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. 2023 Jan 6:16:143-153.
doi: 10.2147/IDR.S387995. eCollection 2023.

Nomogram Prediction Model of Hypernatremia on Mortality in Critically Ill Patients

Zhili Qi #  1 , Jiaqi Lu #  2 , Pei Liu  1 , Tian Li  1 , Ang Li  3 , Meili Duan  1
Affiliations

Nomogram Prediction Model of Hypernatremia on Mortality in Critically Ill Patients

Zhili Qi et al. Infect Drug Resist. .

Abstract

Objective: To investigate the value of hypernatremia in the intensive care unit (ICU) for the risk prediction of mortality in severe patients.

Methods: Clinical data of critically ill patients admitted to the ICU of Beijing Friendship Hospital, were collected for retrospective analysis. Univariate and multivariate logistic regression analyses were employed to analyze the influencing factors. Nomograms predicting the mortality were constructed with R software and validated with repeated sampling.

Results: A total of 442 cases were eligible for this study. Hypernatremia within 48 hours of ICU admission, change in sodium concentration (CNa+) within 48 hours, septic shock, APACHE II score, hyperlactatemia within 48 hours, use of continuous renal replacement therapy (CRRT) within 48 hours, and the use of mechanical ventilation (MV) within 48 hours of ICU admission were all identified as independent risk factors for death within 28 days of ICU admission. These predictors were included in a nomogram of 28-day mortality in severe patients, which was constructed using R software.

Conclusion: The nomogram could predict the individualized risk of 28-day mortality based on the above factors. The model has better discrimination and accuracy and has high clinical application value.

Keywords: 28 day mortality; hypernatremia; intensive care unit; serum sodium.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Study design. A total of 443 ICU patients with complete relevant data were enrolled in this study.
Figure 2
Figure 2
Nomogram to predict the outcomes of death.
Figure 3
Figure 3
Calibration curves of the nomogram in the training dataset (A) and validation dataset (B). The calibration curves show calibration of the nomogram in terms of agreement between the predicted risk of death.
Figure 4
Figure 4
Calibration of the nomogram to predict the death in the training dataset (A) and validation dataset (B).
Figure 5
Figure 5
In the clinical impact curve in the training dataset (A) and validation dataset (B). The red solid line indicates