Abstract
Geriatric patients arriving in a non-traumatic coma (NTC) at the emergency department (ED) present substantial risks and necessitate prompt and appropriate care. The National Early Warning Score (NEWS) is a promising tool that can efficiently evaluate this vulnerable population. Current study was designed to assess the effectiveness of the NEWS for predicting the severity of illness in geriatric patients with NTC and identifying those at highest risk. Current study was performed involving geriatric patients (aged ≥65 years) with NTC who visited Chonnam National University Hospital between January and December 2022. NEWS and Modified Early Warning Scores (MEWS) were calculated at ED visits. The association of NEWS and MEWS for in-hospital death was evaluated by multivariable analysis. Out of 683 patients, 202 were non-survivors (29.6%). The NEWS and MEWS values of non-survivors were higher than those of survivors (NEWS, 7 [5–10] vs. 10 [8–12]; MEWS, 5 [4–6] vs. 7 [5–8]). The NEWS (adjusted odds ratio [aOR]; 1.253, 95% confidence interval [CI]: 1.181–1.329) and MEWS (aOR; 1.323, 95% CI: 1.206–1.451) were also significantly associated with in-hospital death. The area under the curve for the NEWS and MEWS was 0.721 (95% CI: 0.685–0.754) and 0.695 (95% CI: 0.659–0.730), respectively. The NEWS can be an effective tool for predicting severity of illness via in-hospital death in geriatric patients with NTC.
Keywords: Prognosis, Scoring, Coma, Age
1. Introduction
Patients presenting with decreased consciousness in the emergency department (ED) can make diagnosing an etiology particularly difficult and has a critical impact on patient care. The prevalence of decreased consciousness is higher in geriatric patients compared to that in other age groups [1]. Furthermore, geriatric patients with decreased consciousness had worse prognoses compared with their younger counterparts [2,3] since the aging process is characterized by delayed recovery and a higher risk of adverse outcomes [4,5].
A coma, which is defined as a severe decrease in consciousness, presents a more serious risk for geriatric patients and requires immediate care. The rapid assessment of geriatric patients in non-traumatic comas (NTC) is crucial for initiating appropriate interventions and improving patient outcomes. Early identification of the severity of illness for geriatric patients in NTC allows healthcare providers to effectively allocate resources and tailor treatment strategies to meet each patient's specific needs. Currently, specific clinical assessment tools for predicting treatment and outcomes in geriatric patients with NTC are lacking [6]. Therefore, we decided to investigate whether the two widely recognized simple and rapid bedside assessment tools, the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS), could be applied to this patient group. By integrating vital signs and clinical parameters to provide a comprehensive assessment, the NEWS and MEWS are validated tools for assessing clinical conditions and predicting outcomes in critically ill patients [[7], [8], [9], [10], [11], [12], [13], [14], [15]]. Thus, both tools are considered useful for initially assessing geriatric patients with NTC.
We hypothesized that high NEWS and MEWS are associated with the clinical outcomes of geriatric patients in NTC. Therefore, current study was designed to examine the relationships of NEWS and MEWS for outcome in geriatric patients with NTC.
2. Methods
2.1. Study design and population
Current study is a retrospective case-control study of geriatric patients (≥65 years) with NTC who admitted to ED in Chonnam National University Hospital between January and December 2022. The decision to define geriatric patients as those aged 65 years or older was made in accordance with internationally recognized standards, such as those used by the Organization for Economic Co-operation and Development. A Glasgow Coma Scale (GCS) score of less than 9 is defined as coma. Exclusion criteria were traumatic coma, insufficient vital sign data on ED arrival, and lack of laboratory measurements. Current study was approved by the Institutional Review Board (IRB) of Chonnam National University Hospital Biomedical Research Institute (CNUH-2022-231).
