Redefining in-hospital resuscitation: the concept of the medical emergency team

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.

Medication-related Medical Emergency Team activations: a case review study of frequency and preventability

OBJECTIVES

Despite recognition of clinical deterioration and medication-related harm as patient safety risks, the frequency of medication-related Rapid Response System activations is undefined. We aimed to estimate the incidence and preventability of medication-related Medical Emergency Team (MET) activations and describe the associated adverse medication events.

METHODS

A case review study of consecutive MET activations at two acute, academic teaching hospitals in Melbourne, Australia with mature Rapid Response Systems was conducted. All MET activations during a 3-week study period were assessed for a medication cause including identification of the contributing adverse medication event and its preventability, using validated tools and recognised classification systems.

RESULTS

There were 9439 admissions and 628 MET activations during the study period. Of these, 146 (23.2%) MET activations were medication related: an incidence of 15.5 medication-related MET activation per 1000 admissions. Medication-related MET activations occurred a median of 46.6 hours earlier (IQR 22-165) in an admission than non-medication-related activations (p=0.001). Furthermore, this group also had more repeat MET activations during their admission (p=0.021, OR=1.68, 95% CI 1.09 to 2.59). A total of 92 of 146 (63%) medication-related MET activations were potentially preventable. Tachycardia due to omission of beta-blocking agents (10.9%, n=10 of 92) and hypotension due to cumulative toxicity (9.8%, n=9 of 92) or inappropriate use (10.9%, n=10 of 92) of antihypertensives were the most common adverse medication events leading to potentially preventable medication-related MET activations.

CONCLUSIONS

Medications contributed to almost a quarter of MET activations, often early in a patient's admission. One in seven MET activations were due to potentially preventable adverse medication events. The most common of these were omission of beta-blockers and clinically inappropriate antihypertensive use. Strategies to prevent these events would increase patient safety and reduce burden on the MET.

A multidisciplinary safety briefing for acutely ill and deteriorating patients: A quality improvement project

BACKGROUND

Safety briefings can help promoting situational awareness, interprofessional communication and improve patient safety.

LOCAL PROBLEM

A clinical survey highlighted that 90% of the participants including the medical team and the critical care outreach team nurses perceived the meeting for escalating acutely ill and deteriorating patients during the out-of-hours period (20.00 to 08.00) to have unconstructive and unwelcoming atmosphere with belittling, hostility and unhelpful criticisms. The participants reported that the communication across teams lacked in structure and clear information given; but staff also self-reported lacking confidence in communicating key issues.

METHOD

A quality improvement project with Plan-Do-Study-Act was adopted to design and implement a dedicated multidisciplinary safety briefing with a structured format.

RESULTS

The multidisciplinary safety briefing was to 90% of clinicians, and it took a median of 10 min to complete. Delayed referrals to the critical care outreach team were reduced by 46%. Positive changes included increased situational awareness and clearer communication across teams. Barriers identified were variable usage and need for face-to-face presence. Considering all the findings and the time constraint during the SARS-CoV-2 pandemic, we changed to a telephonic safety briefing directly to the team leaders.

CONCLUSION

A structured multidisciplinary safety briefing can improve patient safety and support management of deteriorating and acutely ill patients on the wards during the out-of-hours period.

In-hospital cardiac arrest: the state of the art

In-hospital cardiac arrest (IHCA) is associated with a high risk of death, but mortality rates are decreasing. The latest epidemiological and outcome data from several cardiac arrest registries are helping to shape our understanding of IHCA. The introduction of rapid response teams has been associated with a downward trend in hospital mortality. Technology and access to defibrillators continues to progress. The optimal method of airway management during IHCA remains uncertain, but there is a trend for decreasing use of tracheal intubation and increased use of supraglottic airway devices. The first randomised clinical trial of airway management during IHCA is ongoing in the UK. Retrospective and observational studies have shown that several pre-arrest factors are strongly associated with outcome after IHCA, but the risk of bias in such studies makes prognostication of individual cases potentially unreliable. Shared decision making and advanced care planning will increase application of appropriate DNACPR decisions and decrease rates of resuscitation attempts following IHCA.

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)

OBJECTIVE

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

MATERIALS AND METHODS

Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call.

RESULTS

Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score.

DISCUSSION

Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering.

CONCLUSIONS

MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

Association of out of hospital paediatric early warning score with need for hospital admission in a Scottish emergency ambulance population

OBJECTIVE

Physiological derangement, as measured by paediatric early warning score (PEWS) is used to identify children with critical illness at an early point to identify and intervene in children at risk. PEWS has shown some utility as a track and trigger system in hospital and also as a predictor of adverse outcome both in and out of hospital. This study examines the relationship between prehospital observations, aggregated into an eight-point PEWS (Scotland), and hospital admission.

METHODS

A retrospective analysis of all patients aged less than 16 transported to hospital by the Scottish Ambulance Service between 2011 and 2015. Data were matched to outcome data regarding hospital admission or discharge and length of stay.

RESULTS

Full data were available for 21 202 paediatric patients, of whom 6340 (29.9%) were admitted to hospital. Prehospital PEWS Scotland was associated with an odds ratio for admission of 1.189 [95% confidence interval (CI): 1.176-1.202; P < 0.001]. The area under receiver operating curve of 0.617 (95% CI: 0.608-0.625; P < 0.001) suggests poorly predictive ability for hospital admission. There was no association between prehospital PEWS Scotland and length of hospital stay.

CONCLUSION

These data show that a single prehospital PEWS Scotland was a poor predictor of hospital admission for unselected patients in a prehospital population. The decision to admit a child to hospital is not solely based on the physiological derangement of vital signs, and hence physiological-based scoring systems such as PEWS Scotland cannot be used as the sole criteria for hospital admission, from an undifferentiated prehospital population.

Understanding the pre-medical emergency team tier of a mature rapid response system: A content analysis of guidance documents

BACKGROUND

The pre-medical emergency team (pre-MET) tier of rapid response systems (RRSs) includes extended activation criteria to identify earlier clinical deterioration and a ward-based patient review that is undertaken by the affected patient's admitting team or covering doctors. There is limited understanding of the structure and processes of the pre-MET RRS tier that are expected to guide clinicians' actions and subsequent patient safety outcomes.

OBJECTIVE

The aim of the study was to describe the structure and processes of the pre-MET RRS tier in one acute care setting.

METHODS

An exploratory descriptive design involving document analysis was used. Guidance documents (policies, procedures, guidelines, charts, educational materials) were obtained from one health service with a mature, multitiered RRS in Melbourne, Australia. Documents were analysed using content analysis. Concept- and data-driven approaches were used to construct a coding frame.

