Author(s):
Monica D. Silva, Mary J.
Email(s):
monicadsilva2025@gmail.com
DOI:
10.52711/2349-2996.2026.00022 
Address:
Monica D. Silva, Mary J.
Nursing Tutor, Department of Medical Surgical Nursing, St. Martha’s College of Nursing, No.5, Nrupathunga Road, St. Martha’s Hospital, Bangalore - 560001, Karnataka, India.
*Corresponding Author
Published In:
Volume - 16,
Issue - 2,
Year - 2026
ABSTRACT:
Background: Nutritional risk is a key determinant of outcomes in critically ill patients, with malnutrition contributing to increased morbidity, mortality, and ICU stay. Traditional tools are often impractical in ICU settings. The modified NUTRIC (mNUTRIC) score provides a validated approach for assessing nutritional risk and guiding timely interventions. Objective: To assess the association between mNUTRIC scores and clinical outcomes in critically ill patients admitted to the Critical Care Unit (CCU) of Holy Family Hospital, New Delhi. Methods: A prospective correlational study was conducted on 100 adult CCU patients admitted for over 72 hours. Data on demographic and clinical variables, mNUTRIC scores, and outcomes—duration of mechanical ventilation, vasopressor use, renal replacement therapy (RRT), CCU/hospital stay, and 30-day mortality—were analyzed using correlation, t-tests, and chi-square tests. Results: Higher mNUTRIC scores were significantly associated with prolonged mechanical ventilation, increased vasopressor use, and extended CCU stay (p<0.05). No significant association was observed with RRT or total hospital stay. Thirty-day mortality was markedly higher in high-score patients (23.9%) versus none in the low-score group (p=0.002). Non-survivors had significantly higher mNUTRIC scores (8.19±0.54) than survivors (5.06±1.56) (p<0.001). Factors like advanced age, enteral feeding, recent weight loss, reduced intake, higher APACHE II/SOFA scores, and shock were associated with elevated nutritional risk. Conclusion: The mNUTRIC score is an effective tool for early identification of nutritional risk and mortality in critically ill patients, supporting timely nutritional intervention to improve outcomes.
Cite this article:
Monica D. Silva, Mary J. Assessment of Relationship with mNUTRIC Score and Clinical outcome of Critically ill patients in a selected Hospital, New Delhi. Asian Journal of Nursing Education and Research. 2026;16(2):107-2. doi: 10.52711/2349-2996.2026.00022
Cite(Electronic):
Monica D. Silva, Mary J. Assessment of Relationship with mNUTRIC Score and Clinical outcome of Critically ill patients in a selected Hospital, New Delhi. Asian Journal of Nursing Education and Research. 2026;16(2):107-2. doi: 10.52711/2349-2996.2026.00022 Available on: https://www.ajner.com/AbstractView.aspx?PID=2026-16-2-7
REFERENCES:
1. Correia MITD, Waitzberg DL. The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clin Nutr. 2003;22(3):235-239.
2. Rahman A, Hasan RM, Agarwala R, Martin C, Day AG, Heyland DK. Identifying critically-ill patients who will benefit most from nutritional therapy: further validation of the "modified NUTRIC" nutritional risk assessment tool. Clin Nutr. 2016; 35(1): 158-162.
3. Lew CCH, Yandell R, Fraser RJ, Chua AP, Chong MF, Miller M. Association between malnutrition and clinical outcomes in the intensive care unit: A systematic review. JPEN J Parenter Enteral Nutr. 2017; 41(5): 744-758.
4. Heyland DK, Dhaliwal R, Jiang X, Day AG. Identifying critically ill patients who benefit the most from nutrition therapy: the development and initial validation of a novel risk assessment tool. Crit Care. 2011; 15(6): R268.
5. Kondrup J, Allison SP, Elia M, Vellas B, Plauth M. ESPEN guidelines for nutrition screening 2002. Clin Nutr. 2003; 22(4): 415-421.
6. Zhang Z, Xu X, Ni H. Score for the assessment of nutrition risk in critically ill patients is associated with increased mortality in neurocritical care patients. JPEN J Parenter Enteral Nutr. 2015; 39(1): 62-68.
7. Chen Y, Ye S, Zhou Y, Fu Z, Guo J. Modified NUTRIC score is an effective tool to predict mortality in patients with severe acute pancreatitis. Clin Nutr. 2020; 39(10): 2905-2910.
8. Acehan S, Akin S, Sertdemir I, Yildiz H. Prognostic value of nutritional risk tools in ICU patients with pneumonia: A comparison of mNUTRIC and NRS-2002. Clin Nutr ESPEN. 2022;49:202-208.
