Lopez-Jimenez, Francisco

Objective: Cardiovascular disease remains a leading cause of mortality worldwide. Ceramide scores have been associated with adverse outcomes in patients with established coronary artery disease. The prognostic value of ceramide score has not been assessed in the general population. We tested the hypothesis that ceramide scores are associated with major adverse cardiac events (MACE) in a community-based cohort with average coronary artery disease burden at enrollment. Approach and results: In a prospective community-based cohort, we performed passive follow-up using a record linkage system to ascertain the composite outcome of MACE, defined as acute myocardial infarction, coronary revascularization (bypass grafting or percutaneous intervention), stroke, or death. Ceramides were analyzed as log-transformed continuous variables, ratios or scores, and quartiles with adjustment for confounders. We analyzed 1131 subjects, 52% females, mean age±(SD) 64±9 years. After a median follow-up of 13.3 years (Q1, 12.7; Q3, 14.4), 486 patients experienced a MACE: myocardial infarction (80), coronary artery bypass surgery (34), percutaneous coronary intervention (62), stroke (94), and all-cause death (362). Ceramide ratios were significantly associated with MACE independently of LDL-c (low-density lipoprotein cholesterol) and conventional coronary artery disease risk factors. Those in the highest quartile of ceramide score had nearly 1.5-fold risk of MACE, hazard ratio, 1.47 (95% CI, 1.12–1.92). There was a dose-response association across quartiles of ceramide ratios and MACE. Conclusions: Elevated ceramide score is a robust predictor of cardiovascular disease and MACE in the community. The risk conferred by the ceramide score has a dose-response behavior and is independent of conventional risk factors.

Background An artificial intelligence algorithm that detects age using the 12‐lead ECG has been suggested to signal “physiologic age.” This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index of vascular aging, with accelerated physiologic aging gauged by ECG‐derived artificial intelligence–estimated age. Methods and Results This study included 531 patients who underwent ECG and a noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry. Abnormal PMEF was defined as reactive hyperemia peripheral arterial tonometry index ≤2.0. Accelerated or delayed physiologic aging was calculated by the Δ age (ECG‐derived artificial intelligence–estimated age minus chronological age), and the association between Δ age and PMEF as well as its impact on composite major adverse cardiovascular events were investigated. Δ age was higher in patients with abnormal PMEF than in patients with normal PMEF (2.3±7.8 versus 0.5±7.7 years; P =0.01). Reactive hyperemia peripheral arterial tonometry index was negatively associated with Δ age after adjustment for cardiovascular risk factors (standardized β coefficient, –0.08; P =0.048). The highest quartile of Δ age was associated with an increased risk of major adverse cardiovascular events compared with the first quartile of Δ age in patients with abnormal PMEF, even after adjustment for cardiovascular risk factors (hazard ratio, 4.72; 95% CI, 1.24–17.91; P =0.02). Conclusions Vascular aging detected by endothelial function is associated with accelerated physiologic aging, as assessed by the artificial intelligence–ECG Δ age. Patients with endothelial dysfunction and the highest quartile of accelerated physiologic aging have a marked increase in risk for cardiovascular events.

Abstract Aims An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). Methods and results We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02–1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93–0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. Conclusion The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.

