Predicting Cardiac Arrest on the Wards

Abstract

More than 150,000 adult in-hospital cardiac arrests (CAs) occur in the United States each year, with up to half occurring outside of intensive care settings. 1,2 Mortality for these patients is . 80%, but there is evidence to suggest that some of these deaths may be preventable. 1,3 Rapid response teams (RRTs) were created to decrease adverse outcomes by bringing critical care resources to deteriorating patients on the hospital wards. However, results have been mixed, with some studies demonstrating benefi t but others showing no improvement in mortality and hospital-wide CA rates. [4][5][6] A major cited weakness of the RRT is the failure to identify patients in suffi cient time to intervene prior to CA. 7-9 For example, in the Medical Emergency Response Intervention Trial (MERIT) , the only multicenter randomized trial of RRTs, most patients were identifi ed , 15 min before CA, death, or ICU transfer. 6,7 Furthermore, the criteria used to activate the intervention had a sensitivity , 50%. 6,7 Currently, there are . 50 different published criteria designed

National Early Warning Score Parameters at the Emergency Department as In Hospital Cardiac Arrest Predictors

“…Another parameter that has no correlation to the IHCA incidence is the pulse rate score (p = 0.247; r = 0.093). These results are different from previous studies which mention there are differences in pulse rate 48 hours before the IHCA incidence (Churpek et al, 2012). Abnormalities in the patient's physiological status in the ED related to the IHCA incidence are pulse rate (Considine et al, 2016).…”

National Early Warning Score Parameters at the Emergency Department as In Hospital Cardiac Arrest Predictors

“…Another parameter that has no correlation to the IHCA incidence is the pulse rate score (p = 0.247; r = 0.093). These results are different from previous studies which mention there are differences in pulse rate 48 hours before the IHCA incidence (Churpek et al, 2012). Abnormalities in the patient's physiological status in the ED related to the IHCA incidence are pulse rate (Considine et al, 2016).…”

Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study

“…Therefore, the variables inside the black box may not be too ambiguous to be interpreted. Consistent with previous studies indicating that the performance of SVM and RF models is not inferior to that of traditional LR models [ 32 , 33 , 34 ], our findings reveal that SVM or RF should not be a barrier for clinicians in predicting bacteremia episodes in septic ED patients. Recent worldwide advances in EMR have created a suitable environment to leverage SVM and RF to improve the quality of patient care.…”

“…Based on patient demographics and laboratory data available in the ED, the algorithms with proper discriminations in predicting bacteremia episodes among septic patients were evident. Similar to previous studies indicating that the performance of SVM and RF models was not inferior to that of traditional LR models [ 32 , 33 , 34 ], the prediction performance of the SVM and RF models was not inferior to that of the LR-based model in our study.…”

“…The black box is a significant concern for many clinicians in adopting an algorithm-based aspect in their research [ 53 , 54 , 55 ]. Notably, some studies have suggested that the critical variables of SVM and RF models are usually consistent with clinical intuition and significant predictors found in prior studies in the field [ 32 , 33 , 34 ]. Accordingly, the essential variables chosen in the RF model are primarily compatible with the variables from the LR model presented in our study.…”

Performance of patient acuity rating by rapid response team nurses for predicting short-term prognosis

“…Our study identified smaller AUROC values for other early waring scores compared to previous studies [3, 4, 7, 14, 15]. This is likely related to the studies being performed in different medical settings.…”

National Early Warning Score Parameters at the Emergency Department as In Hospital Cardiac Arrest Predictors

“…Another parameter that has no correlation to the IHCA incidence is the pulse rate score (p = 0.247; r = 0.093). These results are different from previous studies which mention there are differences in pulse rate 48 hours before the IHCA incidence (Churpek et al, 2012). Abnormalities in the patient's physiological status in the ED related to the IHCA incidence are pulse rate (Considine et al, 2016).…”

Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study

“…Therefore, the variables inside the black box may not be too ambiguous to be interpreted. Consistent with previous studies indicating that the performance of SVM and RF models is not inferior to that of traditional LR models [ 32 , 33 , 34 ], our findings reveal that SVM or RF should not be a barrier for clinicians in predicting bacteremia episodes in septic ED patients. Recent worldwide advances in EMR have created a suitable environment to leverage SVM and RF to improve the quality of patient care.…”

“…Based on patient demographics and laboratory data available in the ED, the algorithms with proper discriminations in predicting bacteremia episodes among septic patients were evident. Similar to previous studies indicating that the performance of SVM and RF models was not inferior to that of traditional LR models [ 32 , 33 , 34 ], the prediction performance of the SVM and RF models was not inferior to that of the LR-based model in our study.…”

“…The black box is a significant concern for many clinicians in adopting an algorithm-based aspect in their research [ 53 , 54 , 55 ]. Notably, some studies have suggested that the critical variables of SVM and RF models are usually consistent with clinical intuition and significant predictors found in prior studies in the field [ 32 , 33 , 34 ]. Accordingly, the essential variables chosen in the RF model are primarily compatible with the variables from the LR model presented in our study.…”

Performance of patient acuity rating by rapid response team nurses for predicting short-term prognosis

“…Our study identified smaller AUROC values for other early waring scores compared to previous studies [3, 4, 7, 14, 15]. This is likely related to the studies being performed in different medical settings.…”

National Early Warning Score Parameters at the Emergency Department as In Hospital Cardiac Arrest Predictors

“…Another parameter that has no correlation to the IHCA incidence is the pulse rate score (p = 0.247; r = 0.093). These results are different from previous studies which mention there are differences in pulse rate 48 hours before the IHCA incidence (Churpek et al, 2012). Abnormalities in the patient's physiological status in the ED related to the IHCA incidence are pulse rate (Considine et al, 2016).…”

Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study

“…Therefore, the variables inside the black box may not be too ambiguous to be interpreted. Consistent with previous studies indicating that the performance of SVM and RF models is not inferior to that of traditional LR models [ 32 , 33 , 34 ], our findings reveal that SVM or RF should not be a barrier for clinicians in predicting bacteremia episodes in septic ED patients. Recent worldwide advances in EMR have created a suitable environment to leverage SVM and RF to improve the quality of patient care.…”

“…Based on patient demographics and laboratory data available in the ED, the algorithms with proper discriminations in predicting bacteremia episodes among septic patients were evident. Similar to previous studies indicating that the performance of SVM and RF models was not inferior to that of traditional LR models [ 32 , 33 , 34 ], the prediction performance of the SVM and RF models was not inferior to that of the LR-based model in our study.…”

“…The black box is a significant concern for many clinicians in adopting an algorithm-based aspect in their research [ 53 , 54 , 55 ]. Notably, some studies have suggested that the critical variables of SVM and RF models are usually consistent with clinical intuition and significant predictors found in prior studies in the field [ 32 , 33 , 34 ]. Accordingly, the essential variables chosen in the RF model are primarily compatible with the variables from the LR model presented in our study.…”

Performance of patient acuity rating by rapid response team nurses for predicting short-term prognosis

“…Our study identified smaller AUROC values for other early waring scores compared to previous studies [3, 4, 7, 14, 15]. This is likely related to the studies being performed in different medical settings.…”