Current frailty knowledge, awareness, and practices among physicians following the 2022 European consensus document on Frailty in Cardiology.

Aims: Aging-related cardiovascular disease and frailty burdens are anticipated to rise with global aging. In response to directions from major cardiovascular societies, we investigated frailty knowledge, awareness, and practices among cardiologists as key stakeholders in this emerging paradigm a year after the European Frailty in Cardiology consensus document was published. Methods and results: We launched a prospective multinational web-based survey via social networks to broad cardiology communities representing multiple World Health Organization regions, including Western Pacific and Southeast Asia regions. Overall, 578 respondents [38.2% female; ages 35-49 years (55.2%) and 50-64 years (34.4%)] across subspecialties, including interventionists (43.3%), general cardiologists (30.6%), and heart failure specialists (HFSs) (10.9%), were surveyed. Nearly half had read the consensus document (38.9%). Non-interventionists had better perceived knowledge of frailty assessment instruments (fully or vaguely aware, 57.2% vs. 45%, adj. P = 0.0002), exercise programmes (well aware, 12.9% vs. 6.0%, adj. P = 0.001), and engaged more in multidisciplinary team care (frequently or occasionally, 52.6% vs. 41%, adj. P = 0.002) than interventionists. Heart failure specialists more often addressed pre-procedural frailty (frequently or occasionally, 43.5% vs. 28.2%, P = 0.004) and polypharmacy (frequently or occasionally, 85.5% vs. 71%, adj. P = 0.014) and had consistently better composite knowledge (39.3% vs. 21.6%, adj. P = 0.001) and practice responses (21% vs. 11.1%, adj. P = 0.018) than non-HFSs. Respondents with better knowledge responses also had better frailty practices (40.3% vs. 3.6%, adj. P < 0.001). Conclusion: Distinct response differences suggest that future strategies strengthening frailty principles should address practices peculiar to subspecialties, such as pre-procedural frailty strategies for interventionists and rehabilitation interventions for HFSs.

Going beyond summed stress scores: Correlating global and territorial coronary flow reserve by single photon emission tomography with routine myocardial perfusion imaging.

a Background: Technological advancement in the recent years has enabled the application of single photon emission tomography (SPECT) to evaluate myocardial blood flow (MBF). This method offers increased sensitivity in the assessment of coronary health, quantifiable through non-invasive imaging beyond the more conventional methods such as with myocardial perfusion imaging (MPI). b Aims: To correlate MBF, derived by dynamic SPECT, both global and by coronary territories to the summed stress scores (SSS) on conventional MPI. c Methods: Images obtained from dipyridamole-gated SPECT MPI stress and rest studies performed on recruited subjects were examined. We calculated the global and regional coronary flow reserve (CFR) via a standard software package, taken as the ratio of stress MBF to rest MBF, using CFR<2.5 as the cut off. d Results: Amongst the 90 recruited subjects (mean age 67 ± 8 years; of which 76% were males), 49% had MPI within normal limits (summed stress score (SSS) 0-3; Left ventricular ejection fraction (LVEF) > 50%). We observed a progressive reduction in global and regional CFR across the normal SSS category to that of severely abnormal (SSS >13). Reduced global CFR with correspondent lower CFR across the regional arteries were detected in scans within normal limits of MPI scans in subjects who were older (69 ± 7 vs. 62 ± 9 years, p = 0.034). Decreasing CFR was significantly associated with increasing age across the regional arteries. e Conclusion: In our study we depict the global and regional MBF values obtained via SPECT MPI in correlation to the respective SSS categories. Our data proposes that dynamic SPECT has a part in refining cardiac risk stratification, particularly in the older adult population, who are at greater risk.

ß-blockers are not all the same: pharmacologic similarities and differences, potential combinations and clinical implications.

