National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in India.

BACKGROUND: While several parameters have emerged as predictors of prognosis of COVID-19, a simple clinical score at baseline might help early risk stratification. We determined the ability of National Early Warning Score 2 (NEWS2) to predict poor outcomes among adults with COVID-19.
METHODS: A prospective study was conducted on 399 hospitalised adults with confirmed SARS-CoV-2 infection between August and December 2020. Baseline NEWS2 score was determined. Primary outcome was poor outcomes defined as need for mechanical ventilation or death within 28 days. The sensitivity, specificity and Area under the curve were determined for NEWS2 scores of 5 and 6.
RESULTS: Mean age of patients was 55.5 ± 14.8 years and 275 of 399 (68.9%) were male. Overall mortality was 3.8% and 7.5% had poor outcomes. Median (interquartile range) NEWS2 score at admission was 2 (0-6). Sensitivity and specificity of NEWS 2 of 5 or more in predicting poor outcomes was 93.3% (95% CI: 76.5-98.8) and 70.7% (95% CI: 65.7-75.3) respectively [area under curve 0.88 (95% CI: 0.847-0.927)]. Age, baseline pulse rate, baseline oxygen saturation, need for supplemental oxygen and ARDS on chest X ray were independently associated with poor outcomes.
CONCLUSIONS: NEWS2 score of 5 or more at admission predicts poor outcomes in patients with COVID-19 with good sensitivity and can easily be applied for risk stratification at baseline. Further studies are needed in the Indian setting to validate this simple score and recommend widespread use.

Introduction

More than a year has passed since COVID-19 was declared a pandemic. While majority of SARS-CoV-2 infections are asymptomatic or mild, a small proportion develop severe disease that is often fatal despite best available care [1]. Mortality in this group of patients is high [2]. It is estimated that around 15–20% of hospitalised patients with COVID 19 need ICU care or die of severe disease [1,3]. Increasing age and co-morbidities such as diabetes, obesity, cardiovascular and lung disease have been linked to severe disease and death in many studies [4].

Presence of risk factors and clinical presentation is currently being used to triage patients at the time of admission into mild, moderate and severe disease. Further several laboratory parameters like the C-reactive protein, Ferritin and D-dimer have emerged as potential markers of severity [1,5-7], although none have a definite prognostic value. In other diseases such as sepsis and critical illnesses, several scoring systems have been validated for predicting poor outcomes and mortality. The National Early Waring Score 2 (NEWS2) is one such tool that is simple and enables health care staff to identify high risk patients and escalate care [8]. Some bodies have suggested the use of NEWS2 along with clinical examination to triage COVID-19 patients albeit with caution [9]. Such risk stratification may help quicker decision making and enable the treating doctors to divert more attention, time and resources to those identified as high risk for fatal outcome. Unlike most other scores, NEWS2 also includes oxygenation criteria such as hypoxia and supplemental oxygen requirement, which are particularly important in assessment of COVID-19 patients. All these make NEWS2 a good baseline indicator to be explored as a potential predictor of severe disease and death among COVID-19 patients. We therefore aimed to determine whether NEWS 2 score at admission predicts poor outcome in patients with COVID-19 disease.

Methods

A prospective cohort study was conducted between August and December 2020 at the Pondicherry Institute of Medical Sciences, a tertiary care teaching hospital also functioning as a designated testing and treating centre for COVID-19. Inclusion criteria was adults admitted with a diagnosis of COVID-19 confirmed by detection of SARS-CoV-2 by RT-PCR. Following written informed consent, consecutive eligible participants were interviewed to obtain demographic details such as age, residence and contact with confirmed or suspected COVID-19 cases in household or workplace. Presenting symptoms with duration, associated co-morbidities and treatment for the same were documented. At admission, the vital signs including blood pressure, pulse, respiratory rate and oxygen saturation were recorded. The NEWS2 score was calculated on the following parameters: respiratory rate, oxygen saturation (SpO2), need for supplemental oxygen, pulse rate, level of consciousness and temperature (Fig 1). We also collected baseline laboratory investigations as part of routine COVID 19 care. These patients were followed up on a daily basis for improvement/deterioration. We classified patient outcomes at day 28 as discharged, hospitalised but without oxygen, hospitalised on supplemental oxygen, hospitalised on High flow nasal oxygen (HFNO) or Non-invasive ventilation (NIV), mechanically ventilated and expired. The primary outcome was the ability of NEWS2 score at admission to predict poor outcome in patients with COVID 19 disease, defined as need for mechanical ventilation or death within 28 days. Secondary outcomes were association of other clinical (such as diabetes, hypertension, age and chronic lung, kidney or liver diseases) and laboratory variables with poor outcomes.

Fig 1

NEWS2 scoring matrix.

