Association Between Social Isolation With Age-Gap Determined by Artificial Intelligence-Enabled Electrocardiography.

Background: Loneliness and social isolation are associated with poor health outcomes such as an increased risk of cardiovascular diseases. Objectives: The authors aimed to explore the association between social isolation with biological aging which was determined by artificial intelligence-enabled electrocardiography (AI-ECG) as well as the risk of all-cause mortality. Methods: The study included adults aged ≥18 years seen at Mayo Clinic from 2019 to 2022 who respond to a survey for social isolation assessment and had a 12-lead ECG within 1 year of completing the questionnaire. Biological age was determined from ECGs using a previously developed and validated convolutional neural network (AI-ECG age). Age-Gap was defined as AI-ECG age minus chronological age, where positive values reflect an older-than-expected age. The status of social isolation was measured by the previously validated multiple-choice questions based on Social Network Index (SNI) with score ranges between 0 (most isolated) and 4 (least isolated). Results: A total of 280,324 subjects were included (chronological age 59.8 ± 16.4 years, 50.9% female). The mean Age-Gap was -0.2 ± 9.16 years. A higher SNI was associated with a lower Age-Gap (ß of SNI = 4 was -0.11; 95% CI: -0.22 to -0.01; P < 0.001, adjusted to covariates). Cox proportional hazard analysis revealed the association between social connection and all-cause mortality (HR for SNI = 4, 0.47; 95% CI: 0.43-0.5; P < 0.001). Conclusions: Social isolation is associated with accelerating biological aging and all-cause mortality independent of conventional cardiovascular risk factors. This observation underscores the need to address social connection as a health care determinant.

Prediction of presence and severity of metabolic syndrome using regional body volumes measured by a multisensor white-light 3D scanner and validation using a mobile technology.

Aims: To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS). Methods and results: We enrolled 1280 consecutive subjects who completed study protocol measurements, including 3D-BV and myBVI®. Body volumes and demographics were screened using the least absolute shrinkage and selection operator to select features associated with an MS severity score and prevalence. We randomly selected 80% of the subjects to train the models, and performance was assessed in 20% of the remaining observations and externally validated on 133 volunteers who prospectively underwent myBVI® measurements. The mean ± SD age was 43.7 ± 12.2 years, 63.7% were women, body mass index (BMI) was 28.2 ± 6.2 kg/m2, and 30.2% had MS and an MS severity z-score of -0.2 ± 0.9. Features ß coefficients equal to zero were removed from the model, and 14 were included in the final model and used to calculate the body volume index (BVI), demonstrating an area under the receiving operating curve (AUC) of 0.83 in the validation set. The myBVI® cohort had a mean age of 33 ± 10.3 years, 61% of whom were women, 10.5% MS, an average MS severity z-score of -0.8, and an AUC of 0.88. Conclusion: The described BVI model was associated with an increased severity and prevalence of MS compared with BMI and waist-to-hip ratio. Validation of the BVI had excellent performance when using myBVI®. This model could serve as a powerful screening tool for identifying MS.

MedYOLO: A Medical Image Object Detection Framework.

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general-purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on a diverse range of structures even without hyperparameter tuning, reaching mean average precision (mAP) at intersection over union (IoU) 0.5 of 0.861 on BRaTS, 0.715 on the abdominal CT dataset, and 0.995 on the heart CT dataset. However, the models struggle with some structures, failing to converge on LIDC resulting in a mAP@0.5 of 0.0.

Effect of Active Workstations on Neurocognitive Performance and Typing Skills: A Randomized Clinical Trial.

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.

Impact of vaccination education in cardiac rehabilitation on attitudes and knowledge.

Clinical guidelines recommend influenza vaccination for cardiac patients, and COVID-19 vaccination is also beneficial given their increased risk. Patient education regarding vaccination was developed for cardiac rehabilitation (CR); impact on knowledge and attitudes were evaluated. A single-group pre-post design was applied at a Spanish CR program in early 2022. After baseline assessment, a nurse delivered the 40-minute group education. Knowledge and attitudes were re-assessed. Sixty-one (72%) of the 85 participants were vaccinated for influenza, and 40 (47%) for pneumococcus. Most participants perceived vaccines were important, and that the COVID-19 vaccine specifically was important, with three-quarters not influenced by vaccine myths/misinformation. The education intervention resulted in significant improvements in perceptions of the importance of vaccines (Hake's index 69%), understanding of myths (48%), knowledge of the different types of COVID vaccines (92%), and when they should be vaccinated. Vaccination rates are low despite their importance; while further research is needed, education in the CR setting could promote greater uptake.

