Emergency Medicine

Prognostic significance of emergency department modified early warning score trend in critical ill elderly patients

a b s t r a c t

Objective: To explore the relationship between trends in emergency department modified early warning score (EDMEWS) and the prognosis of elderly patients admitted to the intensive care unit (ICU).

Methods: Consecutive non-traumatic elderly ED patients (>=65 years old) admitted to the ICU between July 2018 and June 2019 were enrolled in this retrospective cohort study. The selected patients had at least 2 separate MEWS during their ED stay. Detailed patient information was retrieved initially from the ICU database of our hos- pital and then crosschecked with electronic medical recording system to confirm the completeness and correct- ness of the data. Patients who had do-not-resuscitate order and those with incomplete data of EDMEWS, Acute Physiology and Chronic Health Evaluation II score, or survival information (7-day and 30-day mortal- ity) were excluded. The trends in EDMEWS were determined using the regression line of multiple MEWS mea- sured during ED stay, in which EDMEWS trend progression was defined as the slope of the regression line > zero. The relationship between EDMEWS trend and prognosis was assessed using univariate and multivariate analyses (multiple logistic regression analysis).

Results: Of the 1423 selected patients, 499 (35.1%) had worsening 24-h APACHE II score, 110 (7.7%) died within 7 days, and 233 (16.4%) died within 30 days. Factors that were significantly associated with worsening 24-h APACHE II score, 7-day mortality, and 30-day mortality in univariate analysis were selected for inclusion into multiple logistic regression analyses. After adjusting for other covariates, EDMEWS trend progression was signif- icantly associated with 24-h APACHE II score progression, 7-day mortality, and 30-day mortality.

Conclusions: EDMEWS trend progression was significantly associated with 24-h APACHE II score progression, 7- day mortality, and 30-day mortality in elderly ED patients admitted to the ICU. EDMEWS is a simple and useful tool for precisely monitoring patients’ ongoing condition and predicting prognosis. Keywords: Modified Early Warning Score, Intensive Care Unit, Emergency Department, Prognosis.

(C) 2021

  1. Introduction

The Modified early warning score is a well-known, simple, and rapid tool used for stratifying patients according to disease severity and for identifying patients with a risk of developing catastrophic med- ical events [1,2]. MEWS is based on five physiological variables (systolic blood pressure [SBP], heart rate [HR], respiratory rate [RR], temperature, and neurological status) that are vital signs and are frequently mea- sured in medical care systems [3]. MEWS is almost always evaluated after admission and is easy to interpret; therefore, it is incorporated in the Medical Informatics system as a routine parameter for assessing ad- mitted patients in some countries [4]. It is recommended that more

* Corresponding author at: Department of Emergency Medicine, Taipei-Veterans General Hospital, 201 Sec 2, Shih-Pai Rd., Taipei, Taiwan, ROC.

E-mail address: [email protected] (J.-S. Fan).

studies should be conducted to test the clinical usefulness of MEWS be- cause it is easy, cost-effective, and non-cumbersome to assess [5-7]. Re- cently, the clinical value of MEWS was tested in pre-hospital and emergency departments (ED); the results showed that pre-hospital, tri- age, and maximum ED MEWS were positively correlated with Hospital admission rate, intensive care unit admission rate, and mortality [5,6]. However, most of the studies focused on the relationship between a single time-point MEWS, mostly the maximum MEWS, and resource use (ward or ICU admission) or prognosis (in-hospital mortality or 30- day mortality) [3,6,8].

The single time-point MEWS or the mean value of multiple MEWSs might indicate the current severity; however, it might not reflect the dy- namic clinical condition in patients with a rapidly worsening disease course [9-12]. Theoretically, the MEWS trend indicates the treatment ef- fect or disease progression during this period and might reflect the clin- ical condition more precisely than a single time-point MEWS [9-12].

https://doi.org/10.1016/j.ajem.2021.01.047

0735-6757/(C) 2021

Armagan et al. demonstrated that a worsening of the initial MEWS was associated with an increased risk of poor outcomes in admitted patients [10,13], indicating the potential role of ED MEWS (EDMEWS) trends in disease course and prognosis prediction. It is recommended to evaluate MEWS series trends instead of single time-point values [9,10,12]; nev- ertheless, currently available studies focusing on the usefulness of series MEWSs or MEWS trends in Prognostic prediction are rare [12]. There- fore, we conducted a retrospective study to examine the correlation be- tween EDMEWS trends and early progression (24-h acute physiology and chronic health evaluation (APACHE) II score progression), early mortality (within 7 days), and 30-day mortality in critically ill elderly patients.

