AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors.

BACKGROUND AND OBJECTIVES: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. METHODS: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. RESULTS: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. CONCLUSIONS: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.

BACKGROUND: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear. OBJECTIVE: This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy. STUDY DESIGN: This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05. RESULTS: Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively. CONCLUSION: The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.

Effect of previous emergency psychiatric consultation on suicide re-attempts - A multi-center observational study.

BACKGROUND: The emergency department (ED) is one of the first gateways when suicide attempt patients seek health care services. The purpose of this study was to analyze the hypothesis that people who received emergency psychiatric services in previous suicide attempts will have a lower mortality rate in current ED visits owing to subsequent suicide attempts. METHOD: This retrospective study included patients who visited six EDs, and participated in the injury surveillance and in-depth suicide surveillance for 10 years, from January 2008 to December 2017. The study subjects were adult patients 18 years or older who visited EDs due to suicide attempts. The main explanatory variable is whether psychiatric treatment was provided in previous suicide attempts. The main outcome variable was suicide related mortality. RESULTS: The study included 2144 suicide attempt patients with a previous history of suicide attempts. Among these, 1335 patients (62.2%) had received psychiatric treatment in previous suicide attempts. Mortality was significantly different between the psychiatric consultation group (n = 33, 2.5%) and non-consultation group (n = 47, 5.8%) (P < 0.01). In multivariate logistic regression analysis, previous psychiatric consultation showed a significant association with low mortality (adjusted OR 0.41; 95% CI [0.23-0.72]) and selecting non-fatal suicide methods (adjusted OR 0.47; 95% CI [0.36-0.61]). CONCLUSION: Patients who received psychiatric consultation in previous suicide attempts had a lower suicide-related mortality in current ED visits as compared to patients who did not, and this may have been related to choosing non-fatal suicide methods.

Safety and Efficacy of Zonisamide in Patients with Epilepsy: A Post-Marketing Surveillance Study.

BACKGROUND AND PURPOSE: Zonisamide (ZNS) is one of new antiepileptic drug, which is known to inhibit seizure through multiple mechanisms of action. In Korea, ZNS was approved as an antiepileptic drug in 1992 and has been used for epilepsy patients with partial and generalized seizures. The objective of this study was to investigate the efficacy and tolerability of ZNS in patients with epilepsy and to identify the incidence of adverse events in real clinical setting. METHODS: This study was carried out in patients who received ZNS for epilepsy. Patients who were observed for at least 12 weeks after treatment with ZNS were included as evaluable subjects. Information regarding the status and type of adverse events occurring during the course of treatment with ZNS was obtained regardless of causal relationship to ZNS and efficacy was assessed by the study physicians and patients at 12 weeks post dose of ZNS. RESULTS: A total of 1,948 patients were included in the study, and ZNS efficacy was evaluated in 1,744 patients. ZNS was used as a monotherapy in 1,095 patients and as an adjunctive drug in 853 patients. Of the total patients, 1,345 (69.1%) patients had partial seizure, 563 patients had generalized seizure, and 40 patients were undetermined. Adverse events were reported in 65 patients (3.34%) including 1 case of Stevens-Johnson syndrome, but no incidence of serious unexpected adverse drug reactions were reported. 755 patients (43.29%) became seizure free with ZNS treatment, and additional 322 patients (18.41%) experienced marked improvement with ZNS treatment. CONCLUSIONS: Our study shows the safety and tolerability of ZNS treatment in patients with epilepsy in real clinical setting. In addition, ZNS was found to be an effective option as a monotherapy or in patients with generalized seizure.

Aspergillus spondylitis involving the cervico-thoraco-lumbar spine in an immunocompromised patient: a case report.

Aspergillosis is a rare cause of spondylitis. Moreover, early diagnosis by MR imaging and adequate treatment can prevent the serious complications of fungal infection. To our knowledge, the MR findings of multilevel aspergillus spondylitis in the cervico-thoraco-lumbar spine have not been previously described. Here, we report the MR findings of aspergillus spondylitis involving the cervical, thoracic, and lumbar spine in a liver transplant recipient.