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

Development and validation of the early warning system scores ontology

BACKGROUND

Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems.

METHODS

We apply the Software Development Lifecycle Framework-conceived by Winston Boyce in 1970-to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building.

RESULTS

We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time.

CONCLUSIONS

Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient.

Implementing a Rapid Response Team: A Quality Improvement Project in a Low- to Middle-Income Country

BACKGROUND

The benefits of rapid response teams (RRTs) have been controversial with few studies conducted in low- to middle-income countries.

OBJECTIVE

The aim of this study was to investigate the effectiveness of implementing an RRT on 4 patient outcomes.

METHODS

We conducted a quality improvement pre-and-post design using the Plan-Do-Study-Act model in a tertiary hospital in a low- to middle-income country. We collected data before and after implementing the RRT in 4 phases and over 4 years.

RESULTS

Survival to discharge after cardiac arrest was 25.0% per 1000 discharges in 2016 and increased to 50% in 2019, a 50% increase. The rate of activations per 1000 discharges was 20.45% for the code team in 2016 and 33.6% for the RRT team in 2019. Thirty-one patients who arrested were transferred to a critical care unit before implementing the RRT, and 33% of such patients were transferred after. The time it took the code team to arrive at the bedside was 3.1 minutes in 2016 and decreased to 1.7 minutes for the RRT team to arrive in 2019, a 46% decrease.

DISCUSSION AND CLINICAL IMPLICATIONS

Implementing an RTT led by nurses in a low- to middle-income country increased the survival rate of patients who had a cardiac arrest by 50%. The role of nurses in improving patient outcomes and saving lives is substantial and empowers nurses to call for assistance to save patient lives who show early signs of a cardiac arrest. Hospital administrators should continue to use strategies to improve nurses' timely response to the clinical deterioration of patients and to continue to collect data to assess the effect of the RRT over time.

Is Modified Early Warning Score associated with clinical outcomes of patients admitted to a university internal medicine ward?

OBJECTIVE

To assess the MEWS association with the clinical outcomes (CO) of patients admitted to an internal medicine ward (IMW) at a Brazilian university hospital (UH).

INTRODUCTION

It is important to quickly identify patients with clinical deterioration, especially in wards. The health team must recognize and act before the situation becomes an adverse event. In Brazil, nurses' work to overcome performance myths and the application of standardized predictive scales for patients in wards is still limited.

DESIGN

An observational cohort study designed and developed by a registered nurse that followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

METHODS

Data were collected from the IMW of a UH located in the city of São Paulo, Brazil (2017). An ROC curve was calculated to strengthen the use of a MEWS of < or ≥ 4 as a cutoff. CO of the two subgroups were compared.

RESULTS

Three hundred patients completed the study; their vital signs were recorded consecutively throughout hospitalization in the IMW. The highest MEWS value each day was considered for analysis. Scores < 4 were significantly associated with a higher probability of hospital discharge, a lower chance of transfer to the ICU, a lower total number of days of hospitalization, and a lower risk of death. Score ≥ 4 had worse CO (orotracheal intubation and cardiac monitoring), transfer to the ICU, and increased risk of death.

CONCLUSION

Scores < 4 were associated with positive outcomes, while scores ≥ 4 were associated with negative outcomes. MEWS can help prioritize interventions, increase certainty in decision-making, and improve patient safety, especially in a teaching IMW with medical teams undergoing professional development, thereby ensuring the central role of the nursing team in Brazil.

RELEVANCE FOR CLINICAL PRACTICE

MEWS aid nurses in identifying and managing patients, prioritizing interventions through assertive decision-making.

Immediate postpartum care in low- and middle-income countries: A gap in healthcare quality research and practice

The immediate postpartum period carries significant risks for complications such as postpartum hemorrhage and sepsis. Postpartum monitoring, including taking vital signs and monitoring blood loss, is important for the early identification and management of complications, but many women in low- and middle-income countries receive minimal attention in the period following childbirth to facility discharge. The World Health Organization recently released new guidelines on postnatal care, which include recommendations for immediate postpartum monitoring. In light of the new guidelines, this presented an opportune moment to address the gaps in postpartum monitoring in low- and middle-income countries. In this commentary, we bring attention to the importance of immediate postpartum monitoring. We identified opportunities for strengthening this often overlooked aspect of maternity care through improvements in quality measurement and data availability, research into barriers against high-quality care, and innovations in service delivery design.

