Anticipating events of in-hospital cardiac arrest

Wearable wireless continuous vital signs monitoring on the general ward

PURPOSE OF REVIEW

Wearable wireless sensors for continuous vital signs monitoring (CVSM) offer the potential for early identification of patient deterioration, especially in low-intensity care settings like general wards. This study aims to review advances in wearable CVSM - with a focus on the general ward - highlighting the technological characteristics of CVSM systems, user perspectives and impact on patient outcomes by exploring recent evidence.

RECENT FINDINGS

The accuracy of wearable sensors measuring vital signs exhibits variability, especially notable in ambulatory patients within hospital settings, and standard validation protocols are lacking. Usability of CMVS systems is critical for nurses and patients, highlighting the need for easy-to-use wearable sensors, and expansion of the number of measured vital signs. Current software systems lack integration with hospital IT infrastructures and workflow automation. Imperative enhancements involve nurse-friendly, less intrusive alarm strategies, and advanced decision support systems. Despite observed reductions in ICU admissions and Rapid Response Team calls, the impact on patient outcomes lacks robust statistical significance.

SUMMARY

Widespread implementation of CVSM systems on the general ward and potentially outside the hospital seems inevitable. Despite the theoretical benefits of CVSM systems in improving clinical outcomes, and supporting nursing care by optimizing clinical workflow efficiency, the demonstrated effects in clinical practice are mixed. This review highlights the existing challenges related to data quality, usability, implementation, integration, interpretation, and user perspectives, as well as the need for robust evidence to support their impact on patient outcomes, workflow and cost-effectiveness.

Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts

RATIONALE

Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration.

OBJECTIVES

We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU.

DESIGN

A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a 'static' score of the patient's condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient's risk of deterioration throughout their hospital stay.

SETTING

Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model.

PARTICIPANTS

A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort.

RESULTS

Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653).

CONCLUSIONS

We showed that a scoring system incorporating data from a patient's stay in the ICU has better performance than commonly used EWS systems based on vital signs alone.

TRIAL REGISTRATION NUMBER

ISRCTN32008295.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients' circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6,  ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772-0.993), but lower for the SAEs (AUROC: 0.594-0.611). The time of early warning for the EWS events were 2.8-5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.

Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System

BACKGROUND

In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system.

STUDY DESIGN

This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs).

RESULTS

Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K], p < 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2], p < 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p < 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K]); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0], p < 0.001).

CONCLUSIONS

Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.

Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit

PURPOSE

The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using the trends of four easy-to-collect vital signs.

MATERIALS AND METHODS

The primary training cohort included 1968 patients at the Veterans Health Service Medical Center. The external validation cohort comprised 409 patients at Seoul National University Hospital. Datasets of heart rate, systolic blood pressure, diastolic blood pressure, and peripheral capillary oxygen saturation (SpO2) measured every hour for 10 h were used. The performances of mortality prediction models generated using five machine learning algorithms, Random Forest (RF), XGboost, perceptron, convolutional neural network, and Long Short-Term Memory, were calculated and compared using area under the receiver operating characteristic curve (AUROC) values and an external validation dataset.

RESULTS

The machine learning model generated using the RF algorithm showed the best performance. Its AUROC was 0.922, which is much better than the 0.8408 of the Acute Physiology and Chronic Health Evaluation II. The machine learning model developed using SpO2 showed the best performance (AUROC, 0.89).

CONCLUSIONS

This simple yet powerful new mortality prediction model could be useful for early detection of probable mortality and appropriate medical intervention, especially in rapidly deteriorating patients.

Exercise in Futility or do CART or MEWS Prevent Errors?

How to cite this article: Rao RMG. Exercise in Futility or do CART or MEWS Prevent Errors? Indian J Crit Care Med 2022;26(7):765-766.

Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients

Background

Patients undergoing major surgery are at risk of complications, so‐called serious adverse events (SAE). Continuous monitoring may detect deteriorating patients by recording abnormal vital signs. We aimed to assess the association between abnormal vital signs inspired by Early Warning Score thresholds and subsequent SAEs in patients undergoing major abdominal surgery.

Methods

Prospective observational cohort study continuously monitoring heart rate, respiratory rate, peripheral oxygen saturation, and blood pressure for up to 96 h in 500 postoperative patients admitted to the general ward. Exposure variables were vital sign abnormalities, primary outcome was any serious adverse event occurring within 30 postoperative days. The primary analysis investigated the association between exposure variables per 24 h and subsequent serious adverse events.

Results

Serious adverse events occurred in 37% of patients, with 38% occurring during monitoring. Among patients with SAE during monitoring, the median duration of vital sign abnormalities was 272 min (IQR 110–447), compared to 259 min (IQR 153–394) in patients with SAE after monitoring and 261 min (IQR 132–468) in the patients without any SAE (p = .62 for all three group comparisons). Episodes of heart rate ≥110 bpm occurred in 16%, 7.1%, and 3.9% of patients in the time before SAE during monitoring, after monitoring, and without SAE, respectively (p < .002). Patients with SAE after monitoring experienced more episodes of hypotension ≤90 mm Hg/24 h (p = .001).

Conclusion

Overall duration of vital sign abnormalities at current thresholds were not significantly associated with subsequent serious adverse events, but more patients with tachycardia and hypotension had subsequent serious adverse events.

Trial registration

Clinicaltrials.gov, identifier NCT03491137.

Global variation in the incidence and outcome of emergency medical services witnessed out-of-hospital cardiac arrest: A systematic review and meta-analysis

AIM OF THE REVIEW

To examine global variation in the incidence and outcomes of emergency medical services (EMS) witnessed out-of-hospital cardiac arrest (OHCA).