ED admitted patients requiring strict monitoring of the disease pro- gression might benefit from MEWS trend evaluation [14]. According to previous study findings, the current prognostication system was not ef- fective in elderly patients, and therefore more elderly than younger pa- tients underwent unexpected disease deterioration and death [15]. Furthermore, elderly patients, compared to younger patients, have been shown to have different disease presentations, progression, deteri- oration risks, and mortality [16]. Any kind of physical decompensation with clinical deterioration could develop and progress rapidly due to de- creased functional reserve [16,17]. Moreover, critically ill elderly pa- tients tend to have decreased sensorium and a loss of normal communication abilities, thereby masking the worsening disease and reducing its early identification chances [16-18]. Unfortunately, the vul- nerable population requiring emergency and critical care is rapidly growing; however, there is no simple, rapid, and effective tool with which to determine the short-term treatment efficacy and predict the early disease course in critically ill elderly patients [18]. Based on the above-mentioned reasons, we tested the efficacy of the EDMEWS trends on critically ill elderly patients.

  1. Methods
    1. Patient selection

The current retrospective cohort study was conducted in the ED of our hospital, a 2800-bed tertiary referral center where more than 85,000 ED patients are treated yearly. In our hospital, approximately 1 in 20 ED patients required ICU admission. Between July 1, 2018, and June 30, 2019, consecutive elderly (>=65 years old) patients, without trauma, treated in our ED and then admitted to our ICU were initially identified using the electronic ICU application list of our hospital. Through this patient list, we accessed the ICU database and hospital’s electronic medical recording system (EMRS) and retrieved detailed pa- tient information. The ICU database was a prospective databank primar- ily designed for quality control and improvement of patient management of the critically ill. The database, which is a computer soft- ware (Microsoft Excel, version 2019), contains ICU bed application in- formation, initial and 24-h APACHE II scores, and prognosis information of patients discharged from the ICU. We excluded patients who had do-not-resuscitate (DNR) orders before discharge and those with incomplete EDMEWS, APACHE II score, or survival information (7-day and 30-day mortality). (Fig. 1) The 24-h APACHE II score pro- gression was defined as 24-h score > score measured on ICU admission. Seven-day mortality was defined as death occurring within 7 days after ICU admission. Thirty-day mortality was defined as death occurring within 30 days (including 7-day mortality) after ICU admission. delayed ICU admission was defined as ED stay time >= 6 h. The study was ap- proved by the Institutional Review Board (IRB) of our hospital, and the need for patient informed consent was waived by the IRB.

    1. Patient management

Patients who arrived at our ED were initially triaged using the Taiwan triage and acuity scale (5 scales) at the triage station where

Image of Fig. 1

Fig. 1. Patient selection with final 1423 cases included.

the first MEWS was measured. Thereafter, the patients were sent to different treatment rooms based on acuity. Patients with Triage 1 (emergency) and 2 (urgency) were sent to the resuscitation room for immediate resuscitation and prompt management. After proper resus- citation and stabilization, patients were discharged home or admitted to the ED observation unit, general ward, or ICU. In our ED, half of the patients in the resuscitation room were admitted to the ICU. As the ter- tiary receiving center in the local emergency medical system, we sel- dom transfer patients to other hospitals. Generally, even patients with the mildest condition would have at least two MEWSs recorded during ED stay (first on arrival, second on discharge), and critically ill patients might have at least five or more EDMEWSs recorded. The need for ICU admission was determined by the duty emergency physician , after which the electronic application form and initial APACHE II score were completed. Handover between ED and ICU teams was initiated shortly after the electronic application form was received by the ICU duty intensivist. Our ED patients were treated safely and effectively by board-certified EP staff who were always available. Various resuscita- tive measures such as cardiopulmonary resuscitation (CPR), advanced cardiac life support, endotracheal intubation, vasoactive drug use, extra- corporeal membrane oxygenation (ECMO), and ECMO-CPR were imme- diately performed in our ED.

