Rapid response systems: are they really effective?

Impact of an emergency department rapid response system on inpatient clinical deterioration: A controlled pre-post study

AIM

To determine the impact implementation of Emergency Department Clinical Emergency Response System (EDCERS) on inpatient deterioration events and identify contributing causal factors.

METHODS

EDCERS was implemented in an Australian regional hospital, integrating a single parameter track and trigger criteria for escalation of care, and emergency, specialty and critical care clinician response to patient deterioration. In this controlled pre-post study, electronic medical records of patients who experienced a deterioration event (rapid response call, cardiac arrest or unplanned intensive care admission) on the ward within 72 h of admission from the emergency department (ED) were reviewed. Causal factors contributing to the deteriorating event were assessed using a validated human factors framework.

RESULTS

Implementation of EDCERS reduced the number of inpatient deterioration events within 72 h of emergency admission with failure or delayed response to ED patient deterioration as a causal factor. There was no change in the overall rate of inpatient deterioration events.

CONCLUSION

This study supports wider implementation of rapid response systems in the ED to improve management of deteriorating patients. Tailored implementation strategies should be used to achieve successful and sustainable uptake of ED rapid response systems and improve outcomes in deteriorating patients.

Prognostic Value of Physiological Scoring Systems in COVID-19 Patients: A Prospective Observational Study

The objective of this study was to investigate the accuracy of the Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Rapid Acute Physiology Score (RAPS), Worthing Physiological Scoring System (WPSS), and Revised Trauma Score (RTS) for predicting the inhospital mortality of COVID-19 patients. This diagnostic accuracy study was conducted in Tehran, Iran, from November 15, 2020, to March 10, 2021. The participants consisted of 246 confirmed cases of COVID-19 patients who were admitted to the emergency department. The patients were followed from the point of admission up until discharge from the hospital. The mortality status of patients (survivor or nonsurvivor) was reported at the discharge time, and the receiver operating characteristic curve analysis of each scoring system for predicting inhospital mortality was estimated. The area under the curve of REMS was significantly higher than other scoring systems and in cutoff value of 6 and greater had a sensitivity and specificity of 89.13% and 55.50%, respectively. Among the five scoring systems employed in this study, REMS had the best accuracy to predict the inhospital mortality rate of COVID-19 patients and RAPS had the lowest accuracy for inhospital mortality. Thus, REMS is a useful tool that can be employed in identifying high-risk COVID-19 patients.

Tele-Rapid Response Team (Tele-RRT): The effect of implementing patient safety network system on outcomes of medical patients-A before and after cohort study

BACKGROUND

Rapid Response Teams were developed to provide interventions for deteriorating patients. Their activation depends on timely detection of deterioration. Automated calculation of warning scores may lead to early recognition, and improvement of RRT effectiveness.

METHOD

This was a "Before" and "After" study, in the "Before" period ward nurses activated RRT after manually recording vital signs and calculating warning scores. In the "After" period, vital signs and warning calculations were automatically relayed to RRT through a wireless monitoring network.

RESULTS

When compared to the before group, the after group had significantly lower incidence and rate of cardiopulmonary resuscitation (CPR) (2.3 / 1000 inpatient days versus 3.8 / 1000 inpatient days respectively, p = 0.01), significantly shorter length of hospital stay and lower hospital mortality, but significantly higher number of RRT activations. In multivariable logistic regression model, being in the "After" group decreases odds of CPR by 33% (OR = 0.67 [95% CI: 0.46-0.99]; p = 0.04). There was no difference between groups in ICU admission.

CONCLUSION

Automated activation of the RRT significantly reduced CPR events and rates, improved CPR success rate, reduced hospital length of stay and mortality, but increased the number of RRT activations. There were no differences in unplanned ICU admission or readmission.

Early warning systems and rapid response systems for the prevention of patient deterioration on acute adult hospital wards

BACKGROUND

Early warning systems (EWS) and rapid response systems (RRS) have been implemented internationally in acute hospitals to facilitate early recognition, referral and response to patient deterioration as a solution to address suboptimal ward-based care. EWS and RRS facilitate healthcare decision-making using checklists and provide structure to organisational practices through governance and clinical audit. However, it is unclear whether these systems improve patient outcomes. This is the first update of a previously published (2007) Cochrane Review.

