CC BY-NC-ND 4.0 · Ibnosina Journal of Medicine and Biomedical Sciences 2024; 16(02): 057-067
DOI: 10.1055/s-0044-1782615
Original Article

The Effect of Implementation of the National Early Warning Scoring System 2 on the Outcomes of COVID-19 Hospitalized Patients

1   An-Najah National University Faculty of Nursing, Nablus, Palestine
,
2   Master of Science in Nursing in Critical Care Nursing, Critical Care Registered Nurse, Rafedia Surgical Governmental Hospital, Palestinian Ministry of Health, Nablus, Palestine
› Author Affiliations
Funding None.
 

Abstract

Introduction It is crucial to have tools to predict the clinical deterioration of coronavirus disease 2019 (COVID-19) patients. We aimed to study the efficacy of the National Early Warning Scoring System (NEWS2) application in predicting the risk of clinical relapse and outcomes in hospitalized COVID-19 patients at Palestinian specialized centers for COVID-19.

Patients and Methods A quasi-experimental design was applied. A sample of 384 adult patients was divided into two groups. For the pre-NEWS phase (control group) and the post-NEWS phase (study group), all study participants were observed until death or hospital discharge.

Results Comparing the pre-NEWS phase with the post-NEWS phase, a significant decrease was found in the mean length of hospital stay from 8.1 ± 5.5 to 6.4 ± 5.3 days (p = 0.002) and a reduction in the mortality rate from 19.8% during the pre-NEWS phase to 12.5% during the post-NEWS phase (p = 0.071). The predictive value of NEWS was an excellent predictor of admission to the intensive care unit (ICU), as indicated by an area under the receiver operating characteristic curve of 0.91 (95% confidence interval: 0.87–0.96, p < 0.001). Also, a significant difference in the frequency of monitoring patients' vital signs was observed between the control group (pre-NEWS phase) and the study group (post-NEWS phase) following clinical deterioration (10.1 ± 7.8 [pre-NEWS phase] vs. 23.4 ± 0.7 [post-NEWS phase], p < 0.001).

Conclusion Implementation of NEWS2 showed a significant improvement in hospitalized COVID-19 patient outcomes (length of stay, predicted ICU admissions, mortality rate, and frequency of vital signs measurements), which indirectly raised the follow-up of those patients by the medical team and attributed to a significant prediction of their deterioration.


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Introduction

In numerous areas of medicine, the development of clinical prediction models is prevalent.[1] In 2000, acute hospitals implemented early warning scores (EWSs) for the first time. Today, they are widely used in various clinical contexts as they provide extremely valuable information to help characterize the clinical risk of deterioration more precisely, thereby facilitating risk stratification. Although 99% of acute hospitals utilize EWS to monitor patients[2] (the National Confidential Enquiry into Patient Outcome and Death [NCEPOD], 2015), there has been little progress over the past decade in identifying and responding to deteriorating patients.[3]

EWS systems have been established and widely used in developed nations to identify clinical deterioration as soon as possible.[4] They are common in Australia, the United States, and the Netherlands.[5]

The present study aims to analyze the influence of applying the National Early Warning Score (NEWS) on coronavirus disease 2019 (COVID-19) patient outcomes, such as patient deterioration, admission to an intensive care unit (ICU), length of hospital stays, and patient mortality. We hypothesized that implementing NEWS2 would significantly improve hospitalized COVID-19 patient outcomes by decreasing the length of hospital stay, predicting ICU admissions, reducing the mortality rate, and increasing the frequency of vital sign measurements.


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Patients and Methods

Design and Setting

Among hospitalized COVID-19 patients, a quasi-experimental research design was utilized. This study was performed at inpatient units in the COVID-19 Martyrs Medical Military Complex and the Palestinian Red Crescent Society Hospital, which also functioned as a certified testing and treatment facility for COVID-19 and is located in Nablus, Palestine.


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Study Population

About 384 adult male and female patients who satisfied the inclusion criteria were divided into two groups: the study group (post-NEWS2) (192 patients) and the control group (pre-NEWS2) (192 patients).

By reverse transcriptase polymerase chain reaction, severe acute respiratory syndrome coronavirus-2 was detected in all adult patients admitted to our research with a confirmed diagnosis of COVID-19. Patients who were younger than 18 years, pregnant women, those with spinal cord injuries, non-COVID-19 patients, and patients who were readmitted to the unit during the trial were excluded from the study. The NEWS should not be applied to children or pregnant women since their physiological response to acute illness may be affected. Due to functional abnormalities in the autonomic nervous system, spinal cord injury patients may not be able to rely on the NEWS (especially tetraplegia and severe paralysis). These criteria were developed by the Royal College of Physicians' (RCP) 2017 standards.[6]

All adult COVID-19 patients admitted to both COVID-19 centers and those who satisfied the criteria for inclusion were enrolled in the study during the study periods: July to November 2021 for the pre-NEWS phase and December 2021 to March 2022 for the post-NEWS phase. These patients were observed daily for improvement or deterioration. The researcher categorized patient outcomes until death or discharge. The sample was selected using the following formula, with a 95% level of significance and a margin of error of 0.05:

n = z 2 × p (1 − p)/1 + z 2 × p (1 − p) /e 2 × N; n = (1.96) 2/4 (0.05) 2 = 384.16

Where p is the proportion of the population predicted to be 0.5, e is the margin of error, N is the size of the people, and z is the z-score associated with the selected significance level. The equation yielded a sample size of n = 384.


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Validity and Reliability

The NEWS2 is the standard track and trigger early warning system used in the United Kingdom and provides a uniform observation system for patient care continuity. Ambulance services, general practice, and community assessments also use it.[7] In this study, NEWS2 was superior in its area under the receiver operating characteristic (AUROC) curve when predicting ICU admission, and the cutoff point for NEWS2 to predict in-hospital mortality was higher.


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Study Protocol

For 2 weeks, using lectures, group discussions, and clinical scenarios, the researcher taught and monitored an educational program where nurses practiced using and implementing the NEWS system. The researcher educated nurses for 2 weeks on how to apply the NEWS system through lectures, group discussions, and clinical situations. The program's goals were to create a framework for communication among health care professionals and aid in knowing the physiological measurements, the rationale behind the size, and vital sign abnormalities. The program covered the advantages of NEWS, how it works, the reasons for its use, the score of physiological parameters, its thresholds and triggers, and its clinical response. The score consists of six physiological measures, including respiratory rate, oxygen saturation, pulse rate, systolic blood pressure, body temperature, and consciousness or new confusion. The study instruments are detailed in [Appendix 1].