Cocaine-Induced Toxic Leukoencephalopathy: A Case Report.

We present a case here where a 57-year-old South Asian male with disturbed mental status developed multifocal leukoencephalopathy, which we believe was caused by cocaine usage. Cocaine was detected in the urine toxicological sample. Non-acute CT head, with a follow-up brain MRI demonstrating hyperintensity of the T2 FLAIR signal corresponding to diffusion restriction throughout the whole supratentorial white matter, involving semiovale and subcortical U fibres in the occipital lobes as well as posterior frontal and parietal centrum. It was less likely that the patient had posterior reversible encephalopathy syndrome (PRES), which can potentially manifest similarly in a clinical and imaging context because there was no abrupt rise of blood pressure at presentation or during the patient's stay. Extensive examinations were conducted to exclude additional factors that may contribute to the patient's appearance, including autoimmune, vasculitis, and infectious diseases. Levamisole, a significant chemical that is frequently used to increase the volume of cocaine samples and has been linked to neuronal damage, should be examined in individuals who use cocaine and exhibit these kinds of clinical symptoms. The patient was prescribed 250 mg of methylprednisolone twice daily for five days after it was determined that cocaine-induced neuronal toxicity was the cause of his symptoms. Although no improvement was seen right away, over the course of the next few days, he did exhibit a gradual, albeit slight, improvement in his mental status while residing in the nursing home. It is crucial to comprehend the possible connection between cocaine usage, a commonly abused drug, and people exhibiting similar clinical symptoms. To have a better understanding of the pathophysiology and possible treatment approach, more research is necessary as there is now no recommended therapy regimen.

Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.

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.

Performance of the ACC/AHA Pooled Cohort Cardiovascular Risk Equations in Clinical Practice.

BACKGROUND: The performance of the American College of Cardiology/American Heart Association pooled cohort equation (PCE) for atherosclerotic cardiovascular disease (ASCVD) in real-world clinical practice has not been evaluated extensively. OBJECTIVES: The goal of this study was to test the performance of PCE to predict ASCVD risk in the community, and determine if including individuals with values outside the PCE range (ie, age, blood pressure, cholesterol) or statin therapy initiation over follow-up would significantly affect PCE predictive capabilities. METHODS: The PCE was validated in a community-based cohort of consecutive patients who sought primary care in Olmsted County, Minnesota, between 1997 and 2000, followed-up through 2016. Inclusion criteria were similar to those of PCE derivation. Patient information was ascertained by using the record linkage system of the Rochester Epidemiology Project. ASCVD events (nonfatal and fatal myocardial infarction and ischemic stroke) were validated in duplicate. Calculated and observed ASCVD risk and c-statistics were compared across predefined groups. RESULTS: This study included 30,042 adults, with a mean age of 48.5 ± 12.2 years; 46% were male. Median follow-up was 16.5 years, truncated at 10 years for this analysis. Mean ASCVD risk was 5.6% ± 8.73%. There were 1,555 ASCVD events (5.2%). The PCE revealed good performance overall (c-statistic 0.78) and in sex and race subgroups; it was highest among non-White female subjects (c-statistic 0.81) and lowest in White male subjects (c-statistic 0.77). Out-of-range values and initiation of statin medication did not affect model performance. CONCLUSIONS: The PCE performed well in a community cohort representing real-world clinical practice. Values outside PCE ranges and initiation of statin medication did not affect performance. These results have implications for the applicability of current strategies for the prevention of ASCVD.

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age.

OBJECTIVE: To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care. PATIENT AND METHODS: We applied a previously trained convolutional neural network model to predict biological age by electrocardiogram (ECG) [Artificial Intelligence (AI)-ECG age] to LMNA patients evaluated by multiple ECGs from January 1, 2003, to December 31, 2019. The age gap was the difference between chronological age and AI-ECG age. Findings were compared with age-/sex-matched controls. RESULTS: Thirty-one LMNA patients who had a total of 271 ECGs were studied. The median age at symptom onset was 22 years (range, <1-53 years; n=23 patients); eight patients were asymptomatic family members carrying the LMNA mutation. Cardiac involvement was detected by ECG and echocardiogram in 16 patients and consisted of ventricular arrhythmias (13), atrial fibrillation (12), and cardiomyopathy (6). Four patients required cardiac transplantation. Fourteen patients had neurological manifestations, mainly muscular dystrophy. LMNA mutation carriers, including asymptomatic carriers, were 16 years older by AI-ECG than non-LMNA carriers, suggesting accelerated biological age. Most LMNA patients had an age gap of more than 10 years, compared with controls (P<.001). Consecutive AI-ECG analysis showed accelerated aging in the LMNA group compared with controls (P<.0001). There were no significant differences in age-gap among LMNA patients based on phenotype. CONCLUSION: AI-ECG predicted that LMNA patients have a biological age older than chronological age and accelerated aging even in the absence of cardiac abnormalities by traditional methods. Such a finding could translate into early medical intervention and serve as a disease biomarker.