DATA SOURCES

We systematically reviewed four electronic databases for studies between 1990 and 5th April 2021 reporting EMS-witnessed OHCA populations. Studies were included if they reported sufficient data to calculate the primary outcome of survival to hospital discharge or 30-day survival. Random-effects models were used to pool incidence and survival outcomes, and meta-regression was used to examine sources of heterogeneity. Study quality was appraised using the Joanna Briggs Institute critical appraisal tools.

RESULTS

The search returned 1178 non-duplicate titles of which 66 articles comprising 133,981 EMS-witnessed patients treated by EMS across 33 countries were included. All but one study was observational and only 12 studies (18%) were deemed to be at low risk of bias. The pooled incidence of EMS-treated cases was 4.1 per 100,000 person-years (95% CI: 3.5, 4.7), varying almost 4-fold across continents. The pooled proportion of survivors to hospital discharge or 30-days was 20% overall (95% CI: 18%, 22%; I² = 98%), 43% (95% CI: 37%, 49%; I² = 94%) for initial shockable rhythms and 6% (95% CI: 5%, 8%; I² = 79%) for initial non-shockable rhythms. In the meta-regression analysis, only region and aetiology were significantly associated with survival. When compared to studies from North America, pooled survival was significantly higher in studies from Europe (14% vs. 26%; p = 0.04) and Australasia (14% vs. 31%, p < 0.001).

CONCLUSION

We identified significant global variation in the incidence and survival outcome of EMS-witnessed OHCA. Further research is needed to understand the factors contributing to these variations.

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

BACKGROUND

The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change.

OBJECTIVES

To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions.

POPULATION

There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing.

MODEL

A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression.

RESULTS

Discrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R² was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors.

CONCLUSIONS

The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.

Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit

Objectives

Predictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers.

Methods

This retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts’ eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to those generated by an automated system.

Results

The automated system was able to rapidly and efficiently tag the complete database utilizing available clinical data. The overall agreement rate between the automated system and the clinicians for respiratory and hemodynamic deterioration tags was 89.4% and 87.1%, respectively. The automatic system did not add substantial variability beyond that seen among the reviewers.

Conclusions

We demonstrated that a simple rule-based tagging system could provide a rapid and accurate tool for mass tagging of a compound database. These types of tagging systems may replace human reviewers and save considerable resources when trying to create a validated, labeled database used to train artificial intelligence algorithms. The ability to harness the power of artificial intelligence depends on efficient clinical validation of targeted conditions; hence, these systems and the methodology used to validate them are crucial.

Development and validation of early warning score system: A systematic literature review

OBJECTIVES

This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies.

METHODOLOGY

A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy.

RESULTS

A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database.

CONCLUSION

This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.

Opportunities for machine learning to improve surgical ward safety

BACKGROUND

Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety.

DATA SOURCES

Fifty-eight articles from Cochrane Library, EMBASE, and PubMed databases were included.

CONCLUSIONS

Only 15-20% of patients suffering ward arrest survive. In most cases, subtle signs of instability often occur prior to critical illness and arrest, and underlying pathology is reversible. Coarse risk assessments lead to under-triage of high-risk patients to wards, where surveillance for complications depends on time-consuming manual review of health records, infrequent patient assessments, prediction models that lack accuracy and autonomy, and biased, error-prone decision-making. Streaming electronic heath record data, wearable continuous monitors, and recent advances in deep learning and reinforcement learning can promote efficient and accurate risk assessments, earlier recognition of instability, and better decisions regarding diagnosis and treatment of reversible underlying pathology.

Is my patient ready for a safe transfer to a lower-intensity care setting? Nursing complexity as an independent predictor of adverse events risk after ICU discharge

BACKGROUND

Patients discharged from intensive care units (ICUs) are at risk for adverse events (AEs). Establishing safe discharge criteria is challenging. No available criteria consider nursing complexity among risk factors.

OBJECTIVES

To investigate whether nursing complexity upon ICU discharge is an independent predictor for AEs.

METHODS

Prospective observational study. The Patient Acuity and Complexity Score (PACS) was developed to measure nursing complexity. Its predictive power for AEs was tested using multivariate regression analysis.

RESULTS

The final regression model showed a very-good discrimination power (AUC 0.881; p<0.001) for identifying patients who experienced AEs. Age, ICU admission reason, PACS, cough strength, PaCO₂, serum creatinine and sodium, and transfer to Internal Medicine showed to be predictive of AEs. Exceeding the identified PACS threshold increased by 3.3 times the AEs risk.

CONCLUSIONS

The level of nursing complexity independently predicts AEs risk and should be considered in establishing patient's eligibility for a safe ICU discharge.

Analyse des courriers de plainte adressés au service d’urgence d’un centre hospitalier général

Objectif : L’objectif principal de notre travail était d’analyser les courriers de plainte (CP) adressés à un service d’urgence (SU). L’objectif secondaire était de déterminer s’il existait une corrélation entre CP et temporalité de passage au SU.

Matériel et méthodes : Étude rétrospective monocentrique sur six ans portant sur les CP concernant les prises en charge en SU.

Résultats : Quatre-vingt-un CP ont été analysés, la moyenne annuelle et l’incidence étaient de 13,5 CP/an et de 5,5 CP/ 10 000 passages. L’incidence était de 3,5 CP/10 000 passages pour les enfants vs 6,1 CP/10 000 passages pour les adultes (p = 0,11). Le plaignant était la famille dans 44 CP (55 %) et le patient dans 34 CP (42 %). Il n’y avait pas de différence significative entre le jour et la nuit (5,7 CP/10 000 vs 4,8 CP/ 10 000 passages ; p = 0,57) ni entre horaires de garde et horaires hors garde (5,6 CP/10 000 vs 5,2 CP/10 000 passages ; p = 0,78). Nous avons dénombré 133 doléances, avec une cause médicale dans 64 CP (48 %), une cause organisationnelle dans 44 CP (33 %), une cause relationnelle dans 25 CP (19 %). Une indemnisation financière a été demandée dans 13 CP (16 %) et a été accordée pour deux dossiers (2 %). Aucun CP n’a entraîné de poursuite en justice.