RESULTS

Nineteen guidance documents supporting the pre-MET RRS tier on general wards were analysed. The coding frame consisted of seven main categories: Defining the Pre-MET RRS Tier, Essential Resources for Operationalisation, Recognising Pre-MET Events, Pathways for Activation, Exceptions to the Rule, Clinician Responses to Pre-MET Events, and Recording Pre-MET Events. The structures and processes of the pre-MET RRS tier were largely consistent with national guidelines, but there were internal inconsistencies in pre-MET activation criteria and unclear recommendations for modifying criteria. Pathways for activating the pre-MET RRS tier were complex and involved many steps, including validation processes before escalation of care to doctors. Responses to pre-MET events were seldom aligned to specific clinician types or groups, with nurses and allied health clinicians being under-represented.

CONCLUSIONS

We identified opportunities to improve guidance documents supporting the pre-MET RRS tier that may assist other health services engaged in planning or evaluating pre-MET strategies. Further research is needed to understand clinicians' use of the pre-MET RRS tier to inform targeted strategies to optimise its design and implementation.

Quels sont le fonctionnement, les caractéristiques, les effets et les modalités d’implantation des équipes d’intervention rapide ? Une revue de la littérature

Introduction&#160;: Hospitalized patients are at risk of unrecognized clinical deterioration that may lead to adverse events.Context&#160;: Rapid Response Teams (RRTs) exist around the world as a strategy to improve patient safety.Objective&#160;: To explore how RRTs work, their characteristics, impacts, and methods of implementation.Design&#160;: Literature review.Method&#160;: Consultation of the databases CINAHL, MEDLINE, PUBMED, COCHRANE library, SCOPUS, and PROQUEST Dissertations and Theses. Keywords&#160;: &#8220;health care team&#8221; and &#8220;rapid response team&#8221;.Results&#160;: 121 articles were included. The collected data were divided into five categories&#160;: 1) composition and operation of RRTs, 2) benefits and limitations of RRTs, 3) perceptions of RRTs by health care teams, organizations, and patients, 4) implementation strategies, and 5) facilitators and barriers to implementation.Discussion&#160;: Although there are many articles related to RRTs, it appears that&#160;: 1) few studies analyze the difference in outcomes in hospitalized patients related to the composition of RRTs, 2) few studies describe how RRTs should work, 3) more studies are needed on the impacts of RRTs on hospitalized patients, 4) organizations&#8217; and patients&#8217; perceptions of RRTs are not well studied, and 5) more studies are needed on the best way to implement an RRT.Conclusion&#160;: The results show that there is a lack of studies on the difference in outcomes in hospitalized patients related to the composition of RRTs, on how RRTs should work, on the impacts of RRTs on hospitalized patients, on organizations&#8217; and patients&#8217; perceptions of RRTs, and on the factors that influence the success or failure of the implementation of an RRT.

Improved Outcomes After Regional Implementation of Sepsis Alert: A Novel Triage Model

OBJECTIVES

To assess whether the triage model Sepsis Alert for Emergency Departments results in improved initial care of patients with severe infections.

DESIGN

Interventional study comparing patient care before and after the start of a new triage model, including 90-day follow-up.

SETTING

Eight emergency departments in Skåne County, Sweden.

SUBJECTS

Patients with suspected severe infection.

INTERVENTIONS

Patients with severely deviating vital signs and suspected infection were triaged into a designated sepsis line called Sepsis Alert, for rapid evaluation supported by an infectious disease specialist. Also, all emergency department staff participated in a designated sepsis education before the model was introduced.

MEASUREMENTS AND MAIN RESULTS

Medical records were evaluated for a 3-month period 1 year before the triage system was started in 2016 and for a 3-month period 1 year after. Of 195,607 patients admitted to these emergency departments during two 3-month periods, a total of 5,321 patients presented severely abnormal vital signs. Of these, 1,066 patients who presented with fever greater thanor equal to 38°C or history of fever/chills were considered to be patients at risk of having severe sepsis. Among patients triaged according to Sepsis Alert, 89.3% received antibiotic treatment within 1 hour after arrival to the emergency department (median time to antibiotics, 26 min), which was significantly better than before the start of the new triage: 67.9% (median time to antibiotics, 37 min) (p < 0.001). Additionally, sepsis treatment quality markers were significantly improved after the introduction of Sepsis Alert, including number of blood cultures and lactate measurements taken, percentage of patients receiving IV fluids, and appropriate initial antibiotic treatment. There were no differences in 28- or 90-day mortality rates.

CONCLUSIONS

The implementation of the new triage model Sepsis Alert with special attention to severe sepsis patients led to faster and more accurate antibiotic treatment and improved diagnostic procedures and supportive care.

Clinical deterioration in patients with ST-elevation myocardial infarction during and for 24 h after percutaneous coronary intervention: An observational study

BACKGROUND

In-hospital adverse events such as cardiac arrest are preceded by abnormalities in physiological data and are associated with high mortality. Healthcare institutions have implemented rapid response systems such as the medical emergency team for early recognition and response to clinical deterioration. Yet, most cardiac catheterisation laboratories, have yet to formally implement a rapid response system, so the nature and frequency of clinical deterioration is unclear and no published data exist.

OBJECTIVES

To explore the nature and frequency of clinical deterioration in ST- elevation myocardial infarction patients in a cardiac catheterisation laboratory without a Medical emergency team, and 24 hours after percutaneous coronary intervention and the immediate nursing responses to clinical deterioration.

METHOD

An exploratory descriptive study using retrospective medical audit was conducted in a public tertiary teaching hospital in Melbourne, Australia. In 2014, there were 327 ST- elevation myocardial infarction presentations of which 75 were randomly selected. Descriptive statistics were used to analyse the data.

RESULTS

In the cardiac catheterisation laboratory, 82.6% of patients fulfilled medical emergency team activation criteria and deterioration was predominantly cardiovascular. Respiratory rate was not documented for all patients in cardiac catheterisation laboratory. Post percutaneous coronary intervention, 31% of patients fulfilled medical emergency team activation criteria and this deterioration occurred secondary to hypoxia. There were no documented abnormalities in respiratory rate.

CONCLUSION

The ST- elevation myocardial infarction patients admitted to the cardiac catheterisation laboratory are critically ill patients. Failure to monitor for signs of respiratory dysfunction such as respiratory rate in cardiac catheterisation laboratory may delay recognition of clinical deterioration and timely escalation of care. Further research is required to inform changes in the system to improve patient safety.

