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VOLUME 28 , ISSUE 5 ( May, 2024 ) > List of Articles


Modified NUTRIC Score as a Predictor of All-cause Mortality in Critically Ill Patients: A Systematic Review and Meta-analysis

Jay Prakash, Saket Verma, Priyanka Shrivastava, Khushboo Saran, Archana Kumari, Kunal Raj, Amit Kumar, Hemant N Ray, Pradip K Bhattacharya

Keywords : Critically ill patients, Meta-analysis, Modified NUTRIC score, Mortality prediction, Systematic review

Citation Information : Prakash J, Verma S, Shrivastava P, Saran K, Kumari A, Raj K, Kumar A, Ray HN, Bhattacharya PK. Modified NUTRIC Score as a Predictor of All-cause Mortality in Critically Ill Patients: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2024; 28 (5):495-503.

DOI: 10.5005/jp-journals-10071-24706

License: CC BY-NC 4.0

Published Online: 30-04-2024

Copyright Statement:  Copyright © 2024; The Author(s).


Purpose: The purpose of our meta-analysis was to look at the impact of modified nutrition risk in the critically ill (mNUTRIC) on mortality in patients with critical illness. Materials and methods: Literature relevant to this meta-analysis was searched in PubMed, Web of Science, and Cochrane Library till 26 August 2023. Prospective or retrospective studies, patients >18 years of age, studies that reported on mortality and mNUTRIC (mNUTRIC cut-off score) were included. The QUIPS tool was used to evaluate the risk for bias in prognostic factors. Results: A total of 31 studies on mNUTRIC score, involving 13,271 patients were included. The summary area under the curve (sAUC) of 0.80 (95% CI: 0.76–0.83) illustrates the mNUTRIC score's strong discrimination. The pooled sensitivity was 0.79 (95% CI: 0.74–0.84) and pooled specificity was 0.68 (95% CI: 0.63–0.73). We found no discernible variation in the mNUTRIC's prediction accuracy among cut-off values of <5 and >5 in our subgroup analysis and sAUC values were 0.82 (95% CI: 0.78–0.85) and 0.78 (95% CI: 0.74–0.81), respectively. Conclusion: We observed that mNUTRIC can discriminate between critically ill individuals and predict their mortality.

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