BACKGROUND OF THE STUDY:

Optimal nutrition is vital for maintaining physiological, cognitive and immune function, and its imbalance is linked to poor outcomes in critically ill patients.¹ In CCUs, malnutrition increases morbidity, mortality, ventilation duration and ICU stay.²^,³ Early nutritional risk detection is therefore crucial.Conventional tools like SGA, MNA and MUST are difficult to use in sedated or ventilated ICU patients.² To address this, Heyland et al. developed the NUTRIC score using age, comorbidities, pre-ICU stay, APACHE II, SOFA and IL-6.⁴ Because IL-6 testing is limited, the modified NUTRIC (mNUTRIC) score was introduced with similar predictive accuracy.²

Studies show that mNUTRIC ≥5 reliably predicts mortality across ICU populations, including neurocritical care,⁶ severe acute pancreatitis⁷ and COVID-19, where it outperforms NRS-2002, SOFA and APACHE II.⁸^,⁹

In India, where under- and overnutrition coexist and diet quality is constrained by socioeconomic factors, mNUTRIC provides a practical and cost-effective screening tool that may reduce ICU stay, mortality and healthcare costs.⁴^,¹¹

This study aims to assess the association between mNUTRIC score and clinical outcomes in critically ill patients in a New Delhi hospital.

NEED OF THE STUDY:

Malnutrition is a major driver of morbidity and mortality in critically ill patients. In India, this concern is heightened as 74% of people lack access to a healthy diet and 39% cannot afford nutrient-rich food.¹² Globally, both undernutrition and overnutrition remain significant, affecting 390 million underweight and 2.5 billion overweight adults, including 890 million who are obese.¹³

In the ICU, malnutrition affects 38%–78% of patients and is linked to prolonged ventilation, more infections, longer ICU stays and higher mortality.¹⁴ Older adults with inadequate protein intake during hospitalization also show increased mortality, emphasizing the need for timely assessment.¹⁵

The mNUTRIC score is a validated, practical tool for identifying nutritional risk, with scores ≥5 consistently associated with higher mortality and adverse outcomes.⁴^,² Early use of this score can guide nutritional interventions, improve survival and optimize ICU resource use.¹⁶

Thus, this study aims to assess the relationship between mNUTRIC score and clinical outcomes in critically ill patients, supporting improved risk stratification in critical care settings.

OBJECTIVES:

· To assess the relationship between mNUTRIC score and clinical outcomes of critically ill patients

· To determine the association of mNUTRIC score with sample characteristics and clinical parameters of critically ill patients

HYPOTHESIS:

· H1: There is a statistically significant relationship between the mNUTRIC score and clinical outcomes in critically ill patients (p < 0.05).

· H2: There is a statistically significant association between the mNUTRIC score and selected demographic and clinical variables in critically ill patients (p < 0.05).

Theoretical Frame work:

This study uses Ludwig von Bertalanffy’s modified General Systems Theory, consisting of input, throughput, and output phases. Inputs include patient demographics, clinical parameters, and mNUTRIC nutritional risk scores. Throughput involves assessing and processing nutritional risk in critical illness. Outputs are clinical outcomes such as CCU stay, mechanical ventilation, vasopressor use, and 30-day mortality. Feedback is not included.

METHODS:

Study Design:

A Quantitative prospective correlational design was used.

Setting and Participants:

The study was conducted in the CCU of Holy Family Hospital, New Delhi, from January to February 2025. The study included 100 critically ill patients. Inclusion criteria were adult patients (≥18 years) admitted to the CCU for more than 72 hours. Exclusion criteria included patients receiving palliative care, re-admitted cases, patients aged ≤18 years, and those with a CCU stay of less than 72 hours.

Data Collection:

Data collection utilized a self-developed structured questionnaire, record analysis, paper-based methods, and telephonic follow-up for 30-day mortality. Key assessments included baseline characteristics, clinical parameters within 24 hours of admission, and mNUTRIC scores within 48 hours of admission.

Statistical Analysis:

Statistical analysis was performed using IBM SPSS Statistics 20, employing descriptive statistics (mean, SD, percentage) and inferential statistics (t-tests, Karl Pearson correlation, and chi-square tests), with a significance threshold of p<0.05p

RESULTS:

Section I: Sample characteristics and Clinical Parameters:

Out of 100 patients, most critically ill patients were aged 61–75 years (37%) and male (63%), with common height (151–160 cm) and weight (60–70 kg). Overweight BMI (41%) predominated; 58% were vegetarians.