OBJECTIVES

To determine the effect of EWS and RRS implementation on adults who deteriorate on acute hospital wards compared to people receiving hospital care without EWS and RRS in place.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase and two trial registers on 28 March 2019. We subsequently ran a MEDLINE update on 15 May 2020 that identified no further studies. We checked references of included studies, conducted citation searching, and contacted experts and critical care organisations.

SELECTION CRITERIA

We included randomised trials, non-randomised studies, controlled before-after (CBA) studies, and interrupted time series (ITS) designs measuring our outcomes of interest following implementation of EWS and RRS in acute hospital wards compared to ward settings without EWS and RRS.

DATA COLLECTION AND ANALYSIS

Two review authors independently checked studies for inclusion, extracted data and assessed methodological quality using standard Cochrane and Effective Practice and Organisation of Care (EPOC) Group methods. Where possible, we standardised data to rates per 1000 admissions; and calculated risk differences and 95% confidence intervals (CI) using the Newcombe and Altman method. We reanalysed three CBA studies as ITS designs using segmented regression analysis with Newey-West autocorrelation adjusted standard errors with lag of order 1. We assessed the certainty of evidence using the GRADE approach.

MAIN RESULTS

We included four randomised trials (455,226 participants) and seven non-randomised studies (210,905 participants reported in three studies). All 11 studies implemented an intervention comprising an EWS and RRS conducted in high- or middle-income countries. Participants were admitted to 282 acute hospitals. We were unable to perform meta-analyses due to clinical and methodological heterogeneity across studies. Randomised trials were assessed as high risk of bias due to lack of blinding participants and personnel across all studies. Risk of bias for non-randomised studies was critical (three studies) due to high risk of confounding and unclear risk of bias due to no reporting of deviation from protocol or serious (four studies) but not critical due to use of statistical methods to control for some but not all baseline confounders. Where possible we presented original study data which reported the adjusted relative effect given these were appropriately adjusted for design and participant characteristics. We compared outcomes of randomised and non-randomised studies reported them separately to determine which studies contributed to the overall certainty of evidence. We reported findings from key comparisons. Hospital mortality Randomised trials provided low-certainty evidence that an EWS and RRS intervention may result in little or no difference in hospital mortality (4 studies, 455,226 participants; results not pooled). The evidence on hospital mortality from three non-randomised studies was of very low certainty (210,905 participants). Composite outcome (unexpected cardiac arrests, unplanned ICU admissions and death) One randomised study showed that an EWS and RRS intervention probably results in no difference in this composite outcome (adjusted odds ratio (aOR) 0.98, 95% CI 0.83 to 1.16; 364,094 participants; moderate-certainty evidence). One non-randomised study suggests that implementation of an EWS and RRS intervention may slightly reduce this composite outcome (aOR 0.85, 95% CI 0.72 to 0.99; 57,858 participants; low-certainty evidence). Unplanned ICU admissions Randomised trials provided low-certainty evidence that an EWS and RRS intervention may result in little or no difference in unplanned ICU admissions (3 studies, 452,434 participants; results not pooled). The evidence from one non-randomised study is of very low certainty (aOR 0.88, 95% CI 0.75 to 1.02; 57,858 participants). ICU readmissions No studies reported this outcome. Length of hospital stay Randomised trials provided low-certainty evidence that an EWS and RRS intervention may have little or no effect on hospital length of stay (2 studies, 21,417 participants; results not pooled). Adverse events (unexpected cardiac or respiratory arrest) Randomised trials provided low-certainty evidence that an EWS and RRS intervention may result in little or no difference in adverse events (3 studies, 452,434 participants; results not pooled). The evidence on adverse events from three non-randomised studies (210,905 participants) is very uncertain.