Reproduced from: Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS. Updated report of a working party. London: RCP, 2017. The final score is a composite of the points for individual criteria. Higher scores (of 5 and above) usually indicate need for escalating care.

Data were collected using the Epicollect 5 application on tablets in order to minimise contact with contaminated documents. We described the frequency of clinical and laboratory features using means and standard deviations, and proportions with confidence intervals. The sensitivity, specificity, positive and negative predictive value with 95% confidence intervals (CIs) of NEWS2 score at admission in predicting poor outcomes were calculated. We used cut off scores of 5 and 6 for the NEWS2 score for these calculations. Hence for calculating sensitivity at a cut off score of 5, we determined the proportion of patients with NEWS2 score of 5 or more who died or needed mechanical ventilation. Similarly for a cut off of 6 and above we determined the proportion of patients with an admission score of 6 and above who died or needed mechanical ventilation. For calculation of specificities, we determined proportions of patients with these scores who did not have a poor outcome. Univariate analysis was performed with Chi-square test/Fisher’s Exact Test to determine associations between clinical and laboratory parameters and poor outcome of categorical variables. Mann-Whitney test/ t test was done for continuous variables based on normality condition. Independent associations between potential variables and poor outcome were determined and expressed as Odds ratios with 95% Confidence intervals and p values. In this model, those who needed mechanical ventilation or died within 28 days (i.e., poor outcome) were considered as case-patients and patients discharged (i.e., good outcome) were considered as control-patients. Exposure status was defined as potential variables known to affect outcomes such as comorbidities, baseline clinical parameters, laboratory parameters and findings of chest X-ray. Logistic regression was employed to quantify the relationship between these variables and poor outcomes and expressed as Odds ratio with 95% Confidence intervals and p values.Variables which were significant at p < 0.1 in univariate analysis were considered for multiple logistic regression analysis. p value < 0.05 was used to define statistical significance.

The study was approved by the PIMS Institute Ethics Committee (IEC:RC/2020/72) and all data pertaining to this study are available within this article and supplementary information file (S1 Dataset).

Results

During the period between August 2020 and December 2020, 416 patients were admitted with a positive RT-PCR test for SARS-CoV-2. Among these, 16 did not consent for the study and one was referred elsewhere; finally, 399 eligible patients were included in the analysis.

The mean age of patients was 55.5 ± 14.8 years and 199 of 399 (49.9%) were between 40 to 60 years of age. 143 of 399 (35.8%) were over 60 years of age. 275 of 399 (68.9%) patients were males. The most common symptom was fever [304/399 (76.2%)] followed by cough (Table 1). 199 of 399 (49.9%) patients were diabetics while 176 of 399 (44.1%) had hypertension. Table 2 presents the common comorbidities of patients.

Table 1

Frequency of symptoms of patients at presentation (n = 399).

Symptom	Number (%)
Fever	304 (76.2)
Cough	229 (57.4)
Sore throat	134 (33.6)
Breathlessness	147 (36.8)
Myalgia	202 (50.6)
Diarrhea	51 (12.8)
Loss of taste	80 (20.1)
Loss of smell	67 (16.8)

Table 2

Distribution of co-morbidities among patients admitted with COVID-19 (n = 399).

Comorbidity	Number (%)
Diabetes	199 (49.9)
Systemic hypertension	176 (44.1)
Chronic heart disease	37 (9.3)
Chronic kidney disease	17 (4.3)
Chronic lung disease	11 (2.8)
Chronic liver disease	3 (0.8)
Others*	30 (7.5)

*Others include hypothyroidism, malignancy, pancreatitis, seizure disorder, post-renal transplant and depression.

94 out of 399 (23.6%) patients had a history of contact with confirmed case of COVID-19. Based on the WHO clinical criteria, 53 out of 399 (13.3%) patients had severe disease. The overall 28-day mortality was 3.8% (15 out of 399 patients). Other outcomes of patients at day 28 is summarised in Table 3. Poor outcome (defined as death or mechanical ventilation at any time during 28 days) occurred in 30 of the 399 (7.5%) patients. Among these, death occurred in 15 patients. The remaining 15 required mechanical ventilation at some point of time during hospitalisation. This includes 2 patients who needed mechanical ventilation but could be weaned off before day 28 and 13 patients who were still on invasive ventilation at day 28.

Table 3

Outcomes at day 28 of patients admitted with COVID-19 (n = 399).

Outcome	Number (%)
Discharged/hospitalized with no supplemental Oxygen	268 (67.2)
Hospitalized with supplemental Oxygen	77 (19.3)
Hospitalized with HFNO* or NIV#	26 (6.5)
Mechanically ventilated	13 (3.3)
Dead	15 (3.8)

*HFNO–high flow nasal oxygen.

#NIV–non-invasive ventilation.