Appendix 1

Description of the study instruments

Part 1. The patient's medical record, age, gender, admitting diagnosis, prior medical history, patient score, hospital stay duration, and admission date were extracted for the medical and demographic data sheets.

Part 2. NEWS2 is a scale that can “monitor and trigger.” It is an aggregate scoring system with multiple factors that measure, from 0 to 3, the deviation from expected normal levels of respiratory rate, oxygen saturation, systolic blood pressure, heart rate, body temperature, and degree of awareness. This is how the AVPU concept evaluates awareness: A = alert, V = verbal stimulus reaction, P = pain stimulus response, and U = unresponsive. Three points are awarded for the possibility of consciousness-level alteration. Each of these features is assigned a score; the scores are tallied together, and the eventual addition of additional oxygen increases the score by two points. The correlation between the patient's clinical risk and the total score points is as follows: low-risk (aggregate scores 1–4): immediate assessment by the ward nurse within 6 h to determine a change in frequency of monitoring or escalation of clinical treatment; moderate-risk (score of more than 5 or 3 in any single parameter): urgent evaluation by ward-based nurse hourly and recheck with other nurse or doctor to discover the cause and to decide on modification in monitoring frequency or escalation of clinical care; and high-risk score (7 or more): indicates that the NEWS escalation protocol will be started when the nurse contacts the physician in charge of a patient who requires an immediate assessment by the critical care team, typically resulting in patient transfer to higher dependency care settings. The escalation procedure assists clinicians in making clinical decisions. Recommendations are forwarded regarding the level of qualifications required for the condition, the evaluation interval, the level of treatment, and the activation of a medical emergency team.

Part 3. A patient outcome observational checklist was subdivided into primary outcomes. Patient deterioration represented the physiological parameters that were included in the NEWS measures. The use of oxygen support therapy, vaccination status, and the frequency of monitoring or any added special concerns or medical reviews by the health care providers were secondary outcomes, including ICU admission, length of hospital stay, and mortality. Each patient in the trial group received it. These data were used daily after utilizing the NEWS compared with the documented patient's clinical condition, use of oxygen support therapy, vaccination status, frequency of monitoring, length of hospital stay, admission to the ICU, and discharge status of the patient (alive or dead) for the control pre-NEWS implementation phase group.

Abbreviations: ICU, intensive care unit; NEWS, National Early Warning Score.


A score is assigned to each observation. When all six scores are combined, the overall NEWS is generated and configured to activate when a patient is seriously ill or has abnormal physiology. Each physiological parameter is assigned a score, with the value of the score representing how much the parameter deviates from the norm. A NEWS2 score of 5 or 6 is regarded as a critical threshold that may signal clinical worsening and should necessitate an immediate reaction by a physician or team with expertise in assessing and treating severely ill patients. An increased NEWS does not establish a diagnosis, but it does assist in identifying the risky patient who systematically requires immediate clinical assessment. The risk levels are classified as low, low-medium, medium, or high, with suggestions for appropriate clinical responses and more intensive treatment. It also provides a consistent monitoring system and continuity of treatment for patients between wards. Trends in a patient's clinical response can be monitored by regularly recording the score and providing early notifications of clinical deterioration. When early warning systems are used in close collaboration with the medical team, the patients are identified as needing special consideration. A specialist team can treat them immediately, lowering the potential adverse outcomes.[8]


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Statistical Analysis

Application version 25 of the Statistical Package for the Social Sciences (SPSS) was used to tabulate and analyze the data after data collection. The quantitative data were reported using means and standard deviations. The independent sample t-test was used to assess group homogeneity and compare the mean frequency of patient monitoring in both groups. The significance level was established at 5% (p = 0.05). The homogeneity of the two groups in terms of patient outcomes and NEWS escalation categories was assessed using the chi-square proportional test. Using a post hoc chi-square test, it was determined which groups were substantially distinct.


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Results

Characteristics of Patients

Patients in the control (pre-NEWS2) group were slightly but not significantly older than those in the study (post-NEWS2) group, as shown in [Tables 1] and [2]. The mean age of patients in the pre-NEWS phase was 62.1 ± 13.9 years, and the mean age in the post-NEWS phase was 58.1 ± 17.7 years (p = 0.015). On the other hand, more patients were 60 years and older (p = 0.040) in the pre-NEWS phase compared with those admitted during the post-NEWS phase. Patients in both groups did not differ significantly (p > 0.05) in terms of gender, chronic kidney diseases, chronic liver diseases, chronic lung diseases, or cardiovascular diseases.

Table 1

Characteristics of patients included in the pre- and post-NEWS phases of the study

Variable

Pre-NEWS[a]

Post-NEWS[a]

Chi-square test

p-Value

Number

192

192

Age, y: < 60/≥ 60

78 (40.6%)/114 (59.4%)

99 (51.6%)/93 (48.4%)

4.62

0.04

Sex: female/male

98 (51%)/94 (50%)

105 (54.7%)/87 (45.3%)

0.51

0.54

Vaccination: none

101 (52.6%)

101 (52.6%)

0.0

1.0

Vaccination: one dose

67 (34.9%)

67 (34.9%)

Vaccination: two doses

24 (12.5%)

24 (12.5%)

Vaccination: two doses + a booster

0 (0.0%)

0 (0.0%)

Diabetes mellitus: no/yes

89 (46.4%)/103 (53.6%)

115 (59.9%)/77 (40.1%)

7.07

0.01

Hypertension: no/yes

71 (37%)/121 (63%)

119 (62%)/73 (38%)

24.0

0.01

CKD: no/yes

172 (89.6%)/20 (10.4%)

183 (95.3%)/9 (4.7%)

4.51

0.052

Cancer: no/yes

180(93.8%)/12 (6.3%)

166 (86.5%)/26 (13.5%)

5.72

0.025

Chronic liver disease: no/yes

174 (90.6%)/18 (9.4%)

174 (90.6%)/18 (9.4%)

0

1.0

Autoimmune disease: no/yes

155 (80.7%)/37 (19.3%)

174 (90.6%)/18 (9.4%)

7.66

0.008

Chronic lung disease: no/yes

176 (91.7%)/16 (8.3%)

178 (92.7%)/14 (7.3%)

0.14

0.85

Cardiovascular disease: no/yes

132 (68.8%)/60 (31.3%)

130(67.7%)/62 (32.3%)

0.05

0.913

Abbreviations: CKD, chronic kidney disease; NEWS, National Early Warning Score.


a Values are expressed as number (percentage).


