Citations

Abstract Background Cardiopulmonary exercise testing (CPET) has emerged as an important tool for risk stratification and prognosis assessment in patients with cardiac diseases. The Weber and Ventilatory Functional Classification have been correlated with outcomes in patients with Heart Failure (HF). Weber classification is based on VO2: Weber class A, peak VO2 >20 mL/kg per minute; class B, peak VO2 16–20 mL/kg per minute; class C, peak VO2 10–16 mL/kg per minute; and class D, peak VO2 ≤10 mL/kg per minute and Ventilatory classification on VE/VCO2 slope I-IV (Figure A). Although VO2 and VE/VCO2 variables have found altered in the amyloidosis population, the applicability of this classification in a cohort of amyloidosis patients and its correlation with all-cause mortality have not been explored. Purpose To evaluate if the Weber and Ventilatory Classification of HF predicts mortality amyloidosis patients. Methods 194 with diagnosis of amyloidosis who underwent CPET from February 2010 to February 2024 were included in this retrospective cohort study. Patients with at least one CPET complete data were included in the analysis. Data on the type of amyloidosis, comorbidities, and echocardiogram were retrieved from medical records. Cohort was divided into two groups according to the Weber Classification: Weber Class A/B and Weber Class C/D. Individuals with CPET studies after heart transplant were excluded from the analysis. Statistical analyses including Fisher’s exact test, chi-square, Tukey-Kramer (TK), Cox regression model analysis, proportional hazards model for time to event, and Kaplan–Meier curves were performed. A p-value <0.05 was considered significant. Results Out of the 194 patients, 94 were classified as Weber A-B and 100 as Weber B-C. The age at stress test was similar between the groups: Weber A/B 62.9 ± 11.7 years and Weber C/D 62.3 ± 10.7 years (p=0.087). Ejection Fraction was similar between groups, 32% and 28.9% in A-B and C-D respectively (p =0.207). Higher levels of NT-proBNP were found in the Weber C/D group. The prevalence of comorbidities was balanced among the groups except chronic kidney disease, which was higher in Weber Class C/D (40%) vs (20.2%) in Weber Class A/B (Table 1). Cox analysis revealed a strong association between higher Weber class and the outcome HR 2.36 (1.18 – 4.71) p=0.014. For Higher Ventilatory Class (Group III-IV) similar results were found HR 2.95 (1.60 – 5.45) p=0.005(Table 2). Mortality was significantly higher during the follow-up in the Weber C/D group (25%) vs (10%) in the Weber A-B group p=0.007(Figure D). Conclusion Weber and ventilatory Classification based on CPET, besides predicting outcomes in patients with cardiac disease, are strong predictors of mortality in patients with amyloidosis. These findings suggest that this patient group benefits from earlier stratification to assess prognosis and the potential requirement for transplant independently from other factors.

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

Abstract Background Cardiopulmonary exercise testing (CPET) is a widely used tool for risk stratification and prognosis in patients with chronic heart failure. In amyloid patients, cardiac amyloid deposits are associated with poor prognosis, increased risk for arrhythmias, sudden death, and subsequent exercise limitation due to restrictive cardiomyopathy. The type of amyloidosis and its affectation of the heart are different, and little is known about the CPET parameters according to the type of cardiac amyloidosis. Purpose In this study, we aim to compare CPET data in different types of amyloidosis to determine if CPET variables evaluated could have a significant difference that could impact the follow-up or prognosis of the patients based on the type of amyloidosis. Methods We conducted a retrospective cohort study of 194 patients with a diagnosis of cardiac amyloidosis referred for CPET from February 2010 to February 2024. Patients with at least one CPET data were included in our analysis. The type of amyloidosis, comorbidities, and echocardiogram data were retrieved from medical records. Patients were divided into 3 different groups according to the type of amyloidosis: AL Amyloidosis(AL), Variant TTR Amyloidosis (vTRR), and Wild Type TTR Amyloidosis (wtTTR). Individuals with CPET studies after heart transplant were excluded from the analysis. Fisher’s exact test or chi-square was used to compare categorical variables. ANOVA and Tukey-Kramer (TK) were used to compare continuous variables. Adjustment using General Linear Models (GLM) procedure was used to identify any confounder that could affect the results. A p-value <0.05 was considered significant. Results Out of the 194 patients, 66 had AL Amyloidosis, 40 had vTTR Amyloidosis, and 88 had wtTTR Amyloidosis. Most of the patients were male, and the male predominance was higher in the wtTRR group. There were 41 (62.1%) males in the AL group, 35 (87.5%) in the vTRR group, and 83 (94.3%) in the wtTTR group (p<0.0001). Comorbidities were balanced among the groups. Additional demographic and comorbidities data are shown in Table 1. From the evaluated CPET variables, VE/VCO2 nadir and slope were significantly higher in the wtTTR group when compared to the other groups, as well as Peak VE and Peak TV(Figure A). However, after adjusting for sex, BMI, and age, VE/VCO2 nadir was still significantly higher in the wtTRR group (p=0.003) compared to the other groups, as well as Peak VE, which was higher again in vTTR patients (p=0.001), and Peak TV (Table 2). Conclusion Ventilation variables evaluated in CPET showed alterations in the wtTTR group. These findings suggest that wtTRR amyloid patients could have a severe problem in ventilation that could be of significant relevance in future prognosis.

