Association Between Aortic Valve Sclerosis and Clonal Hematopoiesis of Indeterminate Potential.

Background: The mechanism and medical treatment target for degenerative aortic valve disease, including aortic stenosis, is not well studied. In this study, we investigated the effect of clonal hematopoiesis of indeterminate potential (CHIP) on the development of aortic valve sclerosis (AVS), a calcified aortic valve without significant stenosis. Methods: Participants with AVS (valves ≥2 mm thick, high echogenicity, and a peak transaortic velocity of <2.5 m/sec) and an age- and sex-matched control group were enrolled. Twenty-four CHIP genes with common variants in cardiovascular disease were used to generate a next-generation sequencing panel. The primary endpoint was the CHIP detection rate between the AVS and control groups. Inverse-probability treatment weighting (IPTW) analysis was performed to adjust for differences in baseline characteristics. Results: From April 2020 to April 2022, 187 participants (125 with AVS and 62 controls) were enrolled; the mean age was 72.6±8.5 yrs, and 54.5% were male. An average of 1.3 CHIP variants was observed. CHIP detection, defined by a variant allele frequency (VAF) of ≥0.5%, was similar between the groups. However, the AVS group had larger CHIP clones: 49 (39.2%) participants had a VAF of ≥1% (vs. 13 [21.0%] in the control group; P=0.020), and 25 (20.0%) had a VAF of ≥2% (vs. 4 [6.5%]; P=0.028). AVS is independently associated with a VAF of ≥1% (adjusted odds ratio: 2.44, 95% confidence interval: 1.11-5.36; P=0.027). This trend was concordant and clearer in the IPTW cohort. Conclusions: Participants with AVS more commonly had larger CHIP clones than age- and sex-matched controls. Further studies are warranted to identify causality between AVS and CHIP.

Prognostic value of preoperative left ventricular global longitudinal strain for predicting postoperative myocardial injury and mortality in patients undergoing major non-cardiac surgery (SOLOMON study).

BACKGROUND: The usefulness of preoperative measurement of left ventricular global longitudinal strain (LVGLS) for predicting prognosis in patients undergoing non-cardiac surgery has not been evaluated. We analyzed the prognostic value of LVGLS in predicting postoperative 30-day cardiovascular events and myocardial injury after non-cardiac surgery (MINS). METHODS: This prospective cohort study was conducted in two referral hospitals and included 871 patients who underwent non-cardiac surgery <1 month after preoperative echocardiography. Those with ejection fraction <40%, valvular heart disease, and regional wall motion abnormality were excluded. The co-primary endpoints were the (1) composite incidence of all-cause death, acute coronary syndrome (ACS), and MINS and (2) composite incidence of all-cause death and ACS. RESULTS: Among the 871 participants enrolled (mean age: 72.9 years; female: 60.8%), there were 43 cases of the primary endpoint (4.9%): 10 deaths, 3 ACS, and 37 MINS. Participants with impaired LVGLS (≤16.6%) had a higher incidence of the co-primary endpoints (log-rank P < 0.001 and 0.015) than those without. The result was similar after adjustment with clinical variables and preoperative troponin T levels (hazard ratio = 1.30, 95% confidence interval [CI] = 1.03-1.65; P = 0.027). In sequential Cox analysis and net reclassification index, LVGLS had an incremental value for predicting the co-primary endpoints after non-cardiac surgery. Among the 538 (61.8%) participants who underwent serial troponin assay, LVGLS predicted MINS independently from the traditional risk factors (odds ratio = 3.54, 95% CI = 1.70-7.36; P = 0.001). CONCLUSIONS: Preoperative LVGLS has an independent and incremental prognostic value in predicting early postoperative cardiovascular events and MINS. CLINICAL TRIAL REGISTRATION: URL: https://trialsearch.who.int/. Unique identifiers: KCT0005147.

Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.

Coronary artery calcium (CAC), which can be measured in various types of computed tomography (CT) examinations, is a hallmark of coronary artery atherosclerosis. However, despite the clinical value of CAC scores in predicting cardiovascular events, routine measurement of CAC scores is limited due to high cost, radiation exposure, and lack of widespread availability. It would be of great clinical significance if CAC could be predicted by electrocardiograms (ECGs), which are cost-effective and routinely performed during various medical checkups. We aimed to develop binary classification artificial intelligence (AI) models that predict CAC using only ECGs as input. Moreover, we aimed to address the generalizability of our model in different environments by externally validating our model on a dataset from a different institution. Among adult patients, standard 12-lead ECGs were extracted if measured within 60 days before or after the CAC scores, and labeled with the corresponding CAC scores. We constructed deep convolutional neural network models based on residual networks using only the raw waveforms of the ECGs as input, predicting CAC at different levels, namely CAC score ≥100, ≥400 and ≥1,000. Our AI models performed well in predicting CAC in the training and internal validation dataset [area under the receiver operating characteristics curve (AUROC) 0.753 ± 0.009, 0.802 ± 0.027, and 0.835 ± 0.024 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively]. Our models also performed well in the external validation dataset (AUROC 0.718, 0.777 and 0.803 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively), indicating that our model can generalize well to different but plausibly related populations. Model performance in terms of AUROC increased in the order of CAC score ≥100, ≥400, and ≥1,000 model, indicating that higher CAC scores might be associated with more prominent structural changes of the heart detected by the model. With our AI models, a substantial proportion of previously unrecognized CAC can be afforded with a risk stratification of CAC, enabling initiation of prophylactic therapy, and reducing the adverse consequences related to ischemic heart disease.

Clinical impact of echocardiography-defined pulmonary hypertension on the clinical outcome in patients with multiple myeloma.

Pulmonary hypertension (PH) is a complication of multiple myeloma (MM); however, the clinical outcomes and prognosis are relatively not well known. We aimed to investigate the risk factors of transthoracic echocardiography-defined PH and its impact on the clinical outcome in patients with MM.A retrospective study was performed using data from the Chonnam National University Hwasun Hospital database for patients who underwent transthoracic echocardiography (TTE) within 1 month of the MM diagnosis between January 2007 and December 2017. PH was defined as an estimated right ventricular systolic pressure (RVSP) > 40 mmHg. A total of 390 patients were included. TTE-defined PH was observed in 107 patients (27%). During the follow-up period (median, 688 days), all-cause death was noted for 134 patients (34.4%). In the Kaplan-Meier survival analysis, the cumulative overall survival and cardiovascular death-free survival rates were significantly lower in the PH group than in the non-PH group (P < .001). In the propensity score-matched population, RVSP > 40 mmHg on TTE and history of congestive heart failure (CHF) were identified as the significant independent predictors of all-cause and cardiovascular death.This study reports that the prevalence of TTE-defined PH is higher in patients with MM than in the general population. Moreover, TTE-defined PH and a history of CHF are the independent prognostic factors for all-cause and cardiovascular death in patients with MM. These results highlight the risk of associated cardiovascular disease in patients with MM and emphasize the importance of management strategies that prevent the deterioration of cardiac function.