Review of systems for recognising and responding to clinical deterioration in Singapore hospitals: a nationwide cross-sectional study

INTRODUCTION

The Rapid Response System for recognising and responding to clinically deteriorating patients has been progressively implemented in acute care hospitals across the globe. This study sought to review the implementation of this system in acute public hospitals in Singapore.

METHODS

A cross-sectional study using a face-to-face survey questionnaire was conducted.

RESULTS

Five out of seven invited hospitals completed the questionnaire and rated the Rapid Response System as either high priority or essential, and indicated its importance over other patient safety indicators. Sensitivity and specificity of the triggering criteria and non-adherence to the escalation protocol were highlighted issues. Only two hospitals had a dedicated response team for providing emergency help to deteriorating ward patients. Limited manpower resources, unclear roles between the primary and response teams, and the potential deskilling of ward staff were reported barriers that inhibited the uptake of a response team. All hospitals had a committee that oversaw its system operation, provided training to ward staff, and used information technology to support the implementation.

CONCLUSION

A variety of approaches have been taken to support the system of recognising and responding to clinical deterioration. This calls for a national approach to enable the standardisation of clinical processes, sharing of educational resources and multi-site evaluation.

Paediatric early warning scores are predictors of adverse outcome in the pre-hospital setting: A national cohort study

INTRODUCTION

Physiological deterioration often precedes clinical deterioration as patients develop critical illness. Use of a specific Paediatric Early Warning Score (PEWS), based on basic physiological measurements, may help identify children prior to their clinical deterioration. NHS Scotland has adopted a single national PEWS - PEWS (Scotland). We aim to look at the utility of PEWS (Scotland) in unselected paediatric ambulance patients.

METHODS

We performed a retrospective cohort of all ambulance patients aged under 16 years conveyed to hospital in Scotland between 2011 and 2015. Patients were matched to their 30 day mortality and ICU admission using data linkage.

RESULTS

Full results were available for 21,202 children and young people (CYP). On multivariate logistic regression, PEWS (Scotland) was an independent predictor of the primary outcome (ICU admission within 48 h or death within 30 days) with an odds ratio of 1.403 (95%CI 1.349-1.460, p < 0.001). Area Under Receiving Operator Curve (AUROC) for aggregated PEWS was 0.797 (95% CI 0.759 to 0.836, p < 0.001). The optimal PEWS using Youlden's Index was 5.

DISCUSSION

These data show PEWS (Scotland) to be a useful tool in a pre-hospital setting. A single set of physiological observations undertaken prior to arrival at hospital can identify a group of children at higher risk of an adverse in-hospital outcome. Paediatric care is becoming more specialised and focussed on a smaller number of centres. In this context, use of PEWS (Scotland) in the pre-hospital phase may allow changes to paediatric pre-hospital pathways to improve both admission to ICU and child mortality rates.

Do nurse-led critical care outreach services impact inpatient mortality rates?

BACKGROUND

Previous systematic reviews have assessed the effect of critical care outreach services, but none have focused solely on nurse-led services.

AIM

To perform a systematic review examining the impact of nurse-led critical care outreach services on inpatient mortality rates as the primary outcome. Secondary outcomes include arrest call rates and patient length of stay.

METHODS

A comprehensive search of several electronic databases was carried out, including the Cumulative Index to Nursing and Allied Health Literature and Medline. Non-catalogued literature was searched, and contact was made by e-mail with expert authors. All studies were in the English language, and although heterogeneous in design, only quantitative data were extracted for analysis. All included studies were assessed for quality using recognized quality appraisal tools. Meta-analysis was not possible because of heterogeneity.

RESULTS

Ten studies involving almost 72 000 participants were identified. The reduction in mortality rates with nurse-led critical care outreach services was reported to be 80%, but the statistical significance was low - four studies showed reductions ranging from 0·1% to 0·22%. Greater statistical significance was reported in arrest call rates, with two studies claiming decreased rates of 58·7% and 29·6%. Two studies reported a decrease in patient length of stay - the findings of a third study were equivocal. Half of the included studies scored poorly in terms of quality and validity, and all were single-centre studies, thereby limiting generalisability.

CONCLUSION

Nurse-led critical care outreach services demonstrate benefits in terms of patient care and reduction in adverse events. Higher-quality research, including multicentre randomized controlled trials, with meta-analysis is recommended.

RELEVANCE TO CLINICAL PRACTICE

Nurse-led critical care outreach services have the potential to improve patient outcomes. Uniformity of team composition and nomenclature would benefit data collection and reporting.

Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric Patients: The EPOCH Randomized Clinical Trial

IMPORTANCE

There is limited evidence that the use of severity of illness scores in pediatric patients can facilitate timely admission to the intensive care unit or improve patient outcomes.

OBJECTIVE

To determine the effect of the Bedside Paediatric Early Warning System (BedsidePEWS) on all-cause hospital mortality and late admission to the intensive care unit (ICU), cardiac arrest, and ICU resource use.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter cluster randomized trial of 21 hospitals located in 7 countries (Belgium, Canada, England, Ireland, Italy, New Zealand, and the Netherlands) that provided inpatient pediatric care for infants (gestational age ≥37 weeks) to teenagers (aged ≤18 years). Participating hospitals had continuous physician staffing and subspecialized pediatric services. Patient enrollment began on February 28, 2011, and ended on June 21, 2015. Follow-up ended on July 19, 2015.

INTERVENTIONS

The BedsidePEWS intervention (10 hospitals) was compared with usual care (no severity of illness score; 11 hospitals).

MAIN OUTCOMES AND MEASURES

The primary outcome was all-cause hospital mortality. The secondary outcome was a significant clinical deterioration event, which was defined as a composite outcome reflecting late ICU admission. Regression analyses accounted for hospital-level clustering and baseline rates.

RESULTS

Among 144 539 patient discharges at 21 randomized hospitals, there were 559 443 patient-days and 144 539 patients (100%) completed the trial. All-cause hospital mortality was 1.93 per 1000 patient discharges at hospitals with BedsidePEWS and 1.56 per 1000 patient discharges at hospitals with usual care (adjusted between-group rate difference, 0.01 [95% CI, -0.80 to 0.81 per 1000 patient discharges]; adjusted odds ratio, 1.01 [95% CI, 0.61 to 1.69]; P = .96). Significant clinical deterioration events occurred during 0.50 per 1000 patient-days at hospitals with BedsidePEWS vs 0.84 per 1000 patient-days at hospitals with usual care (adjusted between-group rate difference, -0.34 [95% CI, -0.73 to 0.05 per 1000 patient-days]; adjusted rate ratio, 0.77 [95% CI, 0.61 to 0.97]; P = .03).