Table 2

COVID-19 features included in the pre- and post-NEWS phases of the study

Variable

Pre-NEWS

Post-NEWS

Chi-square test

p-Value

Number

192

192

Low-flow oxygen[a]: no/yes

10 (5.2%)/182 (94.8%)

42 (21.9%)/150 (78.1%)

22.78

< 0.001

High-flow nasal cannula no/yes

152 (79.2%)/40 (20.8%)

135 (70.3%)/57 (29.7%)

3.99

0.06

NIPPV: no/yes

166 (86.5%)/26 (13.5%)

167 (87.0%)/25 (13.0%)

0.02

1

Hospital LOS (d): < 6/≥ 6

80 (41.7%)/112 (58.3%)

112 (58.3%)/80 (41.7%)

10.67

0.002

Mechanical ventilation: no/yes

178 (92.7%)/14 (7.3%)

175 (91.1%)/17 (8.9%)

0.32

0.354

ICU admission: no/yes

143 (74.5%)/49 (25.5%)

135 (70.3%)/57 (29.7%)

0.83

0.424

Discharged alive: no/yes

38 (19.8%)/154 (80.2%)

24 (12.5%)/168 (87.5%)

3.77

0.071

Abbreviations: COVID-19, coronavirus disease 2019; ICU, intensive care unit; LOS, length of stay; NEWS, National Early Warning Score; NIPPV, noninvasive positive-pressure ventilation.


Note: Values are expressed as number (percentage).


a SPO2 < 94%.


The distribution of vaccinated and unvaccinated patients was also similar in both phases. However, more patients were 60 years and older (p = 0.040), had diabetes mellitus (p = 0.010), hypertension (p = 0.010), and autoimmune diseases (p = 0.008) in the pre-NEWS phase compared with those admitted during the post-NEWS phase.

During the pre-NEWS phase, 38 (19.8%) patients died, compared with 24 (12.5%) during the post-NEWS phase (p = 0.071). Of the patients admitted during the pre-NEWS phase, 49 (25.5%) required admission to the ICU, compared with 57 (29.7%) during the post-NEWS phase (p = 0.424). In the pre-NEWS phase, more patients received low-flow oxygen (LFO) than those admitted during the post-NEWS phase (p < 0.001). However, patients in both phases did not differ in terms of receiving a high-flow nasal cannula (p = 0.060), noninvasive positive pressure ventilation (NIPPV) (p = 1.000), or mechanical ventilation (p = 0.354). Nonetheless, more patients stayed in the hospital for 6 days or more in the pre-NEWS phase than those admitted during the post-NEWS phase (p = 0.002). The mean duration of hospital stay in the pre-NEWS phase was 8.1 ± 5.5 days, and the mean hospital stay in the post-NEWS phase was 6.4 ± 5.3 days (p = 0.002).


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Association between Patients Outcomes and Different Variables in the Pre-NEWS Phase

[Table 3] shows associations between patient deterioration based on oxygen usage, admission to the ICU, and outcomes based on the length of hospitalization and hospital discharge status in the pre-NEWS phase. Receiving a high-flow nasal cannula, mechanical ventilation, and staying in the hospital for 6 days or more were not significantly associated with any of the variables such as age, gender, vaccination status, diabetes, cancer, hypertension, chronic liver disease, chronic kidney disease, autoimmune disease, or cardiovascular disease. However, receiving LFO was associated with having cancer (p = 0.002) and chronic liver disease (p = 0.008). Receiving NIPPV was not associated with chronic lung disease (p = 0.047). Admission to the ICU was significantly associated with having cancer (p = 0.002) and chronic liver disease (p = 0.021). Discharged dead were associated with older age (p = 0.009), autoimmune disease (p = 0.020), and cardiovascular disease (p = 0.020).

Table 3

Associations between patient deterioration and outcomes with variables in the pre-NEWS phase (N = 185)

Variable

Low-flow oxygen

High-flow nasal cannula

NIPPV

Mechanical ventilation

Length of stay ≥ 6 d

ICU

Discharged alive

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

Age (y)