AUTHORS' CONCLUSIONS

Given the low-to-very low certainty evidence for all outcomes from non-randomised studies, we have drawn our conclusions from the randomised evidence. This evidence provides low-certainty evidence that EWS and RRS may lead to little or no difference in hospital mortality, unplanned ICU admissions, length of hospital stay or adverse events; and moderate-certainty evidence of little to no difference on composite outcome. The evidence from this review update highlights the diversity in outcome selection and poor methodological quality of most studies investigating EWS and RRS. As a result, no strong recommendations can be made regarding the effectiveness of EWS and RRS based on the evidence currently available. There is a need for development of a patient-informed core outcome set comprising clear and consistent definitions and recommendations for measurement as well as EWS and RRS interventions conforming to a standard to facilitate meaningful comparison and future meta-analyses.

Role of a rapid response system and code status discussion as determinants of prognosis for critical inpatients: An observational study in a Japanese urban hospital

Rapid response systems (RRS) have been introduced worldwide to reduce unpredicted in-hospital cardiac arrest (IHCA) and in-hospital mortality. The role of advance care planning (ACP) in the management of critical patients has not yet been fully determined in Japan.We retrospectively assessed the characteristics of all inpatients with unpredicted IHCA in our hospital between 2016 and 2018. Yearly changes in the number of RRS activations and the incidence of unpredicted IHCA with or without code status discussion were evaluated from 2014 to 2018. Hospital standardized mortality ratios were assessed from the data reported in the annual reports by the National Hospital Organization.A total of 81 patients (age: 70.9 ± 13.3 years) suffered an unpredicted IHCA and had multiple background diseases, including heart disease (75.3%), chronic kidney disease (25.9%), and postoperative status (cardiovascular surgery, 18.5%). Most of the patients manifested non-shockable rhythms (69.1%); survival to hospital discharge rate was markedly lower than that with shockable rhythms (26.8% vs 72.0%, P < .001). The hospital standardized mortality ratios was maintained nearly constant at approximately 50.0% for 3 consecutive years. The number of cases of RRS activation markedly increased from 75 in 2014 to 274 patients in 2018; conversely, the number of unpredicted IHCA cases was reduced from 40 in 2014 to 18 in 2018 (P < .001). Considering the data obtained in 2014 and 2015 as references, the RRS led to a reduction in the relative risk of unpredicted IHCA from 2016 to 2018 (ie, 0.618, 95% confidence interval 0.453-0.843). The reduction in unpredicted IHCA was attributed partly to the increased number of patients who had discussed the code status, and a significant correlation was observed between these parameters (R2 = 0.992, P < .001). The reduction in the number of patients with end-stage disease, including congestive heart failure and chronic renal failure, paralleled the incidence of unpredicted IHCA.Both RRS and ACP reduced the incidence of unpredicted IHCA; RRS prevents progression to unpredicted IHCA, whereas ACP decreases the number of patients with no code status discussion and thus potentially reducing the patient subgroup progressing to an unpredicted IHCA.

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System

Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. Methods: This was a retrospective cohort study of patients 16 years of age or above admitted to four UK hospitals. The primary outcome was cardiac arrest or unplanned admission to the ICU. We used patient data (vital signs, laboratory tests, comorbidities, and frailty) from one hospital to train a machine-learning model (gradient boosting trees). We internally and externally validated the model and compared its performance with existing scoring systems (including the National EWS, laboratory-based acute physiology score, and electronic cardiac arrest risk triage score). Measurements and Main Results: We developed the HAVEN model using 230,415 patient admissions to a single hospital. We validated HAVEN on 266,295 admissions to four hospitals. HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.

Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores

AIMS

To identify the most accurate early warning score (EWS) for predicting an adverse outcome in COVID-19 patients admitted to the emergency department (ED).

METHODS

In adult consecutive patients admitted (March 1-April 15, 2020) to the ED of a major referral centre for COVID-19, we retrospectively calculated NEWS, NEWS2, NEWS-C, MEWS, qSOFA, and REMS from physiological variables measured on arrival. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV), and the area under the receiver operating characteristic (AUROC) curve of each EWS for predicting admission to the intensive care unit (ICU) and death at 48 h and 7 days were calculated.

