
==== Front
Indian J Crit Care Med
Indian J Crit Care Med
IJCCM
Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine
0972-5229
1998-359X
Jaypee Brothers Medical Publishers

38738192
10.5005/jp-journals-10071-24706
Systematic Review
Modified NUTRIC Score as a Predictor of All-cause Mortality in Critically Ill Patients: A Systematic Review and Meta-analysis
Prakash Jay 1https://orcid.org/0000-0002-5290-3848

Verma Saket 2https://orcid.org/0000-0002-7015-5257

Shrivastava Priyanka 3https://orcid.org/0000-0002-0595-9945

Saran Khushboo 4https://orcid.org/0000-0001-6106-8445

Kumari Archana 5https://orcid.org/0000-0001-8238-9280

Raj Kunal 6https://orcid.org/0009-0002-1084-6977

Kumar Amit 7https://orcid.org/0000-0003-0970-5333

Ray Hemant N 8https://orcid.org/0009-0003-8112-6974

Bhattacharya Pradip K 9https://orcid.org/0000-0002-0219-385X

1,6,9 Department of Critical Care Medicine, Rajendra Institute of Medical Sciences (RIMS), Ranchi, Jharkhand, India
2 Department of Biochemistry, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
3 Department of Trauma Anaesthesia, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
4 Department of Pathology, Gandhi Nagar Hospital, Central Coalfield, Ranchi, Jharkhand, India
5 Department of Obstetrics and Gynaecology, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
7 Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
8 Department of Cardiology, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
Khushboo Saran, Department of Pathology, Gandhi Nagar Hospital, Central Coalfield, Ranchi, Jharkhand, India, e-mail: khushboosaran2@gmail.com
5 2024
30 4 2024
28 5 495503
23 2 2024
04 4 2024
Copyright © 2024; The Author(s).
2024
https://creativecommons.org/licenses/by-nc/4.0/ © The Author(s). 2024 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Abstract

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.

Prospero

CRD42023460292

How to cite this article

Prakash J, Verma S, Shrivastava P, Saran K, Kumari A, Raj K, et al. 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.

Keywords

Critically ill patients
Meta-analysis
Modified NUTRIC score
Mortality prediction
Systematic review
==== Body
pmcHighlights

Intensive care unit (ICU) patients are typically those with acute or chronic disease. Malnutrition in ICU leads to poor response, long-term infection, increased risk for nosocomial infections, and extended ICU stays in patients. The modified Nutrition Risk in the Critically ill (mNUTRIC) is a screening tool for evaluating nutritional risk. There are several studies that have been conducted to evaluate the predictive power of the mNUTRIC on mortality. The results are inconsistent as the population is heterogeneous, the cutoff point is heterogeneous, and clinical settings are heterogeneous. In order to employ the mNUTRIC in evidence-based healthcare settings, it is necessary to evaluate the pooled prediction power on mortality.

Introduction

Nutritional support is an important aspect of the medical treatment provided to critically ill patients. However, there are constraints to using conventional approaches for nutrition assessment in a hospital setting. Malnutrition is believed to afflict one in every three critically ill patients in underdeveloped countries, ranging from 38 to 50% depending on demographics and screening approach.1–3 Patients admitted to the intensive care unit (ICU) typically have acute or chronic illnesses such as cancer, pneumonia, or sepsis, as well as high catabolic activity in the body. This causes increased release of pro-inflammatory chemicals, oxidative stress, disrupted metabolic activity, and skeletal muscle atrophy, delaying treatment outcomes. Malnourishment in ICU leads to poor response, prolonged infection, risk of nosocomial infection, and increased ICU stay in patients.2,3 Even after acute care, the nutritional need is insufficient to supplement or consumption.4,5

The screening tools like anthropometric measurements, functional assessment, physical examinations, history of patient dietary intake, and clinical diagnosis are mainly for hospitalized patients rather than ICU patients. Employing these screening tools are difficult in ICU setup as the patients are more severe, mechanically ventilated, sedated or less responsive.6 Many of these requirements are difficult to meet in ICU patients because most of them require sedation and ventilatory support. Given the enormous volume necessary to maintain hemodynamic stability, weight swings might be influenced by fluid status.3