    1. EDMEWS and EDMEWS trend progression definition

MEWSs of selected patients were calculated from the values of phys- iological parameters recorded in our EMRS (Table 1). MEWSs were de- rived from five common physiological vital signs: SBP (mmHg); HR (beats per minute); RR (breaths per minute); temperature (T, ?C); and alert, voice, pain, unresponsive (AVPU score) [3]. AVPU score includes four scales: alert and oriented state (A = scale 1), verbal stimulus and voice answer state (V = scale 2), response to pain state (P = scale 3), and unresponsive state (U = scale 4) It was simplified and derived from the Glasgow coma scale (GCS) as follows: A = 14-15, V = 9-13, P = 4-8, U = 3. MEWS ranged from 0 to 14 [3,19]. The EDMEWS

Modified early warning score.

Vital signs

Score

3

2

1

0

1

2

3

Systolic pressure (mmHg)

<=70

71\\80

81\\100

101\\199

>=200

Pulse rate (bpm)

<40

40\\50

51\\100

101\\110

111\\129

>=130

Respiratory rate

<=8

9

10\\18

19\\20

21\\29

>=30

Temperature (?C) AVPU

<=35.0

35.1\\38.4

A

V

>=38.5

P

U

Abbreviations, A/V/P/U = alert/verbal/painful/unresponsive.

trend was determined using a regression line of multiple MEWSs mea- sured during ED stay, where MEWS trend progression was defined as the slope of the EDMEWS regression line >0 (Fig. 2)

    1. Data collection

We retrieved basic patient information such as name, chart number, age, sex, ICU admission time, and initial APACHE II score from the elec- tronic ICU application list. With this basic information, we reviewed pa- tients’ detailed clinical information by entering the ICU database and cross-checking the EMRS information of our hospital to ensure the cor- rectness and completeness of the data required in this study. Moreover, we retrieved patient information on underlying disease, history of smoking and alcohol use, insurance status, marital status, ED arrival time, initial vital signs, GCS score, blood test results, ED diagnostic and treatment procedures, and prognosis and follow-up after discharge from the ICU database and EMRS; thereafter, we inputted the informa- tion into an analytical software (Excel, Microsoft and SPSS version 22.0, IBM).

    1. Data analysis

The distribution of demographic and clinical variables were com- pared between two patient groups (patients with and without 24-h APACHE II score progression, patients who survived and died within 7 days, and patients who survived and died within 30 days) using vari- ous tests, including the t-test (continuous data), chi-square test (normal data), and Mann-Whitney U test (continuous or ordinal data with skewed distribution). Using Statistical Package for the Social Sciences version 22.0 (SPSS Statistics, Chicago, IL, USA), we analyzed the associa- tion between demographic and clinical variables (including EDMEWS progression) and prognosis targets (24-h APACHE II score progression, 7-day mortality, and 30-day mortality). Variables showing significance in univariate analysis were included in multiple logistic regression anal- ysis. We analyzed the prognostic effect of delayed ICU admission in

patients with and without EDMEWS progression using multiple logistic regression. Potential confounders such as age, ED triage, Charlson comorbidityindex, endotracheal intubation, maximum EDMEWS, emergency surgical treatment, the presence of cardiovascular disease, and APACHE II score on ICU admission were adjusted. Charlson cormorbidity index is a widely used scoring system to predict the 1-year mortality for a patient using a total 22 comorbid conditions, such as heart disease, HIV/AIDS, or cancer. Each condition is assigned a score of 1, 2, 3, or 6.