2.2. Data collection
Data collected included age, sex, cause of coma (metabolic or structural), GCS score at ED arrival, vital signs (systolic blood pressure [SBP, mm Hg], pulse rate [bpm], respiratory rate [bpm], oxygen saturation [SpO2, %], and body temperature [BT, °C]) at ED arrival, laboratory parameters (white blood cell count [per μL of blood], neutrophil count [per μL of blood], hemoglobin level [g/dL], neutrophil count [per μL of blood], lymphocyte count [per μL of blood], monocyte count [per μL of blood], platelet counts [per μL of blood], creatinine [mg/dL], sodium [mEq/L], chloride [mEq/L], potassium [mEq/L], blood urea nitrogen [BUN, mg/dL], and calcium [mg/dL]) at ED arrival, and in-hospital death. Blood cell count was measured using DxH 900 (Beckman Coulter), and other evaluations were performed using the AU5800 clinical chemistry analyzer (Beckman Coulter). Laboratory parameters were considered for a comprehensive and objective assessment of geriatric patients with NTC presenting to the ED. These metrics provided valuable insight into the patient's physiological status, helped adjust for confounding factors, and are consistent with actual clinical practice that considers both vital signs and laboratory results. This approach allows for a meaningful comparison between the NEWS and MEWS when predicting patient outcomes. The NEWS and MEWS were calculated based on GCS score and vital signs (SBP, respiratory rate, pulse rate, BT, and SpO2) at ED arrival [7,8]. The in-hospital death was primary outcome, and the mortality within 30 days was secondary outcome.
2.3. Statistical analysis
Continuous variables do not meet the normality test and are expressed as median values with interquartile ranges, and categorical variables are expressed as frequencies and percentages. Statistical significance between groups was evaluated using the Mann–Whitney U test for continuous variables and the Chi-squared tests for categorical variables. We examined the receiver operating characteristics (ROC) curves to evaluate the ability of NEWS and MEWS for in-hospital death. We then calculated the area under the ROC curve (AUC), sensitivity, specificity, negative predictive value, positive predictive value, and cutoff values. Youden's index was used to determine the optimal cutoff value [16].
We examined a multivariable logistic regression analysis using relevant covariates for in-hospital death. The significance levels of several predictive variables tend to vary on multivariable regression analysis [17,18]. Thus, similar to that in several studies, we only included variables in the multivariable analysis if their p values < 0.2 in the univariable analysis [19,20]. The final adjusted regression model was built using a backward stepwise approach and sequentially removing variables with p > 0.10 [18]. Metabolic factors, platelet count, sodium, potassium, and calcium were selected as adjusted variables for in-hospital death (Supplementary Table 1). We assessed calibration using the Hosmer–Lemeshow goodness-of-fit C-statistic, with p > 0.05 indicating good calibration [18]. NEWS and MEWS were also included in the final model.
The results of multivariable analysis for in-hospital death were expressed as odds ratio (OR) and 95% confidence interval (CI). Kaplan-Meier survival curves of NEWS and MEWS for mortality within 30 days were compared using the log-rank test. Data were analyzed using PASW/SPSSTM software, version 28.0 (SPSS Inc., Chicago, IL, USA), while the Kaplan–Meier survival and ROC curves were analyzed and compared using MedCalc version 22.0 (MedCalc Software, BVBA, Ostend, Belgium). We conducted a post-hoc power analysis using g*power. Statistical significance was set at P < 0.05 (two-sided).
3. Results
3.1. Patient selection and characteristics
After exclusions, 689 geriatric patients with NTC were included in current study (Fig. 1). Altogether, 348 patients were male (51.0%), the median age was 80.0 (73.0–85.0), and mortality at hospital discharge was 29.6% (n = 202). Among the total cases, 155 (22.5%) patients were diagnosed as structural NTC group and 534 (77.5%) patients were diagnosed as metabolic NTC group. In the structural NTC group, stroke (intracranial hemorrhage or cerebral infarction) was the most common cause. In the metabolic NTC group, the most common causes were cardiac arrest and extracranial infection (Supplementary Table 2).
Fig. 1.
Schematic diagram showing the number of patients admitted with comatose mental status included in the present study.
3.2. Comparison of clinical parameters between survivors and non-survivors
Table 1 presents the clinical parameters of the surviving and non-surviving patients. Non-survivors had lower SBP, BT, SpO2, platelet counts, hemoglobin levels, and calcium levels, as well as higher creatinine, BUN, chloride, and potassium levels than the survivors. The values of NEWS and MEWS in non-survivors were higher than those in the survivors. Post-hoc power analyses for in-hospital death had representative powers of 100%.
Table 1.
