scholarly journals A Multicenter Validation Study of the Deep Learning-based Early Warning Score for Predicting in-hospital Cardiac Arrest in Patients Admitted to General Wards

2020 ◽  
Author(s):  
Yeon Joo Lee ◽  
Kyung-Jae Cho ◽  
Oyeon Kwon ◽  
Hyunho Park ◽  
Yeha Lee ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Deep Learning ◽  
Early Warning ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Early Warning Score ◽  
Patients At Risk ◽  
Key Aspects ◽  
General Wards ◽  
Hospital Cardiac Arrest

Abstract Background: The recently developed deep learning (DL)-based early warning score (DEWS) has shown a potential in predicting deteriorating patients. We aimed to validate DEWS in multiple centers and compare the prediction, alarming and timeliness performance with those of the modified early warning score (MEWS) to identify patients at risk for in-hospital cardiac arrest (IHCA).Methods: This retrospective cohort study included adult patients admitted to the general wards of five hospitals during a 12-month period. We validated DEWS internally at two hospitals and externally at the other three hospitals. The occurrence of IHCA within 24 hours of vital sign observation was the outcome of interest. We used the area under the receiver operating characteristic curve (AUROC) as the main performance metric.Results: The study population consisted of 173,368 patients (224 IHCAs). The predictive performance of DEWS was superior to that of MEWS in both the internal (AUROC: 0.860 vs. 0.754, respectively) and external (AUROC: 0.905 vs. 0.785, respectively) validation cohorts. At the same specificity, DEWS had a higher sensitivity than MEWS, and at the same sensitivity, DEWS had a lower mean alarm count than MEWS, with nearly half of the alarm rate in MEWS. Additionally, DEWS was able to predict more IHCA patients in the 24 to 0.5 hours before the outcome.Conclusion: Our study showed that DEWS was superior to MEWS in the three key aspects (IHCA predictive, alarming, and timeliness performance). This study demonstrates the potential of DEWS as an effective, efficient screening tool in rapid response systems (RRSs) to identify high-risk patients.

Abstract 10992: Machine Learning to Predict In-Hospital Cardiac Arrest in Patients Admitted from the Emergency Department with COVID-19 and Suspected Pneumonia

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Tsung-Chien Lu ◽  
Eric H Chou ◽  
CHIH-HUNG WANG ◽  
Amir Mostafavi ◽  
Mario Tovar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Early Warning Score ◽  
Gradient Boosting ◽  
Patients At Risk ◽  
End Of Life Decision ◽  
Hospital Cardiac Arrest ◽  
Suspected Pneumonia

Introduction: There are only scarce models developed for stratifying the risk of cardiac arrest from COVID-19 patients presenting to the ED with suspected pneumonia. By using the machine learning (ML) approach, we aimed to develop and validate the ML models to predict in-hospital cardiac arrest (IHCA) in patients admitted from the ED. Hypothesis: We hypothesized that ML approach can serve as a valuable tool in identifying patients at risk of IHCA in a timely fashion. Methods: We included the COVID-19 patients admitted from the EDs of five hospitals in Texas between March and November 2020. All adult (≥ 18 years) patients were included if they had positive RT-PCR for SARS-CoV-2 and also received CXR examination for suspected pneumonia. Patients’ demographic, past medical history, vital signs at ED triage, CXR findings, and laboratory results were retrieved from the EMR system. The primary outcome (IHCA) was identified via a resuscitation code. Patients presented as OHCA or without any blood testing were excluded. Nonrandom splitting strategy based on different location was used to divide the dataset into the training (one urban and two suburban hospitals) and testing cohort (one urban and one suburban hospital) at around 2-to-1 ratio. Three supervised ML models were trained and performances were evaluated and compared with the National Early Warning Score (NEWS) by the area under the receiver operating characteristic curve (AUC). Results: We included 1,485 records for analysis. Of them, 190 (12.8%) developed IHCA. Of the constructed ML models, Random Forest outperformed the others with the best AUC result (0.930, 95% CI: 0.896-0.958), followed by Gradient Boosting (0.929, 95% CI: 0.891-0.959) and Extra Trees classifier (0.909, 95% CI: 0.875-0.943). All constructed ML models performed significantly better than by using the NEWS scoring system (AUC: 0.787, 95% CI: 0.725-0.840). The top six important features selected were age, oxygen saturation at triage, and lab data of APTT, lactic acid, and LDH. Conclusions: The ML approach showed excellent discriminatory performance to identify IHCA for patients with COVID-19 and suspected pneumonia. It has the potential to save more life or provide end-of-life decision making if successfully implemented in the EMR system.

scholarly journals Incidence of in-hospital cardiac arrest at general wards before and after implementation of an early warning score

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andreas Creutzburg ◽  
Dan Isbye ◽  
Lars S. Rasmussen
Keyword(s):  
Cardiac Arrest ◽  
Incidence Rate ◽  
Early Warning ◽  
Spontaneous Circulation ◽  
Early Warning Score ◽  
Medical Emergency ◽  
Before And After ◽  
Rate Ratios ◽  
General Wards ◽  
Hospital Cardiac Arrest