CONCLUSIONS AND RELEVANCE

Implementation of the Bedside Paediatric Early Warning System compared with usual care did not significantly decrease all-cause mortality among hospitalized pediatric patients. These findings do not support the use of this system to reduce mortality.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01260831.

Improved hospital mortality with a low MET dose: the importance of a modified early warning score and communication tool

Rapid response systems have been mandated for the recognition and management of the deteriorating patient. Increasing medical emergency team (MET) dose may be associated with improved outcomes. Large numbers of MET calls may divert resources from the program providing the service unless additional personnel are provided. To describe the implementation and outcomes of a multifaceted rapid response system (RRS) in a teaching hospital, we conducted an observational study. The RRS consisted of the introduction of a MET together with 1) redesign of the ward observation chart with the vital sign variables colour-coded to identify variation from normal; 2) mandated minimum frequency of vital sign measurement; 3) three formal levels of escalation based on the degree of physiological instability as measured by a modified early warning score (MEWS); 4) COMPASS© education and e-learning package with a two-hour face-to-face small group tutorial; 5) practise in escalation and communication using the ISBAR (Identify, Situation, Background, Assessment, Response/Recommendation) communication tool. The primary outcome measures were all-cause hospital mortality rate and hospital standardised mortality ratio (HSMR) compared to peer hospitals calculated by the Health Round Table. There were 161,153 separations and 1,994 hospital deaths from July 2008 to December 2012. The MET call rate was 11.3 per 1000 separations in 2012. There was a decline in all-cause hospital mortality from 13.8 to 11 deaths/1000 separations. The HSMR decreased from 95.7 in 2008 to 66 in the second half of 2012 (below the three standard deviation control limit). A low MET dose may be associated with improved hospital mortality when combined with a MEWS and an intervention to improve communication.

Factors related to monitoring during admission of acute patients

Understanding the use of patient monitoring systems in emergency and acute facilities may help to identify reasons for failure to identify risk patients in these settings. Hence, we investigate factors related to the utilization of automated monitoring for patients admitted to an acute admission unit by introducing monitor load as the proportion between monitored time and length of stay. A cohort study of patients admitted and registered to patient monitors in the period from 10/10/2013 to 1/10/2014 at the acute admission unit of Odense University Hospital in Denmark. Admissions with at least one measurement were analyzed using quantile regression by looking at the impact of distance from nursing office, number of concurrent patients, wing type (medical/surgical), age, sex, comorbidities, and severity conditioned on how much patients were monitored during their admissions. We registered 11,848 admissions, of which we were able to link patient monitor readings to 3149 (26.6 %) with 50 % being monitored <1.4 % of total admission time. Distance from nursing office had little influence on patients monitored <10 % of their admission time. But for other patients, being positioned further away from the office reduced the level of monitoring. Higher levels of severity were related to higher degrees of monitoring, but being admitted to the surgical wing reduce how much patients were monitored, and periods with many concurrent patients lead to a small increase in monitoring. We found a significant variation concerning how much patients were monitored during admission to an acute admission unit. Our results point to potential patient safety improvements in clinical procedures, and advocate an awareness of how patient monitoring systems are utilized.

Delayed Recognition of Deterioration of Patients in General Wards Is Mostly Caused by Human Related Monitoring Failures: A Root Cause Analysis of Unplanned ICU Admissions

Background

An unplanned ICU admission of an inpatient is a serious adverse event (SAE). So far, no in depth-study has been performed to systematically analyse the root causes of unplanned ICU-admissions. The primary aim of this study was to identify the healthcare worker-, organisational-, technical,- disease- and patient- related causes that contribute to acute unplanned ICU admissions from general wards using a Root-Cause Analysis Tool called PRISMA-medical. Although a Track and Trigger System (MEWS) was introduced in our hospital a few years ago, it was implemented without a clear protocol. Therefore, the secondary aim was to assess the adherence to a Track and Trigger system to identify deterioration on general hospital wards in patients eventually transferred to the ICU.

Methods

Retrospective observational study in 49 consecutive adult patients acutely admitted to the Intensive Care Unit from a general nursing ward. 1. PRISMA-analysis on root causes of unplanned ICU admissions 2. Assessment of protocol adherence to the early warning score system.

Results

Out of 49 cases, 156 root causes were identified. The most frequent root causes were healthcare worker related (46%), which were mainly failures in monitoring the patient. They were followed by disease-related (45%), patient-related causes (7, 5%), and organisational root causes (3%). In only 40% of the patients vital parameters were monitored as was instructed by the doctor. 477 vital parameter sets were found in the 48 hours before ICU admission, in only 1% a correct MEWS was explicitly documented in the record.

Conclusions

This in-depth analysis demonstrates that almost half of the unplanned ICU admissions from the general ward had healthcare worker related root causes, mostly due to monitoring failures in clinically deteriorating patients. In order to reduce unplanned ICU admissions, improving the monitoring of patients is therefore warranted.

A Protocolised Once a Day Modified Early Warning Score (MEWS) Measurement Is an Appropriate Screening Tool for Major Adverse Events in a General Hospital Population

Background

The Modified Early Warning Score (MEWS) was developed to timely recognise clinically deteriorating hospitalised patients. However, the ability of the MEWS in predicting serious adverse events (SAEs) in a general hospital population has not been examined prospectively. The aims were to (1) analyse protocol adherence to a MEWS protocol in a real-life setting and (2) to determine the predictive value of protocolised daily MEWS measurement on SAEs: death, cardiac arrests, ICU-admissions and readmissions.

Methods

All adult patients admitted to 6 hospital wards in October and November 2015 were included. MEWS were checked each morning by the research team. For each critical score (MEWS ≥ 3), the clinical staff was inquired about the actions performed. 30-day follow-up for SAEs was performed to compare between patients with and without a critical score.

Results

1053 patients with 3673 vital parameter measurements were included, 200 (19.0%) had a critical score. The protocol adherence was 89.0%. 18.2% of MEWS were calculated wrongly. Patients with critical scores had significant higher rates of unplanned ICU admissions [7.0% vs 1.3%, p < 0.001], in-hospital mortality [6.0% vs 0.8%, p < 0.001], 30-day readmission rates [18.6% vs 10.8%, p < 0.05], and a longer length of stay [15.65 (SD: 15.7 days) vs 6.09 (SD: 6.9), p < 0.001]. Specificity of MEWS related to composite adverse events was 83% with a negative predicting value of 98.1%.

Conclusions

Protocol adherence was high, even though one-third of the critical scores were calculated wrongly. Patients with a MEWS ≥ 3 experienced significantly more adverse events. The negative predictive value of early morning MEWS < 3 was 98.1%, indicating the reliability of this score as a screening tool.

