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

ABSTRACT: OBJECTIVES:This 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. DESIGN:A prospective, single-centred observational cohort study. SETTING:This study was conducted at a tertiary hospital in South China. PARTICIPANTS:A 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. OUTCOMES:The 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. RESULTS:A 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. CONCLUSION:Our 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.

SUBMITTER: Xie X

PROVIDER: S-EPMC6303659 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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

Xie Xiaohua X Huang Wenlong W Liu Qiongling Q Tan Wei W Pan Lu L Wang Lei L Zhang Jian J Wang Yunyun Y Zeng Yingchun Y

BMJ open 20181214 12

<h4>Objectives</h4>This 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.<h4>Design</h4>A prospective, single-centred observational cohort study.<h4>Setting</h4>This study was conducted at a tertiary hospital in South China.<h4>Participants</h4>A total of 383 patients aged 18 years or older who presented to the emergency department from 17 May 2017 thro ...[more]

PMID: 30552276

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Project description:The aim of this study is to evaluate the usefulness of the pre-hospital National Early Warning Score (pNEWS) and the pre-hospital Modified Early Warning Score (pMEWS) for predicting admission and in-hospital mortality in elderly patients presenting to the emergency department (ED). We also compare the value of the pNEWS with that of the ED NEWS (eNEWS) and ED MEWS (eMEWS) for predicting admission and in-hospital mortality. This retrospective, single-centre observational study was carried out in the ED of Jikei University Kashiwa Hospital, in Chiba, Japan, from 1st April 2017 to 31st March 2018. All patients aged 65 years or older were included in this study. The pNEWS/eNEWS were derived from seven common physiological vital signs: respiratory rate, peripheral oxygen saturation, the presence of inhaled oxygen parameters, body temperature, systolic blood pressure, pulse rate and Alert, responds to Voice, responds to Pain, Unresponsive (AVPU) score, whereas the pMEWS/eMEWS were derived from six common physiological vital signs: respiratory rate, peripheral oxygen saturation, body temperature, systolic blood pressure, pulse rate and AVPU score. Discrimination was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC). The median pNEWS, pMEWS, eNEWS and eMEWS were significantly higher at admission than at discharge (p < 0.001). The median pNEWS, pMEWS, eNEWS and eMEWS of non-survivors were significantly higher than those of the survivors (p < 0.001). The AUC for predicting admission was 0.559 for the pNEWS and 0.547 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting admission (p = 0.102). The AUCs for predicting in-hospital mortality were 0.678 for the pNEWS and 0.652 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting in-hospital mortality (p = 0.081). The AUC for predicting admission was 0.628 for the eNEWS and 0.591 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting admission (p < 0.001). The AUC for predicting in-hospital mortality was 0.789 for the eNEWS and 0.720 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting in-hospital mortality (p < 0.001). For admission and in-hospital mortality, the AUC of the eNEWS was significantly greater than that of the pNEWS (p < 0.001, p < 0.001), and the AUC of the eMEWS was significantly greater than that of the pMEWS (p < 0.01, p < 0.05). Our single-centre study has demonstrated the low utility of the pNEWS and the pMEWS as predictors of admission and in-hospital mortality in elderly patients, whereas the eNEWS and the eMEWS predicted admission and in-hospital mortality more accurately. Evidence from multicentre studies is needed before introducing pre-hospital versions of risk-scoring systems.

Project description:BackgroundA 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.MethodsA 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.ResultAfter 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.ConclusionStacking ML methods improve predicted in-hospital mortality than MEWS in adult non-traumatic ED patients, especially in the prediction of delayed mortality.

Project description:ObjectivesIncrease of nucleated RBCs in peripheral blood has been shown to be predictive of mortality in ICU patients. The aim of this study was to explore the prognostic value of nucleated RBCs in the first blood sample taken at admission to the emergency department from patients with suspected sepsis.DesignSingle-center prospective cohort study.SettingEmergency department.PatientsOne-thousand two-hundred thirty-one consecutive adult patients with suspected sepsis were included in a prospective quality register-based cohort study. Inclusion criteria were as follows: patients received in rapid response team with blood cultures taken and immediate antibiotics given in the emergency department.InterventionNot applicable.Measurement and main resultsNucleated RBCs, Sequential Organ Failure Assessment score, Quick Sequential Organ Failure Assessment, Charlson Comorbidity Index, and commonly used laboratory tests measured in the emergency department were compared with 30-day mortality. Nvaucleated RBC counts were divided into five groups, called "Nucleated RBC score," according to nucleated RBC count levels and analyzed with logistic regression together with the Sequential Organ Failure Assessment score and Charlson Comorbidity Index. Of the 262 patients with nucleated RBCs equal to or higher than the detection limit (0.01 × 109/L), 26% died within 30 days, compared with 12% of the 969 patients with nucleated RBCs below the detection limit (p < 0.0001). Mortality was significantly higher for each increase in Nucleated RBC score, except from score 2 to 3, and was 62% in the highest group. In multivariate logistic regression, odds ratios for 30-day mortality were as follows: Nucleated RBC score: 1.33 (95% CI, 1.13-1.56), Sequential Organ Failure Assessment score: 1.32 (1.29-1.56), and Charlson Comorbidity Index: 1.17 (1.09-1.25).ConclusionsMost patients with suspected sepsis in emergency department had undetectable nucleated RBCs at admission to the emergency department. However, increased nucleated RBCs significantly predicted 30-day mortality. Nucleated RBCs may provide additional prognostic information to Sequential Organ Failure Assessment score and other laboratory tests.

