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Early Warning Scores at Time of ICU Admission to Predict Mortality in Critically Ill COVID-19 Patients

Published online by Cambridge University Press:  18 June 2021

Asha Tyagi
Affiliation:
Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
Surbhi Tyagi
Affiliation:
Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
Ananya Agrawal
Affiliation:
Hamdard Institute of Medical Sciences & Research, New Delhi, India
Aparna Mohan
Affiliation:
Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
Devansh Garg*
Affiliation:
Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
Rashmi Salhotra
Affiliation:
Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
Ashok Kumar Saxena
Affiliation:
Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
Ashish Goel
Affiliation:
Department of Medicine, University College of Medical Sciences & GTB Hospital, Delhi, India
*
Corresponding author: Devansh Garg, Email: [email protected].

Abstract

Objective:

To assess ability of National Early Warning Score 2 (NEWS2), systemic inflammatory response syndrome (SIRS), quick Sequential Organ Failure Assessment (qSOFA), and CRB-65 calculated at the time of intensive care unit (ICU) admission for predicting ICU mortality in patients of laboratory confirmed coronavirus disease 2019 (COVID-19) infection.

Methods:

This prospective data analysis was based on chart reviews for laboratory confirmed COVID-19 patients admitted to ICUs over a 1-mo period. The NEWS2, CRB-65, qSOFA, and SIRS were calculated from the first recorded vital signs upon admission to ICU and assessed for predicting mortality.

Results:

Total of 140 patients aged between 18 and 95 y were included in the analysis of whom majority were >60 y (47.8%), with evidence of pre-existing comorbidities (67.1%). The most common symptom at presentation was dyspnea (86.4%). Based upon the receiver operating characteristics area under the curve (AUC), the best discriminatory power to predict ICU mortality was for the CRB-65 (AUC: 0.720 [95% confidence interval [CI]: 0.630-0.811]) followed closely by NEWS2 (AUC: 0.712 [95% CI: 0.622-0.803]). Additionally, a multivariate Cox regression model showed Glasgow Coma Scale score at time of admission (P < 0.001; adjusted hazard ratio = 0.808 [95% CI: 0.715-0.911]) to be the only significant predictor of ICU mortality.

Conclusions:

CRB-65 and NEWS2 scores assessed at the time of ICU admission offer only a fair discriminatory value for predicting mortality. Further evaluation after adding laboratory markers such as C-reactive protein and D-dimer may yield a more useful prediction model. Much of the earlier data is from developed countries and uses scoring at time of hospital admission. This study was from a developing country, with the scores assessed at time of ICU admission, rather than the emergency department as with existing data from developed countries, for patients with moderate/severe COVID-19 disease. Because the scores showed some utility for predicting ICU mortality even when measured at time of ICU admission, their use in allocation of limited ICU resources in a developing country merits further research.

Type
Brief Report
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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References

WHO. COVID-19 Clinical management. Living guidance. 25 January 2021. https://apps.who.int/iris/bitstream/handle/10665/338882/WHO-2019-nCoV-clinical-2021.1-eng.pdf. Accessed February 19, 2021.Google Scholar
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(1):66. doi: 10.1186/s13049-020-00764-3 CrossRefGoogle ScholarPubMed
Hu, H, Yao, N, Qiu, Y. Predictive value of 5 early warning scores for critical COVID-19 patients. Disaster Med Public Health Prep. 2020:1-8. doi: 10.1017/dmp.2020.324 Google Scholar
Covino, M, Matteis, GD, Burzo, ML, et al. Predicting in-hospital mortality in COVID-19 older patients with specifically developed scores. J Am Geriatr Soc. 2021;69(1):37-43. doi: 10.1111/jgs.16956 CrossRefGoogle ScholarPubMed
Hu, H, Yao, N, Qiu, Y. Comparing rapid scoring systems in mortality prediction of critically ill patients with novel coronavirus disease. Acad Emerg Med. 2020;27(6):461-468. doi: 10.1111/acem.13992 CrossRefGoogle ScholarPubMed
Jang, JG, Hur, J, Hong, KS, et al. 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. doi: 10.3346/jkms.2020.35.e234 CrossRefGoogle ScholarPubMed
Singer, M, Deutschman, CS, Seymour, CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810. doi: 10.1001/jama.2016.0287 CrossRefGoogle ScholarPubMed
No Authors Listed. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med. 1992;20(6):864-874.CrossRefGoogle Scholar
Lim, WS, van der Eerden, MM, Laing, R, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377-382. doi: 10.1136/thorax.58.5.377 CrossRefGoogle ScholarPubMed
Fan, J, Upadhye, S, Worster, A. Understanding receiver operating characteristic (ROC) curves. CJEM. 2006;8(1):19-20. doi: 10.1017/s1481803500013336 CrossRefGoogle ScholarPubMed
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. Crit Care Med. 2020;48(11):e1004-e1011. doi: 10.1097/CCM.0000000000004549 CrossRefGoogle Scholar