Comparison of baseline characteristics of geriatric patients with non-traumatic coma according to in-hospital death.
| Variables | Total patients (N = 683) | Survivors (N = 481) | Non-survivors (N = 202) | P-value |
|---|---|---|---|---|
| Age, years | 80.0 (73.0–85.0) | 79.0 (73.0–84.0) | 81.0 (75.0–85.0) | 0.098 |
| Male, n (%) | 348 (51.0) | 241 (50.1) | 107 (53.0) | 0.548 |
| Cause, n (%) | <0.001 | |||
| Metabolic | 528 (77.3) | 348 (72.3) | 180 (89.1) | |
| Structure | 155 (22.7) | 133 (27.7) | 22 (10.9) | |
| GCS score | 7 (3–7) | 7 (7–7) | 7 (3–7) | <0.001 |
| Systolic blood pressure, mmHg | 110 (80–140) | 120 (100–150) | 90 (60–120) | <0.001 |
| Respiratory rate,/min | 20 (20–22) | 20 (20–22) | 20 (20–20) | <0.001 |
| Pulse rate,/min | 91 (76–110) | 90 (76–110) | 92 (74–116) | 0.745 |
| Body temperature, °C | 36.3 (36.0–36.7) | 36.4 (36.0–36.8) | 36.1 (36.0–36.6) | 0.005 |
| Oxygen saturation, % | 97 (93–98) | 97 (94–98) | 95 (88–98) | <0.001 |
| NEWS | 8 (6–10) | 7 (5–10) | 10 (8–12) | <0.001 |
| MEWS | 5 (4–7) | 5 (4–6) | 7 (5–8) | <0.001 |
| Blood cell count | ||||
| White blood cell count, × 109/L | 11.5 (7.9–16.1) | 11.2 (7.9–15.9) | 11.9 (7.6–17.5) | 0.361 |
| Hemoglobin, g/dL | 11.5 (9.8–13.3) | 11.9 (10.3–13.6) | 10.6 (8.8–12.6) | <0.001 |
| Neutrophil count, × 109/L | 8.6 (5.2–13.3) | 8.6 (5.3–13.2) | 8.6 (5.1–14.1) | 0.813 |
| Lymphocyte count, × 109/L | 1.4 (0.8–2.4) | 1.3 (0.8–2.2) | 1.5 (0.7–3.0) | 0.203 |
| Monocyte count, × 109/L | 0.6 (0.4–0.9) | 0.6 (0.4–0.8) | 0.5 (0.3–0.9) | 0.002 |
| Platelet count, × 109/L | 197 (142–260) | 211 (155–266) | 162 (107–231) | <0.001 |
| Kidney function | ||||
| Blood urea nitrogen, mg/dL | 24.4 (17.0–42.8) | 22.5 (16.3–35.8) | 32.9 (19.6–56.8) | <0.001 |
| Creatinine, mg/dL | 1.1 (0.8–1.9) | 1.0 (0.7–1.5) | 1.4 (1.0–2.6) | <0.001 |
| Serum electrolytes | ||||
| Sodium, mmol/L | 139 (136–143) | 139 (136–142) | 140 (136–144) | 0.112 |
| Potassium, mmol/L | 4.2 (3.7–4.8) | 4.1 (3.7–4.6) | 4.5 (3.8–5.1) | <0.001 |
| Chloride, mmol/L | 105 (101–108) | 104 (100–108) | 106 (102–110) | 0.003 |
| Calcium, mg/dL | 8.6 (8.0–9.2) | 8.7 (8.1–9.2) | 8.2 (7.5–8.9) | <0.001 |
GCS, Glasgow Coma Scale; NEWS, National Early Warning Score; MEWS, Modified Early Warning Scores.
3.3. Association of NEWS and MEWS for in-hospital death
Table 2 presents the results of multivariable analysis for in-hospital death. After adjusting for confounders, both NEWS (OR, 1.253; 95% CI, 1.181–1.329) and MEWS (OR, 1.323; 95% CI, 1.206–1.451) were associated with in-hospital death. The Hosmer-Lemeshow goodness-of-fit C statistics showed good calibration for both NEWS and MEWS (chi-square = 7.256; P = 0.509 and chi-square = 5.169; P = 0.739, respectively). Table 3 presents the results of the ROC analysis of NEWS and MEWS for predicting in-hospital death. The AUCs for NEWS and MEWS were 0.721 (95% CI, 0.685–0.754] and 0.695 (95% CI, 0.659–0.730), respectively. Kaplan–Meier survival curves for NEWS (Fig. 2A) and MEWS (Fig. 2B) during the mortality within 30 days were then constructed, which showed that both NEWS >7 and MEWS >5 are able to significantly predict the mortality within 30 days (P < 0.001 and P < 0.001).