Conclusion : L’incidence des CP de notre SU reste dans la moyenne basse des données retrouvées au niveau national et sans relation avec la temporalité du passage. L’analyse des CP permet d’améliorer la qualité de la prise en charge et de proposer des mesures correctives en relation avec la commission des usagers.

Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient Deterioration

OBJECTIVES

Previous studies have looked at National Early Warning Score performance in predicting in-hospital deterioration and death, but data are lacking with respect to patient outcomes following implementation of National Early Warning Score. We sought to determine the effectiveness of National Early Warning Score implementation on predicting and preventing patient deterioration in a clinical setting.

DESIGN

Retrospective cohort study.

SETTING

Tertiary care academic facility and a community hospital.

PATIENTS

Patients 18 years old or older hospitalized from March 1, 2014, to February 28, 2015, during preimplementation of National Early Warning Score to August 1, 2015, to July 31, 2016, after National Early Warning Score was implemented.

INTERVENTIONS

Implementation of National Early Warning Score within the electronic health record and associated best practice alert.

MEASUREMENTS AND MAIN RESULTS

In this study of 85,322 patients (42,402 patients pre-National Early Warning Score and 42,920 patients post-National Early Warning Score implementation), the primary outcome of rate of ICU transfer or death did not change after National Early Warning Score implementation, with adjusted hazard ratio of 0.94 (0.84-1.05) and 0.90 (0.77-1.05) at our academic and community hospital, respectively. In total, 175,357 best practice advisories fired during the study period, with the best practice advisory performing better at the community hospital than the academic at predicting an event within 12 hours 7.4% versus 2.2% of the time, respectively. Retraining National Early Warning Score with newly generated hospital-specific coefficients improved model performance.

CONCLUSIONS

At both our academic and community hospital, National Early Warning Score had poor performance characteristics and was generally ignored by frontline nursing staff. As a result, National Early Warning Score implementation had no appreciable impact on defined clinical outcomes. Refitting of the model using site-specific data improved performance and supports validating predictive models on local data.

A deep learning model for real-time mortality prediction in critically ill children

Background

The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units.

Methods

Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center.

Results

Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts.

Conclusions

PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2561-z) contains supplementary material, which is available to authorized users.

Clinical deterioration: A concept analysis

AIMS AND OBJECTIVES

To present a concept analysis of clinical deterioration and introduce an operational definition.

BACKGROUND

Hospitalised patients who endure cardiopulmonary arrest and unplanned intensive care unit admissions often exhibit physiological signs preceding these events. Clinical deterioration not promptly recognised can result in increased patient morbidity and mortality. A barrier to recognising and responding to clinical deterioration stems from practice variations among healthcare clinicians.

DESIGN

Concept analysis.

METHODS

Eight-step method of concept analysis proposed by Walker and Avant.

RESULTS

Defining attributes include dynamic state, decompensation and objective and subjective determination. Antecedents identified include clinical state, susceptibility, pathogenesis and adverse event. Increased mortality, resuscitation, implementation of higher level of care and prolonged hospital admission were the consequences identified. Defining attributes, antecedents and consequences identified led to an operational definition of clinical deterioration as a dynamic state experienced by a patient compromising hemodynamic stability, marked by physiological decompensation accompanied by subjective or objective findings.

CONCLUSIONS

Clinical deterioration is a key contributor to inpatient mortality, and its recognition is often underpinned by contextual factors and practice variances. Variation in the uniformity of the concept of clinical deterioration causes a gap in knowledge and necessitated clarification of this phenomenon for nursing research and practice.

RELEVANCE TO CLINICAL PRACTICE

Identifying and intervening on clinical deterioration plays a vital role in the inpatient setting demonstrated by the dynamic nature of a patients' condition during hospitalisation. It is anticipated that this concept analysis on clinical deterioration will contribute to further identification of clinically modifiable risk factors and accompanying interventions to prevent clinical deterioration in the inpatient setting.

Detection of physiological deterioration by the SNAP40 wearable device compared to standard monitoring devices in the emergency department: the SNAP40-ED study

BACKGROUND

In recent years, there has been increasing focus on the earlier detection of deterioration in the clinical condition of hospital patients with the aim of instigating earlier treatment to reverse this deterioration and prevent adverse outcomes. This is especially important in the ED, a dynamic environment with large volumes of undifferentiated patients, which carries inherent patient risk. SNAP40 is an innovative medical-grade device that can be worn on the upper arm that continuously monitors patients' vital signs including relative changes in systolic blood pressure, respiratory rate, heart rate, movement, blood oxygen saturation and temperature. It uses automated risk analysis to potentially allow clinical staff to easily and quickly identify high-risk patients. The aim of this study is to investigate whether the SNAP40 device is able to identify deterioration in the vital sign physiology of an ED patient earlier than current standard monitoring and observation charting techniques.

METHODS/DESIGN

Single centre, teaching hospital ED open label, prospective, observational cohort study recruiting 250 high acuity participants aged 16 years or over presenting to the ED. Participants will be approached and enrolled in the ED and after consent will have the SNAP40 wearable monitoring device attached which will be used alongside standard care monitoring. Participants will be observed throughout their time in the ED. Any SNAP40 device alarm, standard monitoring alarms or standard practice vital sign observations indicating a deterioration in a patient's vital sign physiology (defined as an increase in NEWS score) will be recorded. Primary outcome is time to detection of deterioration. Secondary outcomes include staff time spent performing observations and responding to standard monitoring alarms, clinical escalation of care when deterioration is detected and participants and staff rating of experience of both SNAP40 and current monitoring.