Inflammation is a contributing factor to hypermetabolism and muscle atrophy in critically ill patients. Understanding the patient's inflammatory condition is essential, but many standard approaches fall short of this goal. To address this issue, NUTRIC Score (NUTritional risk in Critically ill) was introduced. NUTRIC Score takes into account a patient's age, the number of comorbid conditions, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation (APACHE II), and interleukin 6 (IL-6).7 In these individuals, this score approach highlights the nutritional risk linking malnutrition, inflammation, and outcome. Recent studies propose omitting measurement of IL-6 because it is not a frequently accessible test and applying modified modified Nutrition Risk in the Critically ill (mNUTRIC) as a tool for screening to assess nutritional risk.8

Many studies have evaluated the predictive accuracy of the mNUTRIC for mortality prediction. However, the results are inconsistent because the population is heterogeneous, the cut-off point is different, and the clinical context varies. In order for evidence-based use to be made in healthcare settings, it is necessary to calculate the pooled predictive power for mortality. The goal of the present meta-analysis is, therefore, to determine the pooled predictive value for mortality for the mNUTRIC.9

Materials and Methods

This meta-analysis followed the predefined protocol of this study and followed the guidelines of Preferred Reporting Items for the Systematic Review and Meta-Analysis (PRISMA). The protocol was registered in the PROSPERO (CRD42023460292).

Search Strategy for Identification of Studies

Literatures relevant to this meta-analysis were searched in the different databases such as PubMed, Web of Science, and Cochrane Library until 26 August 2023 using the search terms Modified NUTRIC or Modified NUTRIC score or mNUTRIC and mortality. To determine the mNUTRIC score for predicting mortality in patients admitted to the ICU, we reviewed retrospective or prospective cohort studies. Filters were used for literature published in English.

Selection Criteria for Included Studies

Two authors (Jay Prakash and Khushboo Saran) independently selected relevant references by evaluating title and abstracts preceding complete the study. If there were any differences, they were resolved through discussion, and if necessary, we consulted third authors (Archana Kumari). Throughout the paper review process, exclusion criteria were documented.

Inclusion and Exclusion Criteria

Inclusion criteria: Cohort studies (retrospective or prospective); Patients older than 18 years; studies that reported mortality and the mNUTRIC score with cut-off value.

Exclusion criteria: Review articles, abstract, presentations in conference, case reports, and case series that presented phenomenology without assessing outcomes and articles published other than English language.

Participants

Patients >18 years admitted in ICU

Exposure

Modified Nutrition Risk in the Critically ill score

Comparison

Cut-off value of mNUTRIC score mentioned individual study

Outcome

Mortality

Data Extraction and Quality Evaluation

On pre-made data abstraction forms, the two authors (Jay Prakash and Saket Verma) individually extracted data using a methodical approach. A two-tiered method was implemented to address any dispute. First, we let the authors to debate their concerns; but, if the problems persisted, we recommended that a third author (Archana Kumari) do independent data extraction before engaging in additional discussions to resolve the dispute.

The study features, data on demographics, results, and risk of bias for each study were gathered. We extracted mortality for 28 days as well as for various time periods. We collected data from every study that met the eligibility requirements such as authors, publication, country name, design of the study, sensitivity, specificity, number of survivors and non-survivors, and baseline and demographic parameters like sample size, age and sex of the patients, body mass index, comorbidities such as diabetes mellitus, hypertension, cardiac and respiratory disease, chronic kidney disease (CKD), infection, and coronavirus disease (COVID-19).

The risk of bias was assessed by Quality in Prognosis Studies (QUIPS) tool which provides an overview of the six bias domains, together with overall rating assessments.10 The QUIPS tool ranks each of the following domains as “low,” “moderate,” or “high.”

Statistical Analysis

We included all patients in our meta-analysis to predict mortality. The data were obtained by either by direct extraction or indirect calculation from the published literature. The study's weight was determined using DerSimonian and Laird random-effects models, as well as the inverse variance method. Study heterogeneity was evaluated by I2 statistic and the Cochran Q test for heterogeneity.11 In order to assess publication bias, we also examined the funnel plot visually.

We employed meta-regression analysis for looking into possible factors of study heterogeneity. The following variables were used as covariates or moderators in our analysis for potential sources of heterogeneity: continuous variable as mean age and cut-off value for mNUTRIC score as well as percentage of patients with diabetes and hypertension, male gender, patients with cardiac disease, respiratory disease, and chronic kidney disease.

We subsequently did a subgroup analysis based on cut-off value of mNUTRIC score <5 / >5, taking into consideration the clinical importance. In compliance with the guidelines of the Cochrane Collaboration, standard deviations (SD) were computed from interquartile ranges (IQR), and a normal distribution was used for continuous data.12 Finally, the results were displayed using forest plots. We used STATA version 13.0 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP) for statistical analyses. A p-value below 0.05 was deemed noteworthy.