ß-blockers are a heterogeneous class, with individual agents distinguished by selectivity for ß1- vs. ß2- and α-adrenoceptors, presence or absence of partial agonist activity at one of more ß-receptor subtype, presence or absence of additional vasodilatory properties, and lipophilicity, which determines the ease of entry the drug into the central nervous system. Cardioselectivity (ß1-adrenoceptor selectivity) helps to reduce the potential for adverse effects mediated by blockade of ß2-adrenoceptors outside the myocardium, such as cold extremities, erectile dysfunction, or exacerbation of asthma or chronic obstructive pulmonary disease. According to recently updated guidelines from the European Society of Hypertension, ß-blockers are included within the five major drug classes recommended as the basis of antihypertensive treatment strategies. Adding a ß-blocker to another agent with a complementary mechanism may provide a rational antihypertensive combination that minimizes the adverse impact of induced sympathetic overactivity for optimal blood pressure-lowering efficacy and clinical outcomes benefit.

Characteristics of pulmonary artery strain assessed by cardiovascular magnetic resonance imaging and associations with metabolomic pathways in human ageing.

Background: Pulmonary artery (PA) strain is associated with structural and functional alterations of the vessel and is an independent predictor of cardiovascular events. The relationship of PA strain to metabolomics in participants without cardiovascular disease is unknown. Methods: In the current study, community-based older adults, without known cardiovascular disease, underwent simultaneous cine cardiovascular magnetic resonance (CMR) imaging, clinical examination, and serum sampling. PA global longitudinal strain (GLS) analysis was performed by tracking the change in distance from the PA bifurcation to the pulmonary annular centroid, using standard cine CMR images. Circulating metabolites were measured by cross-sectional targeted metabolomics analysis. Results: Among n = 170 adults (mean age 71 ± 6.3 years old; 79 women), mean values of PA GLS were 16.2 ± 4.4%. PA GLS was significantly associated with age (ß = -0.13, P = 0.017), heart rate (ß = -0.08, P = 0.001), dyslipidemia (ß = -2.37, P = 0.005), and cardiovascular risk factors (ß = -2.49, P = 0.001). Alanine (ß = -0.007, P = 0.01) and proline (ß = -0.0009, P = 0.042) were significantly associated with PA GLS after adjustment for clinical risk factors. Medium and long-chain acylcarnitines were significantly associated with PA GLS (C12, P = 0.027; C12-OH/C10-DC, P = 0.018; C14:2, P = 0.036; C14:1, P = 0.006; C14, P = 0.006; C14-OH/C12-DC, P = 0.027; C16:3, P = 0.019; C16:2, P = 0.006; C16:1, P = 0.001; C16:2-OH, P = 0.016; C16:1-OH/C14:1-DC, P = 0.028; C18:1-OH/C16:1-DC, P = 0.032). Conclusion: By conventional CMR, PA GLS was associated with aging and vascular risk factors among a contemporary cohort of older adults. Metabolic pathways involved in PA stiffness may include gluconeogenesis, collagen synthesis, and fatty acid oxidation.

Longitudinal aortic strain, ventriculo-arterial coupling and fatty acid oxidation: novel insights into human cardiovascular aging.