Incorporating Patient Acuity Rating Score Into Patient Handoffs and the Correlation With Rapid Responses and Unexpected ICU Transfers

Handoffs and rapid response team (RRT) activations have been a focus for quality improvement in hospital medicine. This study aimed to incorporate a previously used scoring system (1-7) for severity of illness on initial encounter as a handoff adjunct and to determine its impact on the number of RRTs and intensive care unit transfers. The Patient Acuity Rating (PAR) scale correlates with subsequent RRTs and transfers to a higher level of care, with higher scores leading to increased rates of RRTs and transfers. Patients who experienced an RRT at any time (mean score 4.69), within 24 hours (4.74), or an unplanned transfer (5.16) had higher PAR scores on assessment than those who did not (4.02; all P < .05). There was an increased likelihood of RRTs and transfers with scores of 6 or higher. There was no reduction in the quantity of RRTs or unplanned intensive care unit transfers comparing preintervention and postintervention data.

Patient risk profiling in acute medicine: the way forward?

BACKGROUND

The identification of high-risk patients could form a basis for targetted intervention following an emergency medical admission.

METHODS

All emergency admissions to our institution over 12 years (2002-13) were included. An Illness Severity method based on admission laboratory parameters, previously developed between 2002 and 2007, was investigated for the 2008-13 cohort. We compared the area under the receiver operating characteristic (AUROC) to predict a 30-day in-hospital death between the original and validating cohorts using logistic multiple variable analyses. We defined six risk subgroups, based on admission laboratory data and examined the frequency of 30-day in-hospital mortality within these subgroups.

RESULTS

About 66 933 admissions were recorded in 36 271 patients. Between 2002 and 2007, the 30-day in-hospital mortality was 11.3% but between 2008 and 2013 was 6.7% (P < 0.001). This represented an absolute risk reduction (ARR) of 4.6%, a relative risk reduction (RRR) of 41.0%, and a number needed to treat of 21.6. The laboratory model was similarly predictive in both cohorts-for 2002-07, the AUROC was 0.82 (95% CI 0.81, 0.82) and for 2008-13 was 0.82 (95% CI 0.81, 0.83). Two high-risk subgroups were identified within each cohort; for 2002-07, these contained 15.0 and 30.2% of admitted patients but 95.5% of in-hospital deaths. For 2008-13, these two groups contained 15.7 and 31.0% of admitted patients but 97.0% of in-hospital deaths.

CONCLUSION

A previously described laboratory score method, based on admission biochemistry, identified patients at high risk for an in-hospital death. Risk profiling at admission is feasible for emergency medical admissions and could offer a means to outcome improvement.

Demystifying theory and its use in improvement

The role and value of theory in improvement work in healthcare has been seriously underrecognised. We join others in proposing that more informed use of theory can strengthen improvement programmes and facilitate the evaluation of their effectiveness. Many professionals, including improvement practitioners, are unfortunately mystified-and alienated-by theory, which discourages them from using it in their work. In an effort to demystify theory we make the point in this paper that, far from being discretionary or superfluous, theory ('reason-giving'), both informal and formal, is intimately woven into virtually all human endeavour. We explore the special characteristics of grand, mid-range and programme theory; consider the consequences of misusing theory or failing to use it; review the process of developing and applying programme theory; examine some emerging criteria of 'good' theory; and emphasise the value, as well as the challenge, of combining informal experience-based theory with formal, publicly developed theory. We conclude that although informal theory is always at work in improvement, practitioners are often not aware of it or do not make it explicit. The germane issue for improvement practitioners, therefore, is not whether they use theory but whether they make explicit the particular theory or theories, informal and formal, they actually use.

Admission and discharge practices: High Dependency Unit audit outcome

This paper presents the findings of a retrospective audit of admission and discharge practices of a nurse led High Dependency Unit (Nurse Specials Unit) in Perth, Western Australia. The aim of the study was: to review the effectiveness of the inclusion and exclusion guidelines for patients admitted to the Nurse Special Unit (NSU); to identify characteristics of admitted patients; and to determine the level of adherence to admission protocols for documentation of patient condition, plan of care, medical and Clinical Nurse Consultant review. The sample comprised all patients admitted to the NSU from September 2004 - March 2005, excluding those (50) in the pilot study (n = 154). This audit revealed patients were primarily elderly and admitted for close nursing supervision. Inclusion and exclusion criterion were adhered to, however documentation of patient data, in relation to current status and plan of care, was poorly completed and frequently absent during intra hospital transfer. This lack of clear documentation poses a significant risk to the patient. Further research is required to determine strategies that result in appropriate and useful transfer documentation. In addition, the content of transfer data that permits continuity of care needs to be determined.

Organizational approaches to improving resuscitation effectiveness

Hemodynamic instability and shock are important causes of mortality worldwide. Improving outcomes for these patients through effective resuscitation is a key priority for the health system. This article discusses several organizational approaches to improving resuscitation effectiveness and outlines key areas for future research and development. The discussion is rooted in a conceptual model of effective resuscitation based on three domains: monitoring systems, response teams, and feedback mechanisms. Targeting each of these domains in a unified approach helps clinicians effectively treat deteriorating patients, ultimately improving outcomes for this high-risk patient group.

Do large-scale hospital- and system-wide interventions improve patient outcomes: a systematic review

BACKGROUND

While health care services are beginning to implement system-wide patient safety interventions, evidence on the efficacy of these interventions is sparse. We know that uptake can be variable, but we do not know the factors that affect uptake or how the interventions establish change and, in particular, whether they influence patient outcomes. We conducted a systematic review to identify how organisational and cultural factors mediate or are mediated by hospital-wide interventions, and to assess the effects of those factors on patient outcomes.

METHODS

A systematic review was conducted and reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Database searches were conducted using MEDLINE from 1946, CINAHL from 1991, EMBASE from 1947, Web of Science from 1934, PsycINFO from 1967, and Global Health from 1910 to September 2012. The Lancet, JAMA, BMJ, BMJ Quality and Safety, The New England Journal of Medicine and Implementation Science were also hand searched for relevant studies published over the last 5 years. Eligible studies were required to focus on organisational determinants of hospital- and system-wide interventions, and to provide patient outcome data before and after implementation of the intervention. Empirical, peer-reviewed studies reporting randomised and non-randomised controlled trials, observational, and controlled before and after studies were included in the review.

RESULTS

Six studies met the inclusion criteria. Improved outcomes were observed for studies where outcomes were measured at least two years after the intervention. Associations between organisational factors, intervention success and patient outcomes were undetermined: organisational culture and patient outcomes were rarely measured together, and measures for culture and outcome were not standardised.

