Predicting cardiac arrest on the wards: a nested case-control study

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.

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.

Respiratory rate at intensive care unit discharge after liver transplant is an independent risk factor for intensive care unit readmission within the same hospital stay: a nested case-control study

PURPOSE

Intensive care unit (ICU) readmission negatively impacts patients' outcomes. We aimed to characterize and determine risk factors for ICU readmission within the initial hospital stay after liver transplant (LT).

MATERIALS AND METHODS

The reference cohort included 369 LT recipients from a Canadian center between 2005 and 2012. One control was randomly selected per each case of ICU readmission within the initial hospital stay after LT. Survival analysis used the Kaplan-Meier method. Associations were studied by conditional logistic regression.

RESULTS

Fifty-two (14%) LT recipients were readmitted to the ICU within the initial hospital stay after LT; they had longer first hospital stay (P < .001) and lower 1-month to 2-year cumulative survival (P < .001). Respiratory failure was the major cause of ICU readmission (61%). Respiratory rate at discharge from the first ICU stay after LT was an independent risk factor for ICU readmission (odds ratio = 1.24). The cutoff point more than 20 breaths per minute prognosticated ICU readmission with a specificity of 90% and a positive predictive value of 80%.

CONCLUSIONS

Intensive care unit readmission within the initial hospital stay after LT negatively impacts LT recipients' outcomes. Monitoring respiratory rate at discharge from the first ICU stay after LT is important to prevent readmission.

The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review

BACKGROUND

Acute deterioration in critical ill patients is often preceded by changes in physiological parameters, such as pulse, blood pressure, temperature and respiratory rate. If these changes in the patient's vital parameters are recognized early, excess mortality and serious adverse events (SAEs) such as cardiac arrest may be prevented. The Early Warning Score (EWS) is a scoring system which assists with the detection of physiological changes and may help identify patients at risk of further deterioration.

OBJECTIVES

The aim of this systematic review is to evaluate the impact of the use of the Early Warning Score (EWS) on particular patient outcomes, such as in-hospital mortality, patterns of intensive care unit admission and usage, length of hospital stay, cardiac arrests and other serious adverse events of adult patients on general wards and in medical admission units.

DESIGN AND SETTING

Systematic review of studies identified from the bibliographic databases of PubMed, EMBASE.com and The Cochrane Library.

SELECTION CRITERIA

All controlled studies which measured in-hospital mortality, ICU mortality, serious adverse events (SAEs), cardiopulmonary arrest, length of stay and documentation of physiological parameters which used a EWS on the ward or the emergency department to identify patients at risk were included in the review.

DATA COLLECTION AND ANALYSIS

Three reviewers (NA, AT and EH) independently screened all potentially relevant titles and abstracts for eligibility, by using a standardized data-worksheet. Meta-analysis was not possible due to heterogeneity.

MAIN RESULTS

Seven studies met the inclusion criteria. The results of our included studies were mixed, with a positive trend towards better clinical outcomes following the introduction of the EWS chart, sometimes coupled with an outreach service. Six of the seven included studies used mortality as an endpoint: two of these studies reported no significant difference in in-hospital mortality rate; two found a significant reduction of in-hospital mortality; two other studies described a trend towards improved survival. Although, both ICU mortality and serious adverse events were not significantly improved, there was a trend towards reduction of these endpoints after introduction of the EWS. However only two studies looked respectively at each endpoint. There were conflicting results concerning cardiopulmonary arrests. One study found a reduction in the incidence of cardiac arrest calls as well as in the mortality of patients who underwent CPR, while another one found an increased incidence of cardio-pulmonary arrests. Neither study met all methodological quality criteria.

CONCLUSION

The EWS itself is a simple and easy to use tool at the bedside, which may be of help in recognizing patients with potential for acute deterioration. Coupled with an outreach service, it may be used to timely initiate adequate treatment upon recognition, which may influence the clinical outcomes positively. However, the use of adapted forms of the EWS together with different thresholds, poor or inadequate methodology makes it difficult in drawing comparisons. A general conclusion can thus not be generated from the lack of use of a single standardized score and the use of different populations. In future large multi-centre trials using one standardized score are needed also in order to facilitate comparison.

Failure events in transition of care for surgical patients

BACKGROUND

Unexpected clinical deterioration (failure events) in surgical patients on standard nursing units (WARDs) could have a significant impact on eventual survival. We sought to investigate failure events requiring intensive care (surgical ICU [SICU]) transfer of surgical patients on WARDs in a single-center academic setting.

STUDY DESIGN

Surgical patients admitted to WARDs over a 12-month period, who developed failure events, were retrospectively reviewed. Time to deterioration since WARD arrival, clinical factors, notification chain, and outcomes were identified. A physician review panel determined the preventability of failure events.

RESULTS

Ninety-eight patients experienced 111 failure events requiring SICU transfer. Most patients (85%) were emergency admissions. Of 111 events, 90% had been previously discharged from an SICU or a postanesthesia care unit (PACU). Recognition of failure was by nursing (54%) and on routine physician rounds (34%). Rapid response or code blue alone was less common (12%). A second physician notification was needed in 29%, with delays due to failure to identify severity of illness. Most commonly, respiratory events prompted notification (77 of 111, 69%). Overall mortality was 26 of 98 (27%). Median time to failure was 2 days and was associated with early transfer from the SICU or PACU. Rapid response or code blue activation was associated with higher mortality than physician notification.

