An eight year audit before and after the introduction of modified early warning score (MEWS) charts, of patients admitted to a tertiary referral intensive care unit after CPR

Prognostic and risk factor analysis of cancer patients after unplanned ICU admission: a real-world multicenter study

To investigate the occurrence and 90-day mortality of cancer patients following unplanned admission to the intensive care unit (ICU), as well as to develop a risk prediction model for their 90-day prognosis. We prospectively analyzed data from cancer patients who were admitted to the ICU without prior planning within the past 7 days, specifically between May 12, 2021, and July 12, 2021. The patients were grouped based on their 90-day survival status, and the aim was to identify the risk factors influencing their survival status. A total of 1488 cases were included in the study, with an average age of 63.2 ± 12.4 years. The most common reason for ICU admission was sepsis (n = 940, 63.2%). During their ICU stay, 29.7% of patients required vasoactive drug support (n = 442), 39.8% needed invasive mechanical ventilation support (n = 592), and 82 patients (5.5%) received renal replacement therapy. We conducted a multivariate COX proportional hazards model analysis, which revealed that BMI and a history of hypertension were protective factors. On the other hand, antitumor treatment within the 3 months prior to admission, transfer from the emergency department, general ward, or external hospital, high APACHE score, diagnosis of shock and respiratory failure, receiving invasive ventilation, and experiencing acute kidney injury (AKI) were identified as risk factors for poor prognosis within 90 days after ICU admission. The average length of stay in the ICU was 4 days, while the hospital stay duration was 18 days. A total of 415 patients died within 90 days after ICU admission, resulting in a mortality rate of 27.9%. We selected 8 indicators to construct the predictive model, which demonstrated good discrimination and calibration. The prognosis of cancer patients who are unplanned transferred to the ICU is generally poor. Assessing the risk factors and developing a risk prediction model for these patients can play a significant role in evaluating their prognosis.

Implementation of early warning system in the clinical teaching unit to reduce unexpected deaths

BACKGROUND

Early detection of patients with clinical deterioration admitted to the hospital is critical. The early warning system (EWS) is developed to identify early clinical deterioration. Using individual patient's vital sign records, this bedside score can identify early clinical deterioration, triggering a communication algorithm between nurses and physicians, thereby facilitating early patient intervention. Although various models have been developed and implemented in emergency rooms and paediatric units, data remain sparse on the utility of the EWS in patients admitted to general internal medicine wards and the processes and challenges encountered during the implementation.

LOCAL PROBLEM

There is a lack of standardised tools to recognise early deterioration of patient condition.

METHODS

This was a quality improvement project piloted in the clinical teaching unit of a tertiary care hospital. Data were collected 24 weeks pre-EWS and 55 weeks post-EWS implementation. A series of Plan, Do, Study, Act cycles were conducted to identify the root cause, develop a driver diagram to understand the drivers of unexpected deaths, run a sham test trial run of the EWS, educate and obtained feedback of clinical care teams involved, assess adherence to the EWS during the pilot project (6 weeks pre-EWS and 6 weeks post-EWS implementation), evaluate outcomes by extending the duration to 24 weeks pre-EWS and 55 weeks post-EWS implementation, and retrospectively review the uptake of the EWS.

INTERVENTIONS

Implementation of a standardised protocol to detect deterioration in patient condition.

RESULTS

During the pre-EWS implementation phase (24 weeks), there were 4.4 events per week (1.2 septic workups, 1.9 observation unit transfers, 0.7 critical care transfers, 0.13 cardiac arrests and 0.46 per week unexpected deaths). In the post-EWS implementation phase (55 weeks), there were 4.2 events per week (1.0 septic workup, 1.9 observation unit transfers, 0.82 critical care transfers, 0.25 cardiac arrests and 0.25 unexpected deaths).

CONCLUSION

The EWS can improve patient care; however, more engagement of stakeholders and electronic vital sign documentation may improve the uptake of the system.

An improved prognostic model for predicting the mortality of critically ill patients: a retrospective cohort study

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients.

Early warning scores to assess the probability of critical illness in patients with COVID-19

OBJECTIVE

Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation.

METHODS

Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation.

RESULTS

In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively.

CONCLUSION

In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.

Between-centre differences in care for in-hospital cardiac arrest: a prospective cohort study

BACKGROUND

Survival after in-hospital cardiac arrest is poor, but current literature shows substantial heterogeneity in reported survival rates. This study aims to evaluate care for patients suffering in-hospital cardiac arrest (IHCA) in the Netherlands by assessing between-hospital heterogeneity in outcomes and to explain this heterogeneity stemming from differences in case-mix or differences in quality of care.

METHODS

A prospective multicentre study was conducted comprising 14 centres. All IHCA patients were included. The adjusted variation in structure and process indicators of quality of care and outcomes (in-hospital mortality and cerebral performance category [CPC] scale) was assessed with mixed effects regression with centre as random intercept. Variation was quantified using the median odds ratio (MOR), representing the expected odds ratio for poor outcome between two randomly picked centres.

RESULTS

After excluding centres with less than 10 inclusions (2 centres), 701 patients were included of whom, 218 (32%) survived to hospital discharge. The unadjusted and case-mix adjusted MOR for mortality was 1.19 and 1.05, respectively. The unadjusted and adjusted MOR for CPC score was 1.24 and 1.19, respectively. In hospitals where personnel received cardiopulmonary resuscitation (CPR) training twice per year, 183 (64.7%) versus 290 (71.4%) patients died or were in a vegetative state, and 59 (20.8%) versus 68 (16.7%) patients showed full recovery (p < 0.001).

CONCLUSION

In the Netherlands, survival after IHCA is relatively high and between-centre differences in outcomes are small. The existing differences in survival are mainly attributable to differences in case-mix. Variation in neurological outcome is less attributable to case-mix.

Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events

OBJECTIVE

To propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events.

MATERIALS AND METHODS

This retrospective study explores the efficacy of machine learning algorithms in classifying clinical deterioration events among patients in intensive care units using sequences of timestamps of vital sign measurements, flowsheets comments, order entries, and nursing notes. We design a data pipeline to partition events into discrete, regular time bins that we refer to as timesteps. Logistic regressions, random forest classifiers, and recurrent neural networks are trained on datasets of different length of timesteps, respectively, against a composite outcome of death, cardiac arrest, and Rapid Response Team calls. Then these models are validated on a holdout dataset.

RESULTS

A total of 6720 intensive care unit encounters meet the criteria and the final dataset includes 830 578 timestamps. The gated recurrent unit model utilizes timestamps of vital signs, order entries, flowsheet comments, and nursing notes to achieve the best performance on the time-to-outcome dataset, with an area under the precision-recall curve of 0.101 (0.06, 0.137), a sensitivity of 0.443, and a positive predictive value of 0. 092 at the threshold of 0.6.

DISCUSSION AND CONCLUSION

This study demonstrates that our recurrent neural network models using only timestamps of longitudinal electronic health record data that reflect healthcare processes achieve well-performing discriminative power.