Aging-induced aortic stiffness has been associated with altered fatty acid metabolism. We studied aortic stiffness using cardiac magnetic resonance (CMR)-assessed ventriculo-arterial coupling (VAC) and novel aortic (AO) global longitudinal strain (GLS) combined with targeted metabolomic profiling. Among community older adults without cardiovascular disease, VAC was calculated as aortic pulse wave velocity (PWV), a marker of arterial stiffness, divided by left ventricular (LV) GLS. AOGLS was the maximum absolute strain measured by tracking the phasic distance between brachiocephalic artery origin and aortic annulus. In 194 subjects (71 ± 8.6 years; 88 women), AOGLS (mean 5.6 ± 2.1%) was associated with PWV (R = -0.3644, p < 0.0001), LVGLS (R = 0.2756, p = 0.0001) and VAC (R = -0.3742, p <0.0001). Stiff aorta denoted by low AOGLS <4.26% (25th percentile) was associated with age (OR 1.13, 95% CI 1.04-1.24, p = 0.007), body mass index (OR 1.12, 95% CI 1.01-1.25, p = 0.03), heart rate (OR 1.04, 95% CI 1.01-1.06, p = 0.011) and metabolites of medium-chain fatty acid oxidation: C8 (OR 1.005, p = 0.026), C10 (OR 1.003, p = 0.036), C12 (OR 1.013, p = 0.028), C12:2-OH/C10:2-DC (OR 1.084, p = 0.032) and C16-OH (OR 0.82, p = 0.006). VAC was associated with changes in long-chain hydroxyl and dicarboxyl carnitines. Multivariable models that included acyl-carnitine metabolites, but not amino acids, significantly increased the discrimination over clinical risk factors for prediction of AOGLS (AUC [area-under-curve] 0.73 to 0.81, p = 0.037) and VAC (AUC 0.78 to 0.87, p = 0.0044). Low AO GLS and high VAC were associated with altered medium-chain and long-chain fatty acid oxidation, respectively, which may identify early metabolic perturbations in aging-associated aortic stiffening. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02791139.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.

BACKGROUND: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND METHODS: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. RESULTS: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. CONCLUSIONS: Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.

Automated anxiety detection using probabilistic binary pattern with ECG signals.

BACKGROUND AND AIM: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. MATERIALS AND METHODS: We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. RESULTS: Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. CONCLUSIONS: The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.

Myocardial Aging among a Population-Based Cohort Is Associated with Adverse Cardiovascular Outcomes and Sex-Specific Differences among Older Adults.

INTRODUCTION: Despite growing calls to tackle aging-related cardiovascular disease (CVD), the role of detecting early diastolic dysfunction such as those observed in aging, prior to clinical disease, is of unclear clinical benefit. METHODS: Myocardial function determined by echocardiography was examined in association with incident cardiovascular outcomes or all-cause death by Cox proportional hazards model. Sex-based differences in outcomes were included. RESULTS: A total of 956 participants (mean age 63 ± 12.9 years, n = 424 males [44%]) were categorized based on mitral peak early-to-late diastolic filling velocity (E/A) ratios: E/A <0.8 (28%), E/A 0.8-1.2 (39%), E/A (29%), E/A >2.0 (4%). Incidence rate (IR) for non-fatal cardiovascular outcomes was 2.83 per 100 person-years (95% CI: 2.24-3.56) and 0.45 per 100 person-years (95% CI: 0.26-0.80) for all-cause death. Event-free survival from non-fatal cardiovascular outcomes was significantly different among E/A categories (log-rank p = 0.0269). E/A <0.8 (HR 1.80, 95% CI: 1.031, 3.14, p = 0.039) was associated with non-fatal cardiovascular outcomes. Among men, IR for cardiovascular outcomes was 3.56 per 100 person-years (95% CI: 2.62-4.84) and 0.75 per 100 person-years (95% CI: 0.39-1.44) for all-cause death. Among women, IR for cardiovascular outcomes was 2.22 per 100 person-years (95% CI: 1.56-3.16) and 0.21 per 100 person-years (95% CI: 0.067-0.64) for all-cause death. For E/A <0.8 category, women had significantly higher risks of non-fatal cardiovascular outcomes, compared to E/A 0.8-1.2 category (HR 2.49, 95% CI: 1.18, 5.23, p = 0.017). CONCLUSION: Myocardial aging was an independent predictor of cardiovascular outcomes in community-dwelling older adults prior to clinical CVD. Impaired myocardial relaxation was prevalent in both sexes but associated with worse outcomes in women, suggestive of sex differences in age-related biology.

Assessment of right ventricular diastolic function in pediatric patients with repaired tetralogy of Fallot by cardiovascular magnetic resonance and echocardiography.