< 60

75

0.49

0.533

17

0.07

0.857

8

1.21

0.293

3

2.31

0.163

39

3.75

0.073

14

3.96

0.063

70

7.52

0.009

≥ 60

107

23

18

11

73

35

84

Gender

Female

95

1.86

0.206

17

1.48

0.286

13

0.01

1.000

8

0.23

0.783

53

1.49

0.244

21

1.76

0.191

80

0.26

0.718

Male

87

23

13

6

59

28

74

Vaccine dose

None

93

3.50

0.171

21

1.36

0.490

16

1.15

0.531

7

0.06

0.971

64

3.50

0.168

27

0.18

0.945

81

3.65

0.160

One

65

12

8

5

33

16

57

Two

24

7

2

2

15

6

16

DM

Yes

96

1.17

0.344

21

0.03

1.000

13

0.16

0.833

9

0.69

0.579

64

1.32

0.304

31

2.45

0.137

78

2.81

0.105

Hypertension

Yes

115

0.04

1.000

24

0.20

0.714

19

1.31

0.283

8

0.22

0.775

70

0.03

0.881

34

1.14

0.309

96

0.16

0.713

ESRD

Yes

18

1.03

0.309

6

1.13

0.380

0

3.48

0.081

0

1.75

0.369

9

1.63

0.235

7

1.06

0.415

15

0.38

0.556

Cancer

Yes

8

20.40

0.002

3

0.13

1.000

2

0.11

1.000

1

0.02

1.000

12

9.14

0.004

8

11.34

0.002

8

1.47

0.259

Chronic liver

Yes

14

11.59

0.008

7

3.91

0.065

2

0.10

1.000

1

0.09

1.000

15

5.11

0.042

9

6.23

0.021

12

2.28

0.209

Autoimmune

Yes

35

0.00

1.000

7

0.10

0.826

3

1.16

0.309

1

1.42

0.312

20

0.35

0.582

5

3.48

0.091

35

5.98

0.020

Chronic lung

Yes

14

1.87

0.198

5

1.14

0.333

5

4.65

0.047

1

0.03

1.000

10

0.13

0.796

5

0.30

0.765

14

0.58

0.537

CVD

Yes

58

0.62

0.511

15

0.92

0.444

10

0.73

0.495

6

0.94

0.373

38

0.90

0.430

18

0.92

0.374

42

5.73

0.020

Abbreviations: CVD, cardiovascular disease; DM, diabetes mellitus; ESRD, end-stage renal disease; ICU, intensive care unit; NEWS, National Early Warning Score; NIPPV, noninvasive positive pressure ventilation.



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Relationship between Patients' Outcomes and Variables in the Post-NEWS Phase

[Table 4] shows associations between patient deterioration based on oxygen usage, admission to the ICU, outcomes based on length of stay in the hospital, and hospital discharge status with different variables in the post-NEWS phase. Receiving LFO was associated with older age (p = 0.001), diabetes (p = 0.001), and hypertension (p = 0.006). Receiving a high-flow nasal cannula was significantly associated with older age (p = 0.011), diabetes (p = 0.004), and hypertension (p < 0.001). Furthermore, receiving NIPPV was significantly associated with older patients (p < 0.001), male patients (p = 0.018), diabetes (p = 0.047), and hypertension (p = 0.025). Receiving mechanical ventilation was significantly associated with older age (p < 0.001). Staying 6 days or more was significantly associated with older age (p = 0.041), diabetes (p = 0.004), hypertension (p < 0.001), and cardiovascular disease (p = 0.029). Admission to the hospital was significantly associated with older age (p < 0.001), male patients (p = 0.027), diabetes (p < 0.001), hypertension (p < 0.001), chronic liver disease (p = 0.005), and cardiovascular disease (p = 0.012). Discharged dead were associated with older age (p < 0.001) and cancer (p = 0.026).

Table 4

Associations between patient deterioration and outcomes with variables in the post-NEWS phase

Variable

Low-flow oxygen

High-flow nasal cannula

NIPPV

Mechanical ventilation

Length of stay ≥ 6 d

ICU

Discharged alive

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

N

Chi-square test

p-Value

Age (y)

< 60

68

10.65

0.001

21

7.03

0.011

4

14.55

0.000

0

19.86

0.000

34

4.51

0.041

19

10.79

0.001

98

24.67

0.000

≥ 60

82

36

21

17

46

38

70

Gender

Female

79

1.13

1.130

27

1.75

0.206

8

5.97

0.018

6

2.83

0.126

41

0.65

0.463

24

5.18

0.027

96

3.27

0.082

Male

71

30

17

11

39

33

72

Vaccine dose

None

84

3.83

0.162

30

0.00

1.000

15

1.69

0.469

11

1.16

0.588

44

0.37

0.856

32

0.45

0.814

84

4.42

0.110

One

50

20

6

4

27

18

63

Two

16

7

4

2

9

7

21

DM

Yes

70

12.29

0.001

32

8.68

0.004

15

4.74

0.047

10

2.72

0.122

42

8.77

0.004

37

20.77

0.000

63

3.79

0.073

Hypertension

Yes

65

8.21

0.006

34

16.09

0.000

15

5.89

0.025

9

1.76

0.200

45

19.34

0.000

38

28.23

0.000

60

3.03

0.114

ESRD

Yes

8

0.64

0.687

3

0.06

1.000

1

0.03

1.000

0

0.91

0.610

4

0.03

1.000

4

0.98

0.454

7

0.81

0.605

Cancer

Yes

22

0.74

0.457

8

0.02

1.000

5

1.02

0.345

4

1.58

0.256

12

0.25

0.672

8

0.02

1.000

19

5.69

0.026

Chronic liver

Yes

15

0.31

0.768

2

3.28

0.102

0

2.96

0.136

0

1.92

0.233

7

0.06

1.000

0

8.39

0.005

18

2.82

0.135

Autoimmune

Yes

16

1.34

0.371

4

0.53

0.593

2

0.06

1.000

2

0.13

1.000

8

0.06

1.000

3

1.61

0.282

16

0.04

1.000

Chronic lung

Yes

13

1.91

0.203

3

0.49

0.561

1

0.46

0.699

1

0.06

1.000

4

1.07

0.403

2

1.71

0.238

13

0.39

0.702

CVD

Yes

53

2.90

0.096

22

1.47

0.240

9

0.18

0.819

6

0.08

0.790

33

5.03

0.029

26

6.58

0.012

54

0.01

1.000

Abbreviations: CVD, cardiovascular diseases; DM, diabetes mellitus; ESRD, end-stage renal disease; ICU, intensive care unit; NEWS, National Early Warning Score; NIPPV, noninvasive positive pressure ventilation.



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Relationship between NEWSs and Variables

Student's t-test and analysis of variance showed that the mean NEWSs were higher for patients who were 60 years and older (p < 0.001), had diabetes (p < 0.001), hypertension (p < 0.001), had cardiovascular disease (p = 0.011), received LFO (p = 0.001), NIPPV (p < 0.001), mechanical ventilation (p < 0.001), were admitted to the ICU (p < 0.001), and discharged dead (p < 0.001). There was a positive correlation between age and the NEWS (Pearson's r = 0.473, p < 0.001). Additionally, there was a positive correlation between NEWSs and length of hospital stay (Pearson's r = 0.184, p < 0.001) ([Table 5]). A multiple linear regression model included the variables significantly associated with the student's t-tests to identify the significant predictors of high and low NEWSs. [Tables 6] and [7] show that high NEWSs were significantly predicted by the need to receive NIPPV (p = 0.013), stay in the hospital for 6 or more days (p = 0.013), be admitted to the ICU (p = 0.006), and be discharged dead (p < 0.001).

