Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study.
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ABSTRACT: Introduction
Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED).Methods
In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC).Results
We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models.Conclusion
The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.
SUBMITTER: van Dam PMEL
PROVIDER: S-EPMC7919920 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesDevelop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19.DesignRetrospective.SettingSecondary care in four large Dutch hospitals.ParticipantsPatients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation.Outcome measuresWe developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots.ResultsOf 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model-COVID outcome prediction in the emergency department (COPE)-with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)).ConclusionsCOPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.
| S-EPMC8449847 | biostudies-literature
Project description:The emergency department (ED) is an inherently high-risk setting. Early death after an ED evaluation is a rare and devastating outcome; understanding it can potentially help improve patient care and outcomes. Using administrative data from an integrated health system, we describe characteristics and predictors of patients who experienced 7-day death after ED discharge.Administrative data from 12 hospitals were used to identify death after discharge in adults aged 18 year or older within 7 days of ED presentation from January 1, 2007, to December 31, 2008. Patients who were nonmembers of the health system, in hospice care, or treated at out-of-network EDs were excluded. Predictors of 7-day postdischarge death were identified with multivariable logistic regression.The study cohort contained a total of 475,829 members, with 728,312 discharges from Kaiser Permanente Southern California EDs in 2007 and 2008. Death within 7 days of discharge occurred in 357 cases (0.05%). Increasing age, male sex, and number of preexisting comorbidities were associated with increased risk of death. The top 3 primary discharge diagnoses predictive of 7-day death after discharge included noninfectious lung disease (odds ratio [OR] 7.1; 95% confidence interval [CI] 2.9 to 17.4), renal disease (OR 5.6; 95% CI 2.2 to 14.2), and ischemic heart disease (OR 3.8; 95% CI 1.0 to 13.6).Our study suggests that 50 in 100,000 patients in the United States die within 7 days of discharge from an ED. To our knowledge, our study is the first to identify potentially "high-risk" discharge diagnoses in patients who experience a short-term death after discharge.
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Project description:OBJECTIVE:The aim of this study was to evaluate and compare the abilities of clinicians and clinical prediction models to accurately triage emergency department (ED) trauma patients. We compared the decisions made by clinicians with the Revised Trauma Score (RTS), the Glasgow Coma Scale, Age and Systolic Blood Pressure (GAP) score, the Kampala Trauma Score (KTS) and the Gerdin et al model. DESIGN:Prospective cohort study. SETTING:Three hospitals in urban India. PARTICIPANTS:In total, 7697 adult patients who presented to participating hospitals with a history of trauma were approached for enrolment. The final study sample included 5155 patients. The majority (4023, 78.0%) were male. MAIN OUTCOME MEASURE:The patient outcome was mortality within 30 days of arrival at the participating hospital. A grid search was used to identify model cut-off values. Clinicians and categorised models were evaluated and compared using the area under the receiver operating characteristics curve (AUROCC) and net reclassification improvement in non-survivors (NRI+) and survivors (NRI-) separately. RESULTS:The differences in AUROCC between each categorised model and the clinicians were 0.016 (95% CI -0.014 to 0.045) for RTS, 0.019 (95% CI -0.007 to 0.058) for GAP, 0.054 (95% CI 0.033 to 0.077) for KTS and -0.007 (95% CI -0.035 to 0.03) for Gerdin et al. The NRI+ for each model were -0.235 (-0.37 to -0.116), 0.17 (-0.042 to 0.405), 0.55 (0.47 to 0.65) and 0.22 (0.11 to 0.717), respectively. The NRI- were 0.385 (0.348 to 0.4), -0.059 (-0.476 to -0.005), -0.162 (-0.18 to -0.146) and 0.039 (-0.229 to 0.06), respectively. CONCLUSION:The findings of this study suggest that there are no substantial differences in discrimination and net reclassification improvement between clinicians and all four clinical prediction models when using 30-day mortality as the outcome of ED trauma triage in adult patients. TRIAL REGISTRATION NUMBER:ClinicalTrials.gov Registry (NCT02838459).
| S-EPMC7044989 | biostudies-literature
Project description:BackgroundDevelopment of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).MethodsUsing National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.ResultsOf 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85-0.87] in the deep neural network vs 0.74 [95%CI 0.72-0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82-0.83] in the deep neural network vs 0.69 [95%CI 0.68-0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.ConclusionsCompared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.