RESULTS

We included 334 patients (119 [35.6%] females, median age 66 [54-78] years). At 7 days, the rates of ICU admission and death were 56/334 (17%) and 26/334 (7.8%), respectively. NEWS was the most accurate predictor of ICU admission within 7 days (AUROC 0.783 [95% CI, 0.735-0.826]; sensitivity 71.4 [57.8-82.7]%; NPV 93.1 [89.8-95.3]%), while REMS was the most accurate predictor of death within 7 days (AUROC 0.823 [0.778-0.863]; sensitivity 96.1 [80.4-99.9]%; NPV 99.4[96.2-99.9]%). Similar results were observed for ICU admission and death at 48 h. NEWS and REMS were as accurate as the triage system used in our ED. MEWS and qSOFA had the lowest overall accuracy for both outcomes.

CONCLUSION

In our single-centre cohort of COVID-19 patients, NEWS and REMS measured on ED arrival were the most sensitive predictors of 7-day ICU admission or death. EWS could be useful to identify patients with low risk of clinical deterioration.

Association between the frequency of admission for pneumonia and the incidence of in-hospital cardiac arrest: A population-based case–control study

Objective:

To examine the association between the frequency of admission for pneumonia and the incidence of in-hospital cardiac arrest.

Methods:

We enrolled 1739 patients with in-hospital cardiac arrest and 6956 randomly selected age- and sex-matched control patients using a longitudinal claims sub-dataset from 1996 to 2011 for 1 million people randomly selected from the population covered by the Taiwan National Health Insurance program. The odds ratio of in-hospital cardiac arrest associated with the number of hospital admissions for pneumonia was calculated.

Results:

During the 15-year study period, the in-hospital cardiac arrest group had a higher frequency (28.4% vs 8.1%, p < 0.0001) of admission for pneumonia compared to the control group. The comorbidities of heart failure, chronic pulmonary disease, diabetes, renal failure, liver disease, lymphoma, alcohol abuse, and drug abuse were higher in the in-hospital cardiac arrest group than in the control group. In addition, the risk of in-hospital cardiac arrest was 3.37 for the patients admitted for pneumonia, and the risk of in-hospital cardiac arrest increased for patients with multiple admissions for pneumonia (once, 3.03; two times, 3.44; and three times, 4.42). In cross-analysis, the more admissions for pneumonia and the greater the number of comorbidities, the higher the risk of in-hospital cardiac arrest (odds ratio = 21.37, 95% confidence interval = 13.6–33.9 for patients with more than three admissions for pneumonia and more than three comorbidities).

Conclusion:

Higher admission frequency for pneumonia was associated with a higher risk of in-hospital cardiac arrest. Awareness of this risk factor may help clinicians provide early prevention or detection for patients with potential in-hospital cardiac arrest risks immediately after admission.

Translation of evidence into policy to improve clinical practice: the development of an emergency department rapid response system

BACKGROUND

Undetected clinical deterioration is a major cause of high mortality events in Emergency Department (ED) patients. Yet, there is no known model to guide the recognition and response to clinical deterioration in the ED, integrating internal and external resources.

METHODS

An integrative review was firstly conducted to identify the critical components of recognising and responding to clinical deterioration in the ED. Components identified from the review were analysed by clinical experts and informed the development of an ED Clinical Emergency Response System (EDCERS).

RESULTS

Twenty four eligible studies were included in the review. Eight core components were identified: 1) vital sign monitoring; 2) track and trigger system; 3) communication plan; 4) response time; 5) emergency nurse response; 6) emergency physician response; 7) critical care team response; and 8) specialty team response. These components informed the development of the EDCERS protocol, integrating responses from staff internal and external to the ED.

CONCLUSIONS

EDCERS was based on the best available evidence and considered the cultural context of care. Future research is needed to determine the useability and impact of EDCERS on patient and health outcomes.

The Medical Emergency Team in Italy: an overview of in-hospital emergencies response

BACKGROUND AND AIM

Medical Emergency Team (MET), implemented in many hospitals worldwide, aims to improve the safety of in-hospital patients whose condition is deteriorating. This study describes MET presence and organization in the Italian National Healthcare System Hospitals.

METHODS

A national survey with an online questionnaire was performed. The questionnaire, created ad hoc, was sent by e-mail to the nursing coordinators and MET referents of the Hospitals affiliated to the Italian National Healthcare System with an Anesthesia and Intensive Care service.

