Prediction models for mortality in adult patients visiting the Emergency Department: a systematic review

2019 ◽  
Vol 18 (3) ◽  
pp. 171-183
Author(s):  
A Brink ◽  
◽  
J Alsma ◽  
AW Fortuin ◽  
WM Bramer ◽  
...  
Keyword(s):  
Emergency Department ◽  
Observational Studies ◽  
Prediction Models ◽  
Model Performance ◽  
Risk Of Bias ◽  
Moderate Risk ◽  
Short Term ◽  
Trauma Population ◽  
Short Term Mortality ◽  
Multiple Prediction

We provide a systematic overview of literature on prediction models for mortality in the Emergency Department (ED). We searched various databases for observational studies in the ED or similar setting describing prediction models for short-term mortality (up to 30 days or in-hospital mortality) in a non-trauma population. We used the CHARMS-checklist for quality assessment. We found a total of 14.768 articles and included 17 articles, describing 22 models. Model performance ranged from AUC 0.63- 0.93. Most articles had a moderate risk of bias in one or more domains. The full model and PARIS model performed best, but are not yet ready for implementation. There is a need for validation studies to compare multiple prediction models and to evaluate their accuracy.

A systematic evaluation of models predicting short-term mortality for cancer patients to highlight uncertainty about model performance and high risk of bias.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13559-e13559
Author(s):  
Sheng-Chieh Lu ◽  
Cai Xu ◽  
Chandler Nguyen ◽  
Larissa Meyer ◽  
Chris Sidey-Gibbons
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Model ◽  
Cancer Mortality ◽  
Model Performance ◽  
Risk Of Bias ◽  
Short Term ◽  
Electronic Health Record Data ◽  
Bias Assessment ◽  
Short Term Mortality

e13559 Background: Short-term cancer mortality prediction has many implications concerning care planning. An accurate prognosis allows healthcare providers to adjust care plans and take appropriate actions, such as initiating end-of-life conversations. Machine learning (ML) techniques demonstrated promising capability to support clinical decision-making via providing reliable predictions for a variety of clinical outcomes, including cancer mortality. However, the evidence has not yet been systematically synthesized and evaluated. The objective of this review was to examine the performance and risk-of-bias for ML models trained to predict short-term (≤ 12 months) cancer mortality. Methods: We identified relevant literature from five electronic databases: Ovid Medline, Ovid EMBASE, Scopus, Web of Science, and IEEE Xplore. We searched each database with predefined MeSH terms and keywords of oncology, machine learning, and mortality using AND/OR statements. Inclusion criteria included: 1) developed/validated ML models for predicting oncology patient mortality within one year using electronic health record data; 2) reported model performance within a dataset that was not used to train the models; 3) original research; 4) peer-reviewed full paper in English; 5) published before 1/10/2020. We conducted risk of bias assessment using prediction model risk of bias assessment tool (PROBAST). Results: Ten articles were included in this review. Most studies focused on predicting 1-year mortality (n = 6) for multiple types of cancer (n = 5). Most studies (n = 7) used a single metric, the area under the receiver operating characteristic curve (AUROC), to examine their models. The AUROC ranged from .69 to .91, with a median of .85. Information on samples (n = 10), resampling methods (n = 6), model tuning approaches (n = 9), censoring (n = 10), and sample size determinations (n = 10) were incomplete or absent. Six studies have a high risk of bias for the analysis domain in the PROBAST. Conclusions: The performance of ML models for short-term cancer mortality appears promising. However, most studies report only a single performance metric that obfuscates evaluation of a model’s true performance. This is especially problematic when predicting rare events such as short-term mortality. We found little-to-no information on a given model’s ability to correctly identify patients at high risk of mortality. The incomplete reporting of model development poses challenges to risk of bias assessment and reduces the confidence in the results. Our findings suggest that future studies should report comprehensive performance metrics using a standard reporting guideline, such as transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD), to ensure sufficient information for replication, justification, and adoption.