Abstract Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, routinely used tool such as the electrocardiogram (ECG) enabled by an artificial intelligence algorithm to predict an individual’s physiologic age (ECG age), will be associated with poor CVH as defined by an adapted version of the AHA LS7. Methods We conducted an observational retrospective study of consecutive patients aged ≥18 years who sought care between June 2019 and March 2022, self-reported behavioral history through a validated intake questionnaire in clinical outpatient visits, had a clinically indicated ECG within one year, and provided research authorization. We applied a previously developed AI-ECG convolutional neural network to derive the ECG-Age. We excluded patients with missing data, those with body mass index (BMI) <18.5 kg/m2, previous history of left-bundle branch block, atherosclerotic cardiovascular disease events, atrial fibrillation, heart failure, and non-skin cancer. An adapted version of the LS7 was used to define CVH, which includes anthropometrics (e.g., BMI), behavioral history (e.g., smoking, physical activity, and diet), vital signs (e.g., blood pressure), and laboratory values (e.g., total cholesterol, and fasting glucose). CVH was stratified as ideal, intermediate, and poor if the LS7 score had values of 9-14, 7-8, and 0-6, respectively, constructed by adding 2,1 or 0 points to each metric that met each category criteria, respectively. Accelerated aging was defined as having a Delta-Age ≥1SD above the mean, whereas Delta-age was defined as ECG minus chronological age. Logistic regression models tested the association between CVH categories and positive Delta-Age while adjusting for chronological age and sex. Results We included 114,214 individuals (53.3% females, 86.9% Caucasian). Mean chronological age was 57.7±14 years and mean AI-ECG- Age was 58 ± 12.44 years (R2 0.80, p-value <0.001). Mean Delta-Age was 0.28 ±8.8 years. LS7 average score was 8.3 ± 2.23; 46.8% had ideal, 31.5% intermediate, and 21.7% had poor CVH (Figure A). Compared to those with ideal CVH, those with poor CVH were more than 50% more likely to have Delta-Age ≥1SD (OR 1.61, CI 95% 1.54-1.69, p-value: <0.001), independent of chronological age and sex. (Figure B). Conclusion Poor CVH, defined by the AHA LS7, is linked with biological aging as determined by AI-enabled ECG, highlighting the importance of promoting CVH in adults as a strategy to decelerate biological aging and reduce its associated mortality.

Abstract Background Diagnosing myocardial infarction (MI) in patients with chronic kidney disease (CKD) is difficult as these patients often have elevated baseline high-sensitivity cardiac troponin T (hs-cTnT) concentrations. Methods Observational U.S. cohort study of emergency department (ED) patients undergoing hs-cTnT measurement. Cases with >1 hs-cTnT increase >99th percentile were adjudicated following the Fourth Universal Definition of MI. Diagnostic performance of baseline and serial 2- and 6-hour hs-cTnT thresholds for ruling-in acute MI was compared between those without and with CKD (eGFR <60 ml/min/1.73m2). Results The study cohort included 1992 patients, amongst whom 501 (25%) had CKD. There were 701 (82%) patients with myocardial injury, 64 (7.5%) with type 1, and 91 (11%) with type 2 MI. In CKD patients with baseline hs-cTnT thresholds of >52, >100, >200 or >300 ng/L, PPVs for acute MI were 37% [95% CI 28-46], 53% (95% CI 39-67), 73% (95% CI 50-89) and 80% (95% CI 44-98), and in those without CKD, 64% (95% CI 50-77), 69% (95% CI 49-85), 59% (95% CI 33-82) and 54% (95% CI 25-81). In CKD patients with a 2-hour hs-cTnT delta of >10, >20 or >30 ng/L, the corresponding PPVs were 65% (95% CI 50-78), 86% (95% CI 68-96) and 88% (95% CI 68-97), and in those without CKD, 65% (95% CI 51-78), 73% (95% CI 57-86) and 75% (95% CI 58-88) (Figure). Conclusion Diagnostic performance of standard baseline and serial 2-hour hs-cTnT thresholds to rule-in MI is suboptimal in patients with CKD. It significantly improves when using higher baseline thresholds and delta values.