  1. Results

In total, 1988 cases were coded as eligible cases during the study pe- riod, and 565 patients were excluded because they signed DNR orders before discharge (n = 514) or had incomplete data (n = 51) (Fig. 1). A total of 1423 consecutive elderly ED patients admitted to the ICU were included in the analysis, and 13,376 EDMEWS were analyzed in this study. On average, each patient had 9 EDMEWSs recorded during ED stay (range: 6-16, IQR: 7-12; mean +- SD = 9.4 +- 3.7). Of the se- lected patients, 334 (23.5%) had worse ED MEWS trends, 499 (35.1%) had worse 24-h APACHE II scores, 110 (7.7%) died within 7 days, and 233 (16.4%) died within 30 days. The baseline characteristics of the study population are outlined in Table 2. EDMEWS progression was highly correlated with worse 24-h APACHE II scores (p < 0.001), 7- day mortality (p < 0.001), and 30-day mortality (p < 0.001) (Table 2). After adjusting for covariates (age, ED triage, Charlson’s index, endotra- cheal intubation, maximum EDMEWS, emergency surgical treatment, the presence of cardiovascular disease, and APACHE II score on ICU admission) in multiple logistic regression analysis, EDMEWS pro- gression independently correlated with worse 24-h APACHE II score (p = 0.001, odds ratio [OR] = 1.569, 95% confidence interval [CI] = 1.202-2.046), 7-day mortality (p < 0.001, OR = 2.743, 95% CI = 1.725-4.359), and 30-day mortality (p = 0.004, OR = 1.607, 95%

CI = 1.163-2.223) (Table 3). Patients were further divided into two groups (with and without EDMEWS trend progression) to analyze the

Image of Fig. 2

Fig. 2. Demonstration of EDMEWS trend. 2A, A patient with EDMEWS trend progression (slope of regression line>0). 2B, A patient without EDMEWS trend progression (slope of regression line<=0). EDMEWS, emergency department modified early warning score.

Data of demographic and clinical characteristics of elderly ED patients admitted to the ICU.

Patient characteristics Worsening 24-h APACHE II score 7-Day mortality 30-Day mortality

Yes, n = 499

No, n = 924

P-value

Yes, n = 110

No, n = 1313

P-value

Yes, n = 233

No, n = 1190

P value

Ageb,c

73(70-79)

73(70-78)

0.064

75(75-80)

73 (70-78)

<0.001

75(72-79)

73(70-78)

<0.001

Sex (female)

182(36.5)

318(34.4)

0.450

42(38.2)(40.2)

458(34.9)

0.533

87(37.3)

413(34.7)

0.454

Coming from home

346 (69.3)

610(66.0)

0.214

31(28.2)

436(33.2)

0.293

75(32.2)

392(32.9)

0.879

Veteran status

166(33.3)

303(32.8)

0.859

34(30.9)

435(33.1)

0.674

73(31.3)

396(33.3)

0.594

Ambulance transport

398(79.8)

697(75.4)

0.065

86(78.2)

1009(76.8)

0.814

180(76.9)

915(77.3)

0.932

Education level lower than junior high school

110(22.0)

207(22.4)

0.894

22(20.0)

295(22.5)

0.634

51(21.9)

266(22.4)

0.931

Charlson’s indexa

6(5-8)

5(4 – 7)

<0.001

5 (4-7)

5 (4-7)

0.464

5(4-7)

5(4-7)

0.846

Current smoker

91(18.2)

181(19.6)

0.572

26(23.6)

250(19.0)

0.258

49(21.0)

227(19.6)

0.526

Alcohol use

105(21.0)

205(22.2)

0.638

27(24.5)

287(21.9)

0.549

52(22.3)

262(22.0)

0.931

Transferred from other hospitals

169(33.9)

302(32.7)

0.680

35(31.8)

436(33.2)

0.833

76(32.6)

395(33.2)

0.879

On weekend

133(26.7)

223(24.1)

0.305

33(30.0)

318(24.2)

0.205

63(27.0)

288(24.2)

0.361

At 8 AM-8 AM

193(38.7)

314(34.0)

0.082

46(41.8)

461(35.1)

0.178

85(36.5)

422(35.5)

0.765

ED triage 1b,c

169(34.5)

319(34.5)

0.639

93(84.5)

395(30.1)

<0.001

138(59.2)

350(29.4)

<0.001

2

316(63.3)

571(61.8)

9(8.2)

878(66.9)

83(35.6)

804(67.6)

3

34(2.4)

14(2.8)

8(7.3)

40(3.0)

12(5.2)