DISCUSSION

The SNAP40-ED study aims to recruit 250 patients. It will be the first study to compare the ability of a novel ambulatory monitoring device to detect deterioration compared to standard care in the ED. It may allow the earlier detection of deterioration in the clinical condition of ED patients and therefore earlier treatment to reverse this deterioration and prevent adverse outcomes.

TRIAL REGISTRATION

NCT03179267 ClinicalTrials.gov. Registered on June 17, 2017.

Correlation of the predictive ability of early warning metrics and mortality for cardiac arrest patients receiving in-hospital Advanced Cardiovascular Life Support

BACKGROUND

The Modified Early Warning Score (MEWS) helps identify patients experiencing a decline in physiological parameters that indicate risk for cardiac arrest (CA).

OBJECTIVES

To assess the association between MEWS values and patient survival following in-hospital CA.

METHODS

Retrospective cohort study of patients who experienced in-hospital CA. The relationship between CA survival and MEWS values as well as other risk factors such as age, gender and type of electrographic cardiac rhythms was analyzed using logistic regression.

RESULTS

Survival rate to hospital discharge was 21%. Strong predictors for survival were MEWS values at hospital admission (p < .002), younger age (p < .005), ventricular fibrillation (p < .0001), and ventricular tachycardia (p < .0001). Gender and MEWS 4 hours prior to CA were not significantly associated with survival.

CONCLUSIONS

Survival following CA was significantly associated with MEWS at hospital admission but not 4 hours prior to CA. The type of cardiac rhythm and age were also predictive of survival.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU

OBJECTIVES

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data.

DESIGN

Retrospective cohort study.

SETTING

Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients.

SUBJECTS

Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve.

CONCLUSIONS

Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

The effect of a staged, emergency department specific rapid response system on reporting of clinical deterioration

BACKGROUND

Despite emerging evidence regarding clinical deterioration in emergency department (ED) patients, the widespread uptake of rapid response systems (RRS) in EDs has been limited.

AIMS

To evaluate the effect of an ED RRS on reporting of clinical deterioration and determine if there were differences between patients who did, and did not, deteriorate during ED care.

METHODS

A retrospective cross sectional design was used to conduct this single site study in Melbourne, Australia. Stratified random sampling identified 50 patients with shortness of breath, chest pain or abdominal pain per each year studied (2009-2012) giving a total of 600 patients. The intervention was an ED RRS implemented in stages.

RESULTS

The frequency of clinical deterioration was 14.8% (318 episodes/89 patients). Unreported deterioration decreased each year (86.7%; 68.8%; 55.3%; 54.0%, p=0.141). Patients who deteriorated during ED care had a longer median ED length of stay (2.8h; p<0.001), were 31.9% more likely to need hospital admission (p<0.001) and 4.9% more likely to die in hospital (p=0.044).

CONCLUSIONS

A staged ED specific RRS decreased the frequency of unreported clinical deterioration. Controlled multi-site studies of ED specific RRSs are needed to examine the effect of formal ED RRSs on patient outcomes.

Modified Early Warning System improves patient safety and clinical outcomes in an academic community hospital

Background and objective

Severe adverse events such as cardiac arrest and death are often heralded by abnormal vital signs hours before the event. This necessitates an organized track and trigger approach of early recognition and response to subtle changes in a patient’s condition. The Modified Early Warning System (MEWS) is one of such systems that use temperature, blood pressure, pulse, respiratory rate, and level of consciousness with each progressive higher score triggering an action. Root cause analysis for mortalities in our institute has led to the implementation of MEWS in an effort to improve patient outcomes. Here we discuss our experience and the impact of MEWS implementation on patient care at our community academic hospital.

Methods

MEWS was implemented in a protocolized manner in June 2013. The following data were collected from non-ICU wards on a monthly basis from January 2010 to June 2014: 1) number of rapid response teams (RRTs) per 100 patient-days (100PD); 2) number of cardiopulmonary arrests ‘Code Blue’ per 100PD; and 3) result of each RRT and Code Blue (RRT progressed to Code Blue, higher level of care, ICU transfer, etc.). Overall inpatient mortality data were also analyzed.

Results

Since the implementation of MEWS, the number of RRT has increased from 0.24 per 100PD in 2011 to 0.38 per 100PD in 2013, and 0.48 per 100PD in 2014. The percentage of RRTs that progressed to Code Blue, an indicator of poor outcome of RRT, has been decreasing. In contrast, the numbers of Code Blue in non-ICU floors has been progressively decreasing from 0.05 per 100PD in 2011 to 0.02 per 100PD in 2013 and 2014. These improved clinical outcomes are associated with a decline of overall inpatient mortality rate from 2.3% in 2011 to 1.5% in 2013 and 1.2% in 2014.

Conclusions

Implementation of MEWS in our institute has led to higher rapid response system utilization but lower cardiopulmonary arrest events; this is associated with a lower mortality rate, and improved patient safety and clinical outcomes. We recommend the widespread use of MEWS to improve patient outcomes.

The significance of pre-arrest factors in out-of-hospital cardiac arrests witnessed by emergency medical services: a report from the Victorian Ambulance Cardiac Arrest Registry

BACKGROUND

The significance of pre-arrest factors in out-of-hospital cardiac arrests (OHCA) witnessed by emergency medical services (EMS) is not well established. The purpose of this study was to assess the association between prodromal symptoms and pre-arrest clinical observations on the arresting rhythm and survival in EMS witnessed OHCA.

METHODS

Between 1st January 2003 and 31st December 2011, 1056 adult EMS witnessed arrests of a presumed cardiac aetiology were identified from the Victorian Ambulance Cardiac Arrest Registry. Pre-arrest prodromal features and clinical characteristics were extracted from the patient care record. Backward elimination logistic regression was used to identify pre-arrest factors associated with an initial shockable rhythm and survival to hospital discharge.