Implementation and Evaluation of an Electronic Maternal Early Warning Trigger Tool to Reduce Maternal Morbidity

OBJECTIVE

We compare maternal morbidity and clinical care metrics before and after the electronic implementation of a maternal early warning trigger (MEWT) tool.

STUDY DESIGN

This is a study of maternal morbidity and clinical care within three linked hospitals comparing 1 year before and after electronic MEWT implementation. We compare severe maternal morbidity overall as well as within the subcategories of hemorrhage, hypertension, cardiopulmonary, and sepsis in addition to relevant process metrics in each category. We describe the MEWT trigger rate in addition to MEWT sensitivity and specificity for morbidity overall and by morbidity type.

RESULTS

The morbidity rate ratio increased from 1.6 per 100 deliveries in the pre-MEWT period to 2.06 per 100 deliveries in the post-MEWT period (incidence rate ratio = 1.28, p = 0.018); however, in cases of septic morbidity, time to appropriate antibiotics decreased (pre-MEWT: 1.87 hours [1.11-2.63] vs. post-MEWT: 0.75 hours [0.31-1.19], p = 0.036) and in cases of hypertensive morbidity, the proportion of cases treated with appropriate antihypertensive medication within 60 minutes improved (pre-MEWT: 62% vs. post-MEWT: 83%, p = 0.040). The MEWT trigger rate was 2.3%, ranging from 0.8% in the less acute centers to 2.9% in our tertiary center. The MEWT sensitivity for morbidity overall was 50%; detection of hemorrhage morbidity was lowest (30%); however, it ranged between 69% for septic morbidity, 74% for cardiopulmonary morbidity, and 82% for cases of hypertensive morbidity.

CONCLUSION

Overall, maternal morbidity did not decrease after implementation of the MEWT system; however, important clinical metrics such as time to antibiotics and antihypertensive care improved. We suspect increased morbidity was related to annual variation and unexpected lower morbidity in the pre-MEWT comparison year. Because MEWT sensitivity for hemorrhage was low, and because hemorrhage dominates administrative metrics of morbidity, process metrics around sepsis, hypertension, and cardiopulmonary morbidity are important to track as markers of MEWT efficacy.

KEY POINTS

· MEWT was not associated with a decrease in maternal morbidity.. · MEWT was associated with improvements in some clinical care metrics.. · MEWT is more sensitive in detecting septic, hypertensive, and cardiopulmonary morbidities than hemorrhage morbidity..

Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events with MEWS

Objective.There have been many efforts to develop tools predictive of health deterioration in hospitalized patients, but comprehensive evaluation of their predictive ability is often lacking to guide implementation in clinical practice. In this work, we propose new techniques and metrics for evaluating the performance of predictive alert algorithms and illustrate the advantage of capturing the timeliness and the clinical burden of alerts through the example of the modified early warning score (MEWS) applied to the prediction of in-hospital code blue events.Approach. Different implementations of MEWS were calculated from available physiological parameter measurements collected from the electronic health records of ICU adult patients. The performance of MEWS was evaluated using conventional and a set of non-conventional metrics and approaches that take into account the timeliness and practicality of alarms as well as the false alarm burden.Main results. MEWS calculated using the worst-case measurement (i.e. values scoring 3 points in the MEWS definition) over 2 h intervals significantly reduced the false alarm rate by over 50% (from 0.19/h to 0.08/h) while maintaining similar sensitivity levels as MEWS calculated from raw measurements (∼80%). By considering a prediction horizon of 12 h preceding a code blue event, a significant improvement in the specificity (∼60%), the precision (∼155%), and the work-up to detection ratio (∼50%) could be achieved, at the cost of a relatively marginal decrease in sensitivity (∼10%).Significance. Performance aspects pertaining to the timeliness and burden of alarms can aid in understanding the potential utility of a predictive alarm algorithm in clinical settings.

Effect of National Early Warning Scoring System Implementation on Cardiopulmonary Arrest, Unplanned ICU Admission, Emergency Surgery, and Acute Kidney Injury in an Emergency Hospital, Egypt

Purpose

To evaluate the effect of national early warning scoring system (NEWS) implementation in identifying patients at risk of clinical deterioration at an emergency hospital.

Background

Early warning score has been developed to facilitate early detection of deterioration by categorizing a patients’ severity of illness and prompting nursing staff to request a medical review at specific trigger points.

Patients and Methods

A prospective, control/intervention groups’, quasi-experimental design was utilized. A sample of 364 adult patients were admitted to the inpatient unit at an emergency hospital for six months. The patients were divided into a study group (174 patients) and a control group (190 patients). All study patients were followed up to either death or hospital discharge before and after implementing a new observation chart. The patients’ outcomes were compared and analyzed between both groups.

Results

In the intervention period, compared to the control period, a significant reduction was seen in the number of cardiopulmonary arrest (4.7% vs 1.1%, p = 0.046), unplanned ICU admission (5.3% vs 1.7%, p = 0.049), emergency surgery (6.3% vs 0%, p = 0.001), acute kidney injury (6.8% vs 1.1%, p = 0.006). As well, there was a significant increase in the number of patients receiving medical reviews following clinical deterioration in terms of escalation plan (3.2% vs 26.4%, p = <0.001).

Conclusion

The implementation of NEWS was associated with a significant improvement in patients’ outcomes in hospital wards, increases in the frequency of vital signs measurements, and an increase in the number of medical reviews following clinical instability.

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Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review

OBJECTIVE

To assess predictive performance of universal early warning scores (EWS) in disease subgroups and clinical settings.

DESIGN

Systematic review.

DATA SOURCES

Medline, CINAHL, Embase and Cochrane database of systematic reviews from 1997 to 2019.

INCLUSION CRITERIA

Randomised trials and observational studies of internal or external validation of EWS to predict deterioration (mortality, intensive care unit (ICU) transfer and cardiac arrest) in disease subgroups or clinical settings.

RESULTS

We identified 770 studies, of which 103 were included. Study designs and methods were inconsistent, with significant risk of bias (high: n=16 and unclear: n=64 and low risk: n=28). There were only two randomised trials. There was a high degree of heterogeneity in all subgroups and in national early warning score (I2=72%-99%). Predictive accuracy (mean area under the curve; 95% CI) was highest in medical (0.74; 0.74 to 0.75) and surgical (0.77; 0.75 to 0.80) settings and respiratory diseases (0.77; 0.75 to 0.80). Few studies evaluated EWS in specific diseases, for example, cardiology (n=1) and respiratory (n=7). Mortality and ICU transfer were most frequently studied outcomes, and cardiac arrest was least examined (n=8). Integration with electronic health records was uncommon (n=9).

CONCLUSION

Methodology and quality of validation studies of EWS are insufficient to recommend their use in all diseases and all clinical settings despite good performance of EWS in some subgroups. There is urgent need for consistency in methods and study design, following consensus guidelines for predictive risk scores. Further research should consider specific diseases and settings, using electronic health record data, prior to large-scale implementation.

