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.

A simple and novel equation to estimate the degree of bleeding in haemorrhagic shock: mathematical derivation and preliminary in vivo validation

Determining blood loss [100% – RBV (%)] is challenging in the management of haemorrhagic shock. We derived an equation estimating RBV (%) via serial haematocrits (Hct1 , Hct2 ) by fixing infused crystalloid fluid volume (N) as [0.015 × body weight (g)]. Then, we validated it in vivo. Mathematically, the following estimation equation was derived: RBV (%) = 24k / [(Hct1 / Hct2 ) – 1]. For validation, nonongoing haemorrhagic shock was induced in Sprague–Dawley rats by withdrawing 20.0%–60.0% of their total blood volume (TBV) in 5.0% intervals (n = 9). Hct1 was checked after 10 min and normal saline N cc was infused over 10 min. Hct 2 was checked five minutes later. We applied a linear equation to explain RBV (%) with 1 / [(Hct1 / Hct2 ) – 1]. Seven rats losing 30.0%–60.0% of their TBV suffered shock persistently. For them, RBV (%) was updated as 5.67 / [(Hct1 / Hct2 ) – 1] + 32.8 (95% confidence interval [CI] of the slope: 3.14–8.21, p = 0.002, R2 = 0.87). On a Bland-Altman plot, the difference between the estimated and actual RBV was 0.00 ± 4.03%; the 95% CIs of the limits of agreements were included within the pre-determined criterion of validation (< 20%). For rats suffering from persistent, non-ongoing haemorrhagic shock, we derived and validated a simple equation estimating RBV (%). This enables the calculation of blood loss via information on serial haematocrits under a fixed N.Clinical validation is required before utilisation for emergency care of haemorrhagic shock.

Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection

Background@#The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the general wards, which could simply be regarded as a general early warning score. However, comparison of unselected admissions to highlight the benefits of introducing qSOFA in hospitals already using Modified Early Warning Score (MEWS) remains unclear. We sought to compare qSOFA with MEWS for predicting clinical deterioration in general ward patients regardless of suspected infection. @*Methods@#The predictive performance of qSOFA and MEWS for in-hospital cardiac arrest (IHCA) or unexpected intensive care unit (ICU) transfer was compared with the areas under the receiver operating characteristic curve (AUC) analysis using the databases of vital signs collected from consecutive hospitalized adult patients over 12 months in five participating hospitals in Korea. @*Results@#Of 173,057 hospitalized patients included for analysis, 668 (0.39%) experienced the composite outcome. The discrimination for the composite outcome for MEWS (AUC, 0.777;95% confidence interval [CI], 0.770–0.781) was higher than that for qSOFA (AUC, 0.684;95% CI, 0.676–0.686; P < 0.001). In addition, MEWS was better for prediction of IHCA (AUC, 0.792; 95% CI, 0.781–0.795 vs. AUC, 0.640; 95% CI, 0.625–0.645; P < 0.001) and unexpected ICU transfer (AUC, 0.767; 95% CI, 0.760–0.773 vs. AUC, 0.716; 95% CI, 0.707–0.718; P < 0.001) than qSOFA. Using the MEWS at a cutoff of ≥ 5 would correctly reclassify 3.7% of patients from qSOFA score ≥ 2. Most patients met MEWS ≥ 5 criteria 13 hours before the composite outcome compared with 11 hours for qSOFA score ≥ 2. @*Conclusion@#MEWS is more accurate that qSOFA score for predicting IHCA or unexpected ICU transfer in patients outside the ICU. Our study suggests that qSOFA should not replace MEWS for identifying patients in the general wards at risk of poor outcome.

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS: The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.

High Incidence and Mortality of Out-of-Hospital Cardiac Arrest on Traditional Holiday in South Korea

BACKGROUND AND OBJECTIVES: This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea. METHODS: We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday). RESULTS: OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays. CONCLUSIONS: The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.

High Incidence and Mortality of Out-of-Hospital Cardiac Arrest on Traditional Holiday in South Korea

BACKGROUND AND OBJECTIVES@#This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea.@*METHODS@#We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday).@*RESULTS@#OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays.@*CONCLUSIONS@#The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

BACKGROUND AND OBJECTIVES@#Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF).@*METHODS@#The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data.@*RESULTS@#The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF.@*CONCLUSIONS@#The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.