Validation of a Brief Dietary Questionnaire for Use in Clinical Practice: Mini-EAT (Eating Assessment Tool).

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.

Electrocardiogram-Artificial Intelligence and Immune-Mediated Necrotizing Myopathy: Predicting Left Ventricular Dysfunction and Clinical Outcomes.

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.

Predictors of Changes in Peak Oxygen Uptake After Outpatient Cardiac Rehabilitation: Importance of Cardiac Rehabilitation Attendance.

Objective: To determine whether the number of cardiac rehabilitation (CR) sessions attended and selected clinical characteristics were predictive of patients who exhibited improvement in peak oxygen uptake (VO2peak) after CR. Patients and Methods: Using the Rochester Epidemiology Project records-linkage system, we identified all consecutive patients aged 18 years or older from Olmsted County, Minnesota, who underwent cardiopulmonary exercise testing before and after CR from 1999 to 2017. Regression models were created to assess the clinical predictors of VO2peak improvement (>0% baseline) after CR. Results: The analysis included 671 patients, of which 524 (78%) patients exhibited VO2peak improvement after CR. The significant univariate predictors of VO2peak improvement included younger age (odds ratio [OR], 0.98; 95% CI, 0.96-0.99), lower pre-CR VO2peak (OR, 0.96; 95% CI, 0.94-0.99), and no history of peripheral artery disease (OR, 0.50; 95% CI, 0.31-0.81) (all, P<.005). The significant independent predictors of VO2peak improvement from the multivariable analysis included the number of CR sessions (OR, 1.04; 95% CI, 1.02-1.05), younger age (OR, 0.96; 95% CI, 0.94-0.98), lower pre-CR VO2peak (OR, 0.92; 95% CI, 0.89-0.95), and no history of peripheral artery disease (OR, 0.47; 95% CI, 0.28-0.78) (all, P<.005). Conclusion: These findings highlight the importance of patient participation in CR sessions and individual clinical characteristics in influencing VO2peak improvement after CR in patients with cardiovascular disease.

Predictors of Rehabilitation Referral Among Cardiovascular Surgical Patients.

Objective: Cardiovascular disease (CVD) continues to be the leading cause of mortality globally. Cardiac rehabilitation (CR) programs act by modifying the evolution of CVD and mortality; however, CR programs are under-used. The aim was to determine the profile of patients that received rehabilitation after cardiac surgery. Patients and Methods: A retrospective observational study was conducted from January 2017 to December 2017 at a single center. The study sample was chosen among patients admitted to the Intensive Care Unit of the Hospital Gregorio Marañón/Gregorio Marañón General University Hospital. Socio-demographic and clinical variables were collected. Results: In the present study, 336 patients underwent cardiac surgery of which 63.8% were men and 87.1% had ≥1 cardiovascular risk factors. Of the total cohort, 24.7% were operated for ischemic heart disease, 47.9% valvulopathy, 11% underwent combined surgery, 3.6% cardiac transplantation, 6.5% aneurysms, and 3.9% congenital disease. In-hospital respiratory rehabilitation was prescribed to all patients. Only 4.8% of the patients received motor rehabilitation and 13.8% were referred to CR. We found higher referral rates among patients with more cardiovascular risk factors, <65 years of age, and those undergoing coronary surgery and heart transplantation. Age, ischemic heart disease, and overweight were independent predictors of CR referral. Conclusion: The benefit of CR programs after cardiac surgery is widely described; however, the referral rate to CR remains low. It is crucial to optimize referral protocols for these patients.

Multichamber Strain Characterization Is a Robust Prognosticator for Both Bicuspid and Tricuspid Aortic Stenosis.