RESULTS

The median age was 73.0 years, 690 (65.3%) were male, and the rhythm of arrest was shockable in 465 (44.0%) cases. The most commonly reported prodromal symptoms prior to arrest were chest pain (48.8%), dyspnoea (41.8%) and altered consciousness (37.8%). An unrecordable systolic blood pressure was observed in 34.4%, a respiratory rate <13 or >24min(-1) was present in 43.1%, and 45.5% had a Glasgow coma score <15. In the multivariable analysis, the following pre-arrest factors were significantly associated with survival: age, public location, aged care facility, chest pain, arm or shoulder pain, dyspnoea, dizziness, vomiting, ventricular tachycardia, pulse rate, systolic blood pressure, respiratory rate, Glasgow coma score, aspirin and inotrope administration.

CONCLUSION

Pre-arrest factors are strongly associated with the arresting rhythm and survival following EMS witnessed OHCA. Potential opportunities to improve outcomes exist by way of early recognition and management of patients at risk of OHCA.

Multicenter development and validation of a risk stratification tool for ward patients

RATIONALE

Most ward risk scores were created using subjective opinion in individual hospitals and only use vital signs.

OBJECTIVES

To develop and validate a risk score using commonly collected electronic health record data.

METHODS

All patients hospitalized on the wards in five hospitals were included in this observational cohort study. Discrete-time survival analysis was used to predict the combined outcome of cardiac arrest (CA), intensive care unit (ICU) transfer, or death on the wards. Laboratory results, vital signs, and demographics were used as predictor variables. The model was developed in the first 60% of the data at each hospital and then validated in the remaining 40%. The final model was compared with the Modified Early Warning Score (MEWS) using the area under the receiver operating characteristic curve and the net reclassification index (NRI).

MEASUREMENTS AND MAIN RESULTS

A total of 269,999 patient admissions were included, with 424 CAs, 13,188 ICU transfers, and 2,840 deaths occurring during the study period. The derived model was more accurate than the MEWS in the validation dataset for all outcomes (area under the receiver operating characteristic curve, 0.83 vs. 0.71 for CA; 0.75 vs. 0.68 for ICU transfer; 0.93 vs. 0.88 for death; and 0.77 vs. 0.70 for the combined outcome; P value < 0.01 for all comparisons). This accuracy improvement was seen across all hospitals. The NRI for the electronic Cardiac Arrest Risk Triage compared with the MEWS was 0.28 (0.18-0.38), with a positive NRI of 0.19 (0.09-0.29) and a negative NRI of 0.09 (0.09-0.09).

CONCLUSIONS

We developed an accurate ward risk stratification tool using commonly collected electronic health record variables in a large multicenter dataset. Further study is needed to determine whether implementation in real-time would improve patient outcomes.

Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*

OBJECTIVE

Over 200,000 in-hospital cardiac arrests occur in the United States each year and many of these events may be preventable. Current vital sign-based risk scores for ward patients have demonstrated limited accuracy, which leads to missed opportunities to identify those patients most likely to suffer cardiac arrest and inefficient resource utilization. We derived and validated a prediction model for cardiac arrest while treating ICU transfer as a competing risk using electronic health record data.

DESIGN

A retrospective cohort study.

SETTING

An academic medical center in the United States with approximately 500 inpatient beds.

PATIENTS

Adult patients hospitalized from November 2008 until August 2011 who had documented ward vital signs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Vital sign, demographic, location, and laboratory data were extracted from the electronic health record and investigated as potential predictor variables. A person-time multinomial logistic regression model was used to simultaneously predict cardiac arrest and ICU transfer. The prediction model was compared to the VitalPAC Early Warning Score using the area under the receiver operating characteristic curve and was validated using three-fold cross-validation. A total of 56,649 controls, 109 cardiac arrest patients, and 2,543 ICU transfers were included. The derived model more accurately detected cardiac arrest (area under the receiver operating characteristic curve, 0.88 vs 0.78; p < 0.001) and ICU transfer (area under the receiver operating characteristic curve, 0.77 vs 0.73; p < 0.001) than the VitalPAC Early Warning Score, and accuracy was similar with cross-validation. At a specificity of 93%, our model had a higher sensitivity than the VitalPAC Early Warning Score for cardiac arrest patients (65% vs 41%).

CONCLUSIONS

We developed and validated a prediction tool for ward patients that can simultaneously predict the risk of cardiac arrest and ICU transfer. Our model was more accurate than the VitalPAC Early Warning Score and could be implemented in the electronic health record to alert caregivers with real-time information regarding patient deterioration.

Risk stratification of hospitalized patients on the wards

Patients who suffer adverse events on the wards, such as cardiac arrest and death, often have vital sign abnormalities hours before the event. Early warning scores have been developed with the aim of identifying clinical deterioration early and have been recommended by the National Institute for Health and Clinical Excellence. In this review, we discuss recently developed and validated risk scores for use on the general inpatient wards. In addition, we compare newly developed systems with more established risk scores such as the Modified Early Warning Score and the criteria used in the Medical Early Response Intervention and Therapy (MERIT) trial in our database of &gt; 59,000 ward admissions. In general we found the single-parameter systems, such as the MERIT criteria, to have the lowest predictive accuracy for adverse events, whereas the aggregate weighted scoring systems had the highest. The Cardiac Arrest Risk Triage (CART) score was best for predicting cardiac arrest, ICU transfer, and a composite outcome (area under the receiver operating characteristic curve [AUC], 0.83, 0.77, and 0.78, respectively), whereas the Standardized Early Warning Score, VitalPAC Early Warning Score, and CART score were similar for predicting mortality (AUC, 0.88). Selection of a risk score for a hospital or health-care system should be guided by available variables, calculation method, and system resources. Once implemented, ensuring high levels of adherence and tying them to specific levels of interventions, such as activation of a rapid response team, are necessary to allow for the greatest potential to improve patient outcomes.

