KSCCM
Search
- Page Path
- HOME > Search
Original Article
- Nutrition
- Association of nutrition risk screening 2002 and Malnutrition Universal Screening Tool with COVID-19 severity in hospitalized patients in Iran
- Ghazaleh Eslamian, Sohrab Sali, Mansour Babaei, Karim Parastouei, Dorsa Arman Moghadam
- Acute Crit Care. 2022;37(3):332-338. Published online July 5, 2022
- DOI: https://doi.org/10.4266/acc.2021.01830
- 2,815 View
- 179 Download
- 2 Web of Science
- 2 Crossref
-
Abstract
PDF - Background
Malnutrition affects normal body function and is associated with disease severity and mortality. Due to the high prevalence of malnutrition reported in patients with coronavirus disease 2019 (COVID-19), the current study examined the association between malnutrition and disease severity in hospitalized adult patients with COVID-19 in Iran.
Methods
In this prospective observational study, 203 adult patients with COVID-19 verified by real-time polymerase chain reaction test and chest computed tomography were recruited from those admitted to a university hospital in Iran. To determine COVID-19 intensity, patients were categorized into four groups. Malnutrition assessment was based on the Malnutrition Universal Screening Tool (MUST) and nutrition risk screening score (NRS-2002). An ordinal regression model was run to assess the association between malnutrition and disease severity.
Results
In the studies sample of Iranian patients with COVID-19, 38.3% of patients had severe COVID-19. According to NRS-2002, 12.9% of patients were malnourished. Based on MUST, 2% of patients were at medium, and 13.4% of patients were at high risk of malnutrition. Malnutrition was associated with a higher odds of extremely severe COVID-19 according to NRS-2002 (odds ratio, 1.38; 95% confidence interval, 0.21–2.56; P=0.021).
Conclusions
Malnutrition was not prevalent in the studies sample of Iranian patients with COVID-19; however, it was associated with a higher odds of extremely severe COVID-19. -
Citations
Citations to this article as recorded by
- Computational methods for studying relationship between nutritional status and respiratory viral diseases: a systematic review
Zakir Hussain, Malaya Dutta Borah, Rezaul Karim Ahmed
Artificial Intelligence Review.2024;[Epub] CrossRef - Predictive value of multiple variable models including nutritional risk score (NRS 2002) on mortality and length of stay of patients with covid-19 infections. The INCOVO study
Ghadamieh Fatemeh, Ginette Fotsing, Pedro Marques-Vidal, Peter Kopp, Mohammed Barigou
Clinical Nutrition ESPEN.2023; 55: 357. CrossRef
- Computational methods for studying relationship between nutritional status and respiratory viral diseases: a systematic review
First
Prev
