Prakash Jay, Saran Khushboo, Verma Vivek, Raj Kunal, Kumari Archana, Bhattacharya Pradip K, Priye Shio, Rochwerg Bram, Kumar Raj
Department of Critical Care Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
Department of Pathology, Gandhi Nagar Hospital, Central Coalfields Ltd, Ranchi, Jharkhand, India.
Indian J Crit Care Med. 2025 May;29(5):449-457. doi: 10.5005/jp-journals-10071-24971. Epub 2025 May 8.
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.
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.
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.
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.
Prakash J, Saran K, Verma V, Raj K, Kumari A, Bhattacharya PK, . 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.
营养不良通过增加死亡率和导致更差的临床结局,对重症患者产生相当大的影响。改良的重症患者营养风险(mNUTRIC)评分常用于评估营养风险和预测死亡;然而,其敏感性、特异性和最佳临界值在不同研究中有所不同。本研究采用贝叶斯方法评估mNUTRIC评分在预测重症患者死亡率方面的准确性。
使用来自31项队列研究的数据进行预先计划的贝叶斯分析,这些研究包括13271名重症监护病房(ICU)患者。该研究调查了mNUTRIC评分的敏感性、特异性、诊断比值比和曲线下面积(AUC)。亚组分析比较了28天、90天和住院时间点的死亡率,以及临界值(<5 vs≥5)。使用R版本3.2.1中的rjags和brms软件包进行贝叶斯建模。这些工具还便于结果的可视化,包括后验分布、森林图和费根诺模图。
贝叶斯分析证实了mNUTRIC评分具有较高的判别能力,合并敏感性为0.84(95%可信区间(CrI):0.80 - 0.88),特异性为0.77(95% CrI:0.73 - 0.80),AUC为0.88(95% CrI:0.83 - 0.92)。临界值<5时敏感性(0.83)和AUC(0.87)更高,而≥5时仍然准确,但敏感性略低。该评分始终能预测28天、90天和住院死亡率。
贝叶斯分析验证了mNUTRIC评分作为重症患者死亡率可靠预测指标的有效性。其出色的诊断性能表明应将其纳入ICU用于早期风险评估和营养干预。
Prakash J, Saran K, Verma V, Raj K, Kumari A, Bhattacharya PK, . 用于预测重症患者死亡率的改良重症患者营养风险(mNUTRIC)评分的贝叶斯分析。《印度重症监护医学杂志》2025;29(5):449 - 457。