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

BACKGROUND

Clinical deterioration of ward patients can result in intensive care unit (ICU) transfer, cardiac arrest (CA), and/or death. These different outcomes have been used to develop and test track and trigger systems, but the impact of outcome selection on the performance of prediction algorithms is unknown.

METHODS

Patients hospitalized on the wards between November 2008 and August 2011 at an academic hospital were included in the study. Ward vital signs and demographic characteristics were compared across outcomes. The dataset was then split into derivation and validation cohorts. Logistic regression was used to derive four models (one per outcome and a combined outcome) for predicting each event within 24h of a vital sign set. The models were compared in the validation cohort using the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 59,643 patients were included in the study (including 109 ward CAs, 291 deaths, and 2638 ICU transfers). Most mean vital signs within 24h of the events differed statistically, with those before death being the most deranged. Validation model AUCs were highest for predicting mortality (range 0.73-0.82), followed by CA (range 0.74-0.76), and lowest for predicting ICU transfer (range 0.68-0.71).

CONCLUSIONS

Despite differences in vital signs before CA, ICU transfer, and death, the different models performed similarly for detecting each outcome. Mortality was the easiest outcome to predict and ICU transfer the most difficult. Studies should be interpreted with these differences in mind.

Derivation of a cardiac arrest prediction model using ward vital signs*

OBJECTIVE

Rapid response team activation criteria were created using expert opinion and have demonstrated variable accuracy in previous studies. We developed a cardiac arrest risk triage score to predict cardiac arrest and compared it to the Modified Early Warning Score, a commonly cited rapid response team activation criterion.

DESIGN

A retrospective cohort study.

SETTING

An academic medical center in the United States.

PATIENTS

All patients hospitalized from November 2008 to January 2011 who had documented ward vital signs were included in the study. These patients were divided into three cohorts: patients who suffered a cardiac arrest on the wards, patients who had a ward to intensive care unit transfer, and patients who had neither of these outcomes (controls).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Ward vital signs from admission until discharge, intensive care unit transfer, or ward cardiac arrest were extracted from the medical record. Multivariate logistic regression was used to predict cardiac arrest, and the cardiac arrest risk triage score was calculated using the regression coefficients. The model was validated by comparing its accuracy for detecting intensive care unit transfer to the Modified Early Warning Score. Each patient's maximum score prior to cardiac arrest, intensive care unit transfer, or discharge was used to compare the areas under the receiver operating characteristic curves between the two models. Eighty-eight cardiac arrest patients, 2,820 intensive care unit transfers, and 44,519 controls were included in the study. The cardiac arrest risk triage score more accurately predicted cardiac arrest than the Modified Early Warning Score (area under the receiver operating characteristic curve 0.84 vs. 0.76; p = .001). At a specificity of 89.9%, the cardiac arrest risk triage score had a sensitivity of 53.4% compared to 47.7% for the Modified Early Warning Score. The cardiac arrest risk triage score also predicted intensive care unit transfer better than the Modified Early Warning Score (area under the receiver operating characteristic curve 0.71 vs. 0.67; p < .001).

CONCLUSIONS

The cardiac arrest risk triage score is simpler and more accurately detected cardiac arrest and intensive care unit transfer than the Modified Early Warning Score. Implementation of this tool may decrease rapid response team resource utilization and provide a better opportunity to improve patient outcomes than the modified early warning score.

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

BACKGROUND

Current rapid response team activation criteria were not statistically derived using ward vital signs, and the best vital sign predictors of cardiac arrest (CA) have not been determined. In addition, it is unknown when vital signs begin to accurately detect this event prior to CA.

METHODS

We conducted a nested case-control study of 88 patients experiencing CA on the wards of a university hospital between November 2008 and January 2011, matched 1:4 to 352 control subjects residing on the same ward at the same time as the case CA. Vital signs and Modified Early Warning Scores (MEWS) were compared on admission and during the 48 h preceding CA.

RESULTS

Case patients were older (64 ± 16 years vs 58 ± 18 years; P = .002) and more likely to have had a prior ICU admission than control subjects (41% vs 24%; P = .001), but had similar admission MEWS (2.2 ± 1.3 vs 2.0 ± 1.3; P = .28). In the 48 h preceding CA, maximum MEWS was the best predictor (area under the receiver operating characteristic curve [AUC] 0.77; 95% CI, 0.71-0.82), followed by maximum respiratory rate (AUC 0.72; 95% CI, 0.65-0.78), maximum heart rate (AUC 0.68; 95% CI, 0.61-0.74), maximum pulse pressure index (AUC 0.61; 95% CI, 0.54-0.68), and minimum diastolic BP (AUC 0.60; 95% CI, 0.53-0.67). By 48 h prior to CA, the MEWS was higher in cases (P = .005), with increasing disparity leading up to the event.

CONCLUSIONS

The MEWS was significantly different between patients experiencing CA and control patients by 48 h prior to the event, but includes poor predictors of CA such as temperature and omits significant predictors such as diastolic BP and pulse pressure index.

