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Nutrition in acute and chronic diseases

An mNUTRIC-based nomogram for predicting the in-hospital death risk in patients with acute stroke

European Journal of Clinical Nutrition volume 76, pages 1464–1469 (2022)Cite this article

Subjects

Abstract

Objectives

To establish a risk prediction model for in-hospital death in acute stroke patients based on nutritional risk scores.

Methods

A retrospective analysis was performed including 268 acute stroke patients. The Nutritional Risk Screening 2002 (NRS2002) and modified Nutritional Risk in the Critically Ill (mNUTRIC) score were used to evaluate the nutritional status of patients with acute stroke after admission to the neurological intensive care unit (NICU), and laboratory parameters and clinical characteristics were collected. Multivariate logistic regression analysis was performed to screen the risk factors for in-hospital death in acute stroke patients, and a nomogram for predicting death based on the nutritional risk score was established.

Results

The mortality of acute stroke in the NICU was 25.8%. Multivariate logistic regression analysis showed that the mNUTRIC score, female sex, lymphocyte count, pulmonary infection and mechanical ventilation were independent risk factors for in-hospital mortality in acute stroke patients (P < 0.001 or 0.05). The above indexes were used to establish a prediction model of the in-hospital death risk for acute stroke patients. The area under the ROC curve, sensitivity, and specificity of the prediction model were 0.891 (95% CI = 0.853–0.928), 82.5%, and 81.7%, respectively. The nomogram was established and then internally validated using bootstrap repeat sampling 2000 times, the C-index was 0.880, and the predicted values of the calibration curve were in agreement with the measured values.

Conclusion

The mNUTRIC-based nomogram model can be used as a reliable tool to predict the in-hospital mortality risk of acute stroke patients.

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Fig. 1: ROC curve analysis of NRS2002, mNUTRIC and logistic regression model for predicting mortality risk.
Fig. 2: Development and application of a nomogram for predicting mortality risk.

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Data availability

The study datasets is not publicly available to protect patient confidentiality but is available from the authors on reasonable request if needed.

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Acknowledgements

We thank all of the patients in this study for their cooperation: WWZ, YTL, AY. We also thank American Journal Experts (http://www.journalexperts.com/) for their English language editing and proofreading.

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Authors and Affiliations

  1. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongching, 400016, China

    Rui-xin Zhang & Guang-wei Liu

  2. Department of Surgery Intensive Care Unit, The First Affiliated Hospital of Chongqing Medical University, Chongching, 400016, China

    Wei-wei Zhang

  3. Department of Nosocomial Infection Control, Second Affiliated Hospital, Army Medical University, Chongching, 404100, China

    Ye-tao Luo

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  1. Rui-xin Zhang
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Contributions

Conceptualization and study design: RXZ and FL. Data acquisition: RXZ and WWZ. Statistical analysis: YTL. Picture editing: AY. Data interpretation: RXZ, LHH, and GWL. Paper preparation: RXZ. All authors have read and approved this version for publication.

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Correspondence to Guang-wei Liu.

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Zhang, Rx., Zhang, Ww., Luo, Yt. et al. An mNUTRIC-based nomogram for predicting the in-hospital death risk in patients with acute stroke. Eur J Clin Nutr 76, 1464–1469 (2022). https://doi.org/10.1038/s41430-022-01127-0

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