Significance of the Modified NUTRIC Score for Predicting Clinical Outcomes in Patients with Severe Community-Acquired Pneumonia

Chia-Cheng Tseng, Chih-Yen Tu, Chia-Hung Chen, Yao-Tung Wang, Wei-Chih Chen, Pin-Kuei Fu, Chin-Ming Chen, Chih-Cheng Lai, Li-Kuo Kuo, Shih-Chi Ku, Wen-Feng Fang, Chia-Cheng Tseng, Chih-Yen Tu, Chia-Hung Chen, Yao-Tung Wang, Wei-Chih Chen, Pin-Kuei Fu, Chin-Ming Chen, Chih-Cheng Lai, Li-Kuo Kuo, Shih-Chi Ku, Wen-Feng Fang

Abstract

Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.

Keywords: clinical outcome; nutrition; severe community-acquired pneumonia.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of population characteristics.
Figure 2
Figure 2
ROC curve analysis for predictability of mortality between different clinical prediction rules.
Figure 3
Figure 3
ROC curve analysis for predictability of treatment outcome between different clinical prediction rules.

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