Cardiopulmonary Arrest Occurring in the Radiology Department: Patient Characteristics, Incidence, and Outcomes

Despite efforts for patient stabilization before radiologic procedures, cardiopulmonary arrests still occur. The purpose of our study was to define the incidence, patient characteristics, and outcomes of patients having cardiopulmonary arrest in a radiology department. We retrospectively reviewed patients sustaining cardiopulmonary arrest in the radiology department from 2002 to 2007. Patient characteristics and outcomes were documented. Descriptive statistics were calculated. Over the 6 years of the study, 1,480,578 radiographic procedures were performed, and 27 patients sustained a cardiopulmonary arrest in the radiology department for an incidence rate of 0.002 per cent. The average patient age was 66 years (range, 35 to 88 years); 12 were male (44%). Radiological procedures were diagnostic in 15 cases and therapeutic in 12 cases. The most common locations for arrest were in the CT area (9 patients) and the vascular procedures area (8 patients). The most common preprocedural patient locations were the hospital wards (11 patients [41%]), the emergency department/trauma bay (5 patients [19%]), the intensive care unit (4 patients [15%]), and an outpatient setting (four patients [15%]). Nineteen patients (70%) survived the initial code, 14 patients (52%) survived 24 hours, and 9 patients (33%) survived until discharge. Survival to discharge was significantly impacted by body mass index ( P = 0.005) and type of radiologic procedure ( P = 0.04) but not by the preprocedure patient location. Cardiopulmonary arrest occurring in the radiology department is a rare but potentially lethal occurrence. Patients undergoing vascular access procedures may be an at-risk group. Further study is needed to evaluate potential risk factors for cardiopulmonary arrest occurring in the radiology department.

Unplanned transfers to the intensive care unit: the role of the shock index

BACKGROUND

Unplanned (unexpected) transfers to the intensive care unit (ICU) are typically preceded by physiologic instability. However, trends toward instability may be subtle and not accurately reflected by changes in vital signs. The shock index (SI) (heart rate/systolic blood pressure as an indicator of left ventricular function, reference value of 0.54) may be a simple alternative means to predict clinical deterioration.

OBJECTIVE

To assess the association of the SI with unplanned ICU transfers.

DESIGN

Retrospective case-control study.

SETTING

Academic medical center.

PATIENTS

Fifty consecutive general medical patients with unplanned ICU transfers between 2003 and 2004 and 50 matched controls admitted to the same general medical unit between 2002 and 2004.

MEASUREMENTS

Demographic data and vital signs abstracted from chart review.

RESULTS

The SI was associated with unplanned ICU transfer at values of 0.85 or greater (P < 0.02; odds ratio, 3.0) and there was a significant difference between the median of worst shock indices of cases and controls (0.87 vs. 0.72; P < 0.005). There was no significant difference in age, race, admission ward, or Charlson Comorbidity Index, but hospital stay for cases was significantly longer (mean [standard deviation, SD], 14.8 [9.7] days vs. 5.7 [6.3] days; P < 0.001).

CONCLUSIONS

SI is associated with unplanned transfers to the ICU from general medical units at values of 0.85 or greater. Future studies will determine whether SI is more accurate than simple vital signs as an indicator of clinical decline. If so, it may be a useful trigger to activate medical emergency or rapid response teams (RRTs).

[Analysis of response reports of an in-hospital emergency team : Three years experience at a maximum medical care hospital]

BACKGROUND

In-hospital emergencies can lead to unexpected admission to the ICU, cardiac arrest or even death. Therefore, hospitals have to implement an adequate in-hospital emergency management. The results of the deployment of the in-hospital emergency team of a hospital providing maximum medical care will be presented.

PATIENTS AND METHODS

In 2003 the Westpfalz-Klinikum, Kaiserslautern introduced a central emergency team. The data of the emergency teams on alarm calls and the patient records from 2004 to 2007 were evaluated.

RESULTS

There were 241 alarm calls (9 alarm calls/100 beds and year). The mean age of the patients was 67 years and 56% were male. In 79% of all alarm calls the vital functions were compromised and in 37% cardiac arrest had occurred. When the emergency team arrived all cardiac arrest patients had received basic life support, however, no early defibrillation had been applied. On arrival of the emergency team 41% of the patients could be left on-site after emergency treatment, 40% had to be admitted to an intensive care or intermediate care unit and 21% died or were already dead (5 patients). In 27% of all cardiac arrests ventricular fibrillation/pulseless ventricular tachycardia was the first detected sign. Restoration of spontaneous circulation could be established in 53% and 20% of all resuscitated patients could be discharged. Respiratory emergencies (21%) and altered states of consciousness (20%) were other leading causes for calling the emergency team.

CONCLUSIONS

The high proportion of patients in a life-threatening condition and cardiac arrests indicates the necessity for closer patient monitoring, more intensive emergency training including early defibrillation and continuing education of hospital staff in the prevention and early detection of emergencies, in addition to the provision of an emergency team.

Wireless cardiac event alert monitoring is feasible and effective in the emergency department and adjacent waiting areas

The need for vigilance for unexpected clinical deterioration in the emergency department (ED) waiting area and in unmonitored treatment areas of the ED continues to increase. We sought to determine in an observational study the feasibility and relative false-alarm burden of, and satisfaction with, a novel wireless automated clinician alert device for cardiac rate and rhythm derangements in a teaching hospital ED. Patients presenting with a variety of complaints who after ED triage were not placed on conventional telemetric monitoring (by standard triage policy) were considered for inclusion. Those enrolled in the study were then monitored in the waiting room and in the ED proper via a wireless, one-lead ECG device that, through relay hubs installed in the ED ceiling, alerted clinicians via desktop computers at regular ED work stations to the presence of asystole, ventricular fibrillation, and bradycardia and tachycardia in those patients. The device is not a conventional telemetry monitor, in that it does not provide streaming visual monitoring capability; instead, it provides alarms and one-lead ECG data when triggered by specific rate and rhythm deviations.A total of 298 ED patients (30.2% patients triaged after a run-in period) were monitored, for a mean duration of 3.53 hours. Productive clinical alarms (those that prompted a change in patient therapy, location, or intensity of monitoring) occurred in 20 patients (6.7%); the most common response to an alert was earlier transition from the waiting room into a clinical space in the ED. There were 10 false-positive asystole or ventricular fibrillation alarms in 4 patients (1.3%), all of which were readily attributable to nonclinical origins, such as poor lead adherence. There was excellent satisfaction with the device from both patients and clinical personnel. Wireless cardiac event monitoring is feasible in the ED, and improves the throughput of ED patients with worsening vital signs, and may improve overall patient safety, without an onerous burden of nonproductive alarms.