OBJECTIVES: Cardiovascular magnetic resonance (CMR) imaging is routinely performed for assessing right ventricular (RV) systolic but not diastolic function. We aimed to investigate CMR-based assessment of RV diastolic function in pediatric patients with repaired tetralogy of Fallot (rTOF), compared to transthoracic echocardiography (TTE) measurements. METHODS: A total of 130 consecutive pediatric patients with rTOF who underwent clinically indicated CMR and same-day TTE were included. Forty-three controls were recruited. Phase-contrast images were used to measure trans-tricuspid valve flow velocities during early (E) and late diastolic (A) phases (measured in cm/s). Feature tracking of the tricuspid annulus was performed to derive early (e') and late diastolic (a') myocardial velocities (measured in cm/s). RV diastolic function was evaluated by E/A ratio, E/e' ratio, and E-wave deceleration time (measured in milliseconds). Regression analyses were utilized to identify potential variables associated with RV diastolic dysfunction (DD). The performance of CMR-derived parameters in diagnosing RV DD was assessed using receiver-operating characteristic analyses. RESULTS: Good agreement was found between CMR and TTE measurements (ICC 0.70-0.89). Patients with RV DD (n = 67) showed significantly different CMR-derived parameters including E and e' velocities, and E/A and E/e' ratio, compared to patients without DD (n = 63) (all p < 0.05). CMR-derived E and e' velocities and E/e' ratio were independent predictors of RV DD. E/e' of 5.8 demonstrated the highest discrimination of RV DD (AUC 0.76, sensitivity 70%, specificity 86%). CONCLUSIONS: CMR-derived parameters showed good agreement with TTE parameters in determining RV DD. CMR-derived E/e' was proved to be the most effective in identifying RV DD. CLINICAL RELEVANCE STATEMENT: This study demonstrated the feasibility and efficacy of CMR in assessing diastolic function in pediatric patients. RV DD was presented in over half of patients according to current TTE guidelines, highlighting the need for assessing RV diastolic function during follow-up. KEY POINTS: • Routinely acquired cine and phase-contrast cardiovascular magnetic resonance (CMR) images yielded right ventricular (RV) diastolic parameters which demonstrated good agreement with transthoracic echocardiography (TTE) measurements. • There was a high prevalence of RV diastolic function impairment in pediatric patients with repaired tetralogy of Fallot (rTOF). • CMR is a reliable complementary modality of TTE for RV diastolic function evaluation.

External validation of a deep learning algorithm for automated echocardiographic strain measurements.

Aims: Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging. Methods and results: We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson's correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): -18.9 ± 4.5% vs. -18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and -15.4 ± 4.1% vs. -15.9 ± 3.6%, respectively, bias -0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80. Conclusion: DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.

Heart-brain mapping: Cardiac atrial function is associated with distinct cerebral regions with high free water in older adults.

Left atrial (LA) dysfunction has been linked to cognitive impairment and cerebrovascular dysfunction. Higher brain free-water (FW) derived from diffusion-MRI was associated with early and subtle cerebrovascular dysfunction and more severe cognitive impairment. We hypothesized that LA dysfunction would correlate with higher brain free-water (FW) among healthy older adults. 56 community older adults (73.13 ± 3.56 years; 24 female) with normal cognition and without known cardiovascular disease who had undergone cardiac-MRI, brain-MRI, and neuropsychological assessments were included. Whole-brain voxel-level general linear models were constructed to correlate brain FW measures with LA indices. We found lower scores in LA function measures were related to higher grey matter (GM) FW in regions including orbital frontal and right temporal regions (p < 0.01, family-wise error corrected). In parallel, LA dysfunction was associated with higher FW in white matter (WM) fibres including superior longitudinal fasciculus, internal capsule, and superior corona radiata. However, LA dysfunction was not related to WM tissue reduction and GM cortical thinning. Moreover, these cardiac-related higher brain FW were associated with lower executive function and higher serum B-type natriuretic peptide (p < 0.05, Holm-Bonferroni corrected). These findings may have implications for anti-ageing preventive strategies targeting cardiac and cerebral vascular functions to improve heart and brain outcomes.

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023).

BACKGROUND AND OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data. METHODS: We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information". RESULTS: We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches. CONCLUSION: AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron.