Table No 1 Frequency and percentage distribution of Clinical Parameters of critically ill patients n=100

Sl. No

Clinical parameters

Frequency (f)

Percentage (%)

Type of feeding

a. NPO

b. Oral

c. NG

d. TPN

47.0

34.0

17.0

2.0

Change in dietary intake in past 2 weeks (before admission)

a. No change in intake

b. Poor intake

c. Slight decrease of intake

d. Increase of intake

38.0

24.0

36.0

2.0

Weight loss over 3 months (before admission)

a. No weight loss

b. Weight loss from 1-3 kg

c. Weight more than 3 kg

d. Unknown

51.0

18.0

10.0

21.0

Comorbidity (choose more than one option)

a. Diabetes Mellitus

b. Obesity

c. Hypertension

d. Myocardial/ Vascular disorders/ACS/HF/CAD

e. Pneumonia/Pulmonary Koch's /ARDS

f. COPD / Emphysema / Asthma

g. AKI / CKD

h. Stroke / TIA / CVA/CVD

i. Seizure / Head Injury /CNS related disorders

j. Chronic Liver disease / GI disorders

k. Infection / Sepsis / Shock

l. Malignancy

42.0

18.0

69.0

20.0

31.0

20.0

44.0

8.0

9.0

6.0

26.0

5.0

Clinically, 47% were Nil Per Oral; 48% did not meet caloric/protein needs. Dietary intake declined in 60% of patients; 10% had >3 kg weight loss. Common comorbidities included hypertension (69%), CKD/AKI (44%), and diabetes (42%).

Table No 2 Frequency and percentage distribution of Clinical Parameters of critically ill patients n=100

Sl. No

Clinical parameters

Frequency (f)

Percentage

(%)

Recommended Calories kg/day received

a. Yes

b. No

52.0

48.0

Recommended Protein kg/day received

a. Yes

b. No

52.0

48.0

APACHE II score

a. ≤15

b. 15-<20

c. 20-<28

d. ≥28

14.0

23.0

34.0

29.0

SOFA score

a. <6

b. 6 - <10

c. ≥10

36.0

34.0

30.0

CRP level

a. <1 mg/L

b. 1-10 mg/L

c. 10-50 mg/L

d. >50 mg/L

13.0

41.0

43.0

3.0

SHOCK Sign (choose more than one option)

a. No Sign of shock

b. SBP < 90 mmHg, HR > 100 bpm, RR > 20 breaths/min

c. Decreased urine output

(< 0.5 mL/kg/hr)

d. Pale/cool skin and sweating, Capillary refill >3 seconds

e. Confusion, agitation, or loss of consciousness

38.0

62.0

18.0

30.0

18.0

APACHE II scores ≥28 were seen in 29%, and SOFA >10 in 30%, indicating high severity. Shock signs included hemodynamic instability (62%), poor perfusion (30%) and low urine output (18%).

Section II: Correlation Between mNUTRIC Score and Clinical Outcomes

Table: 3 Findings related to correlation between mNUTRIC score of critically ill patients with number of days on mechanical ventilator using Karl Pearson correlation n=100

Variables	Mean	SD	‘r’ Value	P value
mNUTRIC score	5.6	1.8	0.424*	<0.001
No of days on mechanical ventilator	5.4	5.6	0.424*	<0.001

*Significant at 0.05 level, p < 0.05 -Moderate positive correlation

Table 3 shows a moderate positive correlation between mNUTRIC score and ventilator days (r = 0.424, p < 0.001), indicating higher scores are linked to prolonged mechanical ventilation.

Table:4 Findings related to correlation between mNUTRIC score of critically ill patients with number of days on Vasopressor using Karl Pearson correlation n=100

Variables	Mean	SD	‘r’ Value	P value
mNUTRIC score	5.6	1.8	0.504*	<0.001
No of days on Vasopressor	3.3	3.1	0.504*	<0.001

*Significant at 0.05 level, p < 0.05 -Moderate positive correlation

Table 4 shows a moderate positive correlation between mNUTRIC score and vasopressor duration (r = 0.504, p < 0.001), indicating higher scores are linked to longer use.