The application value of the Modified Early Warning Score combined with age and injury site scores in the evaluation of injuries in emergency trauma patients

Objective

To explore the application value of the Modified Early Warning Score (MEWS) combined with age and injury site scores in predicting the criticality of emergency trauma patients.

Methods

The traditional MEWS was modified by combining it with age and injury site scores to form a new MEWS combined scoring standard. The clinical data were collected from a total of 372 trauma patients from the emergency department of the Nantong First People's Hospital between June and December 2019. A retrospective analysis was conducted, and the patients were scored using the MEWS combined with age and injury site scores. The patients were grouped according to their prognoses and clinical outcomes. A statistical analysis was conducted based on the ranges of the combined scores, and the results of the combined scores of the different groups were compared.

Results

Among the 372 patients, the average score was 3.68 ± 1.25 points in the survival group, 8.33 ± 2.24 points in the death within 24 h group, and 8.38 ± 1.51 points in the death within 30 days of hospitalization group, and the differences were statistically significant (p < 0.05). The average score was 2.74 ± 0.69 points in the outpatient treatment group, 4.19 ± 0.72 points in the emergency stay group, 5.40 ± 0.70 points in the specialist inpatient group, 8.71 ± 2.31 points in the ICU group, and 7.82 ± 1.66 points in the specialist unplanned transfer to ICU group, with the differences between the groups being statistically significant (p < 0.05). The average length of hospital stay for patients with a joint score within the range of 6-8 points was 10.86 ± 2.47 days, with a direct ICU admission rate of 22.00% and an unplanned ICU admission rate of 16.00%. Patients with a joint score >8 points had an average length of hospital stay of 27.05 ± 4.85 days, with a direct ICU admission rate of 66.67% and an unplanned ICU admission rate of 33.33%.

Conclusion

Age and injury site are important high-risk indicators for trauma assessment, and using them in combination with the MEWS could improve the assessment of emergency patients with trauma, increasing the accuracy of pre-screening triage and reducing rescue time. Therefore, this joint scoring method might be worthy of clinical promotion and application.

Construct and clinical verification of a nurse-led rapid response systems and activation criteria

Background

Effective team leadership and good activation criteria can effectively initiate rapid response system (RRS) to reduce hospital mortality and improve quality of life. The first reaction time of nurses plays an important role in the rescue process. To construct a nurse-led (nurse-led RRS) and activation criteria and then to conduct a pragmatic evaluation of the nurse-led RRS.

Methods

We used literature review and the Delphi method to construct a nurse-led RRS and activation criteria based on the theory of “rapid response system planning.” Then, we conducted a quasi-experimental study to verify the nurse-led RRS. The control group patients were admitted from August to October 2020 and performed traditional rescue procedures. The intervention group patients were admitted from August to October 2021 and implemented nurse-led RRS. The primary outcome was success rate of rescue.

Setting

Emergency department, Gansu Province, China.

Results

The nurse-led RRS and activation criteria include 4 level 1 indicators, 14 level 2 indicators, and 88 level 3 indicators. There were 203 patients who met the inclusion criteria to verify the nurse-led RRS. The results showed that success rate of rescue in intervention group (86.55%) was significantly higher than that in control group (66.5%), the rate of cardiac arrest in intervention group (33.61%) was significantly lower than that in control group (72.62%), the effective rescue time of intervention group (46.98 ± 12.01 min) was shorter than that of control group (58.67 ± 13.73 min), and the difference was statistically significant (P < 0.05). The rate of unplanned ICU admissions in intervention group (42.85%) was lower than that in control group (44.04%), but the difference was not statistically significant (P > 0.05).

Conclusions

The nurse-led RRS and activation criteria can improve the success rate of rescue, reduce the rate of cardiac arrest, shorten the effective time of rescue, effectively improve the rescue efficiency of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-022-01087-7.

Early changes in hospital resuscitation practices during the COVID-19 pandemic

Background

The coronavirus disease 2019 (COVID-19) pandemic resulted in many disruptions in care for patients experiencing in-hospital cardiac arrest (IHCA). We sought to identify changes made in hospital resuscitation practices during progression of the COVID-19 pandemic.

