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.

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.

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.

Quantitative relationship between end-tidal carbon dioxide and CPR quality during both in-hospital and out-of-hospital cardiac arrest.

OBJECTIVE: Cardiopulmonary resuscitation (CPR) guidelines recommend the administration of chest compressions (CC) at a standardized rate and depth without guidance from patient physiologic output. The relationship between CC performance and actual CPR-generated blood flow is poorly understood, limiting the ability to define "optimal" CPR delivery. End-tidal carbon dioxide (ETCO2) has been proposed as a surrogate measure of blood flow during CPR, and has been suggested as a tool to guide CPR despite a paucity of clinical data. We sought to quantify the relationship between ETCO2 and CPR characteristics during clinical resuscitation care. METHODS: Multicenter cohort study of 583 in- and out-of-hospital cardiac arrests with time-synchronized ETCO2 and CPR performance data captured between 4/2006 and 5/2013. ETCO2, ventilation rate, CC rate and depth were averaged over 15-s epochs. A total of 29,028 epochs were processed for analysis using mixed-effects regression techniques. RESULTS: CC depth was a significant predictor of increased ETCO2. For every 10mm increase in depth, ETCO2 was elevated by 1.4mmHg (p<.001). For every 10 breaths/min increase in ventilation rate, ETCO2 was lowered by 3.0mmHg (p<.001). CC rate was not a predictor of ETCO2 over the dynamic range of actual CC delivery. Case-averaged ETCO2 values in patients with return of spontaneous circulation were higher compared to those who did not have a pulse restored (34.5±4.5 vs 23.1±12.9mmHg, p<.001). CONCLUSIONS: ETCO2 values generated during CPR were statistically associated with CC depth and ventilation rate. Further studies are needed to assess ETCO2 as a potential tool to guide care.

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.

Racial disparities in outcomes following PEA and asystole in-hospital cardiac arrests.

AIM: To define the racial differences present after PEA and asystolic IHCA and explore factors that could contribute to this disparity. METHODS: We analyzed PEA and asystolic IHCA in the Get-With-The-Guidelines-Resuscitation database. Multilevel conditional fixed effects logistic regression models were used to estimate the relationship between race and survival to discharge and return of spontaneous circulation (ROSC), sequentially controlling for hospital, patient demographics, comorbidities, arrest characteristic, process measures, and interventions in place at time of arrest. RESULTS: Among the 561 hospitals, there were 76,835 patients who experienced IHCA with an initial rhythm of PEA or asystole (74.8% white, 25.2% black). Unadjusted ROSC rate was 55.1% for white patients and 54.1% for black patients (unadjusted OR: 0.94 [95% CI, 0.90-0.98], p=0.016). Survival to discharge was 12.8% for white patients and 10.4% for black patients (unadjusted OR: 0.83 [95% CI, 0.78-0.87], p<0.001). After adjusting for temporal trends, patient characteristics, hospital, and arrest characteristics, there remained a difference in survival to discharge (OR: 0.85 [95% CI, 0.79-0.92]) and rate of ROSC (OR: 0.88 [95% CI, 0.84-0.92]). Black patients had a worse mental status at discharge after survival. Rates of DNAR placed after survival from were lower in black patients with a rate of 38.3% compared to 44.5% in white patients (p<0.001). CONCLUSION: Black patients are less likely to experience ROSC and survival to discharge after PEA or asystole IHCA. Individual patient characteristics, event characteristics, and hospital characteristics don't fully explain this disparity. It is possible that disease burden and end-of-life preferences contribute to the racial disparity.

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.

Relationship between ICU bed availability, ICU readmission, and cardiac arrest in the general wards.

OBJECTIVE: The decision to admit a patient to the ICU is complex, reflecting patient factors and available resources. Previous work has shown that ICU census does not impact mortality of patients admitted to the ICU. However, the effect of ICU bed availability on patients outside the ICU is unknown. We sought to determine the association between ICU bed availability, ICU readmissions, and ward cardiac arrests. DESIGN: In this observational study using data collected between 2009 and 2011, rates of ICU readmission and ward cardiac arrest were determined per 12-hour shift. The relationship between these rates and the number of available ICU beds at the start of each shift (accounting for census and nursing capacity) was investigated. Grouped logistic regression was used to adjust for potential confounders. SETTING: Five specialized adult ICUs comprising 63 adult ICU beds in an academic medical center. PATIENTS: Any patient admitted to a non-ICU inpatient unit was counted in the ward census and considered at risk for ward cardiac arrest. Patients discharged from an ICU were considered at risk for ICU readmission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data were available for 2,086 of 2,190 shifts. The odds of ICU readmission increased with each decrease in the overall number of available ICU beds (odds ratio = 1.06; 95% CI, 1.00-1.12; p = 0.03), with a similar but not statistically significant association demonstrated in ward cardiac arrest rate (odds ratio = 1.06; 95% CI, 0.98-1.14; p = 0.16). In subgroup analysis, the odds of ward cardiac arrest increased with each decrease in the number of medical ICU beds available (odds ratio = 1.26; 95% CI, 1.06-1.49; p = 0.01). CONCLUSIONS: Reduced ICU bed availability is associated with increased rates of ICU readmission and ward cardiac arrest. This suggests that systemic factors are associated with patient outcomes, and flexible critical care resources may be needed when demand is high.