Table 2.
Multivariate logistic regression analysis of NEWS and MEWS for predicting in-hospital death in geriatric patients with non-traumatic coma.
| Unadjusted OR (95% CI) | P-value | Adjusted OR (95% CI) | P-value | |
|---|---|---|---|---|
| NEWS | 1.287 (1.217–1.360) | <0.001 | 1.253 (1.181–1.329) | <0.001 |
| MEWS | 1.427 (1.310–1.554) | <0.001 | 1.323 (1.206–1.451) | <0.001 |
NEWS, National Early Warning Score; MEWS, Modified Early Warning Scores; OR, odds ratio; CI, confidence interval.
Table 3.
Prognostic performance of NEWS and MEWS for predicting in-hospital death in geriatric patients with non-traumatic coma.
| Cutoff | Sensitivity (95% CI) | Specificity (95% CI) | PPV (95% CI) | NPV (95% CI) | AUC (95% CI) | |
|---|---|---|---|---|---|---|
| NEWS | ≥7 | 78.7 (72.4–84.1) | 52.6 (48.0–57.1) | 41.1 (38.3–44.0) | 85.5 (81.7–88.6) | 0.721 (0.685–0.754) |
| MEWS | ≥5 | 65.8 (58.9–72.4) | 65.9 (61.5–70.1) | 44.8 (40.9–48.7) | 82.1 (79.0–84.9) | 0.695 (0.659–0.730) |
NEWS, National Early Warning Score; MEWS, Modified Early Warning Scores; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the receiver operating characteristics curve.
Fig. 2.
Kaplan–Meier plots of NEWS >7 (A) and MEWS >5 (B) for cumulative 30-day survival. Both NEWS >7 and MEWS >5 are statistically significant for predicting 30-day mortality (P < 0.001 and P < 0.001, respectively).
4. Discussion
Previous studies showed a mortality rate of 5–6% in geriatric patients who visited the ED [21,22]. However, the mortality rate was more than 10% when geriatric patients admitted to ED also demonstrated changes in consciousness [1,4,5]. Moreover, the mortality of geriatric patients with NTC in current study was 29.6%. Thus, geriatric patients with NTC appear to be at high risk of mortality and require rapid evaluation and intervention. In the present study, non-survivors had significantly higher valuse of NEWS and MEWS compared with survivors. In the multivariable analysis, NEWS and MEWS were associated with in-hospital death. In the AUC analysis, NEWS performed moderately for in-hospital death, whereas MEWS did not. A NEWS of 7 or higher was a good predictor for in-hospital death in geriatric patients with NTC. The current study provides evidence of the clinical usefulness of NEWS in geriatric patients with NTC who will need rapid and accurate severity prediction.
Several studies have investigated the etiology of coma and altered mental status in ED patients. Kanich et al. found that 28% of coma and altered mental states had neurological causes and 68% had metabolic causes. They also reported a 9% mortality rate in all ED patients with altered mental status [23]. In two other studies, the prevalence of coma and altered mental status due to structural and metabolic causes was 29.8% vs. 70.2% and 25.3% vs. 74.7%, respectively, excluding trauma [4,5]. Xiao et al. compared patients aged around 60 years and found that, among those aged >60 years, 45.8% of patients experienced coma because of structural causes and 54.2% of patients experienced coma because of metabolic reasons. Furthermore, the mortality rate was higher in this group at 10.8% [4]. A previous study of geriatric patients with altered mental status in the ED found a prevalence of 36.9% in the structural group and 63.1% in the metabolic group, with a mortality rate of 24.7%, excluding trauma [24]. In studies of NTC, one study reported a prevalence of 37.6% in the structural group and 62.4% in the metabolic group, with a mortality rate of 26.5% [25]. In another study of non-surgical ED patients with a GCS ≤10, the structural group accounted for 27.7% and the metabolic group for 72.3%, with a mortality rate of 25.6% [26]. The results of previous studies and the present study have shown similar results, particularly the higher prevalence of metabolic causes for NTC and altered mental status than structural causes. Some differences in etiological outcomes may be related to variations in the patient groups investigated. Unlike some other studies, we investigated geriatric patients with NTC. Mortality is generally higher in older comatose patients, and the mortality rate in current study (29.6%) is similar to previous studies of comatose patients.