BACKGROUND: Cardiac damage (CD) staging is associated with worse survival in aortic stenosis (AS). However, patients with bicuspid aortic valve (BAV) are less likely to develop CD than those with tricuspid aortic valve AS (TAV-AS). The aim of this study was to assess the prognostic value of multichamber strain (MCS) characterization, a sensitive measurement of subclinical CD, in patients with BAV- and TAV-AS. METHODS: Offline two-dimensional echocardiographic strain analysis was performed in 209 consecutive patients with BAV and 369 patients with TAV with more than moderate AS (mean gradient, 47 ± 12 vs 47 ± 14 mm Hg, respectively; >90% severe AS), retrospectively identified from the echocardiography database. The extent of abnormal cardiac mechanics was characterized by the number of chambers with impaired longitudinal strain (left ventricle, left atrium, and right ventricle) as preserved or single-chamber, paired-chamber, or triple-chamber impaired strain. Cutoffs of strain parameters to define impaired versus preserved chamber function were 15% for the left ventricle, 24% for the left atrium, and 25% for the right ventricular free wall. CD was staged using a panel of comprehensive echocardiographic parameters. The primary outcome was all-cause death. RESULTS: MCS (per chamber increase) modestly correlated with CD staging (per stage increase) in both groups (BAV, r = 0.36; TAV, r = 0.43). After a median follow-up period of 4.9 years (interquartile range, 2.2-4.6 years), 262 patients died (5-year survival 86.8 ± 2.4% [BAV-AS] vs 49.6 ± 2.7% [TAV-AS], P < .001). In multivariable analysis, MCS was strongly associated with all-cause death in BAV-AS and TAV-AS, separately, and independent of CD staging (P < .001 for both). MCS provided incremental prognostic value to CD staging in both BAV-AS and TAV-AS (P for χ2 change < .001 for both) and improved risk classification of CD staging in TAV AS (continuous net reclassification index = 0.24, P = .02). CONCLUSION: MCS characterization is a robust prognosticator in patients with greater than moderate AS and provides incremental prognostic value to CD staging in both BAV-AS and TAV-AS. Therefore, MCS characterization could represent an alternative or additive method to CD staging.

Age-Related Differences for Cardiorespiratory Fitness Improvement in Patients Undergoing Cardiac Rehabilitation.

Objective: We investigated age-related differences for peak oxygen uptake (peak VO2) improvement with exercise training during cardiac rehabilitation (CR). Patients and Methods: This was a retrospective cohort study of the Mayo Clinic Rochester CR program including adult patients who attended CR (≥1 session) for any eligible indication between 1999 and 2017 and who had a cardiopulmonary exercise test pre and post CR with VO2 data (peak respiratory exchange ratio ≥1.0). Younger (20-49 yrs), midlife (50-64 yrs), and older adults (≥65 yrs) were compared using ANOVA for delta and percent change in peak VO2; and percentage of peak VO2 responders (>0% change). Results: 708 patients (age: 60.8 ± 12.1 years; 24% female) met inclusion criteria. Delta and percent change in peak VO2 was lower for older adults (1.6 ± 3.2 mL.kg.min-1; 12 ± 27%) compared with younger (3.7 ± 4.0 mL.kg.min-1, p < 0.001; 23 ± 28%, p = 0.002) and midlife adults (2.8 ± 3.8 mL.kg.min-1, p < 0.001; 17 ± 28%, p = 0.04). For midlife, delta change, but not percent change in peak VO2 was significantly lower (p = 0.02) compared with younger. Percentage of responders was only different between older and younger (72 vs. 86%; p = 0.008). Sensitivity analyses in non-surgical patients showed similar differences for delta change, and differences in percent change remained significant between older and younger adults (10 ± 20% vs. 16 ± 18%; p = 0.04). Conclusions: In CR patients, older adults had lower improvement in cardiorespiratory fitness than younger and midlife adults. While excluding surgical patients reduced age-related differences, older adults still had lower cardiorespiratory fitness improvement during CR. These findings may have implications for individualizing CR programming in aging populations to reduce future cardiovascular risk.

Updated Reference Standards for Cardiorespiratory Fitness Measured with Cardiopulmonary Exercise Testing: Data from the Fitness Registry and the Importance of Exercise National Database (FRIEND).