[Medical emergency teams: current situation and perspectives of preventive in-hospital intensive care medicine]

Severe clinical incidents occur in up to 10% of all non-intensive care unit (ICU) patients, which have an estimated mortality of 5-8%. As in the prehospital setting, early clinical warning signs can be identified in the majority of cases. Studies suggest that introduction of an in-hospital medical emergency team (MET) which responds to objective criteria of physiological deterioration, may effectively reduce the incidence of in-hospital cardiac arrests as well as unanticipated or readmissions to the ICU. According to this concept, METs would evaluate and treat non-ICU patients at risk at an early stage before a potentially fatal deterioration of cardiorespiratory parameters occurs. This article reviews available data on preventive in-hospital intensive care medicine and reflects on the circumstances for an implementation of METs in Germany, Austria and Switzerland.

Teaching pre-registration student nurses to assess acutely ill patients: an evaluation of an acute illness management programme

Mortality and physical crisis in hospital can be prevented by accurate assessment and early intervention. Evidence suggests that patients experiencing acute illness outside critical care areas who receive enhanced levels of intervention from appropriately trained nurses prevent the need for admission to high dependency and intensive care beds. Such interventions can lead to reduced mortality and morbidity. This qualitative study, supported by quantitative assessment, evaluated training for student nurses designed to assess patients using a structured programme in the management of acute illness. Questionnaires were used to collect data from 184 third year nursing students eliciting their experiences of the training programme. The responses demonstrated a positive evaluation of the programme. The study has implications for incorporating acute illness assessment into a theoretically oriented curriculum. Exposing student nurses to the programme enhances teaching and learning, as well as having potential for more effective interventions to take place in practice.

The Medical Emergency Team system: a two hospital comparison

AIM

To compare activity and outcomes of a mature Medical Emergency Team (MET) in two hospitals. SETTING AND POPULATIONS: A Tertiary Referral Hospital (TRH) and a Metropolitan General Hospital (MGH) who combined have approximately 82,000 admissions annually with 38,000 patients meeting the eligibility criteria. The population included all admissions to the two hospitals aged 15 years and over with a stay>1 day (12 months period). Admissions that had a MET call originating in general wards were defined as Admissions Associated with a MET call (AAMET).

METHODS

A retrospective analysis of MET call audit forms, a Death Review database, and routinely collected hospital data for the period 1st October 2004 to 30th September 2005, inclusive. Chronic morbidity was calculated as a Charlson Index (CI) score over previous visits and admissions using ICD10 & ICD9 diagnosis and procedure codes.

RESULTS

There were 633 and 349 AAMETs. The incidence rates (MET calls/1000 admissions) were 37.6 and 34.1. They were associated with being elderly; males; higher CI scores; surgical admissions, Emergency Department (ED) admissions, and longer length of stay (LOS). A systolic BP<90mm Hg, and "worried" were the most frequent MET call criteria. There were 27 (4.3%) and 9 (2.6%) deaths following a MET call, of these 17 and 5 had Cardiac Arrest (CA) as the reason for the call. Death occurred for 192 and 54 AAMETs, only 38 (20%) and 14 (26%) were Do Not Attempt Resuscitation (DNAR) deaths. One hundred and forty-seven (23.2%) and eighty-seven (24.9%) AAMETs had a MET call within 24h of transfer from a critical care area; the proportions of transfers differed significantly between the two hospitals.

CONCLUSION

A well established MET system identified similar AAMET populations from two different hospital populations. Sick, elderly, and surgical rather than medical patients were associated with MET activity in both hospitals. Further research is needed to estimate the impact of increased monitoring and interventions on patient outcomes, and the role of MET teams in end of life decision-making.

Medical emergency teams: a role for expanding intensive care?

BACKGROUND

A high incidence of preventable adverse events and deaths in hospitals has triggered initiatives to improve the quality of care of acutely ill in-hospital patients. System changes involving the introduction of medical emergency teams, outreach services or rapid response teams are an integral part of these initiatives. The rationale for implementing a designated team is that early recognition and rapid institution of adequate therapy for the deteriorating patient can improve outcome. The concept of bringing intensive care expertise to any acutely ill patient irrespective of location within the hospital is envisioned as "critical care without walls".

METHODS

Studies were identified by a PubMed search and cited references in key publications provided additional material including www-resources. More than 80 studies were identified and selected for review, however, no formal search strategy for a systematic review or meta-analysis was attempted. Only studies published in English were considered.

RESULTS

Several non-randomized, before-and-after cohort studies demonstrate that implementation of medical emergency teams and equivalents can reduce the incidence of cardiac arrests, unexpected deaths, and unplanned intensive care admissions. However, one recent randomized, controlled trial of medical emergency teams failed to demonstrate any differences in outcomes.

CONCLUSION

Several key operational issues need to be addressed before introducing medical emergency response teams based on current evidence. These issues include differences in healthcare systems and performance, patient case-mix, resources available, composition of the teams and calling criteria, and strategies for education, audit and governance.

A New Role for the ACNP

The implementation of a rapid response team or medical emergency team is 1 of the 6 initiatives of the Institute for Healthcare Improvement's 100,000 Lives Campaign with the goal to reduce the number of cardiopulmonary arrests outside the intensive care unit and inpatient mortality rates. The concept of RRT was pioneered in Australia and is now being implemented in many hospitals across the United States. This article reviews the current literature and describes the implementation of an RRT in a community hospital. The first-quarter data after implementation are described. The unique role of the acute care nurse practitioner in this hospital's model is described.