Table 5

Relationship between mean NEWSs and variables

Variables

N (%)

Mean (SD)

p-Value

Age (y): < 60/≥ 60

99 (51.6%)/93 (48.4%)

3.3 (1.5)/5.0 (2.7)

< 0.001

Sex: Female/male

105 (54.7%)/87 (45.3)

3.9 (2.2)/4.5 (2.4)

0.083

Vaccination status: none

101 (52.6%)

4.5 (0.2)

0.052

Vaccination status: one dose

67 (34.9%)

3.6 (0.2)

Vaccination status: two doses

24 (12.5%)

4.2 (0.5)

Vaccination status: two doses + a booster

0 (0.0%)

NA

Diabetes mellitus: no/yes

115 (59.9%)/77 (40.1%)

3.6 (2.0)/4.9 (2.5)

< 0.001

Hypertension: no/yes

119 (62.0%)/73 (38.0%)

3.6 (2.1)/5.0 (2.4)

< 0.001

CKD: no/yes

183 (95.3%)/9 (4.7%)

4.1 (2.3)/5.2 (3.0)

0.145

Cancer: no/yes

166 (86.5%)/26 (13.5%)

4.1 (2.3)/4.6 (2.3)

0.332

Chronic liver disease: no/yes

174 (90.6%)/18 (9.4%)

4.3 (2.4)/3.1 (1.0)

0.043

Autoimmune disease: no/yes

174 (90.6%)/18(9.4%

4.2 (2.3)/3.7 (2.4)

0.418

Chronic lung disease: no/yes

178 (92.7%)/14 (7.3%)

4.1 (2.4)/4.6 (1.5)

0.466

Cardiovascular disease: no/yes

130 (67.7%)/62 (32.3%)

3.9 (2.2)/4.8 (2.4)

0.011

Use of low-flow oxygen[a]: no/yes

42 (21.9%)/150 (78.1%)

3.2 (2.7)/4/4 (2.1)

0.001

Use of high-flow nasal cannula: no/yes

135 (70.3%)/57 (29.7%)

3.3 (1.7)/6.2 (2.4)

< 0.001

Use of NIPPV: no/yes

167 (87.0%)/25 (13.0%)

3.6(1.6)/8.1/2.4)

< 0.001

Hospital LOS (d): < 6/≥ 6

112 (58.3%)/80 (41.7%)

3.2(1.5)/5.4 (2.6)

< 0.001

Mechanical ventilation: no/yes

175 (91.0%)/17 (8.9%)

3.7 (1.8)/8.6 (2.5)

< 0.001

ICU admission: no/yes

135 (70.3%)/57 (29.7%)

3.2 (1.3)/6.5 (2.4)

< 0.001

Discharged alive: no/yes

24 (12.5%)/168 (87.5%)

3.2 (1.3)/6.5 (2.4)

< 0.001

Abbreviations: CKD, chronic kidney disease; ICU, intensive care unit; LOS, length of stay; NEWS, National Early Warning Score; NIPPV, noninvasive positive-pressure ventilation; SD, standard deviation.


a SPO2 < 94%.


Table 6

Use of NEWSs to predict deterioration and outcomes of patients using both multiple linear regression and ROC curve analysis

Variable

Unstandardized coefficients

SE

Standardized coefficients

T

p-Value

Age

0.25

0.24

0.06

1.07

0.287

Diabetes

0.14

0.25

0.03

0.58

0.565

Hypertension

−0.06

0.26

−0.01

−0.24

0.813

Chronic liver disease

−0.15

0.36

−0.02

−0.41

0.681

Cardiovascular disease

0.34

0.25

0.07

1.39

0.167

Low-flow oxygen

0.46

0.25

0.08

1.84

0.068

High-flow nasal cannula

0.29

0.33

0.06

0.90

0.368

NIPPV

1.35

0.54

0.20

2.52

0.013

Mechanical ventilation

−0.79

0.66

−0.10

−1.20

0.231

Length of hospital stay

0.60

0.24

0.13

2.51

0.013

ICU

1.03

0.38

0.21

2.75

0.006

Discharged alive

−3.20

0.48

−0.46

−6.73

< 0.001

Abbreviations: ICU, intensive care unit; NEWS, National Early Warning Score; NIPPV, noninvasive positive pressure ventilation; ROC, receiver operating characteristic; SE, standard error.


Table 7

Use of NEWSs to predict deterioration and outcomes of patients using ROC curve analysis

Variable

AUC

p-Value

Lower

Upper

Sensitivity

Specificity

Low-flow oxygen

0.74

< 0.001

0.64

0.84

0.527

0.810

High-flow nasal cannula

0.87

< 0.001

0.82

0.92

0.561

0.904

NIPPV

0.95

< 0.001

0.90

0.99

0.920

0.904

Hospital stay ≥ 6 d

0.78

< 0.001

0.72

0.85

0.550

0.812

ICU

0.91

< 0.001

0.87

0.96

0.702

0.896

Mechanical ventilation

0.94

< 0.001

0.88

1.00

0.882

0.897

Mortality

0.96

< 0.001

0.92

1.00

0.875

0.911

Abbreviations: AUC, area under the curve; ICU, intensive care unit; NEWS, National Early Warning Score; NIPPV, noninvasive positive pressure ventilation; ROC, receiver operating characteristic.



#
#

Discussion

To the best of our knowledge, most studies have yet to directly address the impact of NEWS systems in identifying clinical deterioration and outcomes in COVID-19 hospitalized patients.

In all fields of practice, it is vital to identify patients whose conditions are deteriorating. In this investigation, the NEWS2 showed strong discrimination in predicting the combined outcome of the patient's admission to the ICU, the patient's need for intensive respiratory support as an enormous sign of deterioration, a lengthening hospital stay, and in-hospital death.