PROSPERO REGISTRATION NUMBER

PROSPERO CRD42019143141.

European Resuscitation Council Guidelines 2021: Systems saving lives

The European Resuscitation Council (ERC) has produced these Systems Saving Lives guidelines, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include chain of survival, measuring performance of resuscitation, social media and smartphones apps for engaging community, European Restart a Heart Day, World Restart a Heart, KIDS SAVE LIVES campaign, lower-resource setting, European Resuscitation Academy and Global Resuscitation Alliance, early warning scores, rapid response systems, and medical emergency team, cardiac arrest centres and role of dispatcher.

Early Warning Scores to Predict Noncritical Events Overnight in Hospitalized Medical Patients: A Prospective Case Cohort Study

BACKGROUND

Physicians are often called to evaluate patients overnight with varying levels of clinical deterioration. Early warning scores predict critical clinical deterioration in patients; however, it is unknown whether they are able to reliably predict which patients will need to be seen overnight and whether these patients will require further resource use.

METHODS

A prospective case cohort study of 522 patient nights in a single tertiary care hospital in Vancouver, British Columbia, Canada, was conducted to assess the ability of Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) to predict patients who will need to be seen overnight by physicians and will require other healthcare resources. Prediction ability was assessed using area under the receiver operating characteristic curve and logistic regression models.

RESULTS

The MEWS and NEWS both significantly predicted which patients needed to be seen overnight, and area under the receiver operating characteristic curves (95% confidence interval) for MEWS and NEWS were 0.72 (0.66-0.78) and 0.69 (0.63-0.76), respectively. Odds ratios (95% confidence interval) for MEWS and NEWS predicting need to be seen overnight were 1.52 (1.34-1.73) and 1.22 (1.14-1.31), respectively.

CONCLUSIONS

Both MEWS and NEWS have fair ability to predict patients who will need to be seen overnight. This may be useful for improving handover and resource allocation for overnight care.

Prediction of hospital outcome in emergency medical admissions using modified early warning score (MEWS): Indian experience

Objective:

To evaluate the applicability of modified early warning (MEWS) Score for prediction of hospital outcomes of medical emergency patients.

Design:

Prospective hospital based observational study.

Setting:

A tertiary care level medicine emergency unit in a medical college of North India.

Study Population:

300 Patients admitted in medicine emergency.

Method:

Patients of both sexes of age more than 18 years who were admitted in medical emergency unit at MMIMSR, Ambala were evaluated. Patients who were in cardiac arrest at arrival and those who died within the first 24 hours were excluded and 300 patients were included. Modified Early Warning Score based on physiological parameters was recorded at admission for each patient and monitored over the next 24 hours in the emergency unit. Hospital outcome of the patient in terms of mortality, need for critical care, prolonged stay and uneventful discharge were recorded and correlated with MEWS scores over the first 24 hours in the emergency unit. Receiver Operating Characteristic (ROC) curves was generated to evaluate the utility of MEWS as a tool to predict patient outcome in medical emergency setting.

Result:

Of the 300 patients studied, the mean age of patients was 49 years, and the majority of the patients were male (61%). A MEWS Score of >5 at 24 hours of admission was associated significantly with in-hospital mortality of patients (p < 0.0001). The ROC (Receiver Operator Characteristic) curve revealed that in those patients who had a 24 hours MEWS >/= 5, the area under curve was (AUC) = 0.9. (95% CI: 0.95-0.98). Thus, MEWS was an effective predictor of in hospital mortality with sensitivity (78%) and specificity (94%).

Conclusion:

MEWS, a scoring system based on easily recordable physiological parameters can be used as an effective tool to triage and monitor patients in medical emergency units, to identify patients who are at greater risk of clinical deterioration and need close monitoring or early transfer for critical care or other timely interventions. Thus, application of MEWS in medical emergency units can be a useful tool to improve patient care, ensure optimal utilization of resources and prevent inappropriate discharge or neglect of sick patients.

Prognostic significance of emergency department modified early warning score trend in critical ill elderly patients

OBJECTIVE

To explore the relationship between trends in emergency department modified early warning score (EDMEWS) and the prognosis of elderly patients admitted to the intensive care unit (ICU).

METHODS

Consecutive non-traumatic elderly ED patients (≥65 years old) admitted to the ICU between July 2018 and June 2019 were enrolled in this retrospective cohort study. The selected patients had at least 2 separate MEWS during their ED stay. Detailed patient information was retrieved initially from the ICU database of our hospital and then crosschecked with electronic medical recording system to confirm the completeness and correctness of the data. Patients who had do-not-resuscitate order and those with incomplete data of EDMEWS, acute physiology and chronic health evaluation (APACHE) II score, or survival information (7-day and 30-day mortality) were excluded. The trends in EDMEWS were determined using the regression line of multiple MEWS measured during ED stay, in which EDMEWS trend progression was defined as the slope of the regression line > zero. The relationship between EDMEWS trend and prognosis was assessed using univariate and multivariate analyses (multiple logistic regression analysis).

RESULTS

Of the 1423 selected patients, 499 (35.1%) had worsening 24-h APACHE II score, 110 (7.7%) died within 7 days, and 233 (16.4%) died within 30 days. Factors that were significantly associated with worsening 24-h APACHE II score, 7-day mortality, and 30-day mortality in univariate analysis were selected for inclusion into multiple logistic regression analyses. After adjusting for other covariates, EDMEWS trend progression was significantly associated with 24-h APACHE II score progression, 7-day mortality, and 30-day mortality.

CONCLUSIONS

EDMEWS trend progression was significantly associated with 24-h APACHE II score progression, 7-day mortality, and 30-day mortality in elderly ED patients admitted to the ICU. EDMEWS is a simple and useful tool for precisely monitoring patients' ongoing condition and predicting prognosis.

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

Education, Implementation, and Teams: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

For this 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations, the Education, Implementation, and Teams Task Force applied the population, intervention, comparator, outcome, study design, time frame format and performed 15 systematic reviews, applying the Grading of Recommendations, Assessment, Development, and Evaluation guidance. Furthermore, 4 scoping reviews and 7 evidence updates assessed any new evidence to determine if a change in any existing treatment recommendation was required. The topics covered included training for the treatment of opioid overdose; basic life support, including automated external defibrillator training; measuring implementation and performance in communities, and cardiac arrest centers; advanced life support training, including team and leadership training and rapid response teams; measuring cardiopulmonary resuscitation performance, feedback devices, and debriefing; and the use of social media to improve cardiopulmonary resuscitation application.

Education, Implementation, and Teams: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

For this 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations, the Education, Implementation, and Teams Task Force applied the population, intervention, comparator, outcome, study design, time frame format and performed 15 systematic reviews, applying the Grading of Recommendations, Assessment, Development, and Evaluation guidance. Furthermore, 4 scoping reviews and 7 evidence updates assessed any new evidence to determine if a change in any existing treatment recommendation was required. The topics covered included training for the treatment of opioid overdose; basic life support, including automated external defibrillator training; measuring implementation and performance in communities, and cardiac arrest centers; advanced life support training, including team and leadership training and rapid response teams; measuring cardiopulmonary resuscitation performance, feedback devices, and debriefing; and the use of social media to improve cardiopulmonary resuscitation application.