CONCLUSIONS

Common findings show the difficulty of introducing large-scale interventions, and that effective leadership and clinical champions, adequate financial and educational resources, and dedicated promotional activities appear to be common factors in successful system-wide change.The protocol has been registered in the international prospective register of systematic reviews, PROSPERO (Registration No. CRD42103003050).

Documentation of limitation of medical therapy at the time of a rapid response team call

OBJECTIVES

The aims of the present study were to: (1) describe the documentation process of limitation of medical therapy (LMT) orders at the time of a rapid response team (RRT) call; and (2) compare documented LMT orders not associated with an RRT call (control, Group 1) with LMT orders documented at the time of an RRT call (Group 2).

METHODS

A descriptive study, over a 6-month period (February-August 2011), involving the review of the medical records of patients prospectively identified as either Group 1 or Group 2.

RESULTS

There were 994 RRT calls; of these, 50 patients (5%) had an LMT order documented by the RRT. A cardiac arrest was the trigger for the RRT for six patients (12%). Patients in Group 1 (n=50) and Group 2 were of similar median age (80.5 vs 78.5 years; P=0.30), LMTs were recorded at a similar time of day (15:30 vs 15:55 hours; P=0.52) and day of the week (weekend: 32% vs 35%; P=0.72). Comparing group 2 with Group 1, the RRT was less likely to document a not-for-resuscitation (NFR; 31 (62%) vs 49 (98%); P<0.01) or a not-for-ICU (NFICU; 18 (36%) vs 41 (82%); P<0.01) order, but more likely to document a not-for-RRT call (NFRRT; 31 (62%) vs 22 (44%); P=0.04) and modified RRT calling criteria (MRRT; 4 (8%) vs 0 (0%); P=0.04) orders. For Group 2 compared with Group 1 orders, involvement of the patient in the decision making process (9 (18%) vs 25 (50%); P<0.01) or the next of kin (29 (58%) vs 45 (90%); P<0.01) was documented less often.

CONCLUSIONS

Documentation of LMT orders at the time of an RRT call is less likely to include documented involvement of patients or their next of kin, and is more likely to be an NFRRT or MRRT order. These findings have implications for overall clinical governance. What is known about the topic? RRT are not infrequently involved in documenting LMT orders. What does this paper add? This is the first study in Australasia to look into the timing and circumstances surrounding the issuing of a NFR order during an RRT call. The study findings clarify the type of LMT orders documented by RRT and to what extent patients, their carers and senior medical staff are involved. What are the implications for practitioners? Our findings indicate that, in the setting of a rapid response system, there is a need to consider beyond the narrow interpretation of the NFR order, when a NFRRT may also be appropriate. This will require standardisation of such nomenclature, and training and education of those involved in documenting and interpreting such orders. Equally, it will require a different approach to the discussion with patients and their carers as to what the implications of an NFRRT order are. The findings also have significant implications as to the senior medical oversight of LMT, in particular for RRT, for whom it is their first encounter with such patients. Finally, the findings suggest that consideration be given to better delineating the documentation of the role of nursing staff when setting LMT orders.

Activation of a medical emergency team using an electronic medical recording-based screening system*

OBJECTIVES

To evaluate the efficacy of a medical emergency team activated using 24-hour monitoring by electronic medical record-based screening criteria followed by immediate intervention by a skilled team.

DESIGN

Retrospective cohort study.

SETTING

Academic tertiary care hospital with approximately 2,700 beds.

PATIENTS

A total of 3,030 events activated by a medical emergency team from March 1, 2008, to February 28, 2010.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

We collected data for all medical emergency team activations: patient characteristics, trigger type for medical emergency team (electronic medical record-based screening vs calling criteria), interventions during each event, outcomes of the medical emergency team intervention, and 28-day mortality after medical emergency team activation. We analyzed data for 2009, when the medical emergency team functioned 24 hours a day, 7 days a week (period 2), compared with that for 2008, when the medical emergency team functioned 12 hours a day, 7 days a week (period 1). The commonest cause of medical emergency team activation was respiratory distress (43.6%), and the medical emergency team performed early goal-directed therapy (21.3%), respiratory care (19.9%), and difficult airway management (12.3%). For patients on general wards, 51.3% (period 1) and 38.4% (period 2) of medical emergency team activations were triggered by the electronic medical record-based screening system (electronic medical record-triggered group). In 23.4%, activation occurred because of an abnormality in laboratory screening criteria. The commonest activation criterion from electronic medical record-based screening was respiratory rate (39.4%). Over half the patients were treated in the general ward, and one third of the patients were transferred to the ICU. The electronic medical record-triggered group had lower ICU admission with an odds ratio of 0.35 (95% CI, 0.22-0.55). In surgical patients, the electronic medical record-triggered group showed the lower 28-day mortality (10.5%) compared with the call-triggered group (26.7%) or the double-triggered group (33.3%) (odds ratio 0.365 with 95% CI, 0.154-0.867, p = 0.022).

CONCLUSIONS

We successful managed the medical emergency team with electronic medical record-based screening criteria and a skilled intervention team. The electronic medical record-triggered group had lower ICU admission than the call-triggered group or the double-triggered group. In surgical patients, the electronic medical record-triggered group showed better outcome than other groups.

Widely used track and trigger scores: are they ready for automation in practice?

INTRODUCTION

Early Warning Scores (EWS) are widely used for early recognition of patient deterioration. Automated alarm/alerts have been recommended as a desirable characteristic for detection systems of patient deterioration. We undertook a comparative analysis of performance characteristics of common EWS methods to assess how they would function if automated.

METHODS

We evaluated the most widely used EWS systems (MEWS, SEWS, GMEWS, Worthing, ViEWS and NEWS) and the Rapid Response Team (RRT) activation criteria in use in our institution. We compared their ability to predict the composite outcome of Resuscitation call, RRS activation or unplanned transfer to the ICU, in a time-dependent manner (3, 8, 12, 24 and 36 h after the observation) by determining the sensitivity, specificity and positive predictive values (PPV). We used a large vital signs database (6,948,689 unique time points) from 34,898 unique consecutive hospitalized patients.

RESULTS

PPVs ranged from less than 0.01 (Worthing, 3 h) to 0.21 (GMEWS, 36 h). Sensitivity ranged from 0.07 (GMEWS, 3 h) to 0.75 (ViEWS, 36 h). Used in an automated fashion, these would correspond to 1040-215,020 false positive alerts per year.

