Early triaging using the Modified Early Warning Score (MEWS) and dedicated emergency teams leads to improved clinical outcomes in acute emergencies

Nursing Students' Scenario Performance: Game Metrics in a Simulation Game

AIM

The purpose of this study was to examine nursing students' scenario performance in a simulation game by utilizing game metrics.

BACKGROUND

A significant advantage of simulation games is that they can store large amounts of data. Although game metrics enable the objective evaluation and analysis of performance, their use in the evaluation of students' performance is limited.

METHOD

Nursing students (N = 376) played a simulation game at home for 1 week. The resulting data consisted of game metrics stored in the game: number of playthroughs, mean scores, and mean playing times.

RESULTS

The total number of playthroughs was 1,923. Statistically significant differences were found between different scenarios regarding the mean score (p < .0001). Mean playing time was significantly associated with the mean score (p < .05).

CONCLUSION

Game metrics demonstrate nursing students' scenario performance in clinical reasoning skills in different scenarios in a simulation game.

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.

How do we identify acute medical admissions that are suitable for same day emergency care?

Medical emergencies causing unplanned hospital admission place considerable demands on acute healthcare services. Some patients can be assessed and treated through ambulatory pathways without inpatient admission, via same day emergency care (SDEC), potentially benefiting patients and reducing demands on inpatient services. There is currently considerable variation within acute medicine in aspects of SDEC delivery ranging from overall service design to patient selection methods. Scoring systems identifying patients likely to be successfully managed through SDEC services have been suggested, but evidence of utility in diverse populations is lacking. Specific scoring systems exist for some common medical problems, including cardiac chest pain and pulmonary embolism, but further research is needed to demonstrate how these are most effectively incorporated into SDEC services. This review defines SDEC and describes the variation in services nationally. It reviews the evidence for their clinical impact, tools to screen patients for SDEC and current gaps in our knowledge regarding service deployment.

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome

Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.

Evaluation of prognostic value of MEDS, MEWS, and CURB-65 criteria and sepsis I and sepsis III criteria in patients with community-acquired infection in emergency department

Background:

Early and effective treatment of patients with sepsis requires early recognition in emergency department and understanding the severity of the disease. Many studies have been conducted for this purpose, and many of scoring systems have been developed that provide early recognition of these patients and show their severity.

Objectives:

The aim of this study is to evaluate the efficacy of the scoring systems used to determine the mortality of patients with infections admitted in emergency department.

Methods:

In all, 400 patients who admitted to Uludağ University Hospital Emergency Department were prospectively included in this study. In addition to Systemic Inflammatory Response Syndrome score, Quick SOFA score, Mortality in Emergency Department Sepsis score, Modified Early Warning Score, and Charlson Comorbidity Index score in all patients, CURB-65 score was calculated in the patients diagnosed with pneumonia. It has been aimed to determine the power of these scores’ predictive mortality rates and their superiority to each other.

Results:

It was found that Mortality in Emergency Department Sepsis score and Quick SOFA score could be used with similar efficacy (respectively p = 0.761 and p = 0.073) in determining early mortality in emergency department (5th and 14th days) and that MEDS score was more effective (p < 0.001) in predicting the 28th-day mortality. While these recommendations were valid in patients diagnosed with pneumonia, it was determined that CURB-65 score could also be used to estimate 5th-, 14th-, and 28th-day mortalities (respectively, for the 5th day, p = 0.894 and p = 0.256; for the 14th day, p = 0.425 and p = 0.098; and for the 28th day, p = 0.095 and p = 0.158). The power of Systemic Inflammatory Response Syndrome score, previously used to identify sepsis, in predicting mortality was detected to be lower.

Conclusion:

Mortality in Emergency Department Sepsis score and Quick SOFA score could be used with similar efficacy in determining early mortality in emergency department. However, if you want to predict 28th-day mortality rate, it can be better to use Mortality in Emergency Department Sepsis score or CURB-65 (in patients diagnosed with pneumonia).

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.

A retrospective cohort study of the utility of the modified early warning score for interfacility transfer of patients with traumatic injury

OBJECTIVE

The modified early warning score (MEWS) is a 'track and trigger' score using routine physiological vital signs. The objective is to determine if the pretransfer MEWS can be used for predicting outcomes in trauma patients requiring interfacility transfer to higher levels of care.

DESIGN, SETTING AND PARTICIPANTS

Retrospective study of consecutively transferred trauma patients into a level-II trauma centre from 2013 to 2014.

INTERVENTIONS

None.

OUTCOME MEASURES

Mortality, intensive care unit (ICU) admission, operative procedure, MEWS deterioration in-transit, air transport interfacility, secondary overtriage (low injury severity score (ISS) <10, LOS<1 day, discharged home) and severe injury (ISS ≥16). The association between the pretransfer MEWS and outcomes were analysed with Cochran-Armitage trend tests, receiver operator characteristic (ROC) curves and univariate logistic regression.

RESULTS

There were 587 transferred patients; outcomes were reported in 339 patients with complete data on all five vital signs used to calculate the MEWS. The MEWS ranged from 0 to 9 (median of 1). There was a significant linear relationship between MEWS and study outcomes, especially mortality, ICU admission, air medical transport and severe injury (p<0.001 for all). A threshold score ≥4 was identified by ROC analysis; 11.2% of patients had MEWS ≥4. Outcomes were significantly worse in patients with MEWS ≥4 versus <4: mortality (26.2% vs 3.0%, OR=11.59, p<0.001); ICU admission (73.7% vs 47.2%, OR=3.14, p=0.003); air transfer (42.1% vs 15.6%, OR=3.93, p<0.001) and severe injury (59.5% vs 27.2%, OR=3.9, p<0.001). The MEWS was not associated with surgery, in-transit MEWS deterioration or secondary overtriage.

CONCLUSION

Pretransfer MEWS ≥4 may be used by the receiving facility for predicting injury severity, mortality, air transport and ICU resource use. In the interfacility transport setting, the MEWS may be useful for identifying patients with less obvious need for transfer or requiring more expeditious transfer.