The mean admission NEWS2 score was 3.3 ± 3.3 and median (IQR) NEWS2 score at admission was 2 (0–6). The baseline NEWS2 score was 5 or more in 136 of the 399 (34.1%) patients while it was 6 or more in 115 of the 399 (28.8%) patients.

Sensitivity and specificity of NEWS2 score

We determined predictive accuracy of NEWS2 at score of 5 as well as 6. Accordingly, 28 out of 30 patients with poor outcomes had a NEWS2 score of 5 or more while only 2 had score less than 5. Similarly, a NEWS2 score of 5 or more was seen in 108 out of 369 patients with good outcome while 261 of them had a score of less than 5. Thus the sensitivity, specificity, positive predictive value and negative predictive value of an admission NEWS2 score of 5 or more to predict poor outcome was 93.3% (95% CI: 76.5–98.8), 70.7% (95% CI: 65.7–75.3), 20.6% (95% CI: 14.3–28.5) and 99.2% (955 CI: 97.0–99.9) respectively (S1 Table). Using a score of 6 as cut off, 27 out of 30 patients with poor outcomes had NEWS2 score of 6 or more compared to 3 who had a score less than 6. NEWS2 score of 6 or more had a sensitivity of 90% (95% CI: 72.3–97.4) and specificity of 76.2% (95% CI: 71.4–80.3) (Table 4). The Area under the curve (AUC) to predict poor outcome was also high (0.887; 95% CI: 0.847–0.927) which is considered excellent for discrimination (Fig 2).

Table 4

Comparison of diagnostic accuracy parameters of scores of 5 and 6.

Characteristic	NEWS2 score of 5 or more (136; 34.1%)	NEWS2 score of 6 or more (115; 28.8%)
Sensitivity	93.3% (95% CI: 76.5–98.8)	90.0% (95% CI: 72.3–97.4)
Specificity	70.7% (95% CI: 65.7–75.3)	76.2% (95% CI: 71.4–80.3)
Positive predictive value	20.6% (95% CI: 14.3–28.5)	23.5% (95% CI: 16.3–32.5)
Negative predictive	99.2% (95% CI: 97.0–99.9)	98.9% (95% CI: 96.9–99.6)

Fig 2

Receiver Operating Characteristics (ROC) curve showing area under curve for NEWS2 scores in predicting poor outcomes among COVID-19 patients.

On univariate analysis, age, chronic kidney disease, chronic heart disease, need for supplemental oxygen at admission and ARDS on baseline Chest X ray were associated with poor outcomes. Other factors associated with poor outcome included baseline pulse rate, baseline respiratory rate, baseline oxygen saturation, leucocyte counts, Neutrophil to Lymphocyte ratio, serum creatinine, ESR, CRP, D-Dimer, Ferritin, Blood urea nitrogen and AST (Table 5). However, on multiple logistic regression only the following factors were associated with poor outcomes: age, baseline pulse rate, baseline oxygen saturation, need for supplemental Oxygen, and ARDS on Chest X ray (Table 6). Eight of 30 (26.7%) patients with poor outcome had ARDS compared to 3 of 369 (0.8%) who had good outcomes, indicating that ARDS on admission was strongly associated with poor outcomes (Odds ratio– 17.2).

Table 5

Univariate analysis of factors associated with poor outcomes at day 28.

Variables	N (percentage to total)	Good outcome (n = 369)	Poor outcome (n = 30)	p value
Age*	-	54.7 ± 14.8	65.3 ± 10.6	< 0.001
Male@	275 (68.9%)	252 (68.3%)	23 (76.7%)	0.341
Diabetes@	199 (49.9%)	180 (48.8%)	19 (63.3%)	0.125
Hypertension@	176 (44.1%)	161 (43.6%)	15 (50.0%)	0.499
Chronic Lung Disease@	11 (2.8%)	11 (3.0%)	0 (0%)	1.000
Chronic Liver Disease@	3 (0.8%)	3 (0.8%)	0 (0%)	1.000
Chronic Kidney Disease@	17 (4.3%)	13 (3.5%)	4 (13.3%)	0.031
Chronic Heart Disease@	37 (9.3%)	31 (8.4%)	6 (20.0%)	0.047
Need for oxygen supplementation@	146 (36.6%)	118 (32.0%)	28 (93.3%)	< 0.001
Chest X-ray ARDS@	11 (2.8%)	3 (0.8%)	8 (26.7%)	< 0.001
Baseline Pulse rate	-	90.0 (81.5–101.0)	110 (93.8–116.8)	< 0.001
Baseline systolic	-	130.0 (120.0–140.0)	125.0 (120.0–140.0)	0.950
Baseline Diastolic	-	80.0 (70.0–90.0)	80.0 (70.0–82.5)	0.368
Baseline respirate rate	-	21.0 (18.0–26.0)	30.0 (25.0–30.0)	< 0.001
Baseline oxygen saturation	-	96.0 (93.0–98.0)	87.0 (79.5–93.0)	< 0.001
Haemoglobin	-	13.0 (11.4–14.3)	12.5 (11.0–13.6)	0.131
Total Leukocyte count	-	5840.0 (4600.0–7650.0)	8200.0 (4950.0–11812.5)	0.005
Neutrophil to Lymphocyte ratio	-	3.0 (2.0–4.0)	4.0 (3.4–9.1)	< 0.001
ESR	-	29.0 (11.0–51.5)	46.0 (28.0–82.0)	< 0.001
CRP		12.0 (4.0–36.0)	30.0 (18.5–96.0)	< 0.001
S Ferritin		278.0 (112.0–602.5)	695.5 (341.8–1161.0)	< 0.001
D Dimer		0.47 (0.25–0.87)	1.17 (0.71–2.70)	< 0.001
AST		28.0 (20.0–43.0)	38.5 (29.5–58.5)	0.009
ALT		27.0 (19.0–41.0)	32.5 (24.5–41.3)	0.064
BUN		18.0 (12.0–27.0)	25.0 (20.0–47.5)	0.001
S Creatinine		0.9 (0.7–1.0)	1.0 (0.8–1.3)	0.001