The prevalence and management of deteriorating patients in an Australian emergency department

BACKGROUND

Complex human and system factors impact the effectiveness of Rapid Response Systems (RRS). Emergency Department (ED) specific RRS are relatively new and the factors associated with their effectiveness are largely unknown. This study describes the period prevalence of deterioration and characteristics of care for deteriorating patients in an Australia ED and examine relationships between system factors and escalation of care.

METHODS

A retrospective medical record audit of all patients presenting to an Australian ED in two weeks.

RESULTS

Period prevalence of deterioration was 10.08% (n=269). Failure to escalate care occurred in nearly half (n=52, 47.3%) of the patients requiring a response (n=110). Appropriate escalation practices were associated with where the patient was being cared for (p=0.01), and the competence level of the person documenting deterioration (p=0.005). Intermediate competence level nurses were nine times more likely to escalate care than novices and experts (p=0.005). While there was variance in escalation practice related to system factors, these associations were not statistically significant.

CONCLUSION

The safety of deteriorating ED patients may be improved by informing care based on the escalation practices of staff with intermediate ED experience and competence levels.

A machine learning approach for mortality prediction only using non-invasive parameters

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between the mortality prediction performance with and without laboratory measurement variables for the four machine learning methods. After reducing the number of feature variables to no more than 50, the machine learning models still outperform the traditional scoring systems, with AUC higher than 0.83. The machine learning approaches only using non-invasive parameters achieved an excellent mortality prediction performance and can equal those using extra laboratory measurements, which can be applied in rural areas and remote battlefield for mortality risk evaluation. Graphical abstract.

Modified Early Warning System: Quality Improvement with the Help of Healthcare Failure Modes and Effect Analysis

Introduction: Hospitals struggle to implement MEWS. This study aims to improve MEWS implementation in the studied hospital.Objective: Improve the implementation of MEWS with the help of HFMEA.Materials: HFMEA together with training is used to improve the implementation.Results: The pre-intervention RPN got reduced from 1558 to 516 in the post-implementation phase.Application: This demonstrates improvement in the implementation of MEWS with the help of HFMEA, this study design can be widely used.Conclusion: The HFMEA is an effective tool to use for the improvement of MEWS implementation by the hospital nurses.

[Internal hospital emergency management : Concepts for optimization of patient safety in hospitals]

Critical incidents in hospitals can often be predicted hours before the event and can mostly be detected earlier and presumably avoided. Quality management programs from US hospitals to reduce deaths following a severe postoperative complication (failure to rescue, FTR), have in this form not yet become established in Germany. A sensitive score-based early warning system for looming complications is decisive for successful in-hospital emergency management. In addition to measurement rounds where the frequency is adapted to the severity, this includes effective communication of the results to the ward physician, who in the best case scenario solves the problem alone. If the deployment of a medical rapid response emergency team (MET) is necessary, there must be clear chain of alarm pathways and the personnel on the ward must be able to take initial bridging action until the MET arrives. The MET provides 24/7 emergency and intensive medical expertise for peripheral wards and must be familiar with the location, well-equipped and trained. Communication skills are particularly required not only to be able to handle the immediate emergency situation but also to organize the downstream diagnostics and escalation of treatment; however, the MET is only one of the links in the in-hospital rescue chain, which can only improve the patient outcome when alerted in a timely manner. Feedback systems, such as participation in the German Resuscitation Registry, allow reflection of one's own performance in a national comparison. The chances offered by a MET will only be fully realized when it is integrated into an in-hospital emergency concept and this determines the added value for patient safety.

Adjusting Early Warning Score by clinical assessment: a study protocol for a Danish cluster-randomised, multicentre study of an Individual Early Warning Score (I-EWS)

INTRODUCTION

Track and trigger systems (TTSs) based on vital signs are implemented in hospitals worldwide to identify patients with clinical deterioration. TTSs may provide prognostic information but do not actively include clinical assessment, and their impact on severe adverse events remain uncertain. The demand for prospective, multicentre studies to demonstrate the effectiveness of TTSs has grown the last decade. Individual Early Warning Score (I-EWS) is a newly developed TTS with an aggregated score based on vital signs that can be adjusted according to the clinical assessment of the patient. The objective is to compare I-EWS with the existing National Early Warning Score (NEWS) algorithm regarding clinical outcomes and use of resources.

METHOD AND ANALYSIS

In a prospective, multicentre, cluster-randomised, crossover, non-inferiority study. Eight hospitals are randomised to use either NEWS in combination with the Capital Region of Denmark NEWS Override System (CROS) or implement I-EWS for 6.5 months, followed by a crossover. Based on their clinical assessment, the nursing staff can adjust the aggregated score with a maximum of -4 or +6 points. We expect to include 150 000 unique patients. The primary endpoint is all-cause mortality at 30 days. Coprimary endpoint is the average number of times per day a patient is NEWS/I-EWS-scored, and secondary outcomes are all-cause mortality at 48 hours and at 7 days as well as length of stay.

ETHICS AND DISSEMINATION

The study was presented for the Regional Ethics committee who decided that no formal approval was needed according to Danish law (J.no. 1701733). The I-EWS study is a large prospective, randomised multicentre study that investigates the effect of integrating a clinical assessment performed by the nursing staff in a TTS, in a head-to-head comparison with the internationally used NEWS with the opportunity to use CROS.

TRIAL REGISTRATION NUMBER

NCT03690128.

Low daily MEWS scores as predictors of low-risk hospitalized patients

BACKGROUND

The Modified Early Warning System (MEWS) is a well-validated tool used by hospitals to identify patients at high risk for an adverse event to occur. However, there has been little evaluation into whether a low MEWS score can be predictive of patients with a low likelihood of an adverse event.

AIM

The present study aims to evaluate the MEWS score as a method of identifying patients at low risk for adverse events.

DESIGN

Retrospective cohort study of 5676 patient days and analysis of associated MEWS scores, medical comorbidities and adverse events. The primary outcome was the association of average daily MEWS scores in those who had an adverse event compared with those who did not.

RESULTS

Those with an average MEWS score of >2 were over 9 times more likely to have an adverse event compared with those with an average MEWS score of 1-2, and over 15 times more likely to have an adverse event compared to those with an average MEWS score of <1.

CONCLUSIONS

Our study shows that those with average daily MEWS scores <2 are at a significantly lower likelihood of having an adverse event compared with a score of >2, deeming them 'low-risk patients'. Formal recognition of such patients can have major implications in a hospital setting, including more efficient resource allocation in hospitals and better patient satisfaction and safety by adjusting patient monitoring according to their individual risk profile.

Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support

BACKGROUND

Clinical predictive tools quantify contributions of relevant patient characteristics to derive likelihood of diseases or predict clinical outcomes. When selecting predictive tools for implementation at clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever-growing number of tools, most of which have never been implemented or assessed for comparative effectiveness. To overcome this challenge, we have developed a conceptual framework to Grade and Assess Predictive tools (GRASP) that can provide clinicians with a standardised, evidence-based system to support their search for and selection of efficient tools.

METHODS

A focused review of the literature was conducted to extract criteria along which tools should be evaluated. An initial framework was designed and applied to assess and grade five tools: LACE Index, Centor Score, Well's Criteria, Modified Early Warning Score, and Ottawa knee rule. After peer review, by six expert clinicians and healthcare researchers, the framework and the grading of the tools were updated.

RESULTS

GRASP framework grades predictive tools based on published evidence across three dimensions: 1) Phase of evaluation; 2) Level of evidence; and 3) Direction of evidence. The final grade of a tool is based on the highest phase of evaluation, supported by the highest level of positive evidence, or mixed evidence that supports a positive conclusion. Ottawa knee rule had the highest grade since it has demonstrated positive post-implementation impact on healthcare. LACE Index had the lowest grade, having demonstrated only pre-implementation positive predictive performance.

CONCLUSION

GRASP framework builds on widely accepted concepts to provide standardised assessment and evidence-based grading of predictive tools. Unlike other methods, GRASP is based on the critical appraisal of published evidence reporting the tools' predictive performance before implementation, potential effect and usability during implementation, and their post-implementation impact. Implementing the GRASP framework as an online platform can enable clinicians and guideline developers to access standardised and structured reported evidence of existing predictive tools. However, keeping GRASP reports up-to-date would require updating tools' assessments and grades when new evidence becomes available, which can only be done efficiently by employing semi-automated methods for searching and processing the incoming information.

Early Warning Score Communication Bundle: A Pilot Study

BACKGROUND

Unplanned admissions of patients to intensive care units from medical-surgical units often result from failure to recognize clinical deterioration. The early warning score is a clinical decision support tool for nurse surveillance but must be communicated to nurses and implemented appropriately. A communication process including collaboration with experienced intensive care unit nurses may reduce unplanned transfers.

OBJECTIVE

To determine the impact of an early warning score communication bundle on medical-surgical transfers to the intensive care unit, rapid response team calls, and morbidity of patients upon intensive care unit transfer.

METHODS

After an early warning score was electronically embedded into medical records, a communication bundle including notification of and telephone collaboration between medical-surgical and intensive care unit nurses was implemented. Data were collected 3 months before and 21 months after implementation.

RESULTS

Rapid response team calls increased nonsignificantly during the study period (from 6.47 to 8.29 per 1000 patient-days). Rapid response team calls for patients with early warning scores greater than 4 declined (from 2.04 to 1.77 per 1000 patient-days). Intensive care unit admissions of patients after rapid response team calls significantly declined (P = .03), as did admissions of patients with early warning scores greater than 4 (P = .01), suggesting that earlier intervention for patient deterioration occurred. Documented reassessment response time declined significantly to 28 minutes (P = .002).

CONCLUSION

Electronic surveillance and collaboration with experienced intensive care unit nurses may improve care, control costs, and save lives. Critical care nurses have a role in coaching and guiding less experienced nurses.

Detection of deteriorating patients after Whipple surgery by a modified early warning score (MEWS)

Background

The modified early warning score (MEWS) was set up to supply prompt recognition of clinically deteriorating patients before they undergo a severe and life-threatening event. The study aimed to describe the probable usefulness of the MEWS in identifying deteriorating post-Whipple patients in hospital wards.

Methods

We performed a study to analyze the relationship between the vital parameters and postoperative severe adverse events of patients after Whipple surgery in Guangdong Provincial People's Hospital from 2000 to 2017. In the retrospective study, a total of 13,651 sets of vital parameters in 236 Whipple postoperative patients were included. Subsequently, we applied a MEWS scoring system and explored the accuracy of the MEWS in evaluating the patients' final events versus advanced mathematical models. We then put the MEWS into the ward warning system and confirmed the accuracy of the MEWS based on the results of prospective studies again.

Results

We assessed the ability of the MEWS to predict postoperative complications with an accuracy rate of 90.86-91.23%, a sensitivity of 83.04-90.88%, and a specificity of 90.85-95.73%.

Conclusions

The MEWS model was applied to identify post-Whipple patients at risk of complication. Once the MEWS ≥2, interventions were needed to minimize the adverse events. Our data suggest that the MEWS is comparable to the advanced mathematical models, but MEWS is more accessible to perform and more generally applicable.

Rapid Response and Cardiac Arrest Teams: A Descriptive Analysis of 103 American Hospitals

Despite improvements in the management of in-hospital cardiac arrest over the past decade, in-hospital cardiac arrest continues to be associated with poor prognosis. This has led to the development of rapid response systems, hospital-wide efforts to improve patient outcomes by centering on prompt identification of decompensating patients, expert clinical management, and continuous quality improvement of processes of care. The rapid response system may include cardiac arrest teams, which are centered on identification and treatment of patients with in-hospital cardiac arrest. However, few evidence-based guidelines exist to guide the formation of such teams, and the degree of their variation across the United States has not been well described.

DESIGN

Descriptive cross-sectional, internet-based survey.

SETTING

Cohort of preidentified clinicians involved in their hospital's adult rapid response system across the United States.

SUBJECTS

Clinicians who had been identified by study team members using personal and professional contacts over a 7-month period from June 2018 to December 2018.

INTERVENTIONS

An 80-item survey was developed by the investigators. It sought information on the afferent (identification and notification of providers) and efferent (response of providers to patient) limbs of the rapid response system, as well as management of patients post in-hospital cardiac arrest.

MEASUREMENTS AND MAIN RESULTS

One-hundred fourteen surveys were distributed. Of these, 109 (96%) were completed. Six were duplicates and were excluded, leaving a total of 103 surveys from 103 hospitals in 30 states. Seventy-six percent of hospitals were academic, 30% were large hospitals (> 750 inpatient beds), and 58% had large ICUs (> 50 ICU beds). We found wide variation in the structure and function in both the afferent and efferent limbs of the rapid response system. The majority of hospitals had a rapid response team and a cardiac arrest team. Most rapid response teams contained a provider, a critical care nurse, and a respiratory therapist. In hospitals with training programs in internal medicine, anesthesia, emergency medicine, or critical care, 45% of rapid response teams and 75% of cardiac arrest teams were led by trainees, with inconsistent attending presence. Targeted temperature management and coronary catheterization were widely used post in-hospital cardiac arrest, but indications varied considerably.

CONCLUSIONS

We have demonstrated substantial variation in the structure and function of rapid response systems as well as in management of patients during and after in-hospital cardiac arrest.