Abstract Background Older persons with obesity aged 65+ residing in rural areas have reduced access to weight management programs due to geographic isolation. The ability to integrate technology into health promotion interventions shows a potential to reach this underserved population. Methods A 12-week pilot in 28 older rural adults with obesity (body mass index [BMI] ≥ 30 kg/m2) was conducted at a community aging center. The intervention consisted of individualized, weekly dietitian visits focusing on behavior therapy and caloric restriction with twice weekly physical therapist-led group strengthening training classes in a community-based aging center. All participants were provided a Fitbit Flex 2. An aerobic activity prescription outside the strength training classes was provided. Results Mean age was 72.9 ± 5.3 years (82% female). Baseline BMI was 37.1 kg/m2, and waist circumference was 120.0 ± 33.0 cm. Mean weight loss (pre/post) was 4.6 ± 3.2 kg (4.9 ± 3.4%; p < .001). Of the 40 eligible participants, 33 (75%) enrolled, and the completion rate was high (84.8%). Objective measures of physical function improved at follow-up: 6-minute walk test improved: 35.7 ± 41.2 m (p < .001); gait speed improved: 0.10 ± 0.24 m/s (p = .04); and five-times sit-to-stand improved by 2.1 seconds (p < .001). Subjective measures of late-life function improved (5.2 ± 7.1 points, p = .003), as did Patient-Reported Outcome Measurement Information Systems mental and physical health scores (5.0 ± 5.7 and 4.4 ± 5.0, both p < .001). Participants wore their Fitbit 93.9% of all intervention days, and were overall satisfied with the trial (4.5/5.0, 1–5 low–high) and with Fitbit (4.0/5.0). Conclusions A multicomponent obesity intervention incorporating a wearable device is feasible and acceptable to older adults with obesity, and potentially holds promise in enhancing health.

Importance. Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation. Objective. To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort. Design. Retrospective observational analysis. Setting. China from October 2016 to December 2017. Participants. Mobile application users with a minimum of 2 weights (baseline and ≥35 days). Intervention. A commercial (Weijian Technologies) weight loss program consisting of a dietary replacement, self-monitoring using a wireless home scale, and frequent guidance via mobile application. Main Outcome. Mean weight change around 42, 60, 90, and 120 days after program initiation with subgroup analysis by gender, age, and frequency of use. Results. 251,718 individuals, with a mean age of 37.3 years (SD: 9.86) (79% female), were included with a mean weight loss of 4.3 kg (CI: ±0.02) and a mean follow-up of 120 days (SD: 76.8 days). Mean weight loss at 42, 60, 90, and 120 d was 4.1 kg (CI: ±0.02), 4.9 kg (CI: ±0.02), 5.6 kg (CI: ±0.03), and 5.4 kg (CI: ±0.04), respectively. At 120 d, 62.7% of participants had lost at least 5% of their initial weight. Both genders and all usage frequency tertiles showed statistically significant weight loss from baseline at each interval (P<0.001), and this loss was greater in men than in women (120 d: 6.5 vs. 5.2 kg; P<0.001). The frequency of recording (categorized as high-, medium-, or low-frequency users) was associated with greater weight loss when comparing high, medium, and low tertile use groups at all time intervals investigated (e.g., 120 d: −8.6, −5.6, and −2.2 kg, respectively; P<0.001). Conclusions. People following a commercially available hybrid weight loss program using a mobile application, wireless scale, and nutritional program without face-to-face interaction on average achieved clinically significant short- and midterm weight loss. These results support the implementation of comparable technologies for weight control in a large population.

Background: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG. Methods: We performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction ≤35% from the 12-lead ECG. Results: The convolutional neural network that was previously derived in a homogeneous population (derivation cohort, n=44 959; 96.2% non-Hispanic white) demonstrated consistent performance to detect low left ventricular ejection fraction across a range of racial/ethnic subgroups in a separate testing cohort (n=52 870): non-Hispanic white (n=44 524; area under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African American (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and American Indian/Native Alaskan (n=223; AUC, 0.938). In secondary analyses, a separate neural network was able to discern racial subgroup category (black/African American [AUC, 0.84], and white, non-Hispanic [AUC, 0.76] in a 5-class classifier), and a network trained only in non-Hispanic whites from the original derivation cohort performed similarly well across a range of racial/ethnic subgroups in the testing cohort with an AUC of at least 0.930 in all racial/ethnic subgroups. Conclusions: Our study demonstrates that while ECG characteristics vary by race, this did not impact the ability of a convolutional neural network to predict low left ventricular ejection fraction from the ECG. We recommend reporting of performance among diverse ethnic, racial, age, and sex groups for all new artificial intelligence tools to ensure responsible use of artificial intelligence in medicine.