*: Mean ± standard deviation; @: Number (percentage); remaining median (inter quartile range).

Table 6

Multiple logistic regression of factors associated with poor outcome at day 28.

Variable	Odds ratio	95% CI	p value
Age	1.048	1.003–1.095	0.036*
Chronic heart disease	1.007	0.247–4.107	0.992
Chronic kidney disease	0.663	0.087–5.066	0.692
Need for oxygen supplementation	6.071	1.114–33.070	0.037*
Baseline pulse	1.040	1.003–1.078	0.034*
Baseline respiratory rate	0.988	0.932–1.048	0.692
Baseline oxygen saturation	0.933	0.873–0.997	0.039*
Total leucocyte count	1.00	1.00–1.00	0.085
Neutrophil to Lymphocyte ratio	1.073	0.990–1.163	0.088
ESR	1.006	0.989–1.024	0.464
CRP	1.004	0.993–1.015	0.461
Ferritin	1.00	0.999–1.001	0.566
D-Dimer	1.034	0.933–1.144	0.526
Blood urea nitrogen	1.003	0.986–1.019	0.743
AST	0.998	0.969–1.027	0.869
ALT	1.002	0.975–1.030	0.868
ARDS on Chest X ray	17.238	2.186–135.929	0.007*

*Denotes statistically significant variables (p < 0.05).

Note: Variables with p value less than or equal to 0.1 on univariate analysis were chosen for multiple logistic regression.

Discussion

This study conducted at a tertiary care hospital that tests and treats COVID-19 disease of all degrees of severity identified that NEWS2 score applied to patients at admission has high sensitivity and reasonable specificity to predict progression to mechanical ventilation or death at 28 days. A score of 5 or more at baseline had maximum area under the curve for prediction of poor outcomes. Further, we found that increasing age, need for supplemental oxygen at baseline, pulse rate at baseline and an initial chest X ray showing features of ARDS were independently associated with poor outcomes.

Since the beginning of COVID-19 pandemic several studies have identified factors associated with risk of death. Early studies from Wuhan indicated that older age, smoking, admission body temperature, Neutrophil to Lymphocyte (NLR) ratio, platelet counts, D-dimer and serum creatinine correlated with increased risk of death from COVID-19 [10]. Another small retrospective study early in the pandemic identified higher Sequential Organ Failure Assessment (SOFA) scores to be a predictor of death in addition to older age and high D-dimer levels [4]. As more data emerged, it was evident that death in COVID-19 disease was due to pulmonary as well as non-pulmonary complications such as acute cardiac injury and heart failure, and data from large cohorts using electronic health records showed that male gender, uncontrolled diabetes and severe asthma were strongly associated with death [11]. Since then a long list of factors attributable to mortality in COVID-19 has emerged including several laboratory parameters such as D-dimer [2].

Despite this, it has been a tough task to predict which patients progress to death or poor outcome such as need for mechanical ventilation [12], especially at the time of admission or first encounter. Many studies reported markers such as D-dimer, Lactate Dehydrogenase (LDH) and interleukins to be associated with severe disease and death; however, most of these are either unavailable or prohibitively expensive to be used routinely for risk stratification [13].