CONCLUSIONS

When the evaluation is performed in a time-sensitive manner, the most widely used weighted track-and-trigger scores do not offer good predictive capabilities for use as criteria for an automated alarm system. For the implementation of an automated alarm system, better criteria need to be developed and validated before implementation.

Benchmarking the use of a rapid response team by surgical services at a tertiary care hospital

BACKGROUND

Rapid response teams (RRT) are used to prevent adverse events in patients with acute clinical deterioration, and to save costs of unnecessary transfer in patients with lower-acuity problems. However, determining the optimal use of RRT services is challenging. One method of benchmarking performance is to determine whether a department's event rate is commensurate with its volume and acuity.

STUDY DESIGN

Using admissions between 2009 and 2011 to 18 distinct surgical services at a tertiary care center, we developed logistic regression models to predict RRT activation, accounting for days at-risk for RRT and patient acuity, using claims modifiers for risk of mortality (ROM) and severity of illness (SOI). The model was used to compute observed-to-expected (O/E) RRT use by service.

RESULTS

Of 45,651 admissions, 728 (1.6%, or 3.2 per 1,000 inpatient days) resulted in 1 or more RRT activations. Use varied widely across services (0.4% to 6.2% of admissions; 1.39 to 8.73 per 1,000 inpatient days, unadjusted). In the multivariable model, the greatest contributors to the likelihood of RRT were days at risk, SOI, and ROM. The O/E RRT use ranged from 0.32 to 2.82 across services, with 8 services having an observed value that was significantly higher or lower than predicted by the model.

CONCLUSIONS

We developed a tool for identifying outlying use of an important institutional medical resource. The O/E computation provides a starting point for further investigation into the reasons for variability among services, and a benchmark for quality and process improvement efforts in patient safety.

The Cardiac Children's Hospital Early Warning Score (C-CHEWS)

Inpatient pediatric cardiovascular patients have higher rates of cardiopulmonary arrests than other hospitalized children. Pediatric early warning scoring tools have helped to provide early identification and treatment to hospitalized children experiencing deterioration thus preventing arrests from occurring. However, the tools have rarely been used and have not been validated in the pediatric cardiac population. This paper describes the modification of a pediatric early warning scoring system for cardiovascular patients, the implementation of the tool, and its companion escalation of care algorithm on an inpatient pediatric cardiovascular unit.

The role of organisational and cultural factors in the implementation of system-wide interventions in acute hospitals to improve patient outcomes: protocol for a systematic literature review

INTRODUCTION

Little is known about the role of the organisational culture in the success and sustainability of the hospital-wide interventions, and how local culture affects patient outcomes in acute hospitals.

METHODS AND ANALYSIS

A systematic literature review will be conducted to identify organisational factors influencing hospital-wide interventions and patient outcomes. A search of English language articles will be performed in MEDLINE, CINAHL, EMBASE, Web of Science, PsychInfo and Global Health databases using Medical Subject Headings and keywords. Randomised controlled trials, quasi-randomised trials, controlled before and after design studies and interrupted time-series analysis studies will be included. 'Grey literature' will be excluded, however peer-reviewed journals that are likely to publish relevant studies (JAMA, BMJ, BMJ Quality and Safety, Lancet and New England Journal of Medicine and Implementation Science) will be hand searched for the last 5 years. Two reviewers will independently undertake a title and abstract review using inclusion and exclusion criteria. Studies will be excluded only after discussion between at least two reviewers, who will assess and agree on the inclusion, risk of bias and quality rating of the studies. One author will extract summary descriptive data from these studies; the other author will review this documentation for accuracy and completeness.

RESULTS

It is likely that the studies will be heterogeneous in nature, therefore a narrative synthesis of the findings will be conducted.

CONCLUSIONS

We will discuss characteristics of the studies and stratify the results according to the type of hospital-wide interventions, organisational factors associated with them and outcomes measured.

A review of rapid response team activation parameters in New Zealand hospitals

OBJECTIVE

To review current systems for recognising and responding to clinically deteriorating patients in all New Zealand public hospitals.

DESIGN

A cross-sectional study of recognition and response systems in all New Zealand public hospitals was conducted in October 2011. Copies of all current vital sign charts and/or relevant policies were requested. These were examined for vital sign based recognition and response systems. The charts or policies were also used to determine the type of system in use and the vital sign parameters and trigger thresholds that provoke a call to the rapid response team.

SETTING

All New Zealand District Health Boards (DHBs).

MAIN OUTCOME MEASURES

Physiological parameters used to trigger rapid response, the weighting of any early warning score assigned to them, type of system used, values of physiological derangement that trigger maximal system response.

RESULTS

All DHBs use aggregate scoring systems to assess deterioration and respond. A total of 9 different physiological parameters were scored with most charts (21%) scoring 6 different parameters. All scored respiratory rate, heart rate, systolic blood pressure and conscious level. 86% scored oliguria, 14% polyuria, 33% oxygen saturation and 24% oxygen administration. All systems used either aggregate scores or a single extreme parameter to elicit a maximal system response. The extremes of physiological derangement to which scores were assigned varied greatly with bradypnoea having the greatest range for what was considered grossly abnormal.

CONCLUSION

A large variance exists in the criteria used to detect deteriorating patients within New Zealand hospitals. Standardising both the vital signs chart and escalation criteria is likely to be of significant benefit in the early detection of and response to patient deterioration.

Early recognition of acutely deteriorating patients in non-intensive care units: assessment of an innovative monitoring technology

BACKGROUND

Continuous vital sign monitoring has the potential to detect early clinical deterioration. While commonly employed in the intensive care unit (ICU), accurate and noninvasive monitoring technology suitable for floor patients has yet to be used reliably.

OBJECTIVE

To establish the accuracy of the Earlysense continuous monitoring system in predicting clinical deterioration.

DESIGN

Noninterventional prospective study with retrospective data analysis.

SETTING

Two medical wards in 2 academic medical centers.

PATIENTS

Patients admitted to a medical ward with a diagnosis of an acute respiratory condition.

INTERVENTION

Enrolled patients were monitored for heart rate (HR) and respiration rate (RR) by the Earlysense monitor with the alerts turned off.

MEASUREMENTS

Retrospective analysis of vital sign data was performed on a derivation cohort to identify optimal cutoffs for threshold and 24-hour trend alerts. This was internally validated through correlation with clinical events recognized through chart review.

RESULTS

Of 113 patients included in the study, 9 suffered major clinical deterioration. Alerts were found to be infrequent (2.7 and 0.2 alerts per patient-day for threshold and trend alert, respectively). For the threshold alerts, sensitivity and specificity in predicting deterioration was found to be 82% and 67%, respectively, for HR and 64% and 81%, respectively, for RR. For trend alerts, sensitivity and specificity were 78% and 90% for HR, and 100% and 64% for RR, respectively.