36(3.0)

Endotracheal intubationa,b,c

229(45.9)

299(32.4)

<0.001

59(53.6)

469(35.7)

<0.001

112(35.0)

416(48.1)

<0.001

Inotrope use

371(74.3)

667(72.2)

0.416

83(75.5)

955(72.7)

0.578

174(74.7)

864(72.6)

0.573

Emergent surgerya

83(16.6)

114(12.3)

0.030

21(19.1)

176(13.4)

0.113

40(17.2)

157(13.2)

0.119

Maximal MEWS during ED staya,b,c

6(6-8)

5(5-8)

<0.001

9(8-10)

6(5-8)

<0.001

8(6-9)

6(5-8)

<0.001

ED MEWS progressiona,b,c

149(29.9)

185(20.0)

<0.001

57(51.8)

277(21.1)

<0.001

84(36.1)

250(21.0)

<0.001

APACHE II score on ICU admissiona,b,c Main disease categorya,b,c

15(13-21)

14(12-20)

<0.001

<0.001

22(21-24)

15(13-19)

<0.001

0.240

20(15-23)

15(13-19)

<0.001

0.298

Pulmonary

85(17.0)

224(24.2)

22(24.0)

287(21.9)

40(17.2)

269(22.6)

Cardiovascular

110(22.0)

108(11.7)

16(14.5)

202(15.4)

35(15.0)

183(15.4)

Neurological

58(11.6)

133(14.4)

7(6.4)

184(14.0)

29(12.4)

162(13.6)

Gastrointestinal

51(10.2)

99(10.7)

10(9.1)

140(10.7)

32(13.7)

118(9.9)

Nephrology/urology

42(8.4)

89(9.6)

13(11.8)

1189.0)

21(9.0)

110(9.2)

Endocrinology

27(5.4)

39(4.2)

8(7.3)

58(4.4)

8(3.4)

58(4.9)

Sepsis

70(14.0)

127(13.7)

18(16.4)

179(13.6)

36(15.5)

161(13.5)

Others

56(11.2)

105(11.4)

16(14.5)

145(11.0)

32(13.7)

129(10.8)

Abbreviations: ED, emergency department; ICU, intensive care unit; MEWS, modified early warning score; APACHE, acute physiology and chronic health evaluation.

Data are expressed as number (percentage) for categorical variables, mean (+- SD) for normally distributed numerical variables, and median (interquartile range) for skewed data (age, Charlson’s index, maximal ED MEWS, and APACHE II score).

a p < 0.5 for worsening 24-h APACHE II score.

b p < 0.5 for 7-day mortality.

c p < 0.05 for 30-day mortality.

effect of delayed ICU admission (ED stay time >= 6 h) on prognoses [20]. In patients with EDMEWS trend progression, delayed ICU admission was significantly associated with higher 7-day mortality. Interestingly, in those without EDMEWS trend progression, delayed ICU admission was significantly associated with a lower rate of worse24-h APACHE II score (Table 4).

  1. Discussion

The ED is a unique area in the hospital where patients with varied acuity, including those in a critical condition, are initially managed [21]. Many studies demonstrated that uninterrupted and highly effective

care provided in the initial phase of a critical illness markedly influences patient survival [22]. Almost all critically ill patients are admitted to different kinds of ICUs after ED resuscitation and stabilization [23]. EDMEWS trend is a simple and easy tool to obtain summative informa- tion reflecting the efficacy of ED management and the dynamics of the disease course. From the emergency physicians’ perspective, EDMEWS trends can help in identifying patients with a high risk of clinical deteri- oration and mortality; both of these are crucial for resource allocation and assessment of patient disposition. When patients are transferred from the ED to the ICU, the EDMEWS trend provides patient infor- mation to the ICU team which makes handing over more proper and efficient [23,24]. This is the first study evaluating the relationship

Table 3

Multiple logistic regression for factors associated with worsening 24-h APACHE II score, 7-day mortality, and 30-day mortality in elderly ED patients admitted to the ICU.