Methods

We conducted a descriptive qualitative study using in-depth interviews of clinical staff leadership involved with resuscitation care at a select group of U.S. acute care hospitals in the national American Heart Association Get With The Guidelines-Resuscitation registry for IHCA. We focused interviews on resuscitation practice changes for IHCA since the initiation of the COVID-19 pandemic. We used rapid analysis techniques for qualitative data summarization and analysis.

Results

A total of 6 hospitals were included with interviews conducted with both physicians and nurses between November 2020 and April 2021. Three topical themes related to shifts in resuscitation practice through the COVID-19 pandemic were identified: 1) ensuring patient and provider safety and wellness (e.g., use of personal protective equipment); 2) changing protocols and training for routine educational practices (e.g., alterations in mock codes and team member roles); and 3) goals of care and end of life discussions (e.g., challenges with visitor and family policies). We found advances in leveraging technology use as an important topic that helped institutions address challenges across all 3 themes.

Conclusions

Early on, the COVID-19 pandemic resulted in many changes to resuscitation practices at hospitals placing an emphasis on enhanced safety, training, and end of life planning. These lessons have implications for understanding how systems may be better designed for resuscitation efforts.

A Retrospective Cohort Study Evaluating the Use of the Modified Early Warning Score to Improve Outcome Prediction in Neurosurgical Patients

Introduction

The modified early warning score (mEWS) has been used to identify decompensating patients in critical care settings, potentially leading to better outcomes and safer, more cost-effective patient care. We examined whether the admission or maximum mEWS of neurosurgical patients was associated with outcomes and total patient costs across neurosurgical procedures.

Methods

This retrospective cohort study included all patients hospitalized at a quaternary care hospital for neurosurgery procedures during 2019. mEWS were automatically generated during a patient’s hospitalization from data available in the electronic medical record. Primary and secondary outcome measures were the first mEWS at admission, maximum mEWS during hospitalization, length of stay (LOS), discharge disposition, mortality, cost of hospitalization, and patient biomarkers (i.e., white blood cell count, erythrocyte sedimentation rate, C-reactive protein, and procalcitonin).

Results

In 1,408 patients evaluated, a mean first mEWS of 0.5 ± 0.9 (median: 0) and maximum mEWS of 2.6 ± 1.4 (median: 2) were observed. The maximum mEWS was achieved on average one day (median = 0 days) after admission and correlated with other biomarkers (p < 0.0001). Scores correlated with continuous outcomes (i.e., LOS and cost) distinctly based on disease types. Multivariate analysis showed that the maximum mEWS was associated with longer stay (OR = 1.8; 95% CI = 1.6-1.96, p = 0.0001), worse disposition (OR = 0.82, 95% CI = 0.71-0.95, p = 0.0001), higher mortality (OR = 1.7; 95% CI = 1.3-2.1, p = 0.0001), and greater cost (OR = 1.2, 95% CI = 1.1-1.3, p = 0.001). Machine learning algorithms suggested that logistic regression, naïve Bayes, and neural networks were most predictive of outcomes.

Conclusion

mEWS was associated with outcomes in neurosurgical patients and may be clinically useful. The composite score could be integrated with other clinical factors and was associated with LOS, discharge disposition, mortality, and patient cost. mEWS also could be used early during a patient's admission to stratify risk. Increase in mEWS scores correlated with the outcome to a different degree in distinct patient/disease types. These results show the potential of the mEWS to predict outcomes in neurosurgical patients and suggest that it could be incorporated into clinical decision-making and/or monitoring of neurosurgical patients during admission. However, further studies and refinement of mEWS are needed to better integrate it into patient care.

Accurate Ballistocardiogram Based Heart Rate Estimation Using an Array of Load Cells in a Hospital Bed

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by q-values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

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.

Implementation of an Electronic National Early Warning System to Decrease Clinical Deterioration in Hospitalized Patients at a Tertiary Medical Center