CONCLUSIONS

The Earlysense monitor was able to continuously measure RR and HR, providing low alert frequency. The current study provides data supporting the ability of this system to accurately predict patient deterioration.

Medical emergency team response for the non-hospitalized patient

OBJECTIVES

Rapid response systems (RRS) evolved to care for deteriorating hospitalized patients outside of the ICU. However, emergent critical care needs occur suddenly and unexpectedly throughout the hospital campus, including areas with non-hospitalized persons. The efficacy of RRS in this population has not yet been described or tested. We hypothesize that non-hospitalized patients accrue minimal benefit from ICU physician participation in the RRS.

DESIGN

A retrospective review of all RRS events in non-hospitalized patients for a 28 month period was performed in a large, urban university medical center. Location, patient type and age, activation trigger, interventions performed, duration of event and disposition were recorded. Admission diagnosis and length of stay were also recorded for patients admitted to the hospital.

SETTING

Academic medical center.

PATIENTS

Non-hospitalized persons requiring evaluation by the medical emergency team.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were a total of 1778 RRS activations during the study period. 232 (13%) of activations were for non-hospitalized patients. The patient cohort consisted of outpatients, visitors, and staff. Triggers for RRS activation were neurologic change (42%), cardiac (27%), respiratory (16%), and staff concerns (16%). The mean duration of the response was 38 min. The most common interventions performed included administration of oxygen (46%), intravenous fluids (13%) and dextrose (6%). 82% of patients were taken to the emergency department and 32% of the ED cohort were admitted to the hospital.

CONCLUSIONS

Perceived emergencies in non-hospitalized patients occur commonly but require minimal emergent intervention. Restriction of critical care physician involvement to inpatient deteriorations should be considered when designing a RRS. Future studies are needed to evaluate the utility of non-physician provider led rapid response teams with protocol-driven interventions for similar populations.

Patient acuity rating: quantifying clinical judgment regarding inpatient stability

BACKGROUND

New resident work-hour restrictions are expected to result in further increases in the number of handoffs between inpatient care providers, a known risk factor for poor outcomes. Strategies for improving the accuracy and efficiency of provider sign-outs are needed.

OBJECTIVE

To develop and test a judgment-based scale for conveying the risk of clinical deterioration.

DESIGN

Prospective observational study.

SETTING

University teaching hospital.

SUBJECTS

Internal medicine clinicians and patients.

MEASUREMENTS

The Patient Acuity Rating (PAR), a 7-point Likert score representing the likelihood of a patient experiencing a cardiac arrest or intensive care unit (ICU) transfer within the next 24 hours, was obtained from physicians and midlevel practitioners at the time of sign-out. Cross-covering physicians were blinded to the results, which were subsequently correlated with outcomes.

RESULTS

Forty eligible clinicians consented to participate, providing 6034 individual scores on 3419 patient-days. Seventy-four patient-days resulted in cardiac arrest or ICU transfer within 24 hours. The average PAR was 3 ± 1 and yielded an area under the receiver operator characteristics curve (AUROC) of 0.82. Provider-specific AUROC values ranged from 0.69 for residents to 0.85 for attendings (P = 0.01). Interns and midlevels did not differ significantly from the other groups. A PAR of 4 or higher corresponded to a sensitivity of 82% and a specificity of 68% for predicting cardiac arrest or ICU transfer in the next 24 hours.

CONCLUSIONS

Clinical judgment regarding patient stability can be reliably quantified in a simple score with the potential for efficiently conveying complex assessments of at-risk patients during handoffs between healthcare members.

[In-hospital emergencies at a surgical university hospital]

BACKGROUND

Emergency treatment and resuscitation within hospitals are managed by so-called medical emergency teams (MET). The present study examined the circumstances, number, initial treatment and further hospital course of in-hospital emergency cases at a level 1 university hospital.

METHODS

A retrospective study of in-hospital emergencies on the surgical wards of a university hospital including all non-intensive care areas from January 2007 to June 2010 was carried out. A self-developed documentation protocol which was introduced in 2006 was used by the MET to document general patient characteristics and details of the emergency treatment. These data included the place where the emergency situation arose, the patient's assignment to a surgical discipline, a detailed description of the emergency situation, the effectiveness of basic life support measures as well as the further hospital course of the patient.

RESULTS

A total of 235 emergency cases were documented within the study period of 3.5 years. The frequency of in-hospital emergencies was 4/1,000 admitted patients per year. Cardiac arrest was encountered in 31,5%. Out of all patients 54,5% were admitted to an intensive care unit.

CONCLUSION

The tasks of a MET at a surgical university hospital go beyond mere cardiopulmonary resuscitation. Emergency cases within the full spectrum of perioperative complications are encountered. Further multicenter studies with standardized protocols are required to analyze the management of German in-hospital emergencies.

Clinical emergencies and outcomes in patients admitted to a surgical versus medical service

BACKGROUND

The merit of rapid response systems (RRSs) remains controversial. A tailored approach to specific groups may increase the efficacy of these teams. The purpose of this study was to compare differences in triggers for RRS activation, interventions, and outcomes in patients on medical and surgical services.

METHODS

A retrospective review RRS events was performed. The incidence of out of ICU cardiac arrests and hospital mortality were compared 2 years prior to and following RRS implementation. Call trigger, interventions, and disposition between medical and surgical patients were compared over a 15 month period.

RESULTS

Out of ICU cardiac arrest was significantly more prevalent in the medical group both before and after implementation of RRS. The out of ICU cardiac arrest rate decreased 32% in the surgical group (p=0.05) but hospital mortality did not change. Out of ICU cardiac arrest decreased 40% in the medical group (p<0.001) and hospital mortality decreased 25% (p<0.001) following RRS implementation. There were 1082 RRS activations, 286 surgical and 796 medical. Surgical patients were more likely to have received sedation within 24 h of evaluation (14% vs. 4%, p<0.001). The majority of patients in both cohorts were discharged alive.

CONCLUSION

Implementation of a RRS had greater impact on reduction of out of ICU cardiac arrest and mortality in medical inpatients. Triggers for activation and interventions were similar between groups; however, surgical patients demonstrated substantial risk for decompensation within the first 24 h following operation. More research is needed to evaluate the disproportionate benefit observed between cohorts.

Intensive Care Medicine: Where We Are and Where We Want To Go?

Intensive care medicine can be defined as the science and art of detecting and managing patients with impending or established critical illness, in order to prevent further deterioration and revert the disease process or its consequences, so as to achieve the best possible outcomes.