While NEWS and NEWS2 scores have been in vogue in the United Kingdom and many other countries for the triaging and monitoring of hospitalised patients, its use in India and many other nations has not been widespread. Initially used in 2012 for identifying and monitoring sick patients in hospital, the NEWS score was modified in 2017 to NEWS2 by including confusion and oxygen saturation [14]. Shortly after the beginning of COVID-19 pandemic, the Royal College of Physicians UK issued guidance advocating use of NEWS2 score for managing patients with COVID-19 [15]. However, this recommendation was not based on existing evidence but an extrapolation of its use in the pre-pandemic era. This led experts to advise caution against its use in COVID-19 setting particularly in primary care [9]. Subsequently, Myrstad and colleagues determined the ability of NEWS2 score at emergency room admission to predict severe disease and death in COVID-19 patients [16]. NEWS2 score of 6 or more had 80% sensitivity and 84.3% specificity in predicting severe disease, with an area under the curve (AUC) of 0.822. But this study was limited by small sample size (66 patients) and retrospective data collection for certain variables such as comorbidities. Similarly, another small retrospective study from Italy showed NEWS2 score at admission to be a good predictor of ICU admission (AUC of 0.90; 95% CI 0.82–0.97) [17]. An intensive care specialist team from China proposed using a modified NEWS2 score for triaging patients by including age above 65 years since older age stood out as an independent risk factor in most studies [18]. However, this scoring system has not been validated as yet.

Our study in contrast was done on a prospective cohort of 399 patients with COVID-19 and determined NEWS2 score at admission for all of them. We used need for mechanical ventilation or death anytime during 28 days as poor outcome. Further, despite being a hospital-based study, our cohort included patients with varying severity of COVID-19, including mild cases since government guidelines during the study period enabled admission of relatively stable patients as well. We found an admission NEWS 2 score of 5 and above to be better than 6 in contrast to Myrstad et al. At this cut-off, AUC for predicting poor outcomes was high (0.887; 95% CI: 0.847–0.927) and higher than the AUC for score of 6 found by Myrstad et al [16].

Carr and others studied modified versions of NEWS2 score by adding age and a set of other routine blood tests at admission to discriminate severe COVID-19 disease at 14 days [19]. While the former model had poor-to-moderate discrimination, the latter model was affected by calibration issues at different study sites. Adding pre-existing co-morbidities to the model made no difference to risk prediction either. This suggests that adding age or co-morbidities to the NEWS2 score adds little to improve its value, justifying our decision to test the standard score. Further, we found it useful for a longer-term outcome of 28 days.

A retrospective study on 296 hospitalised adults with COVID-19 from a single centre in UK found that NEWS2 score of 5 or more anytime during stay predicted the occurrence of deterioration with a sensitivity and specificity of 0.98 (95% CI 0.96–1.00) and 0.28 (95% CI 0.21–0.35) respectively [20], emphasising its utility in longitudinal monitoring of COVID-19 patients as well. However, the caveat is a high false alarm rate. A larger study that evaluated performance of NEWS and NEWS2 scores among five admission cohorts also demonstrated good discrimination for death or ICU admission within 24 hours for patients with COVID-19 [21]. These results also suggest that NEWS2 score may be used without any modifications in COVID-19 settings.

Marta et al determined the value of NEWS 2 score at admission among 477 in-patients with COVID-19 in predicting in-hospital mortality. Their findings were similar to this study with a score more than 5 being the best cut-off [AUC—0.84 (95% CI 0.79–0.90)] [22]. The in-hospital mortality was higher (11.5%) than our study. On the other hand, ROX index, a simple score based on oxygenation and respiratory rate outperformed NEWS2 score in terms of predicting deterioration in COVID-19 patients [23]. These results however need to be interpreted with caution in view of retrospective design and use of a convenience sample. Another recent study that compared index NEWS and NEWS2 scores found low discrimination for COVID-19 versus non-COVID-19 patients; however, there was higher risk of mortality for COVID-19 patients than non-COVID-19 patients for each value of the admission NEWS2 score [24].

In this study, we also determined associations between potential risk factors and poor outcomes. Univariate analysis yielded 18 variables with possible significant association with risk of mechanical ventilation or death. However, after multivariate analysis only age above 60 years, baseline pulse, oxygen saturation, need for supplemental oxygen and admission chest X ray evidence of ARDS remained significantly associated. Several laboratory parameters found in other studies as poor prognostic indicators including D-dimer, serum Ferritin and CRP were not associated with risk of poor outcome in this study. This could be related to the heterogeneity in outcomes chosen in different studies as well as the high prevalence of co-morbidities like diabetes and hypertension in our population in general with rise in metabolic syndrome and lifestyle diseases.

External validity of our results may be limited by the single centre nature of this study. Further some of the co-morbidities were determined based on history such as diabetes since variables like glycated haemoglobin were not available for all patients. However, the large sample size and the inclusion of patients with all grades of severity of COVID-19 increases generalisability. Moreover, to the best of our knowledge, this is the first large study from India on the usefulness of NEWS2 score in COVID-19 patients. Being a simple scoring system based on physiological variables that can easily be determined even in remote healthcare settings, it could help doctors identify patients at risk of worsening.

