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

Clinical assessment and short-term mortality prediction of older adults with altered mental status using RASS and 4AT tools

BACKGROUND

Altered mental status (AMS) in older adults is a common reason for admission to emergency departments (EDs) and usually results from delirium, stupor, or coma. It is important to proficiently identify underlying factors and anticipate clinical outcomes for those patients.

AIM

The primary objective of this study was to reveal and compare the clinical outcomes and etiologic factors of older patients with delirium, stupor, and coma. The secondary objective was to identify the 30-day mortality risk for those patients.

METHOD

The study was conducted as prospective and observational research. We included patients aged 65 years and older who presented with new-onset neurological and cognitive symptoms or worsening in baseline mental status. Patients who presented no change in their baseline mental status within 48 h and those who needed urgent interventions were excluded. Selected patients were assessed using RASS and 4AT tools and classified into three groups: stupor/coma, delirium, and no stupor/coma or delirium (no-SCD). Appropriate statistical tests were applied to compare these 3 groups. The 30-day mortality risks were identified by Cox survival analysis and Kaplan-Meier curve.

RESULTS

A total of 236 patients were eligible for the study. Based on their RASS and 4AT test scores: 56 (23.7%), 94 (40.6%), and 86 (36.4%) patients formed the stupor/coma, delirium and no-SCD groups, respectively. There was no statistical difference in the three groups for gender, mean age, and medical comorbidities. Neurological (34.7%), infectious (19.4%), and respiratory (19.0%) diseases were the leading factors for AMS. Post-hoc tests showed that CCI scores of the delirium (6, IQR = 3) and stupor/coma (7, IQR = 3) groups were not significantly different. The 30-day mortality rates of stupor/coma, delirium, and no-SCD groups were 42.%, 15.9%, and 12.8%, respectively (p < 0.005). The hazard ratio of the stupor/coma group was 2.79 (CI: 95%, 1.36-5.47, p = 0.005).

CONCLUSION

AMS remains a significant clinical challenge in EDs. Using the RASS and 4AT tests provides benefits and advantages for emergency medicine physicians. Neurological, infectious, and respiratory diseases can lead to life-threatening mental deterioration. Our study revealed that long-term mortality predictor CCI scores were quite similar among patients with delirium, stupor, or coma. However, the short-term mortality was significantly increased in the stupor/coma patients and they had 2.8 times higher 30-day mortality risk than others.

Comparison of the new-Poisoning Mortality Score and the Modified Early Warning Score for predicting in-hospital mortality in patients with acute poisoning

INTRODUCTION

The evaluation of acute poisoning is challenging due to varied toxic substances and clinical presentations. The new-Poisoning Mortality Score was recently developed to assess patients with acute poisoning and showed good performance in predicting in-hospital mortality. The objective of this study is to externally validate the performance of the new-Poisoning Mortality Score and to compare it with the Modified Early Warning Score.

METHODS

This retrospective analysis used data from the 2019-2020 Injury Surveillance Cohort, established by the Korea Center for Disease Control and Prevention, to perform external validation of the new-Poisoning Mortality Score. The statistical performances of the new-Poisoning Mortality and Modified Early Warning Scores were assessed and compared in terms of discrimination and calibration. Discrimination analysis involved metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. For calibration analysis, the Hosmer-Lemeshow goodness-of-fit test was utilized and calibration curves for each score were generated to elucidate the relationship between observed and predicted mortalities.

RESULTS

This study analysed 16,570 patients with acute poisoning. Significant differences were observed between survivors and those who died in-hospital, including age, sex, and vital signs. The new-Poisoning Mortality Score showed better performance over the Modified Early Warning Score in predicting in-hospital mortality, in terms of the area under the receiver operating characteristic curve (0.947 versus 0.800), sensitivity (0.863 versus 0.667), specificity (0.912 versus 0.817), and accuracy (0.911 versus 0.814). When evaluated through calibration curves, the new-Poisoning Mortality Score showed better concordance between predicted and observed mortalities. In subgroup analyses, the score system consistently showed strong performance, excelling particularly in substances with high mortality indices and remaining superior in all substances as a group.

CONCLUSIONS

Our study has helped to validate the new-Poisoning Mortality Score as an effective tool for predicting in-hospital mortality in patients with acute poisoning in the emergency department. The score system demonstrated superior performance over the Modified Early Warning Score in various metrics. Our findings suggest that the new-Poisoning Mortality Score can contribute to the enhancement of clinical decision-making and patient management.

In-Hospital Mortality Prediction Model for Critically Ill Older Adult Patients Transferred from the Emergency Department to the Intensive Care Unit

Purpose

Studies on the prognosis of critically ill older adult patients admitted to the emergency department (ED) but requiring immediate admission to the intensive care unit (ICU) remain limited. This study aimed to develop an in-hospital mortality prediction model for critically ill older adult patients transferred from the ED to the ICU.

Patients and Methods

The training cohort was taken from the Medical Information Mart for Intensive Care IV (version 2.2) database, and the external validation cohort was taken from the Affiliated Dongyang Hospital of Wenzhou Medical University. In the training cohort, class balance was addressed using Random Over Sampling Examples (ROSE). Univariate and multivariate Cox regression analyses were performed to identify independent risk factors. These were then integrated into the predictive nomogram. In the validation cohort, the predictive performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, clinical utility decision curve analysis (DCA), and clinical impact curve (CIC).