Abstract Background Heart failure (HF) with abnormal left ventricular (LV) ejection fraction (EF) manifests in multiple phenotypes, each presenting distinct and potentially actionable therapeutic targets. This study aimed to assess the potential of the ABCDE+ stress echocardiography (SE) protocol in uncovering different phenotypes of non-ischemic HF. Methods With a retrospective analysis of prospectively acquired data, we identified 265 patients (mean age: 65.7±12.6 years; 154 males, 59.9%) with non-ischemic HF with EF<50% (mean =41.4±7.5) enrolled by 23 certified laboratories between 2021 and 2023. Ischemic origin was excluded based on data of angiography and medical history. ABCDE-SE was evaluated with exercise (n=98, 37.0%), dobutamine (n=50, 18.9%), or vasodilators (n=117, 44.1%). The protocol consisted of several steps: A assessed regional wall motion abnormality (RWMA); B evaluated number of B-lines; C measured left ventricular contractile reserve; Step D with coronary flow velocity reserve (CFVR) in the left anterior descending artery (LAD) was completed in 102 patients (38.5%); step E assessed heart rate reserve. Additionally, step F (evaluated in 188 patients at rest and 107 at peak stress) noted the presence of rest or stress-induced moderate-to-severe secondary mitral regurgitation. The SE response was graded on a scale from 0 (all steps normal) to 5 (all ABCDE+ steps abnormal). Results The mean ABCDE score was 1.5±1.2, ranging from 0 (n=59, 22.3%) to 5 (n=2, 0.8%) (see distribution of scores in Figure 1A). Abnormal results were observed in 21 subjects (7.9%) for A, 86 (32.5 %) for B, 147 (55.5%) for C, 42/102 (41.2%) for D, and 101 (38.1%) for E steps. Step F was abnormal in 22/188 (11.8%) at rest and in 16/107 (14.9%) at peak stress. The predominant "solitary phenotypes" (only one step abnormal) were impaired LV contractile function phenotype (step C in 47, 51.1%) followed by chronotropic insufficiency (step E in 17, 18.5%) and congestive phenotype (step B in 17, 18.5%) (see Figure 1B). The most frequent combined phenotype was BCE – congestive phenotype with impaired ventricular and heart rate reserves (28 [62.2%]). Notably, 38 patients (14.3%) demonstrated improved regional contractility, while 27 patients (10.2%) showed new regional wall motion abnormality (see Table). Conclusion Non-ischemic HF patients with abnormal EF exhibit a broad spectrum of phenotypes. Impaired left ventricular contractility, interstitial congestion, and chronotropic insufficiency are the most frequent abnormalities detected by ABCDE-SE. Up to one-quarter of such patients demonstrate normal SE results. This comprehensive approach paves the way for personalized, SE-guided therapy.

Objectives To identify Reddit users’ viewpoints and inquiries about spinal cord stimulation (SCS) for chronic back pain using Reddit databases. Methods We performed a qualitative analysis of public, anonymous threads and comments from targeted subreddits within the Reddit community. We used the Python Reddit API Wrapper to extract relevant data. A qualitative descriptive approach was employed, using natural language processing to identify and categorize common questions, concerns, and opinions shared by patients regarding SCS. Results Our analysis included 112 posts and 448 comments. The tone of comments was neutral (n=231), followed by negative (n=121) and positive (n=96). 13 users actively encouraged other users to try the procedure, while 25 advised against it. The main topics of discussions revolved around pain relief expectations and adverse events. Almost half of users commenting about pain relief expectations reported experiencing considerably lower improvement than anticipated. Pocket pain, lead fracture/migration, infection risk, and scars were common topics of discussion among users. Furthermore, users shared strategies to mitigate postoperative discomfort and offered insights into device selection based on MRI conditionality, reprogramming need, and charging prerequisites. Conclusion Our Reddit analysis identified potential targets for enhanced dialog between physicians and patients around anticipated pain relief, complications, and postoperative care. Reddit and other social media platforms may offer valuable opportunities for healthcare professionals to improve engagement with patients.