Hospital cardiac arrest resuscitation practice in the United States: a nationally representative survey.

BACKGROUND: In-hospital cardiac arrest (IHCA) outcomes vary widely between hospitals, even after adjusting for patient characteristics, suggesting variations in practice as a potential etiology. However, little is known about the standards of IHCA resuscitation practice among US hospitals. OBJECTIVE: To describe current US hospital practices with regard to resuscitation care. DESIGN: A nationally representative mail survey. SETTING: A random sample of 1000 hospitals from the American Hospital Association database, stratified into 9 categories by hospital volume tertile and teaching status (major teaching, minor teaching, and nonteaching). SUBJECTS: Surveys were addressed to each hospital's cardiopulmonary resuscitation (CPR) committee chair or chief medical/quality officer. MEASUREMENTS: A 27-item questionnaire. RESULTS: Responses were received from 439 hospitals with a similar distribution of admission volume and teaching status as the sample population (P = 0.50). Of the 270 (66%) hospitals with a CPR committee, 23 (10%) were chaired by a hospitalist. High frequency practices included having a rapid response team (91%) and standardizing defibrillators (88%). Low frequency practices included therapeutic hypothermia and use of CPR assist technology. Other practices such as debriefing (34%) and simulation training (62%) were more variable and correlated with the presence of a CPR committee and/or dedicated personnel for resuscitation quality improvement. The majority of hospitals (79%) reported at least 1 barrier to quality improvement, of which the lack of a resuscitation champion and inadequate training were the most common. CONCLUSIONS: There is wide variability among hospitals and within practices for resuscitation care in the United States with opportunities for improvement.

Tablet-based cardiac arrest documentation: a pilot study.

AIM: Conventional paper-based resuscitation transcripts are notoriously inaccurate, often lacking the precision that is necessary for recording a fast-paced resuscitation. The aim of this study was to evaluate whether a tablet computer-based application could improve upon conventional practices for resuscitation documentation. METHODS: Nurses used either the conventional paper code sheet or a tablet application during simulated resuscitation events. Recorded events were compared to a gold standard record generated from video recordings of the simulations and a CPR-sensing defibrillator/monitor. Events compared included defibrillations, medication deliveries, and other interventions. RESULTS: During the study period, 199 unique interventions were observed in the gold standard record. Of these, 102 occurred during simulations recorded by the tablet application, 78 by the paper code sheet, and 19 during scenarios captured simultaneously by both documentation methods These occurred over 18 simulated resuscitation scenarios, in which 9 nurses participated. The tablet application had a mean sensitivity of 88.0% for all interventions, compared to 67.9% for the paper code sheet (P=0.001). The median time discrepancy was 3s for the tablet, and 77s for the paper code sheet when compared to the gold standard (P<0.001). CONCLUSIONS: Similar to prior studies, we found that conventional paper-based documentation practices are inaccurate, often misreporting intervention delivery times or missing their delivery entirely. However, our study also demonstrated that a tablet-based documentation method may represent a means to substantially improve resuscitation documentation quality, which could have implications for resuscitation quality improvement and research.

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.

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.

Cardiopulmonary resuscitation outcomes in hospitalized community-dwelling individuals and nursing home residents based on activities of daily living.

OBJECTIVES: To determine whether poor functional status is associated with worse outcomes after attempted cardiopulmonary resuscitation (CPR). DESIGN: Retrospective study of individuals who experienced cardiac arrest stratified according to dependence in activities of daily living (ADLs) and residential status (nursing home (NH) or community dwelling). SETTING: Two hundred thirty-five hospitals throughout North America. PARTICIPANTS: Adult inpatients aged 65 and older who had experienced a cardiac arrest as reported to the Get with the Guidelines-Resuscitation registry between 2000 and 2008. MEASUREMENTS: Primary outcomes were return of spontaneous circulation (ROSC) and survival to discharge. RESULTS: Twenty-six thousand three hundred twenty-nine individuals who experienced cardiac arrest met inclusion criteria. NH residents dependent in ADLs had a lower odds than community-dwelling independent participants of achieving ROSC (odds ratio (OR) = 0.73, 95% confidence interval (CI) = 0.63-0.85), whereas participants dependent in ADLs from either residential setting had lower odds of survival (community-dwelling: OR = 0.76, 95% CI = 0.63-0.92; NH: OR = 0.79, 95% CI = 0.64-0.96) after adjusting for participant and arrest characteristics. Duration of resuscitation and doses of epinephrine or vasopressin were similar between groups and had no significant effect on ROSC or survival, although participants dependent in ADLs were more likely to have a do-not-resuscitate (DNR) order placed after ROSC. Overall, median time to signing a DNR order after resuscitation was 10 hours (interquartile range 2-70). CONCLUSION: Functional and residential status are important predictors of survival after in-hospital cardiac arrest. Contrary to the hypothesis but reassuring from a quality-of-care perspective, less-aggressive attempts at resuscitation do not appear to contribute to poorer outcomes in individuals dependent in ADL, regardless of residential status.

