Poor performance of the modified early warning score for predicting mortality in critically ill patients presenting to an emergency department

BACKGROUND:This study was undertaken to validate the use of the modified early warning score(MEWS) as a predictor of patient mortality and intensive care unit(ICU)/ high dependency(HD)admission in an Asian population.METHODS:The MEWS was applied to a retrospective cohort of 1 024 critically ill patients presenting to a large Asian tertiary emergency department(ED) between November 2006 and December2007.Individual MEWS was calculated based on vital signs parameters on arrival at ED.Outcomes of mortality and ICU/HD admission were obtained from hospital records.The ability of the composite MEWS and its individual components to predict mortality within 30 days from ED visit was assessed.Sensitivity,specificity,positive and negative predictive values were derived and compared with values from other cohorts.A MEWS of ≥4 was chosen as the cut-off value for poor prognosis based on previous studies.RESULTS:A total of 311(30.4%) critically ill patients were presented with a MEWS ≥4.Their mean age was 61.4 years(SD 18.1) with a male to female ratio of 1.10.Of the 311 patients,53(17%)died within 30 days,64(20.6%) were admitted to ICU and 86(27.7%) were admitted to HD.The area under the receiver operating characteristic curve was 0.71 with a sensitivity of 53.0%and a specificity of 72.1%in addition to a positive predictive value(PPV) of 17.0%and a negative predictive value(NPV)of 93.4%(MEWS cut-off of ≥4) for predicting mortality.CONCLUSION:The composite MEWS did not perform well in predicting poor patient outcomes for critically ill patients presenting to an ED.

Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients

Background:Triage trauma scores are utilised to determine patient disposition,interventions and prognostication in the care of trauma patients.Heart rate variability(HRV)and heart rate complexity(HRC)reflect the autonomic nervous system and are derived from electrocardiogram(ECG)analysis.In this study,we aimed to develop a model incorporating HRV and HRC,to predict the need for life-saving interventions(LSI)in trauma patients,within 24 h of emergency department presentation.Methods:We included adult trauma patients(≥18 years of age)presenting at the emergency department of Singapore General Hospital between October 2014 and October 2015.We excluded patients who had non-sinus rhythms and larger proportions of artefacts and/or ectopics in ECG analysis.We obtained patient demographics,laboratory results,vital signs and outcomes from electronic health records.We conducted univariate and multivariate analyses for predictive model building.Results:Two hundred and twenty-five patients met inclusion criteria,in which 49 patients required LSIs.The LSI group had a higher proportion of deaths(10,20.41%vs 1,0.57%,p<0.001).In the LSI group,the mean of detrended fluctuation analysis(DFA)-α1(1.24 vs 1.12,p=0.045)and the median of DFA-α2(1.09 vs 1.00,p=0.027)were significantly higher.Multivariate stepwise logistic regression analysis determined that a lower Glasgow Coma Scale,a higher DFA-α1 and higher DFA-α2 were independent predictors of requiring LSIs.The area under the curve(AUC)for our model(0.75,95%confidence interval,0.66–0.83)was higher than other scoring systems and selected vital signs.Conclusions:An HRV/HRC model outperforms other triage trauma scores and selected vital signs in predicting the need for LSIs but needs to be validated in larger patient populations.