Implementation of an automated early warning scoring system in a surgical ward: Practical use and effects on patient outcomes

Introduction

Early warning scores (EWS) are being increasingly embedded in hospitals over the world due to their promise to reduce adverse events and improve the outcomes of clinical patients.

The aim of this study was to evaluate the clinical use of an automated modified EWS (MEWS) for patients after surgery.

Methods

This study conducted retrospective before-and-after comparative analysis of non-automated and automated MEWS for patients admitted to the surgical high-dependency unit in a tertiary hospital. Operational outcomes included number of recorded assessments of the individual MEWS elements, number of complete MEWS assessments, as well as adherence rate to related protocols. Clinical outcomes included hospital length of stay, in-hospital and 28-day mortality, and ICU readmission rate.

Results

Recordings in the electronic medical record from the control period contained 7929 assessments of MEWS elements and were performed in 320 patients. Recordings from the intervention period contained 8781 assessments of MEWS elements in 273 patients, of which 3418 were performed with the automated EWS system. During the control period, 199 (2.5%) complete MEWS were recorded versus 3991 (45.5%) during intervention period. With the automated MEWS systems, the percentage of missing assessments and the time until the next assessment for patients with a MEWS of ≥2 decreased significantly. The protocol adherence improved from 1.1% during the control period to 25.4% when the automated MEWS system was involved. There were no significant differences in clinical outcomes.

Conclusion

Implementation of an automated EWS system on a surgical high dependency unit improves the number of complete MEWS assessments, registered vital signs, and adherence to the EWS hospital protocol. However, this positive effect did not translate into a significant decrease in mortality, hospital length of stay, or ICU readmissions. Future research and development on automated EWS systems should focus on data management and technology interoperability.

Patient and family-initiated escalation of care: a qualitative systematic review protocol

BACKGROUND

Despite the introduction of rapid response systems and early warning scores, clinical deterioration that is not recognised or responded to early enough prevails in acute care areas. One intervention that aims to address this issue and that is gaining increased attention is patient- and family-initiated escalation of care schemes. Existing systematic review evidence to date has tended to focus on identifying the impact or effectiveness of these schemes in practice. However, they have not tended to focus on qualitative evidence to consider the experience of deterioration and the factors that may promote or hinder engagement with these schemes in the practice setting. This systematic review will address this gap. The aim of this review is to explore patients', relatives' and healthcare professionals' experiences of deterioration and their perceptions of the barriers or facilitators to patient and family-initiated escalation of care in acute adult hospital wards.

METHODS

We will search Medline, CINAHL, Embase and PsycINFO databases using free-text and MESH terms relating to deterioration, family-initiated rapid response, families, patients, healthcare staff, hospital and experiences. We will search grey literature and reference lists of included studies for further published and unpublished literature. All studies with a qualitative design or method will be included. Two reviewers will independently assess studies for eligibility, extract data and appraise the quality of included studies. Data will be synthesised using a thematic synthesis approach, and findings will be presented narratively.

DISCUSSION

Patient- and family-initiated escalation of care schemes have been developed and implemented in several countries including the United States, the United Kingdom and Australia, but there is limited evidence regarding patients' or families' perceptions of deterioration or the barriers and facilitators to using these schemes in practice, particularly in acute adult areas. This systematic review will provide evidence for the development of a patient and family escalation of care scheme that can be tested in a feasibility study.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018106952.

Implications of long-term low-fidelity in situ simulation in acute care and association with a reduction in unexpected cardiac arrests: A retrospective research study

In situ simulation is a new tool for building teamwork during crisis. However, only a few studies have discussed the long-term effects of regular in situ simulations. To better understand these effects, this study retrospectively analyzed the effect of regular (twice a month over a four-year period) in situ simulations in the National Cheng Kung University Hospital acute care ward, which provides care for patients with acute illnesses and requires admission during an emergency room visit. The simulations were held in a real clinical environment using a low-fidelity mannequin and the trainees involved in the simulations were the medical staff of the acute care ward. In this study, we review the effects of such long-term simulations with respect to team performance based on the Ottawa global rating scale (GRS) and incidences of urgent intubation and unexpected cardiac arrest. Our results revealed that among the 84 simulations that were conducted during the study period, 42 could be categorized as “high performance” and the remaining 42 as “low performance” based on the team’s Ottawa GRS. Further, the seniority of nurse leaders and exposure of nurses to repeated simulations did not have any effect on performance. However, although regular simulations did not have any effect on the number of urgent intubations, they caused a marked decrease in the number of unexpected cardiac arrests. The current study did not show that repeated, low-fidelity, regular in situ simulations improve team performance in simulations based on Ottawa GRS, but it was associated with a reduction in the unexpected cardiac arrest rate in the acute care ward. Our results support the use of in situ simulations in acute care wards as an educational tool for first-line caregivers.

Characterising variation in composition and activation criteria of rapid response and cardiac arrest teams: a survey of Medicare participating hospitals in five American states

OBJECTIVES

To characterise the variation in composition, leadership, and activation criteria of rapid response and cardiac arrest teams in five north-eastern states of the USA.

DESIGN

Cross-sectional study consisting of a voluntary 46-question survey of acute care hospitals in north-eastern USA.

SETTING

Acute care hospitals in New York, New Jersey, Rhode Island, Vermont, and Pennsylvania.

PARTICIPANTS

Surveys were completed by any member of the rapid response team (RRT) with a working knowledge of team composition and function. Participants were all Medicare-participating acute care hospitals, including teaching and community hospitals as well as hospitals from rural, urban and suburban areas.

RESULTS

Out of 378 hospitals, contacts were identified for 303, and 107 surveys were completed. All but two hospitals had an RRT, 70% of which changed members daily. The most common activation criteria were clinical concern (95%), single vital sign abnormalities (77%) and early warning score (59%). Eighty one per cent of hospitals had a dedicated cardiac arrest team.RRT composition varied widely, with respiratory therapists, critical care nurses, physicians and nurse managers being the most likely to attend (89%, 78%, 64% and 51%, respectively). Consistent presence of critical care physicians was uncommon and both cardiac arrest teams and teams were frequently led by trainee physicians, often without senior supervision.

CONCLUSIONS

As the largest study to date in the USA, we have demonstrated wide heterogeneity, rapid team turnover and a lack of senior supervision of RRT and cardiac arrest teams. These factors likely contribute to the mixed results seen in studies of RRTs.

Use of National Early Warning Score for observation for increased risk for clinical deterioration during post-ICU care at a surgical ward

Purpose

Patients transferred from an intensive care unit (ICU) to a general ward are at risk for clinical deterioration. The aim of the study was to determine if an increase in National Early Warning Score (NEWS) value predicted worse outcomes in surgical ward patients previously treated in the ICU.

Patients and methods

A retrospective observational study was conducted in a cohort of gastrointestinal surgery patients after transfer from an ICU/high dependency unit (HDU). NEWS values were collected throughout the ward admission. Clinical deterioration was defined by ICU readmission or death. The ability of NEWS to predict clinical deterioration was determined using a linear mixed effect model.