In their single-center study including Japanese geriatric patients, Mitsunaga et al. reported that NEWS had an AUC of 0.789 (95% CI, 0.747–0.829) and could effectively predict in-hospital death [21]. In their single-center study in Korea, Kim et al. reported the AUC for in-hospital death to be 0.820 (95% CI, 0.806–0.833), showing that a high NEWS is significantly correlated with in-hospital death in geriatric patients [22]. These findings suggest a good performance of NEWS in geriatric patients in the ED. Our study, which focused on geriatric patients with NTC, found the performance of NEWS to be lower than that reported in previous studies. These patients, representing a high-risk group, showed a higher mortality rate (29.6%) compared to the lower rates of 5.7% and 5.1% reported in the previous studies, wherein the percentage of alert patients was 84.7% and 95.6%, respectively. Kim et al. reported that only 5.6% of their total patient group had a NEWS score of 7 or higher. Considering these differences, our study results show that NEWS is useful even in high-risk patients. This suggests that NEWS can effectively predict mortality in geriatric patients in the ED.
MEWS has also been reported to show good performance for in-hospital death in ED patients [21,27]. In their single-center study in China, Xie et al. showed that MEWS had an AUC of 0.83 for in-hospital death in ED patients [27]. Our results differ, which is likely because of the study population being adult patients in the ED, including those with trauma. Considering that the patients with MEWS ≥4 in study by Xie et al. had a greater average age and a higher proportion of high-risk patients at the time of triage, MEWS may be useful for high-risk geriatric patients. In the study by Mitsunaga et al., MEWS had an AUC of 0.720 for in-hospital death in geriatric patients in ED [21]. This study also showed better performance for MEWS compared to that in our study, which could be attributed to the same reason for the difference in NEWS results as described earlier. Furthermore, Mitsunaga et al. compared both NEWS and MEWS, and their results are consistent with those of our study; that is, they reported a lower performance of MEWS compared to NEWS [21]. Considering these points, MEWS is expected to be useful in geriatric coma patients, although it does not have good performance. But further studies are needed in this regard.
In the present study, the optimal cutoff value for NEWS was determined to be 7, using the Yuden index. A previous study investigating prehospital NEWS found that a NEWS cutoff value of 7 or higher was related with an increased probability of in-hospital death and ICU admission [28]. In a study of geriatric patients with pneumonia, the high-risk group (NEWS ≥7) had a higher 30-day and in-hospital death than the medium-risk (NEWS of 5–6) and low-risk groups (NEWS ≤4) [29]. Given these findings, it can be suggested that a cutoff value of 7 can be used for NEWS in geriatric patients with NTC, although studies are needed to corroborate these findings.
The current study had several limitations. First, our study was performed at a single center by a retrospective nature, meaning that its findings are not immediately generalizable to the entire population. Second, the various clinical factors were measured and evaluated by different individuals, leading to variability. The respiratory rate, in particular, had limitations in measurement, so it was more difficult to reflect it in the results. Third, it would have been better to compare critical care assessment indicators such as SAPS and APACHE. Fourth, we plotted the survival curves using the Kaplan–Meier method considering mortality within 30 days. The mortality within 30 days was assessed based on 30 days during the hospital stay, and death after discharge was not investigated. This may have resulted in outcomes of some patients being misrepresented. Finally, it was difficult to identify the above indicators with screening tests conducted in the ED to urgently evaluate and determine hospitalization, discharge, and interventional treatment.
5. Conclusion
In the present study, NEWS and MEWS were independently associated with in-hospital death in geriatric patients with NTC admitted to ED. NEWS had a higher performance for in-hospital death compared to MEWS. NEWS can be an effective tool for predicting severity of illness via in-hospital death in geriatric patients with NTC. However, further researches are necessary to strengthen the evidence base and increase the generalizability of these results.
Funding
Current study was supported by a grant from Chonnam National University Hospital Biomedical Research Institute (BCRI23080).
Ethics approval and consent to participate
Chonnam National University Hospital's Institutional Review Board approved the study (CNUH-2022-231) and informed consent was waived because of the retrospective nature of the study.
Data availability statement
The dataset analyzed during the current study are available from the corresponding author on reasonable request.
CRediT authorship contribution statement
Dong Ki Kim: Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. Dong Hun Lee: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Byung Kook Lee: Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Formal analysis.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Not applicable.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e28338.