Conclusion

NEWS2 score of 5 or more at admission predicts mortality and need for mechanical ventilation in COVID-19 patients with a high sensitivity of 93.3% and reasonable specificity of 70.7%. ARDS at admission is strongly associated with risk of poor outcomes; older age, low baseline oxygen saturation and need for supplemental oxygen being other risk factors. Patients with these features must be monitored intensively to detect worsening earlier and institute evidence-based measures available. This score must be further validated in Indian settings on a larger scale and put to use especially in resource poor settings to identify patients at need for referral to tertiary care centres or transfer to intensive care units.

Sensitivity and specificity of NEWS2 scores of 5 and 6 in predicting poor outcomes (death or need for mechanical ventilation) among COVID-19 patients.

(DOCX)

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Raw data sheet of the study with identifying information removed.

(XLSX)

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26 Aug 2021

PONE-D-21-23108

National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in India

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Reviewer #1: The current study has used NEWS2 score while hospital admission of the patients to predict the poor outcome in the COVID19 hospitalization case. They have compared score of 5 (or more) and score of 6 (or more) to predict that score of 5 or more to be more accurate in predicting the poor outcome. Using univariate analysis this study found that around 18 factors associated with the poor outcome, whereas, multiple regression analysis showed that only 5 factors are associated with poor outcome. Authors also claimed the study to be first of its kind in India. There are different studies, which also cited the use of NEWS2 score either 5 (or more) or 6 (or more) to predict poor outcomes. Most of these studies are retrospective in nature, whereas the current study is perspective in nature. Although I found the study interesting, I have few queries that may be explained by the authors.

1. One of the major claim by the study is that a NEWS2 score of 5 or more can predict the poor outcome in the case of COVID19 patients better than the NEWS2 score of 6. There is no comparison between score 5 and 6 mentioned in the result section of this study. If the authors claiming so, need to give a comparison between both the scores showing how one is better than other. “The baseline NEWS2 score was 5 or more in 136 of the 399 (34.1%) patients while it was 6 or more in 115 of the 399 (28.8%) patients.” How many poor outcomes were there in 21 extra patients that was identified by score 5? Can this score be used by the doctors for taking any decision?

2. The result section is poorly mentioned and can be elaborated to make it easily understandable for the readers.

3. Why the odds ratio in the case of ARDs on Chest X-ray is so high, how to explain it? Can the authors describe how they analysed the odds ratio in the result section?

4. There are several studies that suggest that one or more comorbidity condition leads to higher rate of poor outcome. However, this study found that co-morbidities is not associated with the poor outcomes. How authors will explain this? Can they provide any further supporting evidence in this regard?

5. Table 4: Can the authors add one more column to show the %age of each variables in total population. It will be easier for the readers to read and compare the data.

Reviewer #2: The manuscript PNE - E-21-23108 is an attempt to predict poor outcome in hospitallised COVID-19 patients in India using the NEWS-2 scoring system .

This is a good attempt at using the NEWS-2 score in a study over 399 patients with 50% of the patients being diabetics and another 44 % of the patents being hypertensive.

The following observations are made regarding the manuscript :

1. The score of 5 has been used to predict the prognosis in the patients. The authors have also stated that the AUC for a score of 5 was better than 6. How have the authors substantiated this finding ? Details may be sent

2. Other co-morbidities have also been stated in the results and discussion section. Will the inclusion of these as stated for Fig-2 and table -5 change the score to more than 5 as a predictor of poor outcome on THE NEWS-2 scoring system

3. The authors have not included laboratory parameters for the NEWS-2 score. How would the NEWS-2 score compare with another scoring system that combines laboratory with clinical parameters for predicting poor outcome

4. The conclusion is withered and needs to be toned based on the definite results obtainedin the study

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Reviewer #1: No

Reviewer #2: No

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9 Sep 2021

Reviewer 1

Reviewer’s comments

1. One of the major claim by the study is that a NEWS2 score of 5 or more can predict the poor outcome in the case of COVID19 patients better than the NEWS2 score of 6. There is no comparison between score 5 and 6 mentioned in the result section of this study. If the authors claiming so, need to give a comparison between both the scores showing how one is better than other. “The baseline NEWS2 score was 5 or more in 136 of the 399 (34.1%) patients while it was 6 or more in 115 of the 399 (28.8%) patients.” How many poor outcomes were there in 21 extra patients that was identified by score 5? Can this score be used by the doctors for taking any decision?

Response: We thank the reviewer for this observation. We have now included the comparison of diagnostic accuracy measures of the two cut-offs (5 and 6). This has been added as a table in the manuscript.

The sensitivity of score 5 or more was 93.3% compared to 90% for score of 6 or more. Since the score is proposed to be used as a screening tool for identifying patients who are likely to have poor outcomes, we chose the score cut off with higher sensitivity.

This cut off however, identified one poor outcome (death) among the 21 patients.