Results

In the ROSE-balanced training cohort, univariate and multivariate Cox regression analysis identified that age, sex, Glasgow coma scale score, malignant cancer, sepsis, use of mechanical ventilation, use of vasoactive agents, white blood cells, potassium, and creatinine were independent predictors of in-hospital mortality in critically ill older adult patients, and were included in the nomogram. The nomogram showed good predictive performance in the ROSE-balanced training cohort (AUC [95% confidence interval]: 0.792 [0.783-0.801]) and validation cohort (AUC [95% confidence interval]: 0.780 [0.727-0.834]). The calibration curves were well-fitted. DCA and CIC demonstrated that the nomogram has good clinical application value.

Conclusion

This study developed a predictive model for early prediction of in-hospital mortality in critically ill older adult patients transferred from the ED to the ICU, which was validated by external data and has good predictive performance.

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.

Accuracy of Emergency Severity Index in older adults

BACKGROUND AND IMPORTANCE

Emergency Severity Index is a five-level triage tool in the emergency department that predicts the need for emergency department resources and the degree of emergency. However, it is unknown whether this is valid in patients aged greater than or equal to 65 years.

OBJECTIVE

The aim of the study was to compare the accuracy of the Emergency Severity Index triage system between emergency department patients aged 18-64 and greater than or equal to 65 years.

DESIGN, SETTINGS, AND PARTICIPANTS

This was a retrospective observational cohort study of adults who presented to a Finnish emergency department between 1 February 2018 and 28 February 2018. All data were collected from electronic health records.

OUTCOME MEASURES AND ANALYSIS

The primary outcome was 3-day mortality. The secondary outcomes were 30-day mortality, hospital admission, high dependency unit or ICU admission, and emergency department length of stay. The area under the receiver operating characteristic curve and cutoff performances were used to investigate significant associations between triage categories and outcomes. The results of the two age groups were compared.

MAIN RESULTS

There were 3141 emergency department patients aged 18-64 years and 2370 patients aged greater than or equal to 65 years. The 3-day mortality area under the curve in patients aged greater than or equal to 65 years was greater than that in patients aged 18-64 years. The Emergency Severity Index was associated with high dependency unit/ICU admissions in both groups, with moderate sensitivity [18-64 years: 61.8% (50.9-71.9%); greater than or equal to 65 years: 73.3% (63.5-81.6%)] and high specificity [18-64 years: 93.0% (92.0-93.8%); greater than or equal to 65 years: 90.9% (90.0-92.1%)]. The sensitivity was high and specificity was low for 30-day mortality and hospital admission in both age groups. The emergency department length of stay was the longest in Emergency Severity Index category 3 for both age groups. There was no significant difference in accuracy between age groups for any outcome.

CONCLUSION

Emergency Severity Index performed well in predicting high dependency unit/ICU admission rates for both 18-64 years and greater than or equal to 65-year-old patients. It predicted the 3-day mortality for patients aged greater than or equal to 65 years with high accuracy. It was inaccurate in predicting 30-day mortality and hospital admission for both age groups.

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.

Prognostic tools for elderly patients with sepsis: in search of new predictive models

As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. But there are currently no MPMs for adults with sepsis who meet the criteria of "good." Clinicians are unlikely to use complex MPMs that require extensive or expensive data collection to impede workflow. Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.

Systems for recognition and response to deteriorating emergency department patients: a scoping review

Background

Assessing and managing the risk of clinical deterioration is a cornerstone of emergency care, commencing at triage and continuing throughout the emergency department (ED) care. The aim of this scoping review was to assess the extent, range and nature of published research related to formal systems for recognising and responding to clinical deterioration in emergency department (ED) patients.

Materials and methods

We conducted a scoping review according to PRISMA-ScR guidelines. MEDLINE complete, CINAHL and Embase were searched on 07 April 2021 from their dates of inception. Human studies evaluating formal systems for recognising and responding to clinical deterioration occurring after triage that were published in English were included. Formal systems for recognising and responding to clinical deterioration were defined as: i) predefined patient assessment criteria for clinical deterioration (single trigger or aggregate score), and, or ii) a predefined, expected response should a patient fulfil the criteria for clinical deterioration. Studies of short stay units and observation wards; deterioration during the triage process; system or score development or validation; and systems requiring pathology test results were excluded. The following characteristics of each study were extracted: author(s), year, design, country, aims, population, system tested, outcomes examined, and major findings.

Results

After removal of duplicates, there were 2696 publications. Of these 33 studies representing 109,066 patients were included: all were observational studies. Twenty-two aggregate scoring systems were evaluated in 29 studies and three single trigger systems were evaluated in four studies. There were three major findings: i) few studies reported the use of systems for recognising and responding to clinical deterioration to improve care of patients whilst in the ED; ii) the systems for recognising clinical deterioration in ED patients were highly variable and iii) few studies reported on the ED response to patients identified as deteriorating.

Conclusion

There is a need to re-focus the research related to use of systems for recognition and response to deteriorating patients from predicting various post-ED events to their real-time use to improve patient safety during ED care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13049-021-00882-6.