Trevor C. Yuen

Predicting Cardiac Arrest on the Wards : A Nested Case-Control Study

BACKGROUND Current rapid response team activation criteria were not statistically derived using ward vital signs, and the best vital sign predictors of cardiac arrest (CA) have not been determined. In addition, it is unknown when vital signs begin to accurately detect this event prior to CA. METHODS We conducted a nested case-control study of 88 patients experiencing CA on the wards of a university hospital between November 2008 and January 2011, matched 1:4 to 352 control subjects residing on the same ward at the same time as the case CA. Vital signs and Modified Early Warning Scores (MEWS) were compared on admission and during the 48 h preceding CA. RESULTS Case patients were older (64 ± 16 years vs 58 ± 18 years; P = .002) and more likely to have had a prior ICU admission than control subjects (41% vs 24%; P = .001), but had similar admission MEWS (2.2 ± 1.3 vs 2.0 ± 1.3; P = .28). In the 48 h preceding CA, maximum MEWS was the best predictor (area under the receiver operating characteristic curve [AUC] 0.77; 95% CI, 0.71-0.82), followed by maximum respiratory rate (AUC 0.72; 95% CI, 0.65-0.78), maximum heart rate (AUC 0.68; 95% CI, 0.61-0.74), maximum pulse pressure index (AUC 0.61; 95% CI, 0.54-0.68), and minimum diastolic BP (AUC 0.60; 95% CI, 0.53-0.67). By 48 h prior to CA, the MEWS was higher in cases (P = .005), with increasing disparity leading up to the event. CONCLUSIONS The MEWS was significantly different between patients experiencing CA and control patients by 48 h prior to the event, but includes poor predictors of CA such as temperature and omits significant predictors such as diastolic BP and pulse pressure index.

Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients

RATIONALE Most ward risk scores were created using subjective opinion in individual hospitals and only use vital signs. OBJECTIVES To develop and validate a risk score using commonly collected electronic health record data. METHODS All patients hospitalized on the wards in five hospitals were included in this observational cohort study. Discrete-time survival analysis was used to predict the combined outcome of cardiac arrest (CA), intensive care unit (ICU) transfer, or death on the wards. Laboratory results, vital signs, and demographics were used as predictor variables. The model was developed in the first 60% of the data at each hospital and then validated in the remaining 40%. The final model was compared with the Modified Early Warning Score (MEWS) using the area under the receiver operating characteristic curve and the net reclassification index (NRI). MEASUREMENTS AND MAIN RESULTS A total of 269,999 patient admissions were included, with 424 CAs, 13,188 ICU transfers, and 2,840 deaths occurring during the study period. The derived model was more accurate than the MEWS in the validation dataset for all outcomes (area under the receiver operating characteristic curve, 0.83 vs. 0.71 for CA; 0.75 vs. 0.68 for ICU transfer; 0.93 vs. 0.88 for death; and 0.77 vs. 0.70 for the combined outcome; P value < 0.01 for all comparisons). This accuracy improvement was seen across all hospitals. The NRI for the electronic Cardiac Arrest Risk Triage compared with the MEWS was 0.28 (0.18-0.38), with a positive NRI of 0.19 (0.09-0.29) and a negative NRI of 0.09 (0.09-0.09). CONCLUSIONS We developed an accurate ward risk stratification tool using commonly collected electronic health record variables in a large multicenter dataset. Further study is needed to determine whether implementation in real-time would improve patient outcomes.