RESULTS

One hundred-ninety-seven hospitals were interviewed (36.2% of the whole national network). A dedicated MET, composed at least by an intensivist and a nurse, was present only in 118 cases (59.9%). The team was composed by a non-dedicated staff (67.8% of doctors, 69.5% of nurses) and a minimum shared standard of education for the nurse component was absent. One third of the estimated hospitals did not use a warning score for emergency call activation.

DISCUSSION AND CONCLUSION

This survey showed a heterogenous and often lacking organization of in-hospital emergency management in Italy. MET system needs to be implemented in terms of presence in the Italian hospitals, and standardized for personnel structure and training, and equipment availability. A broader study is necessary to compare our data with those of other European Countries to better identify the specific areas which need to be improved more promptly.

The impact of rapid response systems on mortality and cardiac arrests - A literature review

BACKGROUND

Rapid response systems were created to improve recognition of and response to deterioration of general ward patients.

AIM

This literature review aimed to evaluate the evidence on whether rapid response systems decrease in-hospital mortality and non-intensive care unit cardiac arrests.

METHOD

Six databases (MEDLINE, Cochrane Central Register of Controlled Trials, Cumulative Index of Nursing and Allied Health Literature, SCOPUS, Web of Science and PubMed) were systematically searched for primary studies published between 1st January 2014 and 31st October 2017, recruiting general ward patients, where the intervention involved introducing/maintaining a rapid response system, the comparison referred to a hospital setting without a rapid response system and the outcomes included mortality and cardiac arrests.

RESULTS

Fifteen studies met eligibility criteria: one stepped wedge cluster randomised controlled trial, one concurrent cohort controlled study and thirteen historically controlled studies. Thirteen studies investigated mortality of which seven reported statistically significant findings in favour of rapid response systems. Thirteen studies investigated cardiac arrests, of which eight reported statistically significant findings in favour of rapid response systems.

CONCLUSION

Evidence suggests that when the process of introducing/maintaining a rapid response system is successful and under certain favourable conditions, rapid response systems significantly decrease mortality and cardiac arrests.

Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

BACKGROUND

Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning-based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records.

OBJECTIVE

This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential.

METHODS

A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets.

RESULTS

The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non-cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced.

CONCLUSIONS

A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient's care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

Performance of the Afferent Limb of Rapid Response Systems in Managing Deteriorating Patients: A Systematic Review

Introduction

The clinical components of the rapid response system (RRS) are the afferent limb, to ensure identification of in-hospital patients who deteriorate and activation of a response, and the efferent limb, to provide the response. This review aims to evaluate the factors that influence the performance of the afferent limb in managing deteriorating ward patients and their effects on patient outcomes.

Methods

A systematic review was performed for the years 1995–2017 by employing five electronic databases. Articles were included assessing the ability of the ward staffs to monitor, recognize, and escalate care to patient deterioration. The findings were summarized using a narrative approach.

Results

Thirty-one studies met the inclusion criteria. The analysis revealed major themes enclosing several factors affecting management of patients having sudden deterioration. The monitoring and recognition process was conditioned by the lack of recording of physiological parameters, the influence of facilitators, including staff education and training, and barriers, including human and environmental factors, and poor compliance with the calling criteria. The escalation of care process highlighted the influence of cultural barriers and personal judgment on RRS activation. Mainly, delayed team calls were factors strongly associated with the increased risk of unplanned admissions to the intensive care unit and length of stay, hospital length of stay and mortality, and 30-day mortality.

Conclusions

A combination of factors affects the timely identification and response to sudden deterioration by general ward staffs, leading to suboptimal care of patients, delayed or failed activation of RRS teams, and increased risks of worsening outcomes. The research efforts and clinical involvement to improve the governance of the factors limiting the performance of the afferent limb may ensure proper management of hospitalized patients showing physiological deterioration.