Objective.Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events.Approach.The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed.Main Results.The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals 'at risk' and 'not at risk,' respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model.Significance.This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

Stress Perfusion Cardiac Magnetic Resonance vs SPECT Imaging for Detection of Coronary Artery Disease.

BACKGROUND: GadaCAD2 was 1 of 2 international, multicenter, prospective, Phase 3 clinical trials that led to U.S. Food and Drug Administration approval of gadobutrol to assess myocardial perfusion and late gadolinium enhancement (LGE) in adults with known or suspected coronary artery disease (CAD). OBJECTIVES: A prespecified secondary objective was to determine if stress perfusion cardiovascular magnetic resonance (CMR) was noninferior to single-photon emission computed tomography (SPECT) for detecting significant CAD and for excluding significant CAD. METHODS: Participants with known or suspected CAD underwent a research rest and stress perfusion CMR that was compared with a gated SPECT performed using standard clinical protocols. For CMR, adenosine or regadenoson served as vasodilators. The total dose of gadobutrol was 0.1 mmol/kg body weight. The standard of reference was a 70% stenosis defined by quantitative coronary angiography (QCA). A negative coronary computed tomography angiography could exclude CAD. Analysis was per patient. CMR, SPECT, and QCA were evaluated by independent central core lab readers blinded to clinical information. RESULTS: Participants were predominantly male (61.4% male; mean age 58.9 ± 10.2 years) and were recruited from the United States (75.0%), Australia (14.7%), Singapore (5.7%), and Canada (4.6%). The prevalence of significant CAD was 24.5% (n = 72 of 294). Stress perfusion CMR was statistically superior to gated SPECT for specificity (P = 0.002), area under the receiver operating characteristic curve (P < 0.001), accuracy (P = 0.003), positive predictive value (P < 0.001), and negative predictive value (P = 0.041). The sensitivity of CMR for a 70% QCA stenosis was noninferior and nonsuperior to gated SPECT. CONCLUSIONS: Vasodilator stress perfusion CMR, as performed with gadobutrol 0.1 mmol/kg body weight, had superior diagnostic accuracy for diagnosis and exclusion of significant CAD vs gated SPECT.

Age- and sex-specific reference values of biventricular flow components and kinetic energy by 4D flow cardiovascular magnetic resonance in healthy subjects.

BACKGROUND: Advances in four-dimensional flow cardiovascular magnetic resonance (4D flow CMR) have allowed quantification of left ventricular (LV) and right ventricular (RV) blood flow. We aimed to (1) investigate age and sex differences of 4D flow CMR-derived LV and RV relative flow components and kinetic energy (KE) parameters indexed to end-diastolic volume (KEiEDV) in healthy subjects; and (2) assess the effects of age and sex on these parameters. METHODS: We performed 4D flow analysis in 163 healthy participants (42% female; mean age 43 ± 13 years) of a prospective registry study (NCT03217240) who were free of cardiovascular diseases. Relative flow components (direct flow, retained inflow, delayed ejection flow, residual volume) and multiple phasic KEiEDV (global, peak systolic, average systolic, average diastolic, peak E-wave, peak A-wave) for both LV and RV were analysed. RESULTS: Compared with men, women had lower median LV and RV residual volume, and LV peak and average systolic KEiEDV, and higher median values of RV direct flow, RV global KEiEDV, RV average diastolic KEiEDV, and RV peak E-wave KEiEDV. ANOVA analysis found there were no differences in flow components, peak and average systolic, average diastolic and global KEiEDV for both LV and RV across age groups. Peak A-wave KEiEDV increased significantly (r = 0.458 for LV and 0.341 for RV), whereas peak E-wave KEiEDV (r = - 0.355 for LV and - 0.318 for RV), and KEiEDV E/A ratio (r = - 0.475 for LV and - 0.504 for RV) decreased significantly, with age. CONCLUSION: These data using state-of-the-art 4D flow CMR show that biventricular flow components and kinetic energy parameters vary significantly by age and sex. Age and sex trends should be considered in the interpretation of quantitative measures of biventricular flow. Clinical trial registration  https://www. CLINICALTRIALS: gov . Unique identifier: NCT03217240.