Results

The Outcomes of Search and Study Characteristics

At first, 192 studies were determined to have qualifying potential. A total of 96 studies were screened after removing duplicates and titles and abstracts were examined (Fig. 1). We e-mailed the eight authors of the pertinent studies, but regrettably they did not reply. Ultimately, a pooled analysis of 31 cohort studies (retrospective and prospective), involving 13,271 participants, was used to assess the mNUTRIC score's ability to predict all-cause mortality.3,13–43

Fig. 1 PRISMA flow diagram

Table 1 displays the features of every included study that is a part of the current meta-analysis. Bias in the outcome measurement was low in 24 studies,3,13–19,22–27,29–34,36,39–43 moderate in five studies,20,28,35,37,38 and high risk of bias in two studies (Fig. 2).21,39

Table 1 Characteristics of the included studies

S.No	Author, Year	Country	Study design	Sample size	Cut-off	Sensitivity	Specificity	Survivor	Non-survivor	Mortality (Days)	
1	Mukhopadhyay et al., 201713	Singapore	Prospective observational cohort study	401	5	72	63	314	87	28	
2	Wang et al., 202117	China	Prospective observational cohort study	3107	4	61.48	78.81	2,567	540	28	
3	Lin et al., 202121	Taiwan	Retrospective observational study	205	>4	90.5	62.3	184	21	28	
4	de Vries et al., 201827	Netherlands	Retrospective cohort study	475	>4	88.4	48.9	354	121	28	
5	Kalaiselvan 201736	India	Prospective observational study	678	≥5	41.5	73.8	463	215	NA	
6	Kumar et al., 202038	India	Prospective cross-sectional study	1990	6	97.2	74	1,595	395	NA	
7	Gulsoy et al., 202214	Turkey	Prospective cohort study	311	4.5	0.88	0.736	182	129	28	
8	Im et al., 202215	Korea	Prospective observational cohort study	206	≥5	83.3	48.9	176	30	90	
9	Jeong et al., 201816	Korea	Retrospective cohort study	482	6	75	65	324	158	28	
10	Kim et al., 202218	Korea	Retrospective cohort study	220	≥5	81	44.4	162	58	NA	
11	Mendes et al., 20173	Portugal	Multicenter prospective cohort study	1122	≥5	73.25	58.4	879	243	NA	
12	Dsouza et al., 202219	India	Prospective observational study	250	>2	80.9	76.4	161	89	NA	
13	Tsai et al., 201920	Taiwan	Retrospective observational study	131	4.5	92	74.5	106	25	6 WK	
14	Tseng et al., 202122	Taiwan	Multicenter prospective cohort study	815	5.5	80	77.4	678	137	28	
15	Alramly et al., 202023	Jordan	Multicenter retrospective cohort study	737	4.5	67.9	54.8	85	652	30	
16	Acehan et al., 202124	Turkey	Retrospective observational study	125	4.5	73.5	63.2	81	44	NA	
17	Mayr et al., 202026	Germany	Retrospective observational study	114	≥6	78.3	71.1	45	69	90	
18	Chen et al., 202328	China	Retrospective study	234	≥3	100	42.6	216	18	28	
19	Ata Ur-Rehman et al., 201829	Pakistan	Prospective observational study	75	≥5	46.67	49.99	62	13	NA	
20	Hai et al., 202230	Vietnam	Prospective cross-sectional study	 194	5	67.1	81	121	73	NA	
21	Mahmoodpoor et al., 202331	Iran	Prospective observational study	445	>4	92.11	94.1	407	38	NA	
22	Brascher et al., 202042	Brazil	Observational, longitudinal, prospective study	83	5	64.7	66.7	73	10	28	
23	Todur et al., 202232	India	Prospective observational study	216	≥4	82.1	65.9	138	78	NA	
24	Zhang et al., 202233	China	Retrospective observational study	268	≥5	82.5	81.7	199	69	NA	
25	Wang et al., 202234	China	Multicenter prospective observational study	1021	>4	76.45	56.63	694	327	28	
26	Acehan et al., 202035	Turkey	Prospective observational study	251	3.5	60	67.8	193	58	NA	
27	Ma et al., 202237	China	Retrospective cohort study	429	≥1	91.1	52.3	384	45	28	
28	Zheng et al., 202125	China	Retrospective cohort study	4059	4	75	63.98	3,967	92	NA	
29	Kucuk et al., 202239	Turkey	Retrospective study	322	>3.5	67.7	80	137	185	NA	
30	Leoni et al., 202240	Italy	Retrospective cohort study	98	5	75	89	53	45	28	
31	Wełna et al., 202341	Poland	Secondary analysis of prospective study	146	≥6	87.7	61.7	75	71	28	