Emergency response teams in and outside of medicine-structurally crafted to be worlds apart

Medical emergency response teams (MERTs) are widespread throughout inpatient hospital care facilities. Besides the rise of the ubiquitous rapid response team, current MERTs span trauma, stroke, myocardial infarction, and sepsis in many hospitals. Given the multiplicity of teams with widely varying membership, leadership, and functionality, the structure of MERTs is appropriate to review to determine opportunities for improvement. Since nonmedical ERTs predate MERT genesis and are similar across multiple disciplines, nonmedical ERTs provide a standard against which to compare and review MERT design and function.Nonmedical ERTs are crafted to leverage team members who are fully trained and dedicated to that domain, whose skills are regularly updated, with leadership tied to unique skill sets rather than based on hierarchical rank; activity is immediately reviewed at the conclusion of each deployment and teams continue to work together between team deployments. Medical emergency response teams, in sharp contradistinction, often incorporate trainees into teams that do not train together, are not focused on the discipline required to be leveraged, are led based on arrival time or hierarchy, and are usually reviewed at a time remote from team action; teams rapidly disperse after each activity and generally do not continue to work together in between team activations. These differences between ERTs and MERTs may impede MERT success with regard to morbidity and mortality mitigation. Readily deployable approaches to bridge identified gaps include dedicated Advanced Practice Provider (APP) team leadership, reductions in trainee MERT leadership while preserving participation, discipline-dedicated rescue teams, and interteam integration training.Emergency response teams in medical and nonmedical domains share parallels yet lack congruency in structure, function, membership, roles, and performance evaluation. Medical emergency response team structural redesign may be warranted to embrace the beneficial elements of nonmedical ERTs to improve patient outcome and reduce variation in rescue practices and team functionality.

Factors associated with delayed rapid response team activation

Delayed activation of the rapid response team (RRT) is common and has been associated with adverse outcomes. However, little is known about the factors associated with delayed activation. This was an observational study from two hospitals in Ottawa, Canada, including adult inpatients with experiencing an activation of the RRT. Data was collected between May 1, 2012 and May 31, 2016 and groups were divided between those with activation within 1 h of meeting call criteria and those with >1 h (delayed activation). The primary outcome was in-hospital mortality. There were 6131 patients included in the study, of which 1441 (26.0%) experienced a delay. The reasons for RRT call were significantly different (P < 0.001) with respiratory distress (29.3% versus 24.8%), and hypotension (17.4% versus 13.2%) being more common in the delayed group, and dysrhythmias (15.9% versus 18.5%) and altered level of consciousness (13.5% versus 18.7%) being less common. RRT activation was more delayed on non-surgical services (P < 0.001). Delayed activation was associated with increased mortality (Adjusted odds ratio [OR] 1.23, 95% CI 1.07-1.41), ICU admission (Adjusted OR 1.72, 95% CI 1.51-1.96), and hospital length of stay (13 versus 15 days, P < 0.001).

Effect of a Real-Time Electronic Dashboard on a Rapid Response System

A rapid response system (RRS) may have limited effectiveness when inpatient providers fail to recognize signs of early patient decompensation. We evaluated the impact of an electronic medical record (EMR)-based alerting dashboard on outcomes associated with RRS activation. We used a repeated treatment study in which the dashboard display was successively turned on and off each week for ten 2-week cycles over a 20-week period on the inpatient acute care wards of an academic medical center. The Rapid Response Team (RRT) dashboard displayed all hospital patients in a single view ranked by severity score, updated in real time. The dashboard could be seen within the EMR by any provider, including RRT members. The primary outcomes were the incidence rate ratio (IRR) of all RRT activations, unexpected ICU transfers, cardiopulmonary arrests and deaths on general medical-surgical wards (wards). We conducted an exploratory analysis of first RRT activations. There were 6736 eligible admissions during the 20-week study period. There was no change in overall RRT activations (IRR = 1.14, p = 0.07), but a significant increase in first RRT activations (IRR = 1.20, p = 0.04). There were no significant differences in unexpected ICU transfers (IRR = 1.15, p = 0.25), cardiopulmonary arrests on general wards (IRR = 1.46, p = 0.43), or deaths on general wards (IRR = 0.96, p = 0.89). The introduction of the RRT dashboard was associated with increased initial RRT activations but not overall activations, unexpected ICU transfers, cardiopulmonary arrests, or death. The RRT dashboard is a novel tool to help providers recognize patient decompensation and may improve initial RRT notification.