Appendix A. Supplementary data
The following is/are the supplementary data to this article:
References
- 1.McCabe J.J., Kennelly S.P. Acute care of older patients in the emergency department: strategies to improve patient outcomes. Open Access Emerg. Med. 2015;7:45–54. doi: 10.2147/OAEM.S69974. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Erkinjuntti T., Wikström J., Palo J., Autio L. Dementia among medical inpatients. Evaluation of 2000 consecutive admissions. Arch. Intern. Med. 1986;146:1923–1926. [PubMed] [Google Scholar]
- 3.Wofford J.L., Loehr L.R., Schwartz E. Acute cognitive impairment in elderly ED patients: etiologies and outcomes. Am. J. Emerg. Med. 1996;14:649–653. doi: 10.1016/S0735-6757(96)90080-7. [DOI] [PubMed] [Google Scholar]
- 4.Xiao H.Y., Wang Y.X., Xu T.D., Zhu H.D., Guo S.B., Wang Z., et al. Evaluation and treatment of altered mental status patients in the emergency department: life in the fast lane. World J Emerg Med. 2012;3:270–277. doi: 10.5847/wjem.j.issn.1920-8642.2012.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kim K.T., Jeon J.C., Jung C.G., Park J.A., Seo J.G., Kwon D.H. Etiologies of altered level of consciousness in the emergency room. Sci. Rep. 2022;12:4972. doi: 10.1038/s41598-022-09110-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Sutton M., Grimmer-Somers K., Jeffries L. Screening tools to identify hospitalised elderly patients at risk of functional decline: a systematic review. Int. J. Clin. Pract. 2008;62(12):1900–1909. doi: 10.1111/j.1742-1241.2008.01930.x. [DOI] [PubMed] [Google Scholar]
- 7.Subbe C.P., Kruger M., Rutherford P., Gemmel L. Validation of a modified early warning score in medical admissions. Qjm. 2001;94:521–526. doi: 10.1093/qjmed/94.10.521. [DOI] [PubMed] [Google Scholar]
- 8.Smith G.B., Prytherch D.R., Meredith P., Schmidt P.E., Featherstone P.I. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation. 2013;84:465–470. doi: 10.1016/j.resuscitation.2012.12.016. [DOI] [PubMed] [Google Scholar]
- 9.Jarvis S.W., Kovacs C., Badriyah T., Briggs J., Mohammed M.A., Meredith P., et al. Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions. Resuscitation. 2013;84:1494–1499. doi: 10.1016/j.resuscitation.2013.05.018. [DOI] [PubMed] [Google Scholar]
- 10.Jo S., Yoon J., Lee J.B., Jin Y., Jeong T., Park B. Predictive value of the National Early Warning Score–Lactate for mortality and the need for critical care among general emergency department patients. J. Crit. Care. 2016;36:60–68. doi: 10.1016/j.jcrc.2016.06.016. [DOI] [PubMed] [Google Scholar]
- 11.Kivipuro M., Tirkkonen J., Kontula T., Solin J., Kalliomäki J., Pauniaho S.-L., et al. National early warning score (NEWS) in a Finnish multidisciplinary emergency department and direct vs. late admission to intensive care. Resuscitation. 2018;128:164–169. doi: 10.1016/j.resuscitation.2018.05.020. [DOI] [PubMed] [Google Scholar]
- 12.Alam N., Vegting I., Houben E., Van Berkel B., Vaughan L., Kramer M., et al. Exploring the performance of the national early warning score (NEWS) in a European emergency department. Resuscitation. 2015;90:111–115. doi: 10.1016/j.resuscitation.2015.02.011. [DOI] [PubMed] [Google Scholar]
- 13.Dundar Z.D., Ergin M., Karamercan M.A., Ayranci K., Colak T., Tuncar A., et al. Modified early warning score and VitalPac early warning score in geriatric patients admitted to emergency department. Eur. J. Emerg. Med. 2016;23:406–412. doi: 10.1097/MEJ.0000000000000274. [DOI] [PubMed] [Google Scholar]
- 14.Bulut M., Cebicci H., Sigirli D., Sak A., Durmus O., Top A.A., et al. The comparison of modified early warning score with rapid emergency medicine score: a prospective multicentre observational cohort study on medical and surgical patients presenting to emergency department. Emerg. Med. J. 2014;31(6):476–481. doi: 10.1136/emermed-2013-202444. [DOI] [PubMed] [Google Scholar]