Fig. 2 Risk of bias summary

Outcomes

A total of 31,277 patients were included in the analysis from 31 studies meeting inclusion criteria. The summary area under the curve (sAUC) of 0.80 (95% CI: 0.76–0.83) (Fig. 3) illustrates the mNUTRIC score's strong discrimination. In predicting mortality in critically ill patients, the pooled sensitivity was 0.79 (95% CI: 0.74–0.84) and pooled specificity was 0.68 (95% CI: 0.63–0.73) (Fig. 4). A total of I2 statistics showed a high value of heterogeneity/inconsistency (92% for sensitivity and 96% for specificity) (Fig. 4). The diagnostic odds ratio for predicting the mortality by mNUTRIC was 6 (95% CI: 6–15), and the positive likelihood ratios (PLR) and negative likelihood ratios (NLR) were 2 (95% CI: 2.0–3.2) and 0.3 (95% CI: 0.21–0.38), respectively (Fig. 5).

Fig. 3 Summary receiver operating characteristic graph for the included studies. The AUC of mNUTRIC for probability in predicting mortality was 0.80 (95% CI: 0.76–0.83)

Fig. 4 Forest plot of the sensitivity and specificity of mNUTRIC for predicting mortality in critically ill patients. The pooled sensitivity and specificity were 0.79 (95% CI: 0.74–0.84) and 0.68 (95% CI: 0.63–0.73), respectively

Fig. 5 Fagan nomogram showing pretest probability and posttest probability using mNUTRIC for predicting mortality

The funnel plot revealed no discernible publication bias (p = 0.8), which suggest validity of the study's observations (Fig. 6). When 50% was the pretest probability, the PLR was 2 and the posttest mortality was 71%. The NLR was 0.30, whereas the posttest negative predictive value was 23%. We looked at the clinically significant variables (mean age, male gender, BMI, hypertension, diabetes, cardiac disease, respiratory disease, chronic kidney disease, and infection) on the effect size to determine the source of heterogeneity, but with the exception of the cut-off value, which might help to explain the source of heterogeneity, we were unable to uncover any characteristics that could adequately explain the actual source of variation (Fig. 7).

Fig. 6 Deek funnel plot showing publication bias for studies included in the meta-analysis

Fig. 7 Forest plot showing pooled sensitivity and pooled specificity of clinical variables for predicting mortality by mNUTRIC

Subgroup analysis was conducted based on cut-off value and we observed no discernible variation in the mNUTRIC's prediction accuracy between cut-off values of < 5 and > 5 and the sAUC were 0.82 (95% CI: 0.78–0.85) and 0.78 (95% CI: 0.74–0.81), respectively (Table 2).

Table 2 Results of subgroup analysis using mNUTRIC score for predicting mortality

Categories	Number of studies	Sensitivity (95% CI)	Specificity (95% CI)	sAUC (95% CI)	DOR (95% CI)	I2	
Cut-off score	
Cut-off <5	16	0.81 (0.75–0.86)	0.68 (0.60–0.75)	0.82 (0.78–0.85)	9 (6, 15)	Sensitivity: 88.01%
Specificity: 97.27%	
Cut-off ≥5	15	0.77 (0.68–0.84)	0.68 (0.61–0.74)	0.78 (0.74–0.81)	7 (14,–22)	Sensitivity: 94.61%
Specificity: 93.81%	
Mortality (28 days)	
Mortality (28 days)	13	0.82 (0.75–0.87)	0.65 (0.58–0.71)	0.80 (0.76–0.83)	8 (6, 12)	Sensitivity: 87.79%
Specificity: 96.99%	
Mortality (others day)	18	0.77 (0.79–0.84)	0.70 (0.63–0.76)	0.80 (0.76–0.83)	8 (4, 14)	Sensitivity: 94.42%
Specificity: 95.40%	
CI, confidence interval; DOR, diagnostic odds ratio; I2, inconsistency; sAUC, summary area under the curve

We also did subgroup analysis based on 28 days mortality and others day. Our subgroup analysis demonstrated that the results were stable with respect to 28 days mortality (Table 2).