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

2021 ◽  
Author(s):  
Patricia Pauline M. Remalante-Rayco ◽  
Evelyn Osio-Salido
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Early Warning ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Good Prediction ◽  
Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

scholarly journals Systolic blood pressure and short-term mortality in the emergency department and prehospital setting: a hospital-based cohort study

Critical Care ◽  
2015 ◽  
Vol 19 (1) ◽  
Author(s):  
Anders Kasper Bruun Kristensen ◽  
Jon Gitz Holler ◽  
Søren Mikkelsen ◽  
Jesper Hallas ◽  
Annmarie Lassen
Keyword(s):  
Blood Pressure ◽  
Emergency Department ◽  
Cohort Study ◽  
Systolic Blood Pressure ◽  
Prehospital Setting ◽  
Short Term ◽  
Short Term Mortality

scholarly journals An Investigation of Risk Factors Affecting Short Term Mortality of Patients Referring to the Hospital Emergency Department

2020 ◽  
Author(s):  
Elham Peyravi ◽  
Hadid Hamrah ◽  
Mohammad Sadegh Masoudi ◽  
Milad Ahmadi Marzaleh ◽  
Mahmoudreza Peyravi
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Emergency Department ◽  
Systolic Blood Pressure ◽  
Fars Province ◽  
Hospital Emergency ◽  
Short Term ◽  
Factors Affecting ◽  
Emergency Department Overcrowding ◽  
Short Term Mortality

Abstract Background and Objective: One of the causes of short-term mortality in patients is the lower quality of services provided by hospital emergency departments. Given the particular importance of the hospital emergency system and the presence of numerous problems, as well as short term mortality rates in hospitals, this study aimed to investigate the risk factors affecting short term mortality of patients presenting to the Emergency Department at Nemazi Hospital in Shiraz, Fars province in 2019.Methods: This is a retrospective study with a case control-analytical design. The sample size was 768 subjects. In the present study, the emergency department overcrowding was measured by the NEDOCS (National Emergency Department Overcrowding Scale) criterion. The severity of the disease was also evaluated based on the level of the triage of patients through the Emergency Severity Index (ESI) system and vital signs.Results: With each year increase in age, the chance of short-term mortality increases by 0.8%. People with O2 sat% <90% are 7.3 times more likely to experience short term mortality in an emergency department compared to people with O2 sat%> 90%. A significant relationship was noted between short term mortality and SBP (systolic blood pressure) in the hospital's emergency department. It was also found out that as the triage score increases, short term mortality decreases significantly. As hospital stay increases, the chance of the patients' mortality decreases by 0.5%.Conclusion: The percentage of arterial blood oxygen saturation, systolic blood pressure, respiration rate per minute, triage score, the way the patient arrives at the hospital, working shifts, hospitalization duration, age, and comorbidities were regarded as the risk factors for short term mortality. Therefore, promoting professional knowledge and skills of nurses and physicians in the hospitals' emergency department and up-to-dating and reviewing emergency protocols as well as similar research can greatly help reduce short term mortality in the hospital's emergency department.

scholarly journals Corticosteroid use in COVID-19 patients: A systematic review and meta-analysis on clinical outcomes

2020 ◽  
Author(s):  
Judith van Paassen ◽  
Jeroen S. Vos ◽  
Eva M. Hoekstra ◽  
Katinka M.I. Neumann ◽  
Pauline C. Boot ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mechanical Ventilation ◽  
Observational Studies ◽  
Oxygen Therapy ◽  
Meta Analysis ◽  
Viral Clearance ◽  
Care Group ◽  
Short Term ◽  
Secondary Infections ◽  
Short Term Mortality