- 15.van Galen L.S., Dijkstra C.C., Ludikhuize J., Kramer M.H., Nanayakkara P.W. A protocolised once a day Modified Early Warning Score (MEWS) measurement is an appropriate screening tool for major adverse events in a general hospital population. PLoS One. 2016;11 doi: 10.1371/journal.pone.0160811. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Habibzadeh F., Habibzadeh P., Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem. Med. 2016;26:297–307. doi: 10.11613/BM.2016.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Concato J., Feinstein A.R., Holford T.R. The risk of determining risk with multivariable models. Ann. Intern. Med. 1993;118(3):201–210. doi: 10.7326/0003-4819-118-3-199302010-00009. [DOI] [PubMed] [Google Scholar]
- 18.Hosmer D., Lamesow S. John Wiley & Sons; New York, NY: 1999. Applied Survival Analysis: Regression Modeling of Time-To-Event Data. [Google Scholar]
- 19.Bitew D.A., Asmamaw D.B., Belachew T.B., Negash W.D. Magnitude and determinants of women's participation in household decision making among married women in Ethiopia, 2022: based on Ethiopian demographic and health survey data. Heliyon. 2023;9(7) doi: 10.1016/j.heliyon.2023.e18218. Published 2023 Jul 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Park Y.S., Lee J.H., Kwak Y.H., et al. A clinical prediction tool to predict urinary tract infection in pediatric febrile patients younger than 2 years old: a retrospective analysis of a fever registry. Clin Exp Emerg Med. 2021;8(4):314–324. doi: 10.15441/ceem.20.134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mitsunaga T., Hasegawa I., Uzura M., Okuno K., Otani K., Ohtaki Y., et al. Comparison of the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS) for predicting admission and in-hospital mortality in elderly patients in the pre-hospital setting and in the emergency department. PeerJ. 2019;7 doi: 10.7717/peerj.6947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kim I., Song H., Kim H.J., Park K.N., Kim S.H., Oh S.H., et al. Use of the National Early Warning Score for predicting in-hospital mortality in older adults admitted to the emergency department. Clin Exp Emerg Med. 2020;7:61–66. doi: 10.15441/ceem.19.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kanich W., Brady W.J., Huff J.S., Perron A.D., Holstege C., Lindbeck G., et al. Altered mental status: evaluation and etiology in the ED. Am. J. Emerg. Med. 2002;20:613–617. doi: 10.1053/ajem.2002.35464. [DOI] [PubMed] [Google Scholar]
- 24.Aslaner M.A., Boz M., Çelik A., Ahmedali A., Eroğlu S., Aksu N.M., et al. Etiologies and delirium rates of elderly ED patients with acutely altered mental status: a multicenter prospective study. Am. J. Emerg. Med. 2017;35:71–76. doi: 10.1016/j.ajem.2016.10.004. [DOI] [PubMed] [Google Scholar]
- 25.Forsberg S., Höjer J., Ludwigs U. Prognosis in patients presenting with non-traumatic coma. J. Emerg. Med. 2012;42:249–253. doi: 10.1016/j.jemermed.2010.04.021. [DOI] [PubMed] [Google Scholar]
- 26.Forsberg S., Höjer J., Ludwigs U., Nyström H. Metabolic vs structural coma in the ED—an observational study. Am. J. Emerg. Med. 2012;30:1986–1990. doi: 10.1016/j.ajem.2012.04.032. [DOI] [PubMed] [Google Scholar]
- 27.Xie X., Huang W., Liu Q., Tan W., Pan L., Wang L., et al. Prognostic value of modified early warning score generated in a Chinese emergency department: a prospective cohort study. BMJ Open. 2018;8 doi: 10.1136/bmjopen-2018-024120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Abbott T.E., Cron N., Vaid N., Ip D., Torrance H.D., Emmanuel J. Pre-hospital National Early Warning Score (NEWS) is associated with in-hospital mortality and critical care unit admission: a cohort study. Ann Med Surg (Lond). 2018;27:17–21. doi: 10.1016/j.amsu.2018.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kakehi E., Uehira R., Ohara N., Akamatsu Y., Osaka T., Sakurai S., et al. Utility of the New Early Warning Score (NEWS) in combination with the neutrophil-lymphocyte ratio for the prediction of prognosis in older patients with pneumonia. Fam Med Community Health. 2023;11(2) doi: 10.1136/fmch-2023-002239. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset analyzed during the current study are available from the corresponding author on reasonable request.