Association between loop diuretic dose administered in first 24 hours of heart failure admissions and length of hospital stay

Background: Heart failure remains one of the highest disease burdens in the USA and worldwide. Heart failure guidelines recommend starting with a higher or equal to home dose of loop diuretics in acute decompensated heart failure admissions. To date, no study has been published assessing the effect of first 24 h loop diuretic dose on length of hospital stay. Objective: We hypothesize that the higher the first 24 h loop diuretic dose to home dose ratio, the shorter the length of hospital stay will be. Design/Methods: Retrospective chart review was conducted in a community teaching hospital and included patients discharged between February, 2015 and April, 2016, with a primary diagnosis of acute decompensated heart failure. The primary outcome was the length of hospital stay. The study population was divided into three groups based on the hospital to home dose ratio. Results: Among the 609 patients included in the data analysis, there was no statistically significant difference in length of hospital stay among the study groups. Inpatient mortality and incidence of acute kidney injury were highest in the group that received a first-24-hours hospital dose that was less than their home dose. Percentage of weight loss and 30-day readmission were not statistically significantly different among the groups. Conclusion: There was no association between the dose ratio and length of hospital stay in each group. Additional randomized controlled trials need to be conducted to provide more evidence and guidance for dosing loop diuretics in acute decompensated heart failure admissions

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out‐of‐hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single‐lead ECGs that comprised the study data set. ECGs of 7‐s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990–1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7‐s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%–98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871–0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT0366280