A Pilot Study of the Effectiveness of Medical Emergency System Implementation at a Single Center in Korea

Background

An automatic alarm system was developed was developed for unexpected vital sign instability in admitted patients to reduce staffing needs and costs related to rapid response teams. This was a pilot study of the automatic alarm system, the medical emergency system (MES), and the aim of this study was to determine the effectiveness of the MES before expanding this system to all departments.

Methods

This retrospective, observational study compared the performance of patients admitted to the pulmonary department at a single center using patient data from three 3-month periods (before implementation of the MES, December 2013-February 2014; after implementation of the MES, December 2014-February 2015 and December 2015-February 2016).

Results

A total of 571 patients were admitted to the pulmonary department during the three observation periods. During this pilot study, the MES automatically issued 568 alarms for 415 admitted patients. There was no significant difference in the rate of cardiopulmonary resuscitation (CPR) before and after application of the MES. The mortality rate also did not change. However, it appeared that CPR was prevented in four patients admitted from the general ward to the intensive care unit (ICU) during MES implementation. The median length of hospital stay and median length of ICU stay were not significantly different before and after MES implementation.

Conclusions

Although we did not find a significant improvement in outcomes upon MES implementation, the CPR rate and mortality rate did not increase despite increased comorbidities. This was a small pilot study and, based on these results, we believe that the MES may have significant effects in longer-term and larger-scale studies.

Evaluation of a critical care outreach service in a middle-income country: A stepped wedge cluster randomized trial and nested qualitative study

PURPOSE

This trial evaluates implementation of critical care outreach in a middle-income country.

MATERIALS AND METHODS

Critical care outreach delivered by a team of intensive care nurses was implemented across general hospital wards in an Iranian university hospital. The order of implementation was randomized with wards stratified by predicted mortality rates. Effectiveness was evaluated using a stepped wedge cluster randomized controlled trial design, comparing outcomes between patients admitted before and after implementation. The primary outcomes were inhospital mortality and cardiopulmonary resuscitation. A nested qualitative study explored challenges to implementation and contextualized the trial outcomes.

RESULTS

Between July 2010 and December 2011, 13 wards were sequentially randomized to implement the critical care outreach: 7802 patients were admitted before implementation and 10 880 after implementation. There were 370 deaths (4.74%) among patients admitted before implementation and 384 deaths (3.53%) after implementation. Adjusting for clustering and temporal trends, the odds ratio for mortality was 1.03 (95% confidence interval, 0.68-1.53). Results for other outcomes were broadly similar. Focus groups revealed a lack of endorsement of the intervention by management and ward nurses.

CONCLUSIONS

This pragmatic evaluation of critical care outreach in a middle-income country did not show a reduction in mortality or other outcomes.

Evaluation of the five-year operation period of a rapid response team led by an intensive care physician at a university hospital

Objective

To evaluate the implementation of a multidisciplinary rapid response team led by an intensive care physician at a university hospital.

Methods

This retrospective cohort study analyzed assessment forms that were completed during the assessments made by the rapid response team of a university hospital between March 2009 and February 2014.

Results

Data were collected from 1,628 assessments performed by the rapid response team for 1,024 patients and included 1,423 code yellow events and 205 code blue events. The number of assessments was higher in the first year of operation of the rapid response team. The multivariate analysis indicated that age (OR 1.02; 95%CI 1.02 - 1.03; p < 0.001), being male (OR 1.48; 95%CI 1.09 - 2.01; p = 0.01), having more than one assessment (OR 3.31; 95%CI, 2.32 - 4.71; p < 0.001), hospitalization for clinical care (OR 1.77; 95%CI 1.29 - 2.42; p < 0.001), the request of admission to the intensive care unit after the code event (OR 4.75; 95%CI 3.43 - 6.59; p < 0.001), and admission to the intensive care unit before the code event (OR 2.13; 95%CI 1.41 - 3.21; p = 0.001) were risk factors for hospital mortality in patients who were seen for code yellow events.

Conclusion

The hospital mortality rates were higher than those found in previous studies. The number of assessments was higher in the first year of operation of the rapid response team. Moreover, hospital mortality was higher among patients admitted for clinical care.