CONCLUSIONS

Patients most at risk for WARD failures were those with acute surgical emergencies or recently discharged from the SICU or PACU. Respiratory complications were the most common cause of WARD failure events. Many early failures may have been due to premature transfer from the SICU or PACU. Failure events on WARDs can have lethal consequences. Awareness, monitoring, and communication are important components of preventative measures.

Flash mob research: a single-day, multicenter, resident-directed study of respiratory rate

BACKGROUND

Vital signs are critical data in the care of hospitalized patients, but the accuracy with which respiratory rates are recorded in this population remains uncertain. We used a novel flash mob research approach to evaluate the accuracy of recorded respiratory rates in inpatients.

METHODS

This was a single-day, resident-led, prospective observational study of recorded vs directly observed vital signs in nonventilated patients not in the ICU on internal medicine teaching services at six large tertiary-care centers across the United States.

RESULTS

Among the 368 inpatients included, the median respiratory rate was 16 breaths/min for the directly observed values and 18 breaths/min for the recorded values, with a median difference of 2 breaths/min (P &lt; .001). Respiratory rates of 18 or 20 breaths/min accounted for 71.8% (95% CI, 67.1%-76.4%) of the recorded values compared with 13.0% (95% CI, 9.5%-16.5%) of the directly observed measurements. For individual patients, there was less agreement between the recorded and the directly observed respiratory rate compared with pulse rate.

CONCLUSIONS

Among hospitalized patients across the United States, recorded respiratory rates are higher than directly observed measurements and are significantly more likely to be 18 or 20 breaths/min.

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 &gt; 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.

The accuracy, precision and reliability of measuring ventilatory rate and detecting ventilatory pause by rainbow acoustic monitoring and capnometry

BACKGROUND

Current methods for monitoring ventilatory rate have limitations including poor accuracy and precision and low patient tolerance. In this study, we evaluated a new acoustic ventilatory rate monitoring technology for accuracy, precision, reliability, and the ability to detect pauses in ventilation, relative to capnometry and a reference method in postsurgical patients.

METHODS

Adult patients presenting to the postanesthesia care unit were connected to a Pulse CO-Oximeter with acoustic monitoring technology (Rad-87, version 7804, Masimo, Irvine, CA) through an adhesive bioacoustic sensor (RAS-125, rev C) applied to the neck. Each subject also wore a nasal cannula connected to a bedside capnometer (Capnostream20, version 4.5, Oridion, Needham, MA). The acoustic monitor and capnometer were connected to a computer for continuous acoustic and expiratory carbon dioxide waveform recordings. Recordings were retrospectively analyzed by a trained technician in a setting that allowed for the simultaneous viewing of both waveforms while listening to the breathing sounds from the acoustic signal to determine inspiration and expiration reference markers within the ventilatory cycle without using the acoustic monitor- or capnometer-calculated ventilatory rate. This allowed the automatic calculation of a reference ventilatory rate for each device through a software program (TagEditor, Masimo). Accuracy (relative to the respective reference) and precision of each device were estimated and compared with each other. Sensitivity for detection of pauses in ventilation, defined as no inspiration or expiration activity in the reference ventilatory cycle for ≥30 seconds, was also determined. The devices were also evaluated for their reliability, i.e., the percentage of the time when each displayed a value and did not drop a measurement.

RESULTS

Thirty-three adults (73% female) with age of 45 ± 14 years and weight 117 ± 42 kg were enrolled. A total of 3712 minutes of monitoring time (average 112 minutes per subject) were analyzed across the 2 devices, reference ventilatory rates ranged from 1.9 to 49.1 bpm. Acoustic monitoring showed significantly greater accuracy (P = 0.0056) and precision (P- = 0.0024) for respiratory rate as compared with capnometry. On average, both devices displayed data over 97% of the monitored time. The (0.95, 0.95) lower tolerance limits for the acoustic monitor and capnometer were 94% and 84%, respectively. Acoustic monitoring was marginally more sensitive (P = 0.0461) to pauses in ventilation (81% vs 62%) in 21 apneic events.

CONCLUSIONS

In this study of a population of postsurgical patients, the acoustic monitor and capnometer both reliably monitored ventilatory rate. The acoustic monitor was statistically more accurate and more precise than the capnometer, but differences in performance were modest. It is not known whether the observed differences are clinically significant. The acoustic monitor was more sensitive to detecting pauses in ventilation. Acoustic monitoring may provide an effective and convenient means of monitoring ventilatory rate in postsurgical patients.

Multivariate temporal symptomatic characterization of cardiac arrest

We model the temporal symptomatic characteristics of 171 cardiac arrest patients in Intensive Care Units. The temporal and feature dependencies in the data are illustrated using a mixture of matrix normal distributions. We found that the cardiac arrest temporal signature is best summarized with six hours data prior to cardiac arrest events, and its statistical descriptions are significantly different from the measurements taken in the past two days. This matrix normal model can classify these patterns better than logistic regressions with lagged features.

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.

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.