The overall mean length of hospital stay after implementing the score was decreased, from 8.1 ± 5.5 days in pre-NEWS phase to 6.4 ± 5.3 days in post-NEWS phase which in line with those obtained in studies in countries other than China, which revealed that the median length of stay in hospitals varied from 4 to 21 days, but in China, the median length of stay in hospitals ranged from 4 to 53 days. However, an Egyptian quasi-experimental study found that there were no statistically significant changes in the total duration of hospital stays before or after the adoption of NEWS.[9]

Furthermore, the present study not only found a nonsignificant decrease in mortality rate in the study group compared with the control groups but also less than the reported mortality for all hospitalized COVID-19 patients in some European, American, and Chinese studies, with a percentage ranging between 17 and 23.4%.[10] [11] [12] [13] [14] [15]

Additionally, the present study's conclusion supports Farenden et al, who found nonsignificant differences between deaths and outcomes before and after NEWS implementation.[16] But in contrast, Moon et al showed a substantial reduction in in-hospital mortality following the deployment of the NEWS system.[17]

Although the NEWS2 method assesses supplemental oxygen as a binary variable, it cannot distinguish between oxygen supply rates. The RCP has verified this issue, stating that any increase in oxygen therapy among COVID-19 patients should prompt a physician assessment and increased monitoring.[17] While it has been established that oxygen supplementation is an independent risk factor for new coronavirus pneumonia escalating to a serious situation, no previous studies have shown an association between NEWS and supplemental oxygen therapy. Some studies have established a link between the oxygen saturation measures in the score and death as a predictive physiological parameter in patients with COVID-19 infection. Liu et al found that the oxygen saturation level had a good predictive performance for predicting death.[18]

Despite the lack of published statistics on NEWS2's effectiveness in controlling COVID-19 patients' clinical deterioration, this study showed that NEWS2 was good at predicting deteriorating patients based on their oxygen requirement, with an acceptable level of sensitivity and specificity for LFO, high-flow oxygen (HFO), and mechanical ventilation (intermittent mandatory ventilation [IMV]).

In El-Hamshari et al's study,[18] ICU admission was recommended if the patient met ICU admission criteria: hemodynamic instability or an increase in acute respiratory failure demanding an increase in oxygen therapy from LFO to a nasal cannula, a simple facemask, a nonrebreathe mask (NRB), or mechanical ventilation. Receiving ICU-level care depends mainly on patients' advanced requirements for oxygen, such as HFO therapy, NIPPV, or IMV, but some patients were monitored outside the ICU on medical floors due to a lack of ICU beds. Patients' oxygen treatment increased, from NRB to HFO therapy to NIPPV. With the patients' or family's consent, intubation and IMV were considered if none of these interventions raised oxygenation to > 90% and if the patient continued worsening breathing difficulties.

No reliable prognostic score can predict clinical outcomes in patients with COVID-19, regardless of whether they are treated in an inpatient or outpatient setting.[19] The present study found that the mean NEWS during the patient's hospital stay was a good predictor of staying in the hospital for 6 days or more and an excellent predictor of admission to the ICU. Like worldwide hospital policies, most ICU admissions were estimated to take 2.0 days from hospital admission. In this study, the mean NEWS during the patient's hospital stay was an excellent predictor of mortality. These findings aligned with a few small cohort studies that looked at the predictive value of baseline NEWS2 and other clinical scoring systems in predicting clinical outcomes in COVID-19 patients based on a single measurement at the time of hospital admission. In a Chinese study of 654 COVID-19 admissions, the baseline NEWS2 had a better prediction of mortality than CURB-65 and performed better than qSOFA.[20] In a Korean study of 110 COVID-19 inpatients, using a baseline threshold of NEWS more than or equal to 5 resulted in a poor predictive value of 0.98 but a positive predictive value of 0.59 for a future event.[21] The baseline (original) NEWS anticipated an event (defined as ICU admission and death) for qSOFA. The baseline NEWS2 in a Norwegian study of 66 inpatients predicted a composite adverse outcome of inpatient death and ICU admission. This was in comparison with qSOFA's AUROC of 0.62 and CURB-65's AUROC of 0.58.[22] The prognostic qualities of NEWS2 for admission to the ICU were similarly established in 68 patients with severe COVID-19 in another study.[23] “Serious events” were classified as any of the following occurring while the patient was hospitalized: death, an unexpected transfer to an ICU, or the start of noninvasive ventilation. With equivalent AUROC scores for predicting serious events of 0.837 for admission NEWS and 0.846 for admission, NEWS is modified by age.[24]

According to Covino et al (2020), NEWS was the most reliable indicator of ICU admission within 48 hours and 7 days of emergency department entrance. The greatest AUROC values for predicting in-hospital death, according to the findings of Liu et al.[3] However, Knight et al. (2020)[25] found NEWS with in-hospital mortality as an outcome has a good, modest predictive value for ICU admissions, which would help prioritize a huge attribution number of patients with COVID-19 who subsequently deteriorate.

The NEWS risk categorization may exist because it provides a quick and easy approach to spotting deteriorating patients using a united surveillance system that directs medical personnel to patients according to priority. This opinion is consistent with Ludikhuize et al[26] who found that NEWS, based on the decline in vital signs noted at least once in 48 hours before an adverse event, might have identified more than 80% of patients with urgent cases.

The results of this study showed that there were statistically significant differences between the study post-NEWS group and the control pre-NEWS group for the frequency of vital sign measurements as the number of vital sign measurements varied, much like in the control group. In other words, before implementing the NEWS, the staff could take vital signs twice or once during a shift. They typically only measured two parameters. However, after implementing NEWS, the mean number of vital signs monitored in the study group increased noticeably as they measured all vital sign parameters by NEWS standards. Nevertheless, the fact that there is little or no high-quality data confirming trends in the myriads of scoring systems established to predict clinical deterioration should be an important contribution to evidence-based practice.

During the COVID-19 pandemic, the researcher included all patients diagnosed with COVID-19 and admitted to the hospital, except those who met RCP-recommended exclusion criteria. Since hospital admission, we have collected comprehensive data on vital signs, covering every aspect of the NEWS2. Also, the commitment from the medical team to using the score was high, which indicates that the implementation of the score was easy and applicable based on a physiological variable and enhanced the flow of patient monitoring in a cost-effective manner. However, it is important to spell out some of this study's limitations. First, because the sample was taken from a single geographic region in Palestine in a relatively short review time and the post-NEWS data were gathered only 3 months after the score's introduction, the research findings are less likely to be generalized. Also, this was a double-center study in the same geographical area, which limits its generalization. Second, not all ward patients required to be admitted to the ICU based on their NEWS were immediately admitted to the ICU. Third, the patient's nursing staff was called first, delaying some ICU admissions when the ICU team was not available or there was a shortage of ICU beds. Fourth, because patients were not followed up after being discharged from the hospital and the study's outcomes were restricted to a hospital stay, 28- and 90-day mortality rates were not provided. Finally, a potential problem with using a control group is the risk bias caused by other concurrent changes. It could be helpful to improve predictive performance by supplementing the score parameters with additional measures, such as blood tests. Instead of creating a new EWS, which would imply a larger sample size and an external validation cohort, the purpose was to investigate the utility of the existing NEWS2 rating system in the context of COVID-19, as it is already used in medical care. Therefore, this study is expected to add value to the body of research.