The National Early Warning Score (NEWS) is an early warning system that predicts clinical deterioration. The impact of the NEWS on the outcome of healthcare remains controversial. This study was conducted to evaluate the effectiveness of implementing an electronic version of the NEWS (E-NEWS), to reduce unexpected clinical deterioration. We developed the E-NEWS as a part of the Health Information System (HIS) and Nurse Information System (NIS). All adult patients admitted to general wards were enrolled into the current study. The “adverse event” (AE) group consisted of patients who received cardiopulmonary resuscitation (CPR), were transferred to an intensive care unit (ICU) due to unexpected deterioration, or died. Patients without AE were allocated to the control group. The development of the E-NEWS was separated into a baseline (October 2018 to February 2019), implementation (March to August 2019), and intensive period (September. to December 2019). A total of 39,161 patients with 73,674 hospitalization courses were collected. The percentage of overall AEs was 6.06%. Implementation of E-NEWS was associated with a significant decrease in the percentage of AEs from 6.06% to 5.51% (p = 0.001). CPRs at wards were significantly reduced (0.52% to 0.34%, p = 0.012). The number of patients transferred to the ICU also decreased significantly (3.63% to 3.49%, p = 0.035). Using multivariate analysis, the intensive period was associated with reducing AEs (p = 0.019). In conclusion, we constructed an E-NEWS system, updating the NEWS every hour automatically. Implementing the E-NEWS was associated with a reduction in AEs, especially CPRs at wards and transfers to ICU from ordinary wards.

The EMS Modified Early Warning Score (EMEWS): A Simple Count of Vital Signs as a Predictor of Out-of-Hospital Cardiac Arrests

Objective: For patients at risk for out-of-hospital cardiac arrest (OHCA) after Emergency Medical Services (EMS) arrival, outcomes may be mitigated by identifying impending arrests and intervening before they occur. Tools such as the Modified Early Warning Score (MEWS) have been developed to determine the risk of arrest, but involve relatively complicated algorithms that can be impractical to compute in the prehospital environment. A simple count of abnormal vital signs, the "EMS Modified Early Warning Score" (EMEWS), may represent a more practical alternative. We sought to compare to the ability of MEWS and EMEWS to identify patients at risk for EMS-witnessed OHCA.Methods: We conducted a retrospect analysis of the 2018 ESO Data Collaborative database of EMS encounters. Patients without cardiac arrest before EMS arrival were categorized into those who did or did not have an EMS-witnessed arrest. MEWS was evaluated without its temperature component (MEWS-T). The performance of MEWS-T and EMEWS in predicting EMS witnessed arrest was evaluated by comparing receiver-operating characteristic curves.Results: Of 369,064 included encounters, 4,651 were EMS witnessed arrests. MEWS-T demonstrated an area under the curve (AUC) of 0.79 (95% CI: 0.79 - 0.80), with 86.8% sensitivity and 51.0% specificity for MEWS-T ≥ 3. EMEWS demonstrated an AUC of 0.74 (95% CI: 0.73 - 0.75), with 81.3% sensitivity and 53.9% specificity for EMEWS ≥ 2.Conclusions: EMEWS showed a similar ability to predict EMS-witnessed cardiac arrest compared to MEWS-T, despite being significantly simpler to compute. Further study is needed to evaluate whether the implementation of EMEWS can aid EMS clinicians in anticipating and preventing OHCA.

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.

Evaluation of the blue code system established in the health campus of a university hospital

OBJECTIVE

We report the hospital outcomes after implementing the blue code system in our hospital and health campus. We also aimed to determine factors related to mortality.

METHODS

This is a retrospective observational study of the patients who received cardiopulmonary resuscitation (CPR). All blue code calls for all age groups between March 15, 2013, and April 30, 2015 were analyzed. Logistic regression analysis was performed to find independent predictors of in-hospital mortality.

RESULTS

A total of 155 patients from the blue code calls were evaluated. Return of spontaneous circulation was achieved in 45.5% of patients, and 54.8% of the patients had died at the end of the CPR. The hospital discharge rate was 20%. Of all patients, 65% were adults with a survival rate of 7.9%, whereas pediatric patients had a 44.2% survival rate. Asystole and pulseless electrical activity were the predominant electrocardiography rhythms in 92.4% of patients. The comparison of survivors and nonsurvivors revealed that nonsurvivors were older, had more cancer as the comorbidity, had a more cardiac arrest, and sepsis as the underlying cause and had >20 min of CPR. The logistic regression analysis demonstrated the independent risk factors for mortality as arrest at a hospital ward, and sepsis as the underlying cause and being adult patient.

CONCLUSION

The performance of the blue code system should be evaluated periodically. Every effort should be made to prevent unexpected cardiac arrests and increase hospital discharge with good neurologic outcomes.

Comparison of within 7 Day All-Cause Mortality among HDU Patients with Modified Early Warning Score of ≥5 with those with Score of <5

Objective

To compare 7-Day All-Cause Mortality among HDU Patients with Modified Early Warning Score of ≥5 with Those with Score of <5.