Derivation of a cardiac arrest prediction model using ward vital signs

Objective:Rapid response team activation criteria were created using expert opinion and have demonstrated variable accuracy in previous studies. We developed a cardiac arrest risk triage score to predict cardiac arrest and compared it to the Modified Early Warning Score, a commonly cited rapid response team activation criterion. Design:A retrospective cohort study. Setting:An academic medical center in the United States. Patients:All patients hospitalized from November 2008 to January 2011 who had documented ward vital signs were included in the study. These patients were divided into three cohorts: patients who suffered a cardiac arrest on the wards, patients who had a ward to intensive care unit transfer, and patients who had neither of these outcomes (controls). Interventions:None. Measurements and Main Results:Ward vital signs from admission until discharge, intensive care unit transfer, or ward cardiac arrest were extracted from the medical record. Multivariate logistic regression was used to predict cardiac arrest, and the cardiac arrest risk triage score was calculated using the regression coefficients. The model was validated by comparing its accuracy for detecting intensive care unit transfer to the Modified Early Warning Score. Each patient’s maximum score prior to cardiac arrest, intensive care unit transfer, or discharge was used to compare the areas under the receiver operating characteristic curves between the two models. Eighty-eight cardiac arrest patients, 2,820 intensive care unit transfers, and 44,519 controls were included in the study. The cardiac arrest risk triage score more accurately predicted cardiac arrest than the Modified Early Warning Score (area under the receiver operating characteristic curve 0.84 vs. 0.76; p = .001). At a specificity of 89.9%, the cardiac arrest risk triage score had a sensitivity of 53.4% compared to 47.7% for the Modified Early Warning Score. The cardiac arrest risk triage score also predicted intensive care unit transfer better than the Modified Early Warning Score (area under the receiver operating characteristic curve 0.71 vs. 0.67; p < .001). Conclusions:The cardiac arrest risk triage score is simpler and more accurately detected cardiac arrest and intensive care unit transfer than the Modified Early Warning Score. Implementation of this tool may decrease rapid response team resource utilization and provide a better opportunity to improve patient outcomes than the modified early warning score.

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

Objective:Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Design:Observational cohort study. Setting:Five hospitals, from November 2008 until January 2013. Patients:Hospitalized ward patients Interventions:None Measurements And Main Results:Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]). Conclusions:In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

Safety and efficacy of defibrillator charging during ongoing chest compressions: A multi-center study

BACKGROUND Pauses in chest compressions during cardiopulmonary resuscitation have been shown to correlate with poor outcomes. In an attempt to minimize these pauses, the American Heart Association recommends charging the defibrillator during chest compressions. While simulation work suggests decreased pause times using this technique, little is known about its use in clinical practice. METHODS We conducted a multi-center, retrospective study of defibrillator charging at three US academic teaching hospitals between April 2006 and April 2009. Data were abstracted from CPR-sensing defibrillator transcripts. Pre-shock pauses and total hands-off time preceding the defibrillation attempts were compared among techniques. RESULTS A total of 680 charge-cycles from 244 cardiac arrests were analyzed. The defibrillator was charged during ongoing chest compressions in 448 (65.9%) instances with wide variability across the three sites. Charging during compressions correlated with a decrease in median pre-shock pause [2.6s (IQR 1.9-3.8) vs 13.3s (IQR 8.6-19.5); p < 0.001] and total hands-off time in the 30s preceding defibrillation [10.3s (IQR 6.4-13.8) vs 14.8s (IQR 11.0-19.6); p < 0.001]. The improvement in hands-off time was most pronounced when rescuers charged the defibrillator in anticipation of the pause, prior to any rhythm analysis. There was no difference in inappropriate shocks when charging during chest compressions (20.0% vs 20.1%; p = 0.97) and there was only one instance noted of inadvertent shock administration during compressions, which went unnoticed by the compressor. CONCLUSIONS Charging during compressions is underutilized in clinical practice. The technique is associated with decreased hands-off time preceding defibrillation, with minimal risk to patients or rescuers.