We believe that when used in the real life setting where case numbers are very high, this might still be relevant.

2. The result section is poorly mentioned and can be elaborated to make it easily understandable for the readers.

Response: We have elaborated as suggested to make it more understandable

3. Why the odds ratio in the case of ARDs on Chest X-ray is so high, how to explain it? Can the authors describe how they analysed the odds ratio in the result section?

Response: Of the 399 patients in the study, poor outcomes (death or ventilation) was noted in 30.

8 of the 30 who had poor outcomes had ARDS; only 3 out of the 369 who had good outcomes had ARDS.

This considerable difference is the possible reason for the very high Odds Ratio.

We have described this in results section.

4. There are several studies that suggest that one or more comorbidity condition leads to higher rate of poor outcome. However, this study found that co-morbidities is not associated with the poor outcomes. How authors will explain this? Can they provide any further supporting evidence in this regard?

Response: We agree that several studies have identified such association with co-morbidities.

The absence of association in our case could be due to the outcomes chosen. We have included both mechanical ventilation and death as poor outcomes since we thought these are important outcomes in hospitalised patients.

Different studies have used various outcomes (such as severe disease or death alone) explaining the diversity in results.

The other explanation is the high prevalence of co-morbidities in the general population in this area. Many studies have shown that diabetes and hypertension are highly prevalent in general population in India, especially South India.

In this study, overall around 50% of patients were diabetic and 44% hypertensive. Therefore it was unable to detect any significant difference between those with and without poor outcomes.

5. Table 4: Can the authors add one more column to show the %age of each variables in total population. It will be easier for the readers to read and compare the data.

Response: This column has been added and table is now modified as table 5; however since there are few continuous variables, these could not be represented in percentages.

Reviewer 2

Reviewer’s comments

1. The score of 5 has been used to predict the prognosis in the patients. The authors have also stated that the AUC for a score of 5 was better than 6. How have the authors substantiated this finding ? Details may be sent

Response: We thank the reviewer for this observation.

We have now included the comparison of diagnostic accuracy measures of the two cut-offs (5 and 6). This has been added as a table (Table 4) in the manuscript.

The sensitivity of score 5 or more was 93.3% compared to 90% for score of 6 or more. Since the score is proposed to be used as a screening tool for identifying patients who are likely to have poor outcomes, we chose the score cut off with higher sensitivity.

2. Other co-morbidities have also been stated in the results and discussion section. Will the inclusion of these as stated for Fig-2 and table -5 change the score to more than 5 as a predictor of poor outcome on THE NEWS-2 scoring system

Response: The standard NEWS2 score is validated only for the clinical variables included and does not include any of the co-morbidities like hypertension, diabetes etc. therefore we could not include them into the score.

Further, studies have shown that adding pre-existing co-morbidities to the model made no difference to risk prediction. This has been mentioned in discussion section.

3. The authors have not included laboratory parameters for the NEWS-2 score. How would the NEWS-2 score compare with another scoring system that combines laboratory with clinical parameters for predicting poor outcome

Response: The NEWS2 score is a clinical bedside scoring that includes only the respiratory rate, oxygen saturation (SpO2), need for supplemental oxygen, pulse rate, level of consciousness and temperature.

Besides the objective of our study was to determine the ability of NEWS2 score to predict poor outcomes and therefore we did not compare it with any other scores that use laboratory parameters. The advantage of NEWS2 score is that since it does not include laboratory parameters, it can be used readily in resource poor and emergency settings as well.

4. The conclusion is withered and needs to be toned based on the definite results obtained in the study

Response: Thank you for the observation. We have modified the conclusion in alignment with the results of the study.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

20 Oct 2021

PONE-D-21-23108R1National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in IndiaPLOS ONE

Dear Dr. Basheer,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please revise.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In the revised version still the result section is not elaborated. Thus it is difficult to understand. How the sensitivity and specificity were determined? Can the authors describe the calculations in the method section? How the sensitivity and specificity was calculated for the poor outcome? What are their significance? Can authors describe this in the result section? Line 160-168 needs more clarifications.

How the Odd ratio was calculated (line 192) is not clear. Can the authors describe it in the method section or in the result section?

Legend for both the figures should be mentioned to understand the figures.

The explanation for Figure 2 is not clear. What the AUC is mentioning here? Is it for score 5 or 6?

Line 231: been a tough ask to predict: change ask to task

As the current study did not find any association of the comorbidity with the poor outcomes, a statement should be made in the discussion section.

The conclusion can be more elaborative, should present numbers. This will help the readers to understand the concluding remarks of the article.

Reviewer #2: The previous comments from the reviewer have been addressed fully and completely. The manuscript has been modified to read the same

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10 Nov 2021

1. In the revised version still the result section is not elaborated. Thus it is difficult to understand

We regret that the previous revision failed to improve understanding.