#

Conclusion

Adoption of NEWS2 was associated with a significant prediction of patient deterioration and showed a considerable improvement in hospitalized COVID-19 patient outcomes (decreased length of hospital stay, predicted ICU admissions, and modest decrease in mortality rate), an increase in the frequency of vital signs measurements, which indirectly raised the number of medical reviews following the patient's clinical deterioration, and also that the medical team's commitment to using the score was high. This indicates that the implementation of the score was easy, applicable, and based on a physiological variable, and it enhanced the flow of patient monitoring cost-effectively.


#
#

Conflict of Interest

None declared.

Acknowledgments

The authors thank An-Najah National University, Clinical Research Center of An-Najah National University, and the frontline workers during the COVID-19 pandemic, all nurses, and physicians who committed to applying the score.

Authors' Contribution

I.M.T. and J.Q. contributed to literature search, preparation of the manuscript, and revision of the manuscript along with this they also initiated, conceptualized, designed, and organized the research, supervised and critically reviewed the manuscript and finalized the manuscript. All authors approved the final manuscript.


Ethical Approval

An Institutional Review Board approval was granted by the An-Najah National University Research Committee (March 2022/25). Informed consent was obtained from all participants before being enrolled. Ethics approval and consent to participate in this study were carried out in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board of An-Najah National University and was also obtained for all participants who participated in the study. Instructions and voluntary participation statements were included. All data collected were confidential and used only for this study.


Availability of Data

The data sets used for the current study are available from the corresponding author upon reasonable requests.


  • References

  • 1 Modesto dos Santos V, Modesto dos Santos LA, Campos Modesto L, Campos Modesto J. Reinfection with SARS-CoV-2: a challenging event. Microbes Infect Chemother 2021; 1: e1189
  • 2 National Confidential Enquiry into Patient Outcome and Death. Just Say Sepsis! A review of the process of care received by patients with sepsis. NCEPOD, 2015. www.ncepod.org.uk/2015report2/downloads/JustSaySepsis_Summary.pdf
  • 3 Liu F-Y, Sun X-L, Zhang Y. et al. Evaluation of the risk prediction tools for patients with coronavirus disease 2019 in Wuhan, China: A single-centered, retrospective, observational study. Critical care medicine 2020; 48 (11) e1004-e1011
  • 4 Martín-Rodríguez F, López-Izquierdo R, Del Pozo Vegas C. et al. Accuracy of National Early Warning Score 2 (NEWS2) in prehospital triage on in-hospital early mortality: a multi-center observational prospective cohort study. Prehosp Disaster Med 2019; 34 (06) 610-618
  • 5 Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019; 110: 12-22
  • 6 Royal College of Emergency Medicine. Position statement: National Early Warning Score (NEWS) for adult patients attending emergency departments. London: RCEM; 2016. . Accessed August 10, 2017, at: www.rcem.ac.uk/docs/News/CEM10125-Position
  • 7 Welch J, Dean J, Hartin J. Using NEWS2: an essential component of reliable clinical assessment. Clin Med (Lond) 2022; 22 (06) 509-513
  • 8 Gerry S, Bonnici T, Birks J. et al. Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology. BMJ 2020; 369: m1501
  • 9 Badr MN, Khalil NS, Mukhtar AM. Effect of National Early Warning Scoring System implementation on cardiopulmonary arrest, unplanned ICU admission, emergency surgery, and acute kidney injury in an emergency hospital, Egypt. J Multidiscip Healthc 2021; 14: 1431-1442
  • 10 Richardson S, Hirsch JS, Narasimhan M. et al; the Northwell COVID-19 Research Consortium. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 2020; 323 (20) 2052-2059
  • 11 Petrilli CM, Jones SA, Yang J. et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369: m1966
  • 12 Mikami T, Miyashita H, Yamada T. et al. Risk factors for mortality in patients with COVID-19 in New York City. J Gen Intern Med 2021; 36 (01) 17-26
  • 13 Li X, Xu S, Yu M. et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 2020; 146 (01) 110-118
  • 14 Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response. JAMA 2020; 323 (16) 1545-1546
  • 15 Jensen JK, Skår R, Tveit B. Introducing the National Early Warning Score - a qualitative study of hospital nurses' perceptions and reactions. Nurs Open 2019; 6 (03) 1067-1075
  • 16 Farenden S, Gamble D, Welch J. Impact of implementation of the National Early Warning Score on patients and staff. Br J Hosp Med (Lond) 2017; 78 (03) 132-136
  • 17 Moon A, Cosgrove JF, Lea D, Fairs A, Cressey DM. An eight year audit before and after the introduction of modified early warning score (MEWS) charts, of patients admitted to a tertiary referral intensive care unit after CPR. Resuscitation 2011; 82 (02) 150-154
  • 18 El-Hamshari Y, Hamarshi M, Nazzal Z. et al. The outcomes of severe COVID-19 pneumonia managed with supportive care in Palestine: an experience from a developing country. J Infect Dev Ctries 2021; 15 (08) 1094-1103
  • 19 Zhang K, Zhang X, Ding W. et al. The prognostic accuracy of National Early Warning Score 2 on predicting clinical deterioration for patients with COVID-19: a systematic review and meta-analysis. Front Med (Lausanne) 2021; 8: 699880
  • 20 Fan G, Tu C, Zhou F. et al. Comparison of severity scores for COVID-19 patients with pneumonia: a retrospective study. Eur Respir J 2020; 56 (03) 2002113
  • 21 Jang JG, Hur J, Hong KS, Lee W, Ahn JH. Prognostic accuracy of the SIRS, qSOFA, and NEWS for early detection of clinical deterioration in SARS-CoV-2 infected patients. J Korean Med Sci 2020; 35 (25) e234
  • 22 Myrstad M, Ihle-Hansen H, Tveita AA. et al. National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19 - a prospective cohort study. Scand J Trauma Resusc Emerg Med 2020; 28 (01) 66
  • 23 Gidari A, De Socio GV, Sabbatini S, Francisci D. Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2 infection. Infect Dis (Lond) 2020; 52 (10) 698-704
  • 24 Bliss M, Aitken LM. Does simulation enhance nurses' ability to assess deteriorating patients?. Nurse Educ Pract 2018; 28: 20-26
  • 25 Knight SR, Ho A, Pius R. et al; ISARIC4C investigators. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ 2020; 370: m3339
  • 26 Ludikhuize J, Smorenburg SM, de Rooij SE, de Jonge E. Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the Modified Early Warning Score. J Crit Care 2012; 27 (04) 424.e7-424.e13