Methods

All patients of age more than 18 years, of either gender admitted in HDU of Medical Unit-II, CHK between September 2019 to February 2020 were included. MEWS was calculated for each patient at time of admission. Patients with MEWS score of ≥5 were allocated to Group-A and those with score of <5 were allocated to Group-B. Patients were followed for seven days and outcome status of alive, expired or discharged was noted.

Results

Total of 336 patients were selected out of which 168 patients was inducted in Group-A and 168 patients in Group-B. MEWS Score in patients who expired was significantly higher (Mdn=11) than in those who survived (Mdn=4), p <.001. 7-day mortality in Group-A was 62 (39.9%) while in Group-B was 40 (23.8%). ROC was plotted of MEWS Score for mortality, it showed significant area under curve of 68.4% (p <.001, 95% CI = .62 to .75). MEWS Score of 3.5 showed sensitivity of 89.2% and specificity of 65%.

Conclusion

Our results show that MEWS has a positive trend to predict mortality. MEWS score of 3.5 is suggested cut off based on ROC in our study.

MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

Implementation of a Vital Sign Alert System to Improve Outcomes

BACKGROUND

Patients at risk for clinical deterioration often show changes in vital signs up to 24 hours before a critical event. Use of modified early warning scores has demonstrated effectiveness in identifying patients at risk for clinical deterioration and improving outcomes.

LOCAL PROBLEM

Documentation of vital signs, timely recognition of clinical deterioration, and compliance with the sepsis bundles remained a challenge.

METHODS

An interprofessional team developed an electronic vital sign alert (VSA) system with a concurrent running sepsis screen, along with clinical protocols.

INTERVENTIONS

Education was provided and the VSA system was implemented on 3 nursing units.

RESULTS

After implementation, the number of unplanned transfers to the intensive care unit increased. Mortality rate and length of stay in the intensive care unit for patients transferred for respiratory failure and sepsis significantly decreased. There was a 21% increase in identification of sepsis.

CONCLUSIONS

The VSA system was an effective tool to identify patients at risk for clinical deterioration and help to improve outcomes.

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

BACKGROUND

The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.

OBJECTIVE

The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.

METHODS

Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.

RESULTS

The EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.

CONCLUSIONS

In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

The Impact of an Electronic Patient Bedside Observation and Handover System on Clinical Practice: Mixed-Methods Evaluation

Background

Patient safety literature has long reported the need for early recognition of deteriorating patients. Early warning scores (EWSs) are commonly implemented as “track and trigger,” or rapid response systems for monitoring and early recognition of acute patient deterioration. This study presents a human factors evaluation of a hospital-wide transformation in practice, engendered by the deployment of an innovative electronic observations (eObs) and handover system. This technology enables real-time information processing at the patient’s bedside, improves visibility of patient data, and streamlines communication within clinical teams.

Objective

The aim of this study was to identify improvement and deterioration in workplace efficiency and quality of care resulting from the large-scale imposition of new technology.

Methods

A total of 85 hours of direct structured observations of clinical staff were carried out before and after deployment. We conducted 40 interviews with a range of clinicians. A longitudinal analysis of critical care audit and electronically recorded patient safety incident reports was conducted. The study was undertaken in a large secondary-care facility in the United Kingdom.

Results

Roll-out of eObs was associated with approximately 10% reduction in total unplanned admissions to critical care units from eObs-equipped wards. Over time, staff appropriated the technology as a tool for communication, workload management, and improving awareness of team capacity. A negative factor was perceived as lack of engagement with the system by senior clinicians. Doctors spent less time in the office (68.7% to 25.6%). More time was spent at the nurses’ station (6.6% to 41.7%). Patient contact time was more than doubled (2.9% to 7.3%).

Conclusions

Since deployment, clinicians have more time for patient care because of reduced time spent inputting and accessing data. The formation of a specialist clinical team to lead the roll-out was universally lauded as the reason for success. Staff valued the technology as a tool for managing workload and identified improved situational awareness as a key benefit. For future technology deployments, the staff requested more training preroll-out, in addition to engagement and support from senior clinicians.

Initial Rhythm and Resuscitation Outcomes for Patients Developing Cardiac Arrest in Hospital: Data From Low-Middle Income Country

BACKGROUND

Health care resource allocation remains challenging in lower middle income countries such as Kenya with meager resources being allocated to resuscitation and critical care. The causes and outcomes for in-hospital cardiac arrest and resuscitation have not been studied.