Risk Stratification of Hospitalized Patients on the Wards

Patients who suffer adverse events on the wards, such as cardiac arrest and death, often have vital sign abnormalities hours before the event. Early warning scores have been developed with the aim of identifying clinical deterioration early and have been recommended by the National Institute for Health and Clinical Excellence. In this review, we discuss recently developed and validated risk scores for use on the general inpatient wards. In addition, we compare newly developed systems with more established risk scores such as the Modified Early Warning Score and the criteria used in the Medical Early Response Intervention and Therapy (MERIT) trial in our database of > 59,000 ward admissions. In general we found the single-parameter systems, such as the MERIT criteria, to have the lowest predictive accuracy for adverse events, whereas the aggregate weighted scoring systems had the highest. The Cardiac Arrest Risk Triage (CART) score was best for predicting cardiac arrest, ICU transfer, and a composite outcome (area under the receiver operating characteristic curve [AUC], 0.83, 0.77, and 0.78, respectively), whereas the Standardized Early Warning Score, VitalPAC Early Warning Score, and CART score were similar for predicting mortality (AUC, 0.88). Selection of a risk score for a hospital or health-care system should be guided by available variables, calculation method, and system resources. Once implemented, ensuring high levels of adherence and tying them to specific levels of interventions, such as activation of a rapid response team, are necessary to allow for the greatest potential to improve patient outcomes.

Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards

Objective:Over 200,000 in-hospital cardiac arrests occur in the United States each year and many of these events may be preventable. Current vital sign–based risk scores for ward patients have demonstrated limited accuracy, which leads to missed opportunities to identify those patients most likely to suffer cardiac arrest and inefficient resource utilization. We derived and validated a prediction model for cardiac arrest while treating ICU transfer as a competing risk using electronic health record data. Design:A retrospective cohort study. Setting:An academic medical center in the United States with approximately 500 inpatient beds. Patients:Adult patients hospitalized from November 2008 until August 2011 who had documented ward vital signs. Interventions:None. Measurements and Main Results:Vital sign, demographic, location, and laboratory data were extracted from the electronic health record and investigated as potential predictor variables. A person-time multinomial logistic regression model was used to simultaneously predict cardiac arrest and ICU transfer. The prediction model was compared to the VitalPAC Early Warning Score using the area under the receiver operating characteristic curve and was validated using three-fold cross-validation. A total of 56,649 controls, 109 cardiac arrest patients, and 2,543 ICU transfers were included. The derived model more accurately detected cardiac arrest (area under the receiver operating characteristic curve, 0.88 vs 0.78; p < 0.001) and ICU transfer (area under the receiver operating characteristic curve, 0.77 vs 0.73; p < 0.001) than the VitalPAC Early Warning Score, and accuracy was similar with cross-validation. At a specificity of 93%, our model had a higher sensitivity than the VitalPAC Early Warning Score for cardiac arrest patients (65% vs 41%). Conclusions:We developed and validated a prediction tool for ward patients that can simultaneously predict the risk of cardiac arrest and ICU transfer. Our model was more accurate than the VitalPAC Early Warning Score and could be implemented in the electronic health record to alert caregivers with real-time information regarding patient deterioration.

Differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest.

Objectives:Vital signs and composite scores, such as the Modified Early Warning Score, are used to identify high-risk ward patients and trigger rapid response teams. Although age-related vital sign changes are known to occur, little is known about the differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest. We aimed to compare the accuracy of vital signs for detecting cardiac arrest between elderly and nonelderly patients. Design:Observational cohort study. Setting:Five hospitals in the United States. Patients:A total of 269,956 patient admissions to the wards with documented age, including 422 index ward cardiac arrests. Interventions:None. Measurements and Main Results:Patient characteristics and vital signs prior to cardiac arrest were compared between elderly (age, 65 yr or older) and nonelderly (age, < 65 yr) patients. The area under the receiver operating characteristic curve for vital signs and the Modified Early Warning Score were also compared. Elderly patients had a higher cardiac arrest rate (2.2 vs 1.0 per 1,000 ward admissions; p < 0.001) and in-hospital mortality (2.9% vs 0.7%; p < 0.001) than nonelderly patients. Within 4 hours of cardiac arrest, elderly patients had significantly lower mean heart rate (88 vs 99 beats/min; p < 0.001), diastolic blood pressure (60 vs 66 mm Hg; p = 0.007), shock index (0.82 vs 0.93; p < 0.001), and Modified Early Warning Score (2.6 vs 3.3; p < 0.001) and higher pulse pressure index (0.45 vs 0.41; p < 0.001) and temperature (36.4°C vs 36.3°C; p = 0.047). The area under the receiver operating characteristic curves for all vital signs and the Modified Early Warning Score were higher for nonelderly patients than elderly patients (Modified Early Warning Score area under the receiver operating characteristic curve 0.85 [95% CI, 0.82–0.88] vs 0.71 [95% CI, 0.68–0.75]; p < 0.001). Conclusions:Vital signs more accurately detect cardiac arrest in nonelderly patients compared with elderly patients, which has important implications for how they are used for identifying critically ill patients. More accurate methods for risk stratification of elderly patients are necessary to decrease the occurrence of this devastating event.