Results

We included 124 patients, age 65.9±14.5, 60% males with an ICU Simplified Acute Physiology Score II 33.8±12.7. No patients died unexpectedly at the ward and 20 were readmitted to an ICU/HDU. The NEWS values increased by a mean of 0.15 points per hour (intercept 3.7, P<0.001) before ICU/HDU readmission according to the linear mixed effect model. NEWS at transfer from ICU was the only factor that predicted readmission (OR 1.32; 95% CI 1.01–1.72; P=0.04) at the time of admission to the ward.

Conclusion

Clinical deterioration of surgical patients was preceded by an increase in NEWS.

How do nurses use the early warning score in their practice? A case study from an acute medical unit

AIMS AND OBJECTIVES

This study aimed to describe how nurses use the early warning score (EWS) in an acute medical ward and their compliance with the EWS and explore their views and experiences of the EWS.

BACKGROUND

early warning score systems have been implemented in response to upward trends in mortality rates. Nurses play a central role in the use of EWS systems. However, barriers to their use have been identified and include behavioural, cultural and organisational approaches to adherence. Improvement strategies including education and training and electronic devices have assisted in compliance with the system.

DESIGN

A holistic single descriptive case study design was used.

METHODS

Data triangulation was used including non-participant observation, semi-structured interviews with nurses and document analysis. Nurses were observed using EWS and were subsequently interviewed. Data analysis was guided by systematic text condensation (STC), an approach underpinned by Giorgi's phenomenological method, where meaning units and themes are identified. The study adhered to the consolidated criteria for reporting qualitative research (COREQ) guidelines.

RESULTS

Three themes with associated meaning units were found. Protocol Adherence vs. Clinical Judgement addresses nurses' knowledge, skill and experience and patient assessment. Parameter Adjustment and Escalation included parameters not being adjusted or reviewed, junior doctors not being authorised to set parameters and escalation. The final theme Culture highlighted a task-driven approach and deficient communication processes.

CONCLUSION

This study highlights the need for ongoing training, behavioural change and a cultural shift by healthcare professionals and organisations to ensure adherence with EWS escalation protocols.

RELEVANCE TO CLINICAL PRACTICE

Improvements in education and training into recognition, management and communication of a deteriorating patient are required. Also, a cultural shift is needed to improve compliance and adherence with EWS practice. The potential use of electronic data should be explored.

Investigating the discriminative value of Early Warning Scores in patients with respiratory disease using a retrospective cohort analysis of admissions to Nottingham University Hospitals Trust over a 2-year period

OBJECTIVE

Early Warning Scores (EWSs) are used to monitor patients for signs of imminent deterioration. Although used in respiratory disease, EWSs have not been well studied in this population, despite the underlying cardiopulmonary pathophysiology often present. We examined the performance of two scoring systems in patients with respiratory disease.

DESIGN

Retrospective cohort analysis of vital signs observations of all patients admitted to a respiratory unit over a 2-year period. Scores were linked to outcome data to establish the performance of the National EWS (NEWS) compared results to a locally adapted EWS.

SETTING

Nottingham University Hospitals National Health Service Trust respiratory wards. Data were collected from an integrated electronic observation and task allocation system employing a local EWS, also generating mandatory referrals to clinical staff at set scoring thresholds.

OUTCOME MEASURES

Projected workload, and sensitivity and specificity of the scores in predicting mortality based on outcome within 24 hours of a score being recorded.

RESULTS

8812 individual patient episodes occurred during the study period. Overall, mortality was 5.9%. Applying NEWS retrospectively (vs local EWS) generated an eightfold increase in mandatory escalations, but had higher sensitivity in predicting mortality at the protocol cut points.

CONCLUSIONS

This study highlights issues surrounding use of scoring systems in patients with respiratory disease. NEWS demonstrated higher sensitivity for predicting death within 24 hours, offset by reduced specificity. The consequent workload generated may compromise the ability of the clinical team to respond to patients needing immediate input. The locally adapted EWS has higher specificity but lower sensitivity. Statistical evaluation suggests this may lead to missed opportunities for intervention, however, this does not account for clinical concern independent of the scores, nor ability to respond to alerts based on workload. Further research into the role of warning scores and the impact of chronic pathophysiology is urgently needed.

The impact of delayed critical care outreach team activation on in-hospital mortality and other patient outcomes: a historical cohort study

PURPOSE

Early warning scores (EWS) and critical care outreach teams (CCOT) have been developed to respond to decompensating patients. Nevertheless, controversy exists around their effectiveness. The primary objective of this study was to determine if a delay of ≥ 60 min between when a patient was identified as meeting EWS criteria and the CCOT was activated impacted in-hospital mortality.

METHODS

This was a historical cohort study evaluating all new CCOT activations over a four-year study period (1 June 2007 to 31 August 2011) for inpatients ≥ 18 yr of age at two academic tertiary care hospitals in London, Ontario, Canada. Multivariable logistic regression accounting for repeated measures was used to determine the effect of delayed CCOT activation on in-hospital mortality (primary outcome). Differences in outcomes between medical and surgical patients were also examined.

RESULTS

There were 3,133 CCOT activations for 1,684 (53.8%) medical patients and 1,449 (46.2%) surgical patients during the study period. The CCOT was activated < 60 min of a patient meeting EWS criteria in 2,160 (68.9%) cases and ≥ 60 min in 973 (31.1%) cases. Patients with ≥ 60 min delay were more likely be admitted to the intensive care unit (odds ratio [OR], 1.22; 95% confidence interval [CI], 1.07 to 1.47) and to suffer in-hospital mortality (OR, 1.30; 95% CI, 1.08 to 1.56). Irrespective of delay, surgical patients were less likely to experience in-hospital mortality than medical patients (OR, 0.46; 95% CI, 0.39 to 0.55).

CONCLUSION

Including the rates of delay in CCOT activation and the admitting service could be an additional step in exploring the conflicting results seen in the current literature assessing the impact of CCOT on patient outcomes.

Chief physicians' attitudes towards early warning score systems in Switzerland: Results of a cross-sectional survey

RATIONALE, AIMS AND OBJECTIVES

Early warning score systems (EWS-S) have been shown to be valuable tools to recognize otherwise unnoticed clinical deterioration (CDET) of patients. They have been associated with fewer unplanned transfers to the intensive care unit (UTICU) and lower in-hospital mortality. Little is known about their current usage in Switzerland and about the attitudes towards such tools among chief physicians. We aimed to assess the use of EWS-S in Switzerland and the attitudes of chief physicians towards EWS-S depending on previously experienced CDET followed by UTICU, reanimation, or death.

METHODS

Chief physicians of medical and surgical departments from all acute care hospitals in Switzerland were asked to participate within a project that aims to develop recommendations for the use of EWS-S in Switzerland (n = 118). The explorative study assessed perceived CDET, which led to UTICU, reanimation, or death of a patient, the knowledge and usage about different EWS-S and attitudes towards EWS-S in a written questionnaire. Means and percentages were used, and differences were assessed with independent t tests, chi-square, or Fisher exact test, as appropriate.