Table No.5 Findings related to correlation between mNUTRIC score of critically ill patients with number of days on RRT (Renal Replacement Therapy) using Karl Pearson correlation n=100

Variables	Mean	SD	‘r’ Value	P value
mNUTRIC score	5.6	1.8	0.019**	0.851
No of days on RRT	2.6	3.6	0.019**	0.851

**Non-Significant at 0.05 level, p >0.05 - No correlation

Table 5 shows no significant correlation between mNUTRIC score and RRT duration (r = 0.019, p = 0.851).

Table No. 6 Findings related to correlation between mNUTRIC score of critically ill patients with Length of CCU stay using Karl Pearson correlation n=100

Variables	Mean	SD	‘r’ Value	P value
mNUTRIC score	5.6	1.8	0.262*	0.008
Length of CCU stay	11.1	5.2	0.262*	0.008

*Significant at 0.05 level, p < 0.05 - weak positive correlation

Table 6 shows a weak but significant positive correlation between mNUTRIC score and CCU stay (r = 0.262, p = 0.008), indicating higher scores are associated with longer stays.

Table No.7 Findings related to correlation between mNUTRIC score of critically ill patients with Length of Hospital stay using Karl Pearson correlation n=100

Variables	Mean	SD	‘r’ Value	P value
mNUTRIC score	5.6	1.8	0.133**	0.187
Length of hospital stay	16.0	6.5	0.133**	0.187

**Non-Significant at 0.05 level, p < 0.05 - No correlation

Table 7 shows no significant correlation between mNUTRIC score and hospital stay (r = 0.133, p = 0.187), indicating scores do not predict length of stay.

Table No 8 Mean, Mean Difference, Standard Deviation and t-value of mNUTRIC Score in Comparison Between survivors Vs non survivors Using Independent t-Test n=100

30 day Mortality

Mean

Mean Difference

t value

P value

Non Survivors

8.188

3.128

0.5439

7.89*

<0.001

Survivors

5.060

1.5628

*p<0.05 level of Significance

Table 8 shows non-survivors had higher mean mNUTRIC scores than survivors (8.19 ± 0.54 vs. 5.06 ± 1.56; t = 7.89, p < 0.001), indicating a strong link with 30-day mortality.

Figure No 1 : A bar chart showing the percentage distribution of 30 day mortality (Survivors Vs Non Survivors) of critically ill patients

Table No 9 Findings related to association Between Modified NUTRIC Score of Critically Ill Patients and 30 day Mortality Using Chi-Square n=100

mNUTRIC score	30 day Mortality		Chi-square Value	df	P value	Significance
	Non Survivors	Survivors
Low Score	0	33	9.382*	1	0.002	S
High Score	16	51

* p<0.05 level of Significance

Table 9 shows higher mNUTRIC scores were significantly associated with 30-day mortality (23.9% vs. 0%; χ² = 9.382, p = 0.002).

Section III

Table No.10 Findings related to Association between mNUTRIC score of critically ill patients with sample characteristics and clinical parameters using Chi Square n=100

Sample characteristics and Clinical Parameters		Modified NUTRIC score		Chi-square value	df	P value	Significance
Sample characteristics and Clinical Parameters		Low score	High score	Chi-square value	df	P value	Significance
Age (in years)	a. 18-40 b. 41-60 c. 61-75 d. >75	8 11 13 1	2 20 24 21	18.2*	3	<0.001	S
Gender	a. Male b. Female	20 13	43 24	0.121**	1	0.728	NS
BMI in kg/m²	a. Below 18.5 b. 18.5-24.9 c. 25.0-29.9 d. 30.0 and above	2 11 14 6	7 21 27 12	0.525**	3	0.913	NS
Diet	a. Vegetarian b. Non-Vegetarian c. Vegan	18 15 0	40 23 4	2.792**	2	0.248	NS
Type of feeding	a. NPO b. Oral c. NG d. TPN	9 18 5 1	38 16 12 1	10.554*	3	0.014	S
Weight loss over three months (before admission)	a. No weight loss b. Weight loss from 1-3 kg c. Weight more than 3 kg d. Unknown	24 5 1 3	27 13 9 18	10.500*	3	0.015	S
Comorbidity: Seizure / Head Injury /Other CNS disorders	a. Yes b. No	6 27	3 64	5.070*	1	0.024	S
Recommended Calories received	a. Yes b. No	25 8	27 40	11.138*	1	<0.001	S
Recommended Protein received	a. Yes b. No	25 8	27 40	11.138*	1	<0.001	S
APACHE II score	a. ≤15 b. 15-<20 c. 20-<28 d. ≥28	12 14 7 0	2 9 27 29	43.328*	3	<0.001	S
SOFA score	a. <6 b. 6-10 c. >10	21 11 1	15 23 29	22.398*	2	<0.001	S
CRP Level	a. <1 mg/L b. 1-10 mg/L c. 10-50 mg/L d. >50 mg/L	6 13 14 0	7 28 29 3	2.530**	3	0.470	NS
Signs of Shock: SBP < 90 mmHg, HR > 100 bpm, RR > 20 breaths/min	a. Yes b. No	13 20	49 18	10.684*	1	0.001	S

