Estimating Ejection Fraction from the 12 Lead ECG among Patients with Acute Heart Failure.

Background: Identifying patients with low left ventricular ejection fraction (LVEF) in the emergency department using an electrocardiogram (ECG) may optimize acute heart failure (AHF) management. We aimed to assess the efficacy of 527 automated 12-lead ECG features for estimating LVEF among patients with AHF. Method: Medical records of patients >18 years old and AHF-related ICD codes, demographics, LVEF %, comorbidities, and medication were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) identified important ECG features and evaluated performance. Results: Among 851 patients, the mean age was 74 years (IQR:11), male 56% (n=478), and the median body mass index was 29 kg/m2 (IQR:1.8). A total of 914 echocardiograms and ECGs were matched; the time between ECG-Echocardiogram was 9 hours (IQR of 9 hours); ≤30% LVEF (16.45%, n=140). Lasso demonstrated 42 ECG features important for estimating LVEF ≤30%. The predictive model of LVEF ≤30% demonstrated an area under the curve (AUC) of 0.86, a 95% confidence interval (CI) of 0.83 to 0.89, a specificity of 54% (50% to 57%), and a sensitivity of 91 (95% CI: 88% to 96%), accuracy 60% (95% CI:60 % to 63%) and, negative predictive value of 95%. Conclusions: An explainable machine learning model with physiologically feasible predictors may be useful in screening patients with low LVEF in AHF.

Integrative review of electrocardiographic characteristics in patients with reduced, mildly reduced, and preserved heart failure.

INTRODUCTION: Electrocardiographic (ECG) changes in heart failure with reduced, mildly reduced, and preserved ejection fractions can be critical in clinical assessment while waiting to perform echocardiograms or when it is unavailable. This integrative review aimed to identify ECG characteristics among hospitalized patients demonstrating three types of heart failure during acute decompensation. METHODS: We searched an electronic database of PubMed, Web of Science, EMBASE, Scopus, Google Scholar, and ClinicalTrials.gov using medical subject headings (MeSH) terms and keywords. Sixteen studies were synthesized and reported. RESULTS: Heart failure with reduced ejection fraction (HFrEF) was more common in men, comorbid with coronary artery diseases and diabetes mellitus, higher BNP/Pro-BNP, wide QRS, and left bundle branch block on ECG. On average, clients with heart failure with preserved ejection fraction (HFpEF) were older and more likely to have a history of atrial fibrillation, valvular heart diseases, hypertension, chronic obstructive pulmonary, and atrial fibrillation (AF) on ECG. Patients with mildly reduced (HFmrEF) were more similar to HFpEF in older patients, comorbid with hypertension, AF and valvular diseases, and AF on ECG. CONCLUSIONS: ECG characteristics might be related to left ventricular ejection fraction. Demographics, BNP/Pro-BNP, and ECG changes might help differentiate different heart failure types. Therefore, ECG might be a prognostic tool while caring for heart failure patients when highly skilled resources are unavailable. These identified ECG characteristics help generate research hypotheses and warrant validation in future research.

In-Hospital Cardiac Arrest: An Update on Pulseless Electrical Activity and Asystole.

Nonshockable rhythms, including pulseless electrical activity (PEA) and asystole, precede more than 70% of in-hospital cardiac arrests (I-HCA). Compared with shockable rhythms (ventricular fibrillation and ventricular tachycardia), nonshockable rhythms have higher mortality and morbidity. Therefore, investigating the underlying mechanisms of these arrhythmias to improve the quality of care and outcome for patients who suffer cardiac arrest is a priority. As the first responders to I-HCA, nurses must have the proper knowledge and training to provide timely and efficient cardiopulmonary resuscitation therapy. This article provides an overview of nonshockable cardiac arrhythmias preceding I-HCA as a means of addressing the gap between science and clinical practice.