RESULTS

Adverse events based on CDET were reported frequently, and awareness among chief physicians was high. Less than half of the chief physicians knew tools that systematically assess CDET with one-fifth of responders reporting using tools at their department. Previous experiences of UTICU, reanimation, or death after due to CDET were associated with more positive attitudes towards EWS-S.

CONCLUSIONS

Adverse events based on CDET of patients are frequent and the awareness of this problem is high among chief physicians. Positive attitudes were more common with previous experiences of adverse events due to CDET. Our results strengthen the argumentation that the recommendation and future implementation of EWS-S in Switzerland would be meaningful.

Early in-hospital clinical deterioration is not predicted by severity of illness, functional status, or comorbidity

BACKGROUND

Prior studies concentrated on unplanned intensive care unit (ICU) transfer to gauge deterioration occurring shortly following hospital admission. However, examining only ICU transfers is not ideal since patients could stabilize with treatment, refuse ICU admission, or not require ICU evaluation. To further explore etiologies of early clinical deterioration, we used rapid response team (RRT) activation within 48 hours of admission as an index of early clinical worsening.

METHODS

A retrospective analysis of prospectively gathered admissions from the emergency department in an academic medical center was done. Data were reviewed independently by two physicians. We assessed severity of illness, functional status, comorbidity, the frequency of ICU and palliative care consultations, and changes in advance health care directives.

RESULTS

Of 655 rapid responses (RRs) within the study period, 24.6% occurred within 48 hours of admission. Disease trajectory was the most frequent perceived reason for RRs (55.6% and 58.9%, reviewer 1 and 2, respectively) followed by medical error (15.6% and 15.2%). Acute physiology and chronic health evaluation II (APACHE-II) and modified early warning scores (MEWS) were higher at the time of RR compared to admission (p<0.0001). However, admission APACHE-II, MEWS, functional status, and comorbidity scores did not predict early RRs. One third of RRs resulted in ICU consultation and 95% were accepted. Palliative care consults were requested for 15%, the majority (65%) after RR and all resulting in advance directive change.

CONCLUSION

Disease trajectory accounted for most clinical deterioration and medical error contributed to 15%. Our data suggest that it is difficult to predict early clinical deterioration as none of the measured parameters were associated with RRT activation.

Early warning systems and rapid response to the deteriorating patient in hospital: A realist evaluation

AIM

To test the Rapid Response Systems programme theory against actual practice components of the Rapid Response Systems implemented to identify those contexts and mechanisms which have an impact on the successful achievement of desired outcomes in practice.

BACKGROUND

Rapid Response Systems allow deteriorating patients to be recognized using Early Warning Systems, referred early via escalation protocols and managed at the bedside by competent staff.

DESIGN

Realist evaluation.

METHODS

The research design was an embedded multiple case study approach of four wards in two hospitals in Northern Ireland which followed the principles of Realist Evaluation. We used various mixed methods including individual and focus group interviews, observation of nursing practice between June-November 2010 and document analysis of Early Warning Systems audit data between May-October 2010 and hospital acute care training records over 4.5 years from 2003-2008. Data were analysed using NiVivo8 and SPPS.

RESULTS

A cross-case analysis highlighted similar patterns of factors which enabled or constrained successful recognition, referral and response to deteriorating patients in practice. Key enabling factors were the use of clinical judgement by experienced nurses and the empowerment of nurses as a result of organizational change associated with implementation of Early Warning System protocols. Key constraining factors were low staffing and inappropriate skill mix levels, rigid implementation of protocols and culturally embedded suboptimal communication processes.

CONCLUSION

Successful implementation of Rapid Response Systems was dependent on adopting organizational and cultural changes that facilitated staff empowerment, flexible implementation of protocols and ongoing experiential learning.

Daily surveillance with early warning scores help predicthospital mortality in medical wards

BACKGROUND/AIM

To analyze the potency of a modified early warning score (EWS) to help predict hospital mortality when used for surveillance in nonacute medical wards.

MATERIALS AND METHODS

Patients in internal medicine wards were prospectively recruited. First, highest, and last scores; and mean daily score recordings and values were recorded. Nurses calculated scores for each patient upon admission and every 4 h. The last score was the score before death, discharge, or transfer to another ward. The highest scores in total and for each single parameter were used for analysis.

RESULTS

Fifty-nine percent of 182 recruited patients had recordings eligible for data analysis. Patients admitted from the emergency room had higher mortality rates than patients admitted from outpatient clinics (15% vs. 1.5%; P = 0.01) as well as patients whose first (40% vs. 4.9%; P = 0.033) and highest scores (18.8% vs. 1.3%; P = 0.003) were equal to or more than 3. The first recorded EWS was not predictive for mortality while the maximum score during the admission period was.

CONCLUSION

This study underlines the fact that each physiological variable of EWS may not have the same weight in determining the outcome.

Scoping review: The use of early warning systems for the identification of in-hospital patients at risk of deterioration

INTRODUCTION

Early warning systems (EWS) were developed as a means of alerting medical staff to patient clinical decline. Since 85% of severe adverse events are preceded by abnormal physiological signs, the patient bed-side vital signs observation chart has emerged as an EWS tool to help staff identify and quantify deteriorating patients. There are three broad categories of patient observation chart EWS: single or multiple parameter systems; aggregated weighted scoring systems; or combinations of single or multiple parameter and aggregated weighted scoring systems.

OBJECTIVE

This scoping review is an overview of quantitative studies and systematic reviews examining the efficiency of the adult EWS charts in the recognition of in-hospital patient deterioration.

METHOD

A broad search was undertaken of peer-reviewed publications, official government websites and databases housing research theses, using combinations of keywords and phrases.

DATA SOURCES

CINAHL with full text; MedLine, PsycINFO, MasterFILE Premier, GreenFILE and ScienceDirect. Also, the Cochrane Library database, Department of Health government websites and Ethos, ProQuest and Trove databases were searched.

EXCLUSIONS

Paediatric, obstetric and intensive care studies, studies undertaken at the point of hospital admission or pre-admission, non-English publications and editorials.

RESULTS

Five hundred and sixty five publications, government documents, reports and theses were located of which 91 were considered and 21 were included in the scoping review. Of the 21 publications eight studies compared the efficacy of various EWS and 13 publications validated specific EWS.

CONCLUSIONS

There is low level quantitative evidence that EWS improve patient outcomes and strong anecdotal evidence that they augment the ability of the clinical staff to recognise and respond to patient decline, thus reducing the incidence of severe adverse events. Although aggregated weighted scoring systems are most frequently used, the efficiency of the specific EWS appears to be dependent on the patient cohort, facilities available and staff training and attitude. While the review demonstrates support for EWS, researchers caution that given the contribution of human factors to the EWS decision-making process, patient EWS charts alone cannot replace good clinical judgment.

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.