The Modified Early Warning Score as a Predictive Tool During Unplanned Surgical Intensive Care Unit Admission

The Effect of Modified Early Warning Score (MEWS) and Nursing Guide Application on Postoperative Patient Outcomes: A Randomized Controlled Study

PURPOSE

The aim of this study is to determine the effect of nursing guide application (NGA) on patient outcomes in patients followed up according to the modified early warning score (MEWS) in the postoperative period.

DESIGN

A randomized controlled clinical trial.

METHODS

The sample of the study consisted of 252 patients who underwent surgical intervention under general anesthesia in a university hospital between July 29, 2022, and October 31, 2022.

FINDINGS

Results showed that the development of complications was less in the study group (SG) compared to the control group (CG) during anesthesia (P = .027), in the postanesthesia care unit (PACU) (P = .017), and in the clinic (P = .001). It was found that the duration of stay in PACU in the CG was significantly shorter than in the study group (P < .001), and as the duration of stay in PACU in CG decreased, the MEWS increased (r = -0.201, P = .024). We found that there were fewer patients transferred to the intensive care unit (ICU) after PACU (P = .007), the MEWS was lower, and the number of nursing interventions applied to patients was higher (P < .05).

CONCLUSIONS

In patients followed up according to MEWS, NGA had a positive effect on preventing the development of complications and shortening the intervention time for complications, decreasing ICU admission, decreasing MEWS and increasing the number of nursing interventions. Based on the results, it may be recommended to use MEWS+NGA in the early postoperative period as it positively affects patient outcomes.

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.

Comparison between various scoring systems in predicting the need for intensive care unit admission of acute pesticide-poisoned patients

The decision of intensive care unit (ICU) admission in acute pesticide poisoning is often challenging, especially in developing countries with limited resources. This study was conducted to compare the efficacy of the Acute Physiology and Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Poisoning Severity Score (PSS) in predicting ICU admission and mortality of acute pesticide-poisoned patients. This prospective cohort study included all patients admitted to Tanta University Poison Control Center with acute pesticide poisoning from the start of March 2018 to the end of March 2019. Patient data, including demographic and toxicological data, clinical examination, laboratory investigation, and score values, were collected on admission. Out of 337 acute pesticide-poisoned patients, 30.5% were admitted to the ICU, including those poisoned with aluminum phosphide (ALP) (81.5%) and organophosphates (OP) (18.5%). Most non-survivors (86.6%) were ALP poisoning. The PSS had the best discriminatory power in predicting ICU admission and mortality, followed by APACHE II and MEWS. However, no significant difference in predicting ICU admission of OP-poisoned patients was detected between the scores. Additionally, no significant difference in mortality prediction of ALP-poisoned patients was found between the PSS and APACHE II. The PSS, APACHE II, and MEWS are good discriminators for outcome prediction of acute pesticide poisoning on admission. Although the PSS showed the best performance, MEWS was simpler, more feasible, and practicable in predicting ICU admission of OP-poisoned patients. Moreover, the APACHE II has better sensitivity for mortality prediction of ALP-poisoned patients.

Evaluation of the Overall Accuracy of the Combined Early Warning Scoring Systems in the Prediction of In-Hospital Mortality

Introduction Deterioration of clinical condition of in-hospital patients further leads to intensive care unit (ICU) transfer or death which can be reduced by the use of prediction tools. The early warning scoring (EWS) system is a prediction tool used in monitoring medical patients in hospitals, hospital staying length, and inpatient mortality. The present study evaluated four different EWS systems for the prediction of patient survival. Method The present prospective observational study has analyzed 217 patients visiting the emergency department from November 2016 to November 2018, followed by demographic and clinical data collection. Modified Early Warning Score (MEWS), Triage Early Warning Score (TEWS), Leed's Early Warning Score (LEWS), and patient-at-risk scores (PARS) were assigned based upon body temperature, consciousness level, heart rate, blood pressure, respiratory rate, mobility, etc. Data was analyzed with the help of R 4.0.4 (R Foundation, Vienna, Austria) and Microsoft Excel (Microsoft, Redmond, Washington). Results Out of these 217 patients, 205 got shifted to a ward, and 12 died, amongst which the majority belonged to the 31-40 age group. Among patients admitted to ICU had a MEWS greater than 3, TEWS within the range 0 to 2 and 3 to 5, LEWS greater than 7, and PARS greater than 5 on the initial days of admission. The patients who died and those who were shifted to the ward showed significant differences in EWS. A significant association was observed between all the EWS and patient outcomes (p<0.001). Conclusion MEWS, TEWS, LEWS, and PARS were effective in the prediction of inpatient mortality as well as admission to the ICU. With the increase in the EWS, there was an increase in the duration of ICU stay and a decrease in chances of survival.

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

INTRODUCTION

Early identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians' decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.

METHODS AND ANALYSIS

We will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool's user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.

ETHICS AND DISSEMINATION

Ethics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

Failure to rescue: A quality indicator for postoperative care

Postoperative complications occur despite optimal perioperative care and are an important driver of mortality after surgery. Failure to rescue, defined as death in a patient who has experienced serious complications, has emerged as a quality metric that provides a mechanistic pathway to explain disparities in mortality rates among hospitals that have similar perioperative complication rates. The risk of failure to rescue is higher after invasive surgical procedures and varies according to the type of postoperative complication. Multiple patient factors have been associated with failure to rescue. However, failure to rescue is more strongly correlated with hospital factors. In addition, microsystem factors, such as institutional safety culture, teamwork, and other attitudes and behaviors may interact with the hospital resources to effectively prevent patient deterioration. Early recognition through bedside and remote monitoring is the first step toward prevention of failure to rescue followed by rapid response initiatives and timely escalation of care.

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.