Early Cardiac Arrest in Patients Hospitalized With Pneumonia: A Report From the American Heart Association’s Get With the Guidelines-Resuscitation Program

BACKGROUND Pneumonia is the leading infectious cause of death. Early deterioration and death commonly result from progressive sepsis, shock, respiratory failure, and cardiac complications. Recent data suggest that cardiac arrest may also be common, yet few previous studies have addressed this. Accordingly, we sought to characterize early cardiac arrest in patients who are hospitalized with coexisting pneumonia. METHODS We performed a retrospective analysis of a multicenter cardiac arrest database, with data from > 500 North American hospitals. We included in-hospital cardiac arrest events that occurred in community-dwelling adults with pneumonia within the first 72 h after hospital admission. We compared patient and event characteristics for patients with and without pneumonia. For patients with pneumonia, we also compared events according to event location. RESULTS We identified 4,453 episodes of early cardiac arrest in patients who were hospitalized with pneumonia. Among patients with preexisting pneumonia, only 36.5% were receiving mechanical ventilation and only 33.3% were receiving infusions of vasoactive drugs prior to cardiac arrest. Only 52.3% of patients on the ward were receiving ECG monitoring prior to cardiac arrest. Shockable rhythms were uncommon in all patients with pneumonia (ventricular tachycardia or fibrillation, 14.8%). Patients on the ward were significantly older than patients in the ICU. CONCLUSIONS In patients with preexisting pneumonia, cardiac arrest may occur in the absence of preceding shock or respiratory failure. Physicians should be alert to the possibility of abrupt cardiopulmonary collapse, and future studies should address this possibility. The mechanism may involve myocardial ischemia, a maladaptive response to hypoxia, sepsis-related cardiomyopathy, or other phenomena.

Original ResearchChest InfectionsEarly Cardiac Arrest in Patients Hospitalized With Pneumonia: A Report From the American Heart Association's Get With the Guidelines-Resuscitation Program

BACKGROUND Pneumonia is the leading infectious cause of death. Early deterioration and death commonly result from progressive sepsis, shock, respiratory failure, and cardiac complications. Recent data suggest that cardiac arrest may also be common, yet few previous studies have addressed this. Accordingly, we sought to characterize early cardiac arrest in patients who are hospitalized with coexisting pneumonia. METHODS We performed a retrospective analysis of a multicenter cardiac arrest database, with data from > 500 North American hospitals. We included in-hospital cardiac arrest events that occurred in community-dwelling adults with pneumonia within the first 72 h after hospital admission. We compared patient and event characteristics for patients with and without pneumonia. For patients with pneumonia, we also compared events according to event location. RESULTS We identified 4,453 episodes of early cardiac arrest in patients who were hospitalized with pneumonia. Among patients with preexisting pneumonia, only 36.5% were receiving mechanical ventilation and only 33.3% were receiving infusions of vasoactive drugs prior to cardiac arrest. Only 52.3% of patients on the ward were receiving ECG monitoring prior to cardiac arrest. Shockable rhythms were uncommon in all patients with pneumonia (ventricular tachycardia or fibrillation, 14.8%). Patients on the ward were significantly older than patients in the ICU. CONCLUSIONS In patients with preexisting pneumonia, cardiac arrest may occur in the absence of preceding shock or respiratory failure. Physicians should be alert to the possibility of abrupt cardiopulmonary collapse, and future studies should address this possibility. The mechanism may involve myocardial ischemia, a maladaptive response to hypoxia, sepsis-related cardiomyopathy, or other phenomena.