Patient characteristics Worsening 24-h APACHE II score

7-Day mortality 30-Day mortality

OR

95% CI of OR

OR

95% CI of OR

OR

95% CI of OR

Age

NA

NA

1.055

1.020\\1.091

1.027

1.004\\1.051

Charlson’s index

1.101

1.052\\1.153

NA

NA

NA

NA

ED triage 1

NA

NA

5.204

2.312\\11.717

1.730

1.007\\2.972

Endotracheal intubation

1.820

1.431\\2.315

1.711

1.071\\2.733

1.388

1.018\\1.891

Maximal ED MEWS

1.047

0.975\\1.125

1.899

1.575\\2.289

1.284

1.146\\1.440

Emergent surgery

1.381

1.003\\1.901

1.381

1.003\\1.901

NA

NA

Cardiovascular disease

2.334

1.719\\3.169

NA

NA

NA

NA

APACHE II score on ICU admission

1.031

0.999\\1.064

1.283

1.200\\1.372

1.106

1.055\\1.160

MEWS progression during ED stay

1.569

1.202\\2.046

2.743

1.725\\4.359

1.607

1.163\\2.223

Abbreviations: ED, emergency department; MEWS, modified early warning score; ICU, intensive care unit; NA, not applicable; APACHE, acute physiology and chronic health evaluation; OR, odds ratio; CI, confidence interval.

Table 4

Effect of delayed ICU admission in patients with and without EDMEWS trend progression.

Effect of delayed ICU admission

Without EDMEWS trend progression With EDMEWS trend progression Dependent variables

P value

OR

95% CI of OR

P value

OR

95% CI of OR

0.044

0.736

0.546-0.991

0.214

1.349

0.841-2.169

24-Hour worsening APACHE II score

0.101

0.578

0.299-1.112

0.025

2.150

1.103-1.298

7-Day mortality

0.069

0.699

0.469-1.029

0.205

1.424

0.824-2.461

30-Day mortality

Abbreviations: EDMEWS, emergency department modified early warning score; ICU, intensive care unit; APACHE, acute physiology and chronic health evaluation; OR, odds ratio; CI, con-

fidence interval.

between MEWS trend during ED stay and short-term disease course, and we found that EDMEWS trend progression was strongly associated with the early progression of APACHE II score, 7-day mortality, and 30- day mortality in elderly critically ill patients. This suggests the potential clinical value of this novel tool.

Our findings were in agreement with a similar concept that was re- ported in the prospective observational study conducted by Armagan et al. [13] They enrolled 225 hospitalized patients (mostly general ward patients) and observed a correlation between the initial MEWS trend (trend of two MEWSs in separate time-points: the 1st MEWS measured on arrival and the 2nd MEWS measured before admission, with results of improvement being either no change or worse) and com- bined endpoints (coronary care unit admission, ICU admission, or death). However, due to limitation in the number of high-risk patients, the prediction role of MEWS trend in each endpoint was not further an- alyzed. In addition, without a double-blinded design, the risk of the Hawthorne effect could not be completely avoided in this study. Fortu- nately, in many ways (population homogeneity, sample size, and the risk of the Hawthorne effect), our study has overcome these limitations. Many ICU Severity assessment and prognosis prediction systems have been developed in the past decade. APACHE II score is possibly the most widely used today [25]. It involves more than 20 physiological and Biochemical parameters measured over 24 h, making data acquisi- tion time-consuming and laborious [26]. Due to hardware limitations, many biochemical parameters cannot be rapidly assessed in an emer- gency context. Therefore, the use of this system is limited in the ED or during ICU admission [27]. Unlike APACHE II score, MEWS is frequently measured in the ED and almost always assessed during ICU admission. MEWS trend could help better reflect dynamic changes in patient clini- cal condition in a timely manner, and it could present summative infor- mation regarding patient discharge condition and treatment efficacy during ED stay [19,27]. It is notable that EDMEWS trend could not re- place clinical gestalt or other in-use Prognostic systems, such as APACHE II score, because they play different roles in patient manage- ment. In clinical practice, EDMEWS could complement the clinical ge- stalt and existing prognostic prediction system and help furnish the

receiving team with adequate patient information.

