Bayesian Analysis of Modified Nutrition Risk in Critically Ill (mNUTRIC) Score for Mortality Prediction in Critically Ill Patients
Bibliographic record
Abstract
Background and aims: Malnutrition has a considerable influence on critically ill patients by increasing mortality and poorer clinical outcomes. The modified Nutrition Risk in Critically Ill (mNUTRIC) score is commonly used to assess nutritional risk and predict death; however, its sensitivity, specificity, and optimal cut-off values differ between studies. This study uses a Bayesian approach to assess the accuracy of the mNUTRIC score in predicting mortality in critically ill patients. Patients and methods: A preplanned Bayesian analysis was performed using data from 31 cohort studies, which included 13,271 intensive care unit (ICU) patients. The study investigated the mNUTRIC score's sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC). Subgroup analysis compared mortality rates at 28-day, 90-day, and in-hospital time points, along with cut-off values (<5 vs ≥5). Bayesian modeling was performed using the rjags and brms packages in R version 3.2.1. These tools also facilitated the visualization of results, including posterior distributions, forest plots, and Fagan nomograms. Results: Bayesian analysis affirmed the mNUTRIC score's high discriminative capacity, with a pooled sensitivity of 0.84 (95% credible interval (CrI): 0.80-0.88), specificity of 0.77 (95% CrI: 0.73-0.80), and AUC of 0.88 (95% CrI: 0.83-0.92). A cut-off of <5 resulted in higher sensitivity (0.83) and AUC (0.87), whereas ≥5 remained accurate but had somewhat lower sensitivity. The score consistently predicted 28-day, 90-day, and in-hospital mortality. Conclusions: The Bayesian analysis validates the mNUTRIC score as a reliable predictor of mortality in critically ill patients. Its excellent diagnostic performance suggests its incorporation into ICU for early risk assessment and nutritional interventions. How to cite this article: . Bayesian Analysis of Modified Nutrition Risk in Critically Ill (mNUTRIC) Score for Mortality Prediction in Critically Ill Patients. Indian J Crit Care Med 2025;29(5):449-457.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".