OBJECTIVE: To provide updated reference standards for cardiorespiratory fitness (CRF) for the United States derived from cardiopulmonary exercise (CPX) testing when using a treadmill or cycle ergometer. PATIENTS AND METHODS: Thirty-four laboratories in the United States contributed data to the Fitness Registry and the Importance of Exercise National Database. Analysis included 22,379 tests (16,278 treadmill and 6101 cycle ergometer) conducted between January 1, 1968, through March 31, 2021, from apparently healthy adults (aged 20 to 89 years). Percentiles of peak oxygen consumption for men and women were determined for each decade from 20 through 89 years of age for treadmill and cycle exercise modes, as well as when defining maximal effort as respiratory exchange ratio (RER) greater than or equal to 1.0 or RER greater than or equal to 1.1. RESULTS: For both men and women, the 50th percentile scores for each exercise mode decreased with age and were higher in men across all age groups and higher for treadmill compared with cycle CPX. The average rate of decline per decade over a 6-decade period was 13.5%, 4.0 mLO2·kg-1·min-1 for treadmill CPX and 16.4%, 4.3 mLO2·kg-1·min-1 for cycle CPX. Observationally, the mean peak oxygen consumption was similar whether using an RER criterion of greater than or equal to 1.0 or greater than or equal to 1.1 across the different test modes, ages, and for both sexes. The updated reference standards for treadmill CPX were 1.5 - 4.6 mLO2·kg-1·min-1 lower compared with the previous 2015 standards whereas the updated cycling standards were generally comparable to the original 2017 standards. CONCLUSION: These updated cardiorespiratory fitness reference standards improve the representativeness of the US population compared with the original standards.

Association of Bipolar Disorder With Major Adverse Cardiovascular Events: A Population-Based Historical Cohort Study.

OBJECTIVE: This study aimed to assess the association of bipolar disorder (BD) with risk of major adverse cardiac events (MACEs) after adjusting for established cardiovascular disease (CVD) risk factors. METHODS: We conducted a population-based historical cohort study using the Rochester Epidemiology Project. Patients older than 30 years with a clinical encounter from 1998 to 2000 with no prior MACE, atrial fibrillation, or heart failure were followed up through March 1, 2016. BD diagnosis was validated by chart review. Cox proportional hazards regression models were adjusted for established CVD risk factors, alcohol use disorder, other substance use disorders (SUDs), and major depressive disorder (MDD). RESULTS: The cohort included 288 individuals with BD (0.81%) and 35,326 individuals without BD as the reference group (Ref). Median (interquartile range) follow-up was 16.5 (14.6-17.5) years. A total of 5636 MACE events occurred (BD, 59; Ref, 5577). Survival analysis showed an association between BD and MACE (median event-free-survival rates: BD, 0.80; Ref, 0.86; log-rank p = .018). Multivariate regression adjusting for age and sex also yielded an association between BD and MACE (hazard ratio [HR] = 1.93; 95% confidence interval [CI] = 1.43-2.52; p < .001). The association remained significant after further adjusting for smoking, diabetes mellitus, hypertension, high-density lipoprotein cholesterol, and body mass index (HR = 1.66; 95% CI = 1.17-2.28; p = .006), and for alcohol use disorder, SUD, and MDD (HR = 1.56; 95% CI = 1.09-2.14; p = .010). CONCLUSIONS: In this study, BD was associated with an increased risk of MACE, which persisted after adjusting for established CVD risk factors, SUDs, and MDD. These results suggest that BD is an independent risk factor for major clinical cardiac disease outcomes.

Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.

OBJECTIVE: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%). METHODS: Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality. RESULTS: A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P<.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%. CONCLUSION: A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.

Lipidomic Profiling Identifies Signatures of Poor Cardiovascular Health.

Ideal cardiovascular health (CVH) is defined for the presence of ideal behavioral and health metrics known to prevent cardiovascular disease (CVD). The association of circulatory phospho- and sphingo-lipids to primary reduction in cardiovascular risk is unclear. Our aim was to determine the association of CVH metrics with the circulating lipid profile of a population-based cohort. Serum sphingolipid and phospholipid species were extracted from 461 patients of the randomly selected prospective Kardiovize study based on Brno, Czech Republic. Lipids species were measured by a hyphenated mass spectrometry technique, and were associated with poor CVH scores, as defined by the American Heart Association. Phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE) species were significantly lower in ideal and intermediate scores of health dietary metric, blood pressure, total cholesterol and blood fasting glucose compared to poor scores. Current smokers presented higher levels of PC, PE and LPE individual species compared to non-smokers. Ceramide (Cer) d18:1/14:0 was altered in poor blood pressure, total cholesterol and fasting blood glucose metrics. Poor cardiovascular health metric is associated with a specific phospho- and sphingolipid pattern. Circulatory lipid profiling is a potential biomarker to refine cardiovascular health status in primary prevention strategies.