Several studies have been conducted on delayed ICU admission and their effect on patients’ Treatment outcomes [20-22,28]. Although the definitions of delayed admission varied in different studies and the re- sults were contradictory, it is generally believed that prolonged ED boarding was inappropriate for critically ill patients because of ED short- age of resource and overcrowding [21,22]. EDMEWS trend, to an extent, reflected the efficacy of ED treatment, and its results (progression or not) might change the relationship between ED stay time and the risk of poor patient outcome. In patients with EDMEWS progression, longer ED stay time was associated with 7-day mortality (P = 0.025; OR = 2.15; 95% CI 1.103-1.298) and had a trend of higher 1-day APACHE II score progression rate and 30-day mortality rate, which corroborated with the findings of most previous studies (Table 4). Interestingly, in the context of no EDMEWS progression, longer ED stay time was not as- sociated with poor patient outcome in this cohort (Table 4). These re- sults suggested that the exact cause for short-term poor outcomes in

critically ill patients was the ongoing disease progression or treatment inefficacy, rather than the “delay” itself.

In recent years, more discussion on end-of-life Medical issues has

been initiated in the ED through the ways of implementing shared decision-making or providing a DNR order [29]. Discussion on an early initiation of a palliative care plan or DNR-order could help improve end-of-life care quality and reduce futile treatments in the ED and ICU [30]. Based on previous studies, insufficient information about the short-term disease course might impede the initiation of palliative care. A better understanding of the patient’s ongoing condition and early mortality risk might decrease the anxiety caused by the disease course uncertainty and help in improving the patient-physician com- munication through a better relationship [31]. However, in critically ill patients with worsening EDMEWS trend, more aggressive treatment strategies could be administered, such as organ replacement therapies, percutaneous coronary intervention, intra-aortic balloon pump, or ECMO, or new treatment trials could be conducted to investigate treat- ment options suitable for early and timely ICU treatment initiation. For patients who need medical referral, EDMEWS trend provides informa- tion regarding transportation risk and required ambulance care level; furthermore, it helps the receiving hospital be well-prepared, thereby rendering the inter-hospital transfer safer.

    1. Perspectives

Future studies will be directed toward the implementation of an EDMEWS and its trend in the ED information system and prospectively validating the link between EDMEWS and disease course and prognosis. Several studies have been conducted in the machine learning system to predict in-hospital cardiac arrest; however, none of these studies was specifically conducted on ED patients [32,33]. After the implementation of an EDMEWS and its trend in the ED information system, a large num- ber of EDMEWS data could be generatedautomatically. Using machine learning technology, we might map the correlations between different patterns of EDMEWS trends and varied early disease course (or adverse events) before ICU admission. Hence, the early recognition of silent downhill and timely response to those adverse events, including unex- pected cardiac arrest, could be possible. In the future, trend analysis might replace the existing prediction system using a single value (max- imum or average score) [10,34].

    1. Limitations

Our study has several limitations. First, patient management was not standardized due to the retrospective nature of the study; therefore, some patients were excluded because of missing important data. Fortu- nately, resuscitative procedures in our ED are highly standardized and protocolized. Variables included in our study were basic ED parameters and ICU management, and the completeness and correctness of these variables were indicators of the quality of care, and were audited fre- quently internally and externally. Since only few patients were ex- cluded (n = 51) because of missing data, it would not significantly affect the statistical results. Second, the course of EDMEWS (Time-

EDMEWS relationship) might not perfectly fit the linear model applied in this study (Fig. 2), which might affect the prediction precision. How- ever, in our study, the linear regression line was appropriate for differ- entiating improvement versus progression rather than precisely predicting the next-hour MEWS, and its result was much more easily interpreted than those of other complicated trend models [35,36].

  1. Conclusions

EDMEWS trend assessment using the regression line is a simple and practical tool that can provide summative information on ED treatment efficacy and reflect the ongoing disease condition. Incorporating the EDMEWS trend into electronic medical systems and using it as a daily tool for patient assessment might help not only in the early identifica- tion of ongoing disease deterioration but also in the prediction of short-term mortality in critically ill elderly patients in the ED and ICU. Based on these results, EDMEWS trend is an excellent communication and handover tool for transferring patients between units or between facilities.

Declaration of Competing Interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or en- tity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consul- tancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

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