| S-EPMC6387562 | biostudies-literature
Project description:Introduction:The emergency department (ED) is an inherently high-risk setting. Risk scores can help practitioners understand the risk of ED patients for developing poor outcomes after discharge. Our objective was to develop two risk scores that predict either general inpatient admission or death/intensive care unit (ICU) admission within seven days of ED discharge. Methods:We conducted a retrospective cohort study of patients age > 65 years using clinical data from a regional, integrated health system for years 2009-2010 to create risk scores to predict two outcomes, a general inpatient admission or death/ICU admission. We used logistic regression to predict the two outcomes based on age, body mass index, vital signs, Charlson comorbidity index (CCI), ED length of stay (LOS), and prior inpatient admission. Results:Of 104,025 ED visit discharges, 4,638 (4.5%) experienced a general inpatient admission and 531 (0.5%) death or ICU admission within seven days of discharge. Risk factors with the greatest point value for either outcome were high CCI score and a prolonged ED LOS. The C-statistic was 0.68 and 0.76 for the two models. Conclusion:Risk scores were successfully created for both outcomes from an integrated health system, inpatient admission or death/ICU admission. Patients who accrued the highest number of points and greatest risk present to the ED with a high number of comorbidities and require prolonged ED evaluations.
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Project description:RationaleCertain outdoor air pollutants cause asthma exacerbations in children. To advance understanding of these relationships, further characterization of the dose-response and pollutant lag effects are needed, as are investigations of pollutant species beyond the commonly measured criteria pollutants.ObjectivesInvestigate short-term associations between ambient air pollutant concentrations and emergency department visits for pediatric asthma.MethodsDaily counts of emergency department visits for asthma or wheeze among children aged 5 to 17 years were collected from 41 Metropolitan Atlanta hospitals during 1993-2004 (n = 91,386 visits). Ambient concentrations of gaseous pollutants and speciated particulate matter were available from stationary monitors during this time period. Rate ratios for the warm season (May to October) and cold season (November to April) were estimated using Poisson generalized linear models in the framework of a case-crossover analysis.Measurements and main resultsBoth ozone and primary pollutants from traffic sources were associated with emergency department visits for asthma or wheeze; evidence for independent effects of ozone and primary pollutants from traffic sources were observed in multipollutant models. These associations tended to be of the highest magnitude for concentrations on the day of the emergency department visit and were present at relatively low ambient concentrations.ConclusionsEven at relatively low ambient concentrations, ozone and primary pollutants from traffic sources independently contributed to the burden of emergency department visits for pediatric asthma.
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Project description:ObjectivesTo develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters.MethodsAll symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model.ResultsA total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics.ConclusionThe developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.
| S-EPMC8776406 | biostudies-literature
Project description:ObjectivesTo develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters.MethodsAll symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model.ResultsA total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics.ConclusionThe developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.
| S-EPMC8576116 | biostudies-literature
Project description:The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care.
| S-EPMC9610299 | biostudies-literature
Project description:BackgroundOlder patients is a complex group at increased risk of adverse outcomes compared to younger patients, which should be considered in the risk assessment performed in emergency departments. We evaluated whether the predictive ability of different risk assessment models for acutely admitted patients is affected by age.MethodsCohort study of middle-aged and older patients. We investigated the accuracy in discriminating between survivors and non-survivors within 7 days of different risk assessment models; a traditional triage algorithm, a triage algorithm with clinical assessment, vital signs, routine biomarkers, and the prognostic biomarker soluble urokinase plasminogen activator receptor (suPAR).ResultsThe cohort included 22,653 (53.2%) middle-aged patients (age 40-69 years), and 19,889 (46.8%) older patients (aged 70+ years). Death within 7 days occurred in 139 patients (0.6%) in middle-aged patients and 596 (3.0%) of the older patients. The models based on vital signs and routine biomarkers had the highest area under the curve (AUC), and both were significantly better at discriminating 7-day mortality in middle-aged patients compared to older patients; AUC (95% CI): 0.88 (0.84-0.91), 0.75 (0.72-0.78), P < 0.01, and 0.86 (0.82-0.90), 0.76 (0.73-0.78), P < 0.001. In a subgroup of the total cohort (6.400 patients, 15.0%), the suPAR level was available. suPAR had the highest AUC of all individual predictors with no significant difference between the age groups, but further research in this biomarker is required before it can be used.ConclusionThe predictive value was lower in older patients compared to middle-aged patients for all investigated models. Vital signs or routine biomarkers constituted the best models for predicting 7-day mortality and were better than the traditional triage model. Hence, the current risk assessment for short-term mortality can be strengthened, but modifications for age should be considered when constructing new risk assessment models in the emergency department.
| S-EPMC6521424 | biostudies-literature