*S-Significant at 0.05 level, p < 0.05

**NS-Non-Significant at 0.05 level, p > 0.05

Table 10 shows higher mNUTRIC scores were significantly associated with age, feeding type, weight loss, CNS comorbidities, calorie/protein intake, APACHE II and SOFA scores, and shock (p ≤ 0.024), but not with gender, BMI, diet type, or CRP (p > 0.05), reflecting greater clinical severity and nutritional risk.

DISCUSSION:

This study confirms the prognostic value of the mNUTRIC score in critically ill patients. Higher scores were significantly associated with prolonged mechanical ventilation, increased vasopressor use, longer CCU stay, and higher 30-day mortality. These results are consistent with findings from Indian and international studies, including Kalaiselvan et al. (2017)¹⁷ and Reddy et al. (2025)¹⁸, who reported longer ICU stays and more nosocomial infections in high-score groups.

Our observation of prolonged mechanical ventilation in patients with elevated scores aligns with Gupta et al. (2020)¹⁹, Patel et al. (2024)²⁰, Zhang et al. (2024)²¹, and Abu-Shaheen et al. (2021)²². Similarly, higher mortality mirrors results from Rahman et al. (2016)², Almutairi et al. (2022)²³, Gupta et al. (2020)¹⁹, and Mahmoodpoor et al. (2023)²⁴. Increased vasopressor use also corresponds with findings from Sharma et al. (2022)²⁵ and Mahmoodpoor et al. (2023)²⁴. Overall, the mNUTRIC score reliably reflects illness severity and predicts adverse outcomes, supporting its incorporation into routine ICU assessment.

STRENGTHS AND LIMITATIONS OF THE STUDY:

Strengths:

Prospective design, standardized scoring tools (mNUTRIC, APACHE II, SOFA), and analysis of multiple clinically relevant outcomes strengthen the validity of results.

Limitations:

Single-center design, small sample size, short study duration, and unmeasured variations in ICU practices and nutrition strategies limit generalizability.

CONCLUSION:

The mNUTRIC score is a valid predictor of adverse outcomes in critically ill patients. Higher scores were linked to extended ventilation, increased vasopressor use, longer CCU stay and higher 30-day mortality, especially in patients with older age, inadequate intake, weight loss, high APACHE II and SOFA scores, and shock. No significant association was found with renal replacement therapy or total hospital stay. The absence of deaths in low-score groups reinforces its utility for early risk stratification and guiding nutritional interventions.

RELEVANCE TO CLINICAL PRACTICE:

The mNUTRIC score is a practical tool for early nutritional risk assessment in ICUs. Its strong predictive value for ventilation duration, vasopressor use, CCU stay, and mortality supports routine use to identify high-risk patients and optimize timely nutritional and clinical interventions.

ETHICAL APPROVAL:

Approved by Holy Family Hospital Ethics Committee; informed consent obtained and confidentiality maintained.

ACKNOWLEDGMENT:

Thanks to the CCU staff, Multidisciplinary team, and Participants/Relatives for their support.

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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Received on 17.09.2025 Revised on 13.12.2025

Accepted on 31.01.2026 Published on 30.04.2026

Available online from May 02, 2026

Asian J. Nursing Education and Research. 2026;16(2):107-112.

DOI: 10.52711/2349-2996.2026.00022

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