OBJECTIVES

This study sought to determine the initial rhythm and the survival for patients developing in-hospital cardiac arrest.

METHODS

This was a prospective study for in-hospital cardiac arrest in 6 Kenyan hospitals from July 2014 to April 2016. Resuscitation teams were utilized to collect data during resuscitation using a standardized protocol. Patients with do-not-resuscitate orders, trauma, postsurgical, and pregnancy-related complications were excluded. The Modified Early Warning Score (MEWS)- systolic blood pressure, heart rate, respiration rate, temperature, and responsiveness-was determined based on worst parameters at least 4 hours prior to the arrest.

RESULTS

A total of 353 patients with cardiac arrest were included over 19 months. The mean age was 61 years, 53.5% were male, and admission diagnoses included cardiovascular disease (15%), pneumonia 18.13%, and cancer 9%. The mean MEWS was 4.48 and low, intermediate, and high MEWS were found in 25.8%, 29.5%, and 44.8%, respectively. The mean time to cardiopulmonary resuscitation was 0.84 min. The initial rhythm was asystole in 47.6%, pulseless electrical activity in 38.2%, ventricular tachycardia/ventricular fibrillation in 5.4%, and unknown in 8.8%. Return of spontaneous circulation (ROSC) occurred in 29.2% of patients with the mean time to ROSC being 5.3 min. ROSC occurred in 17.3% of patients with asystole, 40.7% in pulseless electrical activity, 57.9% in ventricular tachycardia/ventricular fibrillation, and 25.8% in patients with an unknown rhythm. Of all patients, 16 (4.2%) were discharged alive.

CONCLUSIONS

Nonshockable rhythms account for the majority of the cardiac arrests in hospitals in a lower middle income country and are associated with unfavorable outcomes. Future work should be directed to training health care personnel in recognizing early warning signs and implementing appropriate measures in a resource-scarce environment.

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.

Enhanced early warning system impact on nursing practice: A phenomenological study

AIM

To determine how an enhanced early warning system has an impact on nursing practice.

BACKGROUND

Early warning systems score physiologic measures and alert nurses to subtle changes in patient condition. Critics of early warning systems have expressed concern that nurses would rely on a score rather than assessment skills and critical thinking to determine the need for intervention. Enhancing early warning systems with innovative technology is still in its infancy, so the impact of an enhanced early warning system on nursing behaviours or practice has not yet been studied.

DESIGN

Phenomenological design.

METHODS

Scripted, semistructured interviews were conducted in September 2015 with 25 medical/surgical nurses who used the enhanced early warning system. Data were analysed using thematic analysis techniques (coding and bracketing). Emerging themes were examined for relationships and a model describing the enhanced early warning system experience was developed.

FINDINGS

Nurses identified awareness leading to investigation and ease of prioritization as the enhanced early warning system's most important impact on their nursing practice. There was also an impact on organizational culture, with nurses reporting improved communication, increased collaboration, increased accountability and proactive responses to early changes in patient condition.

CONCLUSIONS

Rather than hinder critical thinking, as many early warning systems' critics claim, nurses in this study found that the enhanced early warning system increased their awareness of changes in a patient's condition, resulting in earlier response and reassessment times. It also had an impact on the organization by improving communication and collaboration and supporting a culture of proactive rather than reactive response to early signs of deterioration.

Maternal early warning systems-Towards reducing preventable maternal mortality and severe maternal morbidity through improved clinical surveillance and responsiveness

Despite increasing awareness of obstetric safety initiatives, maternal mortality and severe maternal morbidity in the United States have continued to increase over the past 20 years. Since results from large-scale surveillance programs suggest that up to 50% of maternal deaths may be preventable, new efforts are focused on developing and testing early warning systems for the obstetric population. Early warning systems are a set of specific clinical signs or symptoms that trigger the awareness of risk and an urgent patient evaluation, with the goal of reducing severe morbidity and mortality through timely diagnosis and treatment. Early warning systems have proven effective at predicting and reducing mortality and severe morbidity in medical, surgical, and critical care patient populations; however, there has been limited research on how to adapt these tools for use in the obstetric population, where physiologic changes of pregnancy render them inadequate. In this article, we review the available obstetric early warning systems and present evidence for their use in reducing maternal mortality and severe maternal morbidity. We also discuss considerations and strategies for implementation and acceptance of these early warning systems for clinical use in obstetrics.

