Modified Early Warning Score as a predictor of intensive care unit readmission within 48 hours: a retrospective observational study

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.

Mortality Rates in Early versus Late Intensive Care Unit Readmission

Background

ICU readmission is associated with poor outcomes. Few studies have directly compared the outcomes of early versus late readmissions, especially in Saudi Arabia.

Objective

To compare the outcomes between early and late ICU readmissions, mainly with regards to hospital mortality.

Methods

This retrospective study included unique patients who, within the same hospitalization, were admitted to the ICU, discharged to the general wards, and then readmitted to the ICU of King Saud Medical City, Riyadh, Saudi Arabia, between January 01, 2015, and June 30, 2022. Patients readmitted within 2 calendar days were grouped into the Early readmission group, while those readmitted after 2 calendar days were in the Late readmission group.

Results

A total of 997 patients were included, of which 753 (75.5%) belonged to the Late group. The mortality rate in the Late group was significantly higher than that in the Early group (37.6% vs. 29.5%, respectively; 95% CI: 1%-14.8%; P = 0.03). The readmission length of stay (LOS) and severity score of both groups were similar. The odds ratio of mortality for the Early group was 0.71 (95% CI: 0.51-0.98, P = 0.04); other significant risk factors were age (OR = 1.023, 95% CI: 1.016-1.03; P < 0.001) and readmission LOS (OR = 1.017, 95% CI: 1.009-1.026; P < 0.001). The most common reason for readmission in the Early group was high Modified Early Warning Score, while in the Late group, it was respiratory failure followed by sepsis or septic shock.

Conclusion

Compared with late readmission, early readmission was associated with lower mortality, but not with lower LOS or severity score.

The risk assessment tool for intensive care unit readmission: A systematic review and meta-analysis

OBJECTIVE

To review and evaluate existing risk assessment tools for intensive care unitreadmission.

METHODS

Nine electronic databases (Medline, CINAHL, Web of Science, Cochrane Library, Embase, Sino Med, CNKI, VIP, and Wan fang) were systematically searched from their inception to September 2022. Two authors independently extracted data from the literature included. Meta-analysis was performed under the bivariate modeling and summary receiver operating characteristic curve method.

RESULTS

A total of 29 studies were included in this review, among which 11 were quantitatively Meta-analyzed. The results showed Stability and Workload Index for Transfer: Sensitivity = 0.55, Specificity = 0.65, Area under curve = 0.63. And Early warning score: Sensitivity = 0.78, Specificity = 0.83, Area under curve = 0.88. The remaining tools included scores, nomograms, machine learning models, and deep learning models. These studies, with varying reports on thresholds, case selection, data preprocessing, and model performance, have a high risk of bias.

CONCLUSION

We cannot identify a tool that can be used directly in intensive care unit readmission risk assessment. Scores based on early warning score are moderately accurate in predicting readmission, but there is heterogeneity and publication bias that requires model adjustment for local factors such as resources, demographics, and case mix. Machine learning models present a promising modeling technique but have a high methodological bias and require further validation.

IMPLICATIONS FOR CLINICAL PRACTICE

Using reliable risk assessment tools is essential for the early identification of unplanned intensive care unit readmission risk in critically ill patients.  A reliable risk assessment tool must be developed, which is the focus of further research.

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSION

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

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Background

Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge.

Methods

This multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients’ outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS.

Results

A total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88–0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84–0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001).

Conclusion

National Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients’ discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.

Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Background

The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization.

Methods

We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan–Meier survival curves were used in survival rate.

Results

In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001).

Conclusion

We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.

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.