Project description:High-sensitivity troponin assay brought new challenges as we detect elevated concentration in many other diseases, and it became difficult to distinguish the real cause of this elevation. In this notion, diagnosis of acute coronary syndrome (ACS) remains a challenge in emergency department (ED). We aim to examine different approaches for rule-in and rule-out of ACS using risk scores, copeptin, and coronary computed tomography angiography (CCTA). A prospective observational study was designed to evaluate chest pain patients. Consecutive adult patients admitted to the ED with a chief complaint of chest pain due to any cause were included. All patients were followed-up for 6 months after discharge for major adverse cardiovascular events and readmissions. Admission data, ED processes, and diagnoses were analyzed. One hundred forty-six patients were included, average age was 63 ± 13.4 years, and 95 (65.1%) were male. Global Registry of Acute Coronary Events (GRACE) and History, ECG, Age, Risk factors, Troponin (HEART) scores showed good prognostic abilities, but HEART combination with copeptin improves diagnoses of myocardial infarction (area under the curve [AUC] 0.764 vs AUC 0.864 P = .0008). Patients with elevated copeptin were older, had higher risk scores, and were more likely to be admitted to hospital and diagnosed with ACS in ED. For copeptin, AUC was 0.715 (95% confidence interval 0.629-0.803), and for combination with troponin, AUC of 0.770 (0.703-0.855) did not improve rule-in of myocardial infarction. High-sensitivity troponin I assay alongside prior stroke, history of carotid stenosis, dyslipidemia, use of diuretics, and electrocardiogram changes (left bundle branch block or ST depression) are good predictors of myocardial infarction (χ² = 52.29, AUC = 0.875 [0.813-0.937], P < .001). The regression analysis showed that combination of copeptin and CCTA without significant stenosis can be used for ACS rule-out (χ² = 26.36, P < .001, AUC = 0.772 [0.681-0.863], negative predictive value of 96.25%). For rule-in of ACS, practitioner should consider not only scores for risk stratification but carefully analyze medical history and nonspecific electrocardiogram changes and even with normal troponin results, we strongly suggest thorough evaluation in chest pain unit. For rule-out of ACS combination of copeptin and CCTA holds great potential.

Project description:BACKGROUND:Emergency department (ED) crowding leads to prolonged emergency department length of stay (ED-LOS) and adverse patient outcomes. No uniform definition of ED crowding exists. Several scores have been developed to quantify ED crowding; the best known is the Emergency Department Work Index (EDWIN). Research on the EDWIN is often applied to limited settings and conducted over a short period of time. OBJECTIVES:To explore whether the EDWIN as a measure can track occupancy at a Dutch ED over the course of one year and to identify fluctuations in ED occupancy per hour, day, and month. Secondary objective is to investigate the discriminatory value of the EDWIN in detecting crowding, as compared with the occupancy rate and prolonged ED-LOS. METHODS:A retrospective cohort study of all ED visits during the period from September 2010 to August 2011 was performed in one hospital in the Netherlands. The EDWIN incorporates the number of patients per triage level, physicians, treatment beds and admitted patients to quantify ED crowding. The EDWIN was adjusted to emergency care in the Netherlands: modified EDWIN (mEDWIN). ED crowding was defined as the 75th percentile of mEDWIN per hour, which was ?0.28. RESULTS:In total, 28,220 ED visits were included in the analysis. The median mEDWIN per hour was 0.15 (Interquartile range (IQR) 0.05-0.28); median mEDWIN per patient was 0.25 (IQR 0.15-0.39). The EDWIN was higher on Wednesday (0.16) than on other days (0.14-0.16, p<0.001), and a peak in both mEDWIN (0.30-0.33) and ED crowding (52.9-63.4%) was found between 13:00-18:00 h. A comparison of the mEDWIN with the occupancy rate revealed an area under the curve (AUC) of 0.86 (95%CI 0.85-0.87). The AUC of mEDWIN compared with a prolonged ED-LOS (?4 hours) was 0.50 (95%CI 0.40-0.60). CONCLUSION:The mEDWIN was applicable at a Dutch ED. The mEDWIN was able to identify fluctuations in ED occupancy. In addition, the mEDWIN had high discriminatory power for identification of a busy ED, when compared with the occupancy rate.

Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/?L or lower than 4000/?L; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ?18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.

Dataset Information

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

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

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