Abstract Background: In the current SARS-CoV-2 pandemic, there has been worldwide debate on the use of corticosteroids in COVID-19. In the recent RECOVERY trial, evaluating the effect of dexamethasone, a reduced 28-day mortality in patients requiring oxygen therapy or mechanical ventilation was shown. Their results have led to considering amendments in guidelines or actually already recommending corticosteroids in COVID-19. However, the effectiveness and safety of corticosteroids still remain uncertain, and reliable data to further shed light on the benefit and harm are needed. Objectives: The aim of this systematic review and meta-analysis was to evaluate the effectiveness and safety of corticosteroids in COVID-19. Methods: A systematic literature search of RCTS and observational studies on adult patients was performed across Medline/PubMed, Embase, and Web of Science from 1st of December 2019 until 1 st of October 2020, according to the PRISMA guidelines. Primary outcomes were short-term mortality and viral clearance (based on RT-PCR in respiratory specimens). Secondary outcomes were: need for mechanical ventilation, other oxygen therapy, length of hospital stay and secondary infections. Results: Forty-four studies were included, covering 20.197 patients. In twenty-two studies, the effect of corticosteroid use on mortality was quantified. The overall pooled estimate (observational studies and RCTs) showed a significant reduced mortality in the corticosteroid group (OR 0.72 (95%CI 0.57-0.87). Furthermore, viral clearance time ranged from 10-29 days in the corticosteroid group and from 8-24 days in the standard of care group. Fourteen studies reported a positive effect of corticosteroids on need for and duration of mechanical ventilation. A trend towards more infections and antibiotic use was present. Conclusions: Our findings from both observational studies and RCTs confirm a beneficial effect of corticosteroids on short-term mortality and a reduction of need for mechanical ventilation. And although data in the studies were too sparse to draw any firm conclusions, there might be a signal of delayed viral clearance and an increase in secondary infections.

scholarly journals Validating the RISE UP score for predicting prognosis in patients with COVID-19 in the emergency department, a retrospective study

2020 ◽  
Author(s):  
Paul M.E.L. van Dam ◽  
Noortje Zelis ◽  
Patricia M. Stassen ◽  
Daan J.L. van Twist ◽  
Peter W. de Leeuw ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Outcome ◽  
Vital Signs ◽  
Favourable Outcome ◽  
Secondary Outcome ◽  
Care Hospital ◽  
Short Term ◽  
Short Term Mortality ◽  
Patients At Risk ◽  
System Information

AbstractObjectiveTo mitigate the burden of COVID-19 on the healthcare system, information on the prognosis of the disease is needed. The recently developed RISE UP score has very good discriminatory value with respect to short-term mortality in older patients in the emergency department (ED). It consists of six items: age, abnormal vital signs, albumin, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), and bilirubin. We hypothesized that the RISE UP score could have discriminatory value with regard to 30-day mortality in ED patients with COVID-19.SettingTwo EDs of the Zuyderland Medical Centre (MC), secondary care hospital in the Netherlands.ParticipantsThe study sample consisted of 642 adult ED patients diagnosed with COVID-19 between March 3rd until May 25th 2020. Inclusion criteria were: 1) admission to the hospital with symptoms suggestive of COVID-19, and 2) positive result of the polymerase chain reaction (PCR), or (very) high suspicion of COVID-19 according to the chest computed tomography (CT) scan.OutcomePrimary outcome was 30-day mortality, secondary outcome was a composite of 30-day mortality and admission to intensive care unit (ICU).ResultsWithin 30 days after presentation, 167 patients (26.0%) died and 102 patients (15.9%) were admitted to ICU. The RISE UP score showed good discriminatory value with respect to 30-day mortality (AUC 0.77, 95% CI 0.73-0.81), and to the composite outcome (AUC 0.72, 95% CI 0.68-0.76). Patients with RISE UP scores below 10% (121 patients) had favourable outcome (0% mortality and 5% ICU admissions). Patients with a RISE UP score above 30% (221 patients) were at high risk of adverse outcome (46.6% mortality and 19% ICU admissions).ConclusionThe RISE UP score is an accurate prognostic model for adverse outcome in ED patients with COVID-19. It can be used to identify patients at risk of short-term adverse outcome, and may help guiding decision-making and allocating healthcare resources.

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