scholarly journals Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11988
Author(s):  
Kuan-Han Wu ◽  
Fu-Jen Cheng ◽  
Hsiang-Ling Tai ◽  
Jui-Cheng Wang ◽  
Yii-Ting Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Hospital Mortality ◽  
Early Warning ◽  
Performance Test ◽  
Model Development ◽  
Training Data ◽  
Early Warning Score ◽  
Prediction Ability ◽  
Specific Patient

Background A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a ML model to predict in-hospital morality of the adult non traumatic ED patients for different time stages, and comparing performance with other ML models and MEWS. Methods A retrospective observational cohort study was conducted in five Taiwan EDs including two tertiary medical centers and three regional hospitals. All consecutively adult (>17 years old) non-traumatic patients admit to ED during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. MEWS was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking ML model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic (AUROC) and the area under the precision and recall curve (AUPRC) as the comparative measures. Result After excluding 182,001 visits (7.46%), study group was consisted of 24,37,326 ED visits. The dataset was split into 67% training data and 33% test data for ML model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the AUROC of MEW and ML mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking ML model outperform other ML model as well. For the prediction of in-hospital mortality over 48-hours, AUPRC performance of MEWS drop below 0.1, while the AUPRC of ML mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, ML model achieved statistically significant higher AUROC and AUPRC than MEWS (all P < 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of AUROC values between two model increases gradually (P < 0.001). Three MEWS thresholds (score >3, >4, and >5) were determined as baselines for comparison, ML mode consistently showed improved or equally performance in sensitivity, PPV, NPV, but not in specific. Conclusion Stacking ML methods improve predicted in-hospital mortality than MEWS in adult non-traumatic ED patients, especially in the prediction of delayed mortality.

scholarly journals A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

10.2196/24246 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e24246 ◽  
Author(s):  
Siavash Bolourani ◽  
Max Brenner ◽  
Ping Wang ◽  
Thomas McGinn ◽  
Jamie S Hirsch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Respiratory Failure ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Predictive Accuracy ◽  
Invasive Mechanical Ventilation ◽  
Laboratory Data ◽  
Early Warning Score ◽  
Learning Approaches

Background Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Objective Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Methods Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. Results The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. Conclusions The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Emergency Department Triage Early Warning Score (TREWS) predicts in-hospital mortality in the emergency department

2020 ◽  
Vol 38 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Sang Bong Lee ◽  
Dong Hoon Kim ◽  
Taeyun Kim ◽  
Changwoo Kang ◽  
Soo Hoon Lee ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Mortality ◽  
Early Warning ◽  
Early Warning Score ◽  
Emergency Department Triage

scholarly journals Prognostic value of Modified Early Warning Score generated in a Chinese emergency department: a prospective cohort study

BMJ Open ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e024120 ◽  
Author(s):  
Xiaohua Xie ◽  
Wenlong Huang ◽  
Qiongling Liu ◽  
Wei Tan ◽  
Lu Pan ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Cohort Study ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Early Warning ◽  
Predictive Value ◽  
Mortality Prediction ◽  
Early Warning Score ◽  
Secondary Outcome

ObjectivesThis study aimed to validate the performance of the Modified Early Warning Score (MEWS) in a Chinese emergency department and to determine the best cut-off value for in-hospital mortality prediction.DesignA prospective, single-centred observational cohort study.SettingThis study was conducted at a tertiary hospital in South China.ParticipantsA total of 383 patients aged 18 years or older who presented to the emergency department from 17 May 2017 through 27 September 2017, triaged as category 1, 2 or 3, were enrolled.OutcomesThe primary outcome was a composite of in-hospital mortality and admission to the intensive care unit. The secondary outcome was using MEWS to predict hospitalised and discharged patients.ResultsA total of 383 patients were included in this study. In-hospital mortality was 13.6% (52/383), and transfer to the intensive care unit was 21.7% (83/383). The area under the receiver operating characteristic curve of MEWS for in-hospital mortality prediction was 0.83 (95% CI 0.786 to 0.881). When predicting in-hospital mortality with the cut-off point defined as 3.5, 158 patients had MEWS >3.5, with a specificity of 66%, a sensitivity of 87%, an accuracy of 69%, a positive predictive value of 28% and a negative predictive value of 97%, respectively.ConclusionOur findings support the use of MEWS for in-hospital mortality prediction in patients who were triaged category 1, 2 or 3 in a Chinese emergency department. The cut-off value for in-hospital mortality prediction defined in this study was different from that seen in many other studies.