Abstract Background In order to reduce the incidence of in-hospital cardiac arrest (IHCA) at general wards, medical emergency teams (MET) were implemented in the Capital Region of Denmark in 2012 as the efferent part of a track and trigger system. The National Early Warning Score (NEWS) system became the afferent part. This study aims at investigating the incidence of IHCA at general wards before and after the implementation of the NEWS system. Material and methods We included patients at least 18 years old with IHCA at general wards in our hospital in the periods of 2006 to 2011 (pre-EWS group) and 2013 to 2018 (post-EWS group). Data was obtained from a local database and the National In-Hospital Cardiac Arrest Registry (DANARREST). We calculated incidence rate ratios (IRR) for IHCA at general wards with 95% confidence interval (95% CI). Odds ratios (OR) for return of spontaneous circulation (ROSC) and 30-day survival were also calculated with 95% CI. Results A total of 444 IHCA occurred before the implementation of NEWS at general wards while 494 IHCA happened afterwards. The incidence rate of IHCA at general wards was 1.13 IHCA per 1000 admissions in the pre-EWS group (2006–2011) and 1.11 IHCA per 1000 admissions in the post-EWS group (2013–2018). The IRR between the two groups was 0.98 (95% CI [0.86;1.11], p = 0.71). The implementation did not affect the chance of ROSC with a crude OR of 1.14 (95% CI [0.88;1.47], p = 0.32) nor did it change the 30-day survival with a crude OR 1.30 (95% CI [0.96;1.75], p = 0.09). Conclusion Implementation of the EWS system at our hospital did not decrease the incidence rate of in-hospital cardiac arrest at general wards.

scholarly journals Use of a modified early warning score system to reduce the rate of in-hospital cardiac arrest

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Isao Nishijima ◽  
Shouhei Oyadomari ◽  
Shuuto Maedomari ◽  
Risa Toma ◽  
Chisato Igei ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Early Warning ◽  
Early Warning Score ◽  
Score System ◽  
Hospital Cardiac Arrest

scholarly journals Periarrest Modified Early Warning Score (MEWS) predicts the outcome of in-hospital cardiac arrest

2016 ◽  
Vol 115 (2) ◽  
pp. 76-82 ◽  
Author(s):  
An-Yi Wang ◽  
Cheng-Chung Fang ◽  
Shyr-Chyr Chen ◽  
Shin-Han Tsai ◽  
Wei-Fong Kao
Keyword(s):  
Cardiac Arrest ◽  
Early Warning ◽  
Early Warning Score ◽  
Hospital Cardiac Arrest

scholarly journals Modified Early Warning Score Changes Prior to Cardiac Arrest in General Wards

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0130523 ◽  
Author(s):  
Won Young Kim ◽  
Yu Jung Shin ◽  
Jin Mi Lee ◽  
Jin Won Huh ◽  
Younsuck Koh ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Early Warning ◽  
Early Warning Score ◽  
General Wards

scholarly journals Predictive value of pre-arrest albumin level with GO-FAR score in patients with in-hospital cardiac arrest

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seok-In Hong ◽  
Youn-Jung Kim ◽  
Yeon Joo Cho ◽  
Jin Won Huh ◽  
Sang-Bum Hong ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Serum Albumin ◽  
Characteristic Curve ◽  
Tertiary Care ◽  
Predictive Performance ◽  
Albumin Level ◽  
Ordinal Scale ◽  
Care Hospital ◽  
Neurologic Outcomes ◽  
Hospital Cardiac Arrest

AbstractWe investigated whether combining the pre-arrest serum albumin level could improve the performance of the Good Outcome Following Attempted Resuscitation (GO-FAR) score for predicting neurologic outcomes in in-hospital cardiac arrest patients. Adult patients who were admitted to a tertiary care hospital between 2013 and 2017 were assessed. Their pre-arrest serum albumin levels were measured within 24 h before the cardiac arrest. According to albumin levels, the patients were divided into quartiles and were assigned 1, 0, 0, and, − 2 points. Patients were allocated to the derivation (n = 419) and validation (n = 444) cohorts. The proportion of favorable outcome increased in a stepwise manner across increasing quartiles (p for trend < 0.018). Area under receiver operating characteristic curve (AUROC) of the albumin-added model was significantly higher than that of the original GO-FAR model (0.848 vs. 0.839; p = 0.033). The results were consistent in the validation cohort (AUROC 0.799 vs. 0.791; p = 0.034). Net reclassification indices of the albumin-added model were 0.059 (95% confidence interval [CI] − 0.037 to 0.094) and 0.072 (95% CI 0.013–0.132) in the derivation and validation cohorts, respectively. An improvement in predictive performance was found by adding the ordinal scale of pre-arrest albumin levels to the original GO-FAR score.

scholarly journals Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1255
Author(s):  
Minsu Chae ◽  
Sangwook Han ◽  
Hyowook Gil ◽  
Namjun Cho ◽  
Hwamin Lee
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Deep Learning ◽  
Early Warning ◽  
Short Term Memory ◽  
Sudden Cardiac Arrest ◽  
Early Warning Systems ◽  
Early Warning Score ◽  
Unit Model ◽  
Low Sensitivity

Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

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