Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study.

OBJECTIVE: Assess the accuracy of 3 early warning scores for predicting severe adverse events in postoperative inpatients. SUMMARY OF BACKGROUND DATA: Postoperative clinical deterioration on inpatient hospital services is associated with increased morbidity, mortality, and cost. Early warning scores have been developed to detect inpatient clinical deterioration and trigger rapid response activation, but knowledge regarding the application of early warning scores to postoperative inpatients is limited. METHODS: This was a retrospective cohort study of adult patients hospitalized on the wards after surgical procedures at an urban academic medical center from November, 2008 to January, 2016. The accuracies of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and the electronic cardiac arrest risk triage (eCART) score were compared in predicting severe adverse events (ICU transfer, ward cardiac arrest, or ward death) in the postoperative period using the area under the receiver operating characteristic curve (AUC). RESULTS: Of the 32,537 patient admissions included in the study, 3.8% (n = 1243) experienced a severe adverse outcome after the procedure. The accuracy for predicting the composite outcome was highest for eCART [AUC 0.79 (95% CI: 0.78-0.81)], followed by NEWS [AUC 0.76 (95% CI: 0.75-0.78)], and MEWS [AUC 0.75 (95% CI: 0.73-0.76)]. Of the individual vital signs and labs, maximum respiratory rate was the most predictive (AUC 0.67) and maximum temperature was an inverse predictor (AUC 0.46). CONCLUSION: Early warning scores are predictive of severe adverse events in postoperative patients. eCART is significantly more accurate in this patient population than both NEWS and MEWS.

Characteristics of Rapid Response Calls in the United States: An Analysis of the First 402,023 Adult Cases From the Get With the Guidelines Resuscitation-Medical Emergency Team Registry.

OBJECTIVES: To characterize the rapid response team activations, and the patients receiving them, in the American Heart Association-sponsored Get With The Guidelines Resuscitation-Medical Emergency Team cohort between 2005 and 2015. DESIGN: Retrospective multicenter cohort study. SETTING: Three hundred sixty U.S. hospitals. PATIENTS: Consecutive adult patients experiencing rapid response team activation. INTERVENTIONS: Rapid response team activation. MEASUREMENTS AND MAIN RESULTS: The cohort included 402,023 rapid response team activations from 347,401 unique healthcare encounters. Respiratory triggers (38.0%) and cardiac triggers (37.4%) were most common. The most frequent interventions-pulse oximetry (66.5%), other monitoring (59.6%), and supplemental oxygen (62.0%)-were noninvasive. Fluids were the most common medication ordered (19.3%), but new antibiotic orders were rare (1.2%). More than 10% of rapid response teams resulted in code status changes. Hospital mortality was over 14% and increased with subsequent rapid response activations. CONCLUSIONS: Although patients requiring rapid response team activation have high inpatient mortality, most rapid response team activations involve relatively few interventions, which may limit these teams' ability to improve patient outcomes.

Real-Time Risk Prediction on the Wards: A Feasibility Study.

OBJECTIVE: Failure to detect clinical deterioration in the hospital is common and associated with poor patient outcomes and increased healthcare costs. Our objective was to evaluate the feasibility and accuracy of real-time risk stratification using the electronic Cardiac Arrest Risk Triage score, an electronic health record-based early warning score. DESIGN: We conducted a prospective black-box validation study. Data were transmitted via HL7 feed in real time to an integration engine and database server wherein the scores were calculated and stored without visualization for clinical providers. The high-risk threshold was set a priori. Timing and sensitivity of electronic Cardiac Arrest Risk Triage score activation were compared with standard-of-care Rapid Response Team activation for patients who experienced a ward cardiac arrest or ICU transfer. SETTING: Three general care wards at an academic medical center. PATIENTS: A total of 3,889 adult inpatients. MEASUREMENTS AND MAIN RESULTS: The system generated 5,925 segments during 5,751 admissions. The area under the receiver operating characteristic curve for electronic Cardiac Arrest Risk Triage score was 0.88 for cardiac arrest and 0.80 for ICU transfer, consistent with previously published derivation results. During the study period, eight of 10 patients with a cardiac arrest had high-risk electronic Cardiac Arrest Risk Triage scores, whereas the Rapid Response Team was activated on two of these patients (p < 0.05). Furthermore, electronic Cardiac Arrest Risk Triage score identified 52% (n = 201) of the ICU transfers compared with 34% (n = 129) by the current system (p < 0.001). Patients met the high-risk electronic Cardiac Arrest Risk Triage score threshold a median of 30 hours prior to cardiac arrest or ICU transfer versus 1.7 hours for standard Rapid Response Team activation. CONCLUSIONS: Electronic Cardiac Arrest Risk Triage score identified significantly more cardiac arrests and ICU transfers than standard Rapid Response Team activation and did so many hours in advance.