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.

Aortic flow is associated with aging and exercise capacity.

Aims: Increased blood flow eccentricity in the aorta has been associated with aortic (AO) pathology, however, its association with exercise capacity has not been investigated. This study aimed to assess the relationships between flow eccentricity parameters derived from 2-dimensional (2D) phase-contrast (PC) cardiovascular magnetic resonance (CMR) imaging and aging and cardiopulmonary exercise test (CPET) in a cohort of healthy subjects. Methods and Results: One hundred and sixty-nine healthy subjects (age 44 ± 13 years, M/F: 96/73) free of cardiovascular disease were recruited in a prospective study (NCT03217240) and underwent CMR, including 2D PC at an orthogonal plane just above the sinotubular junction, and CPET (cycle ergometer) within one week. The following AO flow parameters were derived: AO forward and backward flow indexed to body surface area (FFi, BFi), average flow displacement during systole (FDsavg), late systole (FDlsavg), diastole (FDdavg), systolic retrograde flow (SRF), systolic flow reversal ratio (sFRR), and pulse wave velocity (PWV). Exercise capacity was assessed by peak oxygen uptake (PVO2) from CPET. The mean values of FDsavg, FDlsavg, FDdavg, SRF, sFRR, and PWV were 17 ± 6%, 19 ± 8%, 29 ± 7%, 4.4 ± 4.2 mL, 5.9 ± 5.1%, and 4.3 ± 1.6 m/s, respectively. They all increased with age (r = 0.623, 0.628, 0.353, 0.590, 0.649, 0.598, all P < 0.0001), and decreased with PVO2 (r = -0.302, -0.270, -0.253, -0.149, -0.219, -0.161, all P < 0.05). A stepwise multivariable linear regression analysis using left ventricular ejection fraction (LVEF), FFi, and FDsavg showed an area under the curve of 0.769 in differentiating healthy subjects with high-risk exercise capacity (PVO2 ≤ 14 mL/kg/min). Conclusion: AO flow haemodynamics change with aging and predict exercise capacity. Registration: NCT03217240.

Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.

Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.

Measurement Properties of the 15-Item Singapore Caregiver Quality of Life Scale (SCQOLS-15) in Family Caregivers of Patients with Heart Diseases.

PURPOSE: To evaluate the measurement properties of the 15-item Singapore Caregiver Quality of Life Scale (SCQOLS-15) in family caregivers of patients with heart diseases. METHODS: The SCQOLS-15 survey was self-administered by family caregivers of patients with chronic heart diseases, at baseline and 1 week later. The criterion validity of SCQOLS-15 and its domain scores was assessed by calculating the Spearman correlation coefficient (ρ) with the Brief Assessment Scale for Caregivers (BASC), Caregiver Reaction Assessment (CRA), and their sub-scores. Known-group validity was assessed using the New York Heart Association (NYHA) functional class. Test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). RESULTS: Of the 327 caregivers included, 65% were adult children and 28% were spouses. The distribution of NYHA classes of the patients was I: 27%, II: 40%, III: 24%, and IV: 9%. There was a positive correlation between the SCQOLS-15 and BASC total scores (ρ = 0.7). SCQOLS-15 domain scores were also correlated with BASC and CRA sub-scores as per a priori hypotheses, with absolute values of ρ ranging from 0.4 to 0.6. The mean values of SCQOLS-15 total and all domain scores were lower among caregivers of patients with NYHA class III/IV compared to those of class I/II patients (each P < 0.05). Among 146 caregivers who completed the follow-up and self-rated a stable quality-of-life, ICCs for test-retest reliability of SCQOLS-15 total and all domain scores were ≥ 0.8. CONCLUSION: The SCQOLS-15 is a valid and reliable instrument for measuring the quality of life in caregivers of heart disease patients.

Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction.

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.