Address for correspondence

Jamal Qaddumi, PhD
An-Najah National University Faculty of Nursing
Nablus, PO Box 7, 707
Palestine   

Publication History

Article published online:
08 May 2024

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  • References

  • 1 Modesto dos Santos V, Modesto dos Santos LA, Campos Modesto L, Campos Modesto J. Reinfection with SARS-CoV-2: a challenging event. Microbes Infect Chemother 2021; 1: e1189
  • 2 National Confidential Enquiry into Patient Outcome and Death. Just Say Sepsis! A review of the process of care received by patients with sepsis. NCEPOD, 2015. www.ncepod.org.uk/2015report2/downloads/JustSaySepsis_Summary.pdf
  • 3 Liu F-Y, Sun X-L, Zhang Y. et al. Evaluation of the risk prediction tools for patients with coronavirus disease 2019 in Wuhan, China: A single-centered, retrospective, observational study. Critical care medicine 2020; 48 (11) e1004-e1011
  • 4 Martín-Rodríguez F, López-Izquierdo R, Del Pozo Vegas C. et al. Accuracy of National Early Warning Score 2 (NEWS2) in prehospital triage on in-hospital early mortality: a multi-center observational prospective cohort study. Prehosp Disaster Med 2019; 34 (06) 610-618
  • 5 Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019; 110: 12-22
  • 6 Royal College of Emergency Medicine. Position statement: National Early Warning Score (NEWS) for adult patients attending emergency departments. London: RCEM; 2016. . Accessed August 10, 2017, at: www.rcem.ac.uk/docs/News/CEM10125-Position
  • 7 Welch J, Dean J, Hartin J. Using NEWS2: an essential component of reliable clinical assessment. Clin Med (Lond) 2022; 22 (06) 509-513
  • 8 Gerry S, Bonnici T, Birks J. et al. Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology. BMJ 2020; 369: m1501
  • 9 Badr MN, Khalil NS, Mukhtar AM. Effect of National Early Warning Scoring System implementation on cardiopulmonary arrest, unplanned ICU admission, emergency surgery, and acute kidney injury in an emergency hospital, Egypt. J Multidiscip Healthc 2021; 14: 1431-1442
  • 10 Richardson S, Hirsch JS, Narasimhan M. et al; the Northwell COVID-19 Research Consortium. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 2020; 323 (20) 2052-2059
  • 11 Petrilli CM, Jones SA, Yang J. et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369: m1966
  • 12 Mikami T, Miyashita H, Yamada T. et al. Risk factors for mortality in patients with COVID-19 in New York City. J Gen Intern Med 2021; 36 (01) 17-26
  • 13 Li X, Xu S, Yu M. et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 2020; 146 (01) 110-118
  • 14 Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response. JAMA 2020; 323 (16) 1545-1546
  • 15 Jensen JK, Skår R, Tveit B. Introducing the National Early Warning Score - a qualitative study of hospital nurses' perceptions and reactions. Nurs Open 2019; 6 (03) 1067-1075
  • 16 Farenden S, Gamble D, Welch J. Impact of implementation of the National Early Warning Score on patients and staff. Br J Hosp Med (Lond) 2017; 78 (03) 132-136
  • 17 Moon A, Cosgrove JF, Lea D, Fairs A, Cressey DM. An eight year audit before and after the introduction of modified early warning score (MEWS) charts, of patients admitted to a tertiary referral intensive care unit after CPR. Resuscitation 2011; 82 (02) 150-154
  • 18 El-Hamshari Y, Hamarshi M, Nazzal Z. et al. The outcomes of severe COVID-19 pneumonia managed with supportive care in Palestine: an experience from a developing country. J Infect Dev Ctries 2021; 15 (08) 1094-1103
  • 19 Zhang K, Zhang X, Ding W. et al. The prognostic accuracy of National Early Warning Score 2 on predicting clinical deterioration for patients with COVID-19: a systematic review and meta-analysis. Front Med (Lausanne) 2021; 8: 699880
  • 20 Fan G, Tu C, Zhou F. et al. Comparison of severity scores for COVID-19 patients with pneumonia: a retrospective study. Eur Respir J 2020; 56 (03) 2002113
  • 21 Jang JG, Hur J, Hong KS, Lee W, Ahn JH. Prognostic accuracy of the SIRS, qSOFA, and NEWS for early detection of clinical deterioration in SARS-CoV-2 infected patients. J Korean Med Sci 2020; 35 (25) e234
  • 22 Myrstad M, Ihle-Hansen H, Tveita AA. et al. National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19 - a prospective cohort study. Scand J Trauma Resusc Emerg Med 2020; 28 (01) 66
  • 23 Gidari A, De Socio GV, Sabbatini S, Francisci D. Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2 infection. Infect Dis (Lond) 2020; 52 (10) 698-704
  • 24 Bliss M, Aitken LM. Does simulation enhance nurses' ability to assess deteriorating patients?. Nurse Educ Pract 2018; 28: 20-26
  • 25 Knight SR, Ho A, Pius R. et al; ISARIC4C investigators. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ 2020; 370: m3339
  • 26 Ludikhuize J, Smorenburg SM, de Rooij SE, de Jonge E. Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the Modified Early Warning Score. J Crit Care 2012; 27 (04) 424.e7-424.e13