Testing the functional assessment of mentation: A mobile application based assessment of mental status.

BACKGROUND: Altered mental status is a significant predictor of mortality in hospitalized patients and a prerequisite component to the diagnosis of delirium. However, the detection of altered mental status is often incomplete, inaccurate, and resource intensive. OBJECTIVE: To identify the clinical utility and feasibility of the Functional Assessment of Mentation (FAM(TM) ), a mobile application for evaluating attention and recall. DESIGN: Prospective observational pilot study. SETTING: Tertiary care medical center. PARTICIPANTS: Nine hundred thirty-one adult subjects (612 nonhospitalized and 319 hospitalized). MEASUREMENTS: Score distribution and time to FAM(TM) completion were compared between nonhospitalized and hospitalized subjects (as well as between hospitalized subjects discharged home and those not discharged home). Additionally, in the hospitalized subgroup, FAM(TM) was compared to the Glasgow Coma Scale (GCS), using the Short Portable Mental Status Questionnaire (SPMSQ) as our criterion standard for altered mental status assessment. RESULTS: Median time to completion of FAM(TM) was 55 seconds (interquartile range [IQR], 45-67 seconds). Our data identified a graded reduction in score comparing nonhospitalized subjects to hospitalized subjects discharged home and not discharged home (median 5 [IQR 4-7] vs 5 [IQR 3-6] vs 3 [IQR 1-5]; P < 0.001). In the hospitalized subset, FAM(TM) scores were more highly correlated to SPMSQ (Spearman ρ = 0.27, P < 0.001) compared to GCS (Spearman ρ = 0.05, P = 0.40). CONCLUSIONS: FAM(TM) is a rapid and clinically feasible tool that can identify minor alterations in mental status often missed by GCS. Journal of Hospital Medicine 2016;11:463-466. 2016 Society of Hospital Medicine.

Comparison of mental-status scales for predicting mortality on the general wards.

BACKGROUND: Altered mental status is a significant predictor of mortality in inpatients. Several scales exist to characterize mental status, including the AVPU (Alert, responds to Voice, responds to Pain, Unresponsive) scale, which is used in many early-warning scores in the general-ward setting. The use of the Glasgow Coma Scale (GCS) and Richmond Agitation Sedation Scale (RASS) is not well established in this population. OBJECTIVE: To compare the accuracies of AVPU, GCS, and RASS for predicting inpatient mortality. DESIGN: Retrospective cohort study. SETTING: Single, urban, academic medical center. PARTICIPANTS: Adult inpatients on the general wards. MEASUREMENTS: Nurses recorded GCS and RASS on consecutive adult hospitalizations. AVPU was extracted from the eye subscale of the GCS. We compared the accuracies of each scale for predicting in-hospital mortality within 24 hours of a mental-status observation using area under the receiver operating characteristic curves (AUC). RESULTS: There were 295,974 paired observations of GCS and RASS obtained from 26,873 admissions; 417 (1.6%) resulted in in-hospital death. GCS and RASS more accurately predicted mortality than AVPU (AUC 0.80 and 0.82, respectively, vs 0.73; P < 0.001 for both comparisons). Simultaneous use of GCS and RASS produced an AUC of 0.85 (95% confidence interval: 0.82-0.87, P < 0.001 when compared to all 3 scales). CONCLUSIONS: In ward patients, both GCS and RASS were significantly more accurate predictors of mortality than AVPU. In addition, combining GCS and RASS was more accurate than any scale alone. Routine tracking of GCS and/or RASS on general wards may improve the accuracy of detecting clinical deterioration.