Discussion

The mNUTRIC is one of the most reliable indicators for predicting mortality among patients with critical illness. This meta-analysis, which uses a robust literature search, pre-registered methodology, the QUIPS tool for risk of bias analysis, and the inclusion of up to thirty-one existing studies, suggests that mNUTRIC has a strong ability to distinguish between critically ill patients and predict their mortality. This is the first mNUTRIC meta-analysis on all-cause mortality prediction in critically ill patients that we know of.

Because nutritional risk in the intensive care unit is linked to inflammation and a hypermetabolic state, most nutrition screening methods were ineffective before the mNUTRIC score was developed. These crucial factors were overlooked by the previous methods. In this context, a cutting-edge tool created specifically for critically ill patients is the mNUTRIC score. The importance of the mNUTRIC score in predicting outcomes for critically ill patients has been demonstrated by numerous studies.27,43 The effectiveness of the mNUTRIC score is limited in some ways like score might not identify patients who would benefit from supplementation (antioxidants, for example). The main emphasis of the score is on the supply of macronutrients, protein, and calories. The development of the NUTRIC score does not completely account for nutritional background and practices.

Several studies have reported varied mNUTRIC cut-off for predicting mortality. As a result, clinicians are unsure of the best mNUTRIC thresholds to use in order to predict outcomes. 28.2% of critically ill patients admitted to the intensive care unit (ICU) were found to be at high nutritional risk (mNUTRIC scores >5).17 These results were in line with a previous study, where 22.4% of the patients were rated as having high scores (between 5 and 9).44

We found that risk stratification could be accomplished with the mNUTRIC score. In accordance to our meta-analysis, mNUTRIC may be able to distinguish between critically ill patients with a sAUC of 0.80 (95% CI: 0.76–0.83), sensitivity of 0.79 (95% CI: 0.74–0.84), and specificity of 0.68 (95% CI: 0.63–0.73). Inconsistency, or heterogeneity (I2), was considerable (86% for sensitivity and 85% for specificity).

Using meta-regression analysis, we looked into the sources of variables that might affect variation in the studies. However, the presence of cardiac disease, respiratory conditions, chronic kidney disease, infection, BMI, and other possible clinical conditions did not explain the source of heterogeneity. Examples of such clinical conditions include the proportion of hypertension, diabetes, male gender, and mean age. Nonetheless, the cut-off value might be able to shed light on the source of heterogeneity and the absence of publication bias corroborated the findings of our meta-analysis.

The included studies used a variety of cut-off values, ranging from 2 to 6, to get a consistent and clinically acceptable cut-off. Early outcome prediction is essential in order to give patients the best care possible, and categorization in the early hours of life is necessary to predict mortality. Nevertheless, based on included studies that looked at the mNUTRIC, we observed after subgroup analysis that the mNUTRIC's discriminatory power was equal to what we looked at when all studies were taken into consideration.

Considering to the present meta-analysis's findings, the mNUTRIC may help to identify those who are more likely to experience poor outcomes.

Limitations

One of our study limitations was that the validity of the mNUTRIC score's prediction accuracy was limited because none of the studies that were part of the meta-analysis showed model calibration and validation. Additionally, as shown by I2, we observe a significant degree of heterogeneity among the studies, underscoring the significance of carrying out well-planned prospective studies. The varying cut-off value of mNUTRIC may have resulted from differences in patient clinical settings. Furthermore, the inability to collect data from eight trials diminished the study's power.

Conclusion

Our meta-analysis showed that the mNUTRIC has a strong ability to discriminate between patients who are critically ill and predict their mortality. To support the current findings, more extensive multicenter studies at certain time intervals and with uniform cut-offs are required.

Acknowledgment

This paper won the 1st prize in the “Best Paper Award” category in the 3rd World Conference on Comprehensive Critical Care (W4C) at Jaipur in 2023.

Orcid

Jay Pralash https://orcid.org/0000-0002-5290-3848

Saket Verma https://orcid.org/0000-0002-7015-5257

Priyanka Shrivastava https://orcid.org/0000-0002-0595-9945

Khushboo Saran https://orcid.org/0000-0001-6106-8445

Archana Kumari https://orcid.org/0000-0001-8238-9280

Kunal Raj https://orcid.org/0009-0002-1084-6977

Amit Kumar https://orcid.org/0000-0003-0970-5333

Hemant N Ray https://orcid.org/0009-0003-8112-6974

Pradip K Bhattacharya https://orcid.org/0000-0002-0219-385X

Source of support: Nil

Conflict of interest: None
==== Refs
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