We have added more details on number of patients who had score of 5 and above and went on to develop poor outcomes. Similar description has been given for score of 6 and above. We hope these will improve the understanding of the section.

Please see:

Lines 166 and 167

Lines 176 to 180

Lines 183 to 185

2. How the sensitivity and specificity were determined? Can the authors describe the calculations in the method section? How the sensitivity and specificity was calculated for the poor outcome? What are their significance? Can authors describe this in the result section?

Sensitivity refers to a test's ability to designate an individual with disease as positive. In this study it meant the ability of NEWS2 score to identify the proportion of people with the disease who will have a poor outcome. A highly sensitive test means that there are few false negative results, and thus fewer cases of disease are missed and is good at including most people who have the condition.

In this study, in order to calculate the sensitivity of a score of 5, we determined the proportion of patients with a baseline score of 5 and above who developed poor outcome. Poor outcome was defined as death or mechanical ventilation (mentioned in methods). Similarly, sensitivity was calculated for score of 6.

On the other hand, Specificity refers the ability of a test to correctly identify people without the disease. In this study it meant the ability of NEWS2 score to identify the proportion of people with the disease who will have a better outcome.

Hence in this study, in order to find the specificity of score of 5, we determined the proportion of patients with a baseline score of 5 and above who did not develop a poor outcome (or had good outcome). Same method was used for specificity of score 6.

We have added this explanation in the methods section and also elaborated the calculation in results and added supplementary tables (S2 tables).

Please see:

Lines 183 and 184.

3. Line 160-168 needs more clarifications

In this study Table 4 and lines 160 – 166 explains about best cut off for the NEWS2 score.

We have now added descriptions of how the calculations were made.

Please see:

Lines 176 to 180

Lines 184 and 185

Supplementary tables 1 and 2

4. How the Odd ratio was calculated (line 192) is not clear. Can the authors describe it in the method section or in the result section?

The odds ratio quantifies the relationship between an exposure and a disease and tells us how much higher the odds of exposure are among cases than among controls. In this study those who need for mechanical ventilation or death within 28 days (i.e., Poor outcome) are considered as the case-patients and patients discharged (i.e., Good outcome) are considered as the control-patients. Exposure status is defined as findings of the Chest X-ray viz., ARDS and no ARDS. Logistic regression, a standard technique to calculate odds ratio, was employed to quantify the relationship between Chest X-ray and poor outcome and expressed as Odds ratio with 95% Confidence intervals and p values.

This information has been added in methods section covering Odds ratio calculation for various variables.

Please see:

Lines 122 to 130

5. Legend for both the figures should be mentioned to understand the figures.

The explanation for Figure 2 is not clear. What the AUC is mentioning here? Is it for score 5 or 6?

Legend for figure 1 has been elaborated.

Lines 105 and 106

Regarding Legend for figure 2:

The Area Under the Curve (AUC) is the area under the receiver operating characteristic curve (ROC curve). It is a number between zero and one, because the ROC curve fits inside a unit square. In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding. Thus higher the AUC, the better the performance of the model at distinguishing between the positive and negative cases. In this study the AUC to predict poor outcome i.e., need for mechanical ventilation or death within 28 daysbased on NEWS2 score at admission was 0.887 which is considered excellent.

The sensitivity and specificity can be calculated for each cut off value of the test score and it does not depend upon AUC. In other words, AUC is not for a specific cut off score. The typo error “for this cut off” has therefore been removed since this is not applicable for AUC.

We regret the confusion caused by the previous wording.

Please see:

Line 187 and 188.

6. Line 231: been a tough ask to predict: change ask to task

We thank the reviewer for identifying this mistake. We have now changed it.

Please see:

Line 273

7. As the current study did not find any association of the comorbidity with the poor outcomes, a statement should be made in the discussion section.

We thank you for this suggestion.

In response to a similar suggestion in the first review, we have already added this in the discussion.

Please see:

Line 338 to 343.

8. The conclusion can be more elaborative, should present numbers. This will help the readers to understand the concluding remarks of the article

We thank you for the valuable suggestion. We have now added the numbers pertaining to sensitivity and specificity which are the primary outcomes of the study. We have not included more details as it may be a repetition of results.

Please see:

Line 354 to 356

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

1 Dec 2021

National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in India

PONE-D-21-23108R2

Dear Dr. Basheer,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Robert Jeenchen Chen, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In the revised manuscript the authors have addressed all the queries raised during previous revision.

Reviewer #3: The large sample size play a major determinant for your study to be accepted.

Relating COVID cases with simple, doable scores for every centres is important especially with high patients load.

My suggestion is to only include significant and non significant but important factors for table 5 & 6

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

3 Dec 2021

PONE-D-21-23108R2

National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in India

Dear Dr. Basheer:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Robert Jeenchen Chen

Academic Editor

PLOS ONE