9. Leoni S, Masetti G, Consales G, Busani S, Venturelli C, Girardis M. The Modified Nutrition Risk in Critically Ill Score (mNUTRIC) is associated with 28-day mortality in critically ill COVID-19 patients: A prospective observational study. Clin Nutr. 2021; 40(5): 2454-2458.
10. Agarwal A, Shukla S, Singh A. Nutrition in critical care in Indian scenario: Present status and future direction. Indian J Crit Care Med. 2017; 21(3): 122-125.
11. Barazzoni R, Bischoff SC, Krznaric Z, Pirlich M, Singer P; endorsed by the ESPEN Council. ESPEN expert statements and practical guidance for nutritional management of individuals with SARS-CoV-2 infection. Clin Nutr. 2020; 39(6): 1631-1638.
12. FAO, IFAD, UNICEF, WFP, WHO. The State of Food Security and Nutrition in the World 2023: Urbanization, agrifood systems transformation and healthy diets across the rural–urban continuum. Rome: FAO; 2023. Available from: https://doi.org/10.4060/cc3017en
13. WHO. Obesity and Overweight: Key Facts. World Health Organization; 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
14. Tripathy S, Mishra JC, Dash SC. Nutritional risk assessment in critically ill patients using modified NUTRIC score. Indian J Crit Care Med. 2018; 22(4): 263–266. doi:10.4103/ijccm.IJCCM_474_17
15. Allard JP, Keller H, Teterina A, Jeejeebhoy KN, Laporte M, Duerksen DR, et al. Malnutrition at hospital admission—Contributors and effect on length of stay: A prospective cohort study from the Canadian Malnutrition Task Force. JPEN J Parenter Enteral Nutr. 2016; 40(4): 487–497. doi:10.1177/0148607114567902
16. Chen Y, Tang Y, Zeng J, Cai D, Duan Y, Fan J. Predictive value of the modified NUTRIC score for mortality in patients with severe acute pancreatitis. Clin Nutr ESPEN. 2021; 41: 173–178. doi:10.1016/j.clnesp.2021.01.026
17. Kalaiselvan MS, Renuka MK, Arunkumar AS. NUTRIC score for nutritional risk in ventilated patients. Indian J Crit Care Med. 2017; 21(5): 253–6. doi:10.4103/ijccm.IJCCM_24_17
18. Reddy S, Banerjee A, Jha V, Mehta K. Role of mNUTRIC score in predicting nosocomial infections and length of ICU stay in critically ill patients. Int J Clin Nutr Crit Care. 2025; 12(2): 41–47.
19. Gupta S, Thakur S, Agrawal A. Comparison of APACHE II, SOFA, and mNUTRIC scores for predicting mortality and need for mechanical ventilation in ICU patients. Indian J Crit Care Med. 2020; 24(10): 929–33. doi:10.5005/jp-journals-10071-23562.
20. Patel R, Singh M, Thomas P, Rani R. Clinical utility of mNUTRIC score in predicting ICU outcomes: A multicenter prospective study. Indian J Clin Nutr. 2024; 11(1): 25–30.
21. Hang W, Wang X, Xue R, Zhang Y, Liu D. The mNUTRIC score predicts clinical outcomes in critically ill patients: A retrospective study. BMC Anesthesiol. 2024; 24: 50. doi:10.1186/s12871-024-0286
22. Abu-Shaheen AK, Ameen NA, Nazer RI. Nutritional risk and outcomes among mechanically ventilated critically ill patients in Jordan. Int J Gen Med. 2021;14:8033–41. doi:10.2147/IJGM.S334706.
23. Almutairi MM, Alshamrani SM, Alkhathlan B, Alharbi N, Al-Mazrou A, Alamri AA, et al. The impact of mNUTRIC score on 30-day mortality among critically ill patients with COVID-19 in Saudi Arabia. Ann Saudi Med. 2022; 42(3): 236–42. doi:10.5144/0256-4947.2022.236.
24. Mahmoodpoor A, Sanaie S, Sarfaraz T, et al. Prognostic value of mNUTRIC. BMC Anesthesiol. 2023; 23(1): 131. doi:10.1186/s12871-023-02086-0
25. Sharma K, Gupta A, Yadav R. Association of mNUTRIC score with clinical outcomes in critically ill patients in a tertiary care hospital in North India. Indian J Crit Care Med. 2022; 26(8): 932–937. doi:10.5005/jp-journals-10071-24216
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