Predicting clinical deterioration in the hospital: the impact of outcome selection.

BACKGROUND: Clinical deterioration of ward patients can result in intensive care unit (ICU) transfer, cardiac arrest (CA), and/or death. These different outcomes have been used to develop and test track and trigger systems, but the impact of outcome selection on the performance of prediction algorithms is unknown. METHODS: Patients hospitalized on the wards between November 2008 and August 2011 at an academic hospital were included in the study. Ward vital signs and demographic characteristics were compared across outcomes. The dataset was then split into derivation and validation cohorts. Logistic regression was used to derive four models (one per outcome and a combined outcome) for predicting each event within 24h of a vital sign set. The models were compared in the validation cohort using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 59,643 patients were included in the study (including 109 ward CAs, 291 deaths, and 2638 ICU transfers). Most mean vital signs within 24h of the events differed statistically, with those before death being the most deranged. Validation model AUCs were highest for predicting mortality (range 0.73-0.82), followed by CA (range 0.74-0.76), and lowest for predicting ICU transfer (range 0.68-0.71). CONCLUSIONS: Despite differences in vital signs before CA, ICU transfer, and death, the different models performed similarly for detecting each outcome. Mortality was the easiest outcome to predict and ICU transfer the most difficult. Studies should be interpreted with these differences in mind.

Supplementing cross-cover communication with the patient acuity rating.

BACKGROUND: Patient hand-offs at physician shift changes have limited ability to convey the primary team's longitudinal insight. The Patient Acuity Rating (PAR) is a previously validated, 7-point scale that quantifies physician judgment of patient stability, where a higher score indicates a greater risk of clinical deterioration. Its impact on cross-covering physician understanding of patients is not known. OBJECTIVE: To determine PAR contribution to sign-outs. DESIGN: Cross-sectional survey. SUBJECTS: Intern physicians at a university teaching hospital. INTERVENTIONS: Subjects were surveyed using randomly chosen, de-identified patient sign-outs, previously assigned PAR scores by their primary teams. For each sign-out, subjects assigned a PAR score, then responded to hypothetical cross-cover scenarios before and after being informed of the primary team's PAR. MAIN MEASURE: Changes in intern assessment of the scenario before and after being informed of the primary team's PAR were measured. In addition, responses between novice and experienced interns were compared. KEY RESULTS: Between May and July 2008, 23 of 39 (59 %) experienced interns and 25 of 42 (60 %) novice interns responded to 480 patient scenarios from ten distinct sign-outs. The mean PAR score assigned by subjects was 4.2 ± 1.6 vs. 3.8 ± 1.8 by the primary teams (p < 0.001). After viewing the primary team's PAR score, interns changed their level of concern in 47.9 % of cases, their assessment of the importance of immediate bedside evaluation in 48.7 % of cases, and confidence in their assessment in 43.2 % of cases. For all three assessments, novice interns changed their responses more frequently than experienced interns (p = 0.03, 0.009, and <0.001, respectively). Overall interns reported the PAR score to be theoretically helpful in 70.8 % of the cases, but this was more pronounced in novice interns (81.2 % vs 59.6 %, p < 0.001). CONCLUSIONS: The PAR adds valuable information to sign-outs that could impact cross-cover decision-making and potentially benefit patients. However, correct training in its use may be required to avoid unintended consequences.

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.

The impact of a step stool on cardiopulmonary resuscitation: a cross-over mannequin study.

OBJECTIVE: Shallow chest compressions and incomplete recoil are common during cardiopulmonary resuscitation (CPR) and negatively affect outcomes. A step stool has the potential to alter these parameters when performing CPR in a bed but the impact has not been quantified. METHODS: We conducted a cross-over design, simulated study of in-hospital cardiac arrest. Rescuers performed a total of four 2-min segments of uninterrupted chest compressions, half of which were on a step stool. Compression characteristics were measured using a CPR-sensing defibrillator and subjective impressions were obtained from rescuer surveys. Paired analyses were performed to measure the impact of the step stool, taking into account rescuer characteristics, including height. RESULTS: Fifty subjects, of whom 36% were men, with a median height of 169.8 cm (range 148.6-190.5) volunteered to participate. Use of a step stool resulted in an average increase in compression depth of 4 mm (p<0.001) and 18% increase in incomplete recoil (p<0.001). However, unlike with incomplete recoil, the effect was more pronounced in rescuers in the lowest height tertile (9±9 mm vs 2±6 mm for those rescuers taller than 167 cm, p=0.006). CONCLUSIONS: Using a step stool when performing CPR in a bed results in a trade-off between increased compression depth and increased incomplete recoil. Given the nonlinear relationship between the increase in compression depth and rescuer height, the benefit of a step stool may outweigh the risks of incomplete release for rescuers ≤167 cm in height. The benefit is less clear in taller rescuers.

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.

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.