHO region using generalized linear mixed models. Findings: 111/203 (54.7%) countries in the world offer CR; data were collected in 93 (83.8%; N = 1082 surveys, 32.1% program response rate). The most commonly-accepted indications were: myocardial infarction (n = 832, 97.4%), percutaneous coronary intervention (n = 820, 96.1%; 0.10), and coronary artery bypass surgery (n = 817, 95.8%). Most programs were led by physicians (n = 680; 69.1%). The most common CR providers (mean = 5.9 ± 2.8/program) were: nurses (n = 816, 88.1%; low in Africa, p

Background: Despite the epidemic of cardiovascular disease and the benefits of cardiac rehabilitation (CR), availability is known to be insufficient, although this is not quantified. This study ascertained CR availability, volumes and its drivers, and density. Methods: A survey was administered to CR programs globally. Cardiac associations and local champions facilitated program identification. Factors associated with volumes were assessed using generalized linear mixed models, and compared by World Health Organization region. Density (i.e. annual ischemic heart disease [IHD] incidence estimate from Global Burden of Disease study divided by national CR capacity) was computed. Findings: CR was available in 111/203 (54.7%) countries; data were collected in 93 (83.8% country response; N = 1082 surveys, 32.1% program response rate). Availability by region ranged from 80.7% of countries in Europe, to 17.0% in Africa (p