House staff-led interdisciplinary morbidity and mortality conference promotes systematic improvement

BACKGROUND

Improvements in patient safety are critical to improving clinical outcomes. We present a resident-led interdisciplinary morbidity and mortality (M&M) conference utilizing postconference task forces to identify unique system issues, classify key contributors to interdisciplinary complications, and implement systems solutions. The conference also served to facilitate resident involvement in quality improvement projects.

MATERIALS AND METHODS

Members of the UNC Housestaff Council designed and implemented a hospital-wide M&M conference. Cases involving two or more service lines and resulting from systematic failures were selected for presentation by an interdisciplinary group of residents involved in the patient's care. Postconference task forces addressed problems and developed initiatives to improve care.

RESULTS

Of the 15 cases presented, 60% were attributable to an error in judgment, 26% to an error in diagnosis, and 13% to an error in technique. Communication (67%), coordination/care utilization (47%), poor process/workflow (40%), and inadequate training (33%) were the main associated contributing factors. Poor communication contributed to all complications resulting from an error in judgment. Inadequate training and poor workflow were the most common contributing factors with an error in technique. Poor utilization of care and inadequate processes were most common with an error in diagnosis. Postconference task forces identified system-based improvement projects in 73% (11 of 15) of cases with 82% (9 of 11) of projects successfully implemented or in process.

CONCLUSIONS

House staff-led interdisciplinary M&M conference utilizing postconference task forces is an ideal setting to identify unique system issues and implement system-based improvement strategies.

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.

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

Background

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

Methods

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

Results

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

Conclusions

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

A computational approach to mortality prediction of alcohol use disorder inpatients

BACKGROUND

Health information technologies can assist clinicians in the Intensive Care Unit (ICU) by providing additional analysis of patient stability. However, because patient diagnoses can be confounded by chronic alcohol use, the predictive value of existing systems is suboptimal. Through the use of Electronic Health Records (EHR), we have developed computer software called AutoTriage to generate accurate predictions through multi-dimensional analysis of clinical variables. We analyze the performance of AutoTriage on the Alcohol Use Disorder (AUD) subpopulation in this study, and build on results we reported for AutoTriage performance on the general population in previous work.

METHODS

AUD-related ICD-9 codes were used to obtain a patient population from MIMIC III ICU dataset for a retrospective study. Patient mortality risk score is generated through analysis of eight EHR-based clinical variables. The score is determined by combining weighted subscores, each of which are obtained from singlets, doublets or triplets of one or more of the eight continuous-valued clinical variable inputs. A temporally updating risk score is computed with a continuously revised 12-hour mortality prediction.

RESULTS

Among AUD patients, in a non-overlapping test set, AutoTriage outperforms existing systems with an Area Under Receiver Operating Characteristic (AUROC) value of 0.934 for 12-h mortality prediction. At a sensitivity of 90%, AutoTriage achieves a specificity of 80%, positive predictive value of 40%, negative predictive value of 89%, and an Odds Ratio of 36.

CONCLUSIONS

For mortality prediction, AutoTriage demonstrates improvements in both the accuracy and the Odds Ratio over current systems among the AUD patient population.

Emergency department Modified Early Warning Score association with admission, admission disposition, mortality, and length of stay

BACKGROUND

Geisinger Health System implemented the Modified Early Warning Score (MEWS) in 2011 and is fully integrated to the Electronic Medical Record (EMR). Our objective was to assess whether the emergency department (ED) MEWS (auto-calculated by EMR) is associated with admission to the hospital, admission disposition, inpatient mortality, and length of stay (LOS) 4 years after its implementation.

METHODS

A random sample of 3,000 patients' first encounter in the ED was extracted in the study period (between January 1, 2014 and May 31, 2015). Logistic regression was done to analyze whether mean, maximum, and median ED MEWS is associated with admission disposition, mortality, and LOS.

RESULTS

Mean, maximum, and median ED MEWS is associated with admission to the hospital, admission disposition, and mortality. It correlates weakly with LOS.

CONCLUSION

MEWS can be integrated to the EMR, and the score automatically generated still helps predict catastrophic events. MEWS can be used as a triage tool when deciding whether and where patients should be admitted.