scholarly journals Clinical impact of implementing a rapid-response team based on the Modified Early Warning Score in wards that offer emergency department support

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259577
Author(s):  
Lorena Micheline Alves Silva ◽  
Diego Marques Moroço ◽  
José Paulo Pintya ◽  
Carlos Henrique Miranda
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Early Warning ◽  
Rapid Response Team ◽  
Rapid Response ◽  
Early Warning Score ◽  
Before And After ◽  
Response Team

Background Emergency department (ED) crowding is a frequent situation. To decrease this overload, patients without a life-threating condition are transferred to wards that offer ED support. This study aimed to evaluate if implementing a rapid response team (RRT) triggered by the modified early warning score (MEWS) in high-risk wards offering ED support is associated with decreased in-hospital mortality rate. Methods A before-and-after cross-sectional study compared in-hospital mortality rates before and after implementation of an RRT triggered by the MEWS ≥4 in two wards of a tertiary hospital that offer ED support. Results We included 6863 patients hospitalized in these wards before RRT implementation from July 2015 through June 2017 and 6944 patients hospitalized in these same wards after RRT implementation from July 2018 through June 2020. We observed a statistically significant decrease in the in-hospital mortality rate after intervention, 449 deaths/6944 hospitalizations [6.47% (95% confidence interval (CI) 5.91%– 7.07%)] compared to 534 deaths/6863 hospitalizations [7.78% (95% CI 7.17–8.44)] before intervention; with an absolute risk reduction of -1.31% (95% CI -2.20 –-0.50). Conclusion RRT trigged by the MEWS≥4 in high-risk wards that offer ED support was found to be associated with a decreased in-hospital mortality rate. A further cluster-randomized trial should evaluate the impact of this intervention in this setting.

scholarly journals The Utility of the Rapid Emergency Medicine Score (REMS) compared with SIRS, qSOFA and NEWS for predicting in-Hospital Mortality Among Patients with Suspicion of Sepsis in an Emergency Department

2020 ◽  
Author(s):  
Onlak Ruangsomboon ◽  
Phetsinee Boonmee ◽  
Chok Limsuwat ◽  
Tipa Chakorn ◽  
Apichaya Monsomboon
Keyword(s):  
Emergency Department ◽  
Emergency Medicine ◽  
Hospital Mortality ◽  
Early Warning ◽  
Risk Model ◽  
Adverse Outcomes ◽  
Early Warning Score ◽  
Baseline Risk ◽  
Secondary Outcome ◽  
Net Benefit

Abstract Background Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes secondary to sepsis in the Emergency Department (ED). These EWSs include the Systemic Inflammatory Response Syndrome criteria (SIRS), the quick Sequential Organ Failure Assessment (qSOFA) and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has never been validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of SIRS, qSOFA and NEWS for predicting mortality in patients with suspicion of sepsis in the ED.Methods We conducted a retrospective study at the ED of Siriraj Hospital Mahidol University, Thailand. Adult patients suspected of having sepsis in the ED between August 2018 and July 2019 were included. Their EWSs were calculated. The primary outcome was all-cause in-hospital mortality. The secondary outcome was 7-day mortality.Results A total of 1622 patients were included in the study; 574 (28.2%) died at hospital discharge. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.62 (95% confidence interval (CI) 0.59, 0.65)), which was significantly higher than qSOFA (AUROC 0.58 (95%CI 0.55, 0.60); p=0.005) and SIRS (AUROC 0.52 (95%CI 0.49, 0.55); p<0.001) but not significantly superior to NEWS (AUROC 0.61 (95%CI 0.58, 0.64); p=0.27). REMS was the best EWS in terms of calibration and association with the outcome. It could also provide the highest net benefit from the decision curve analysis. Comparison of EWSs plus baseline risk model showed similar results. REMS also performed better than other EWSs for 7-day mortality.ConclusionREMS was an early warning score with higher accuracy than sepsis-related scores (qSOFA and SIRS) and had the highest utility in terms of net benefit compared to SIRS, qSOFA and NEWS in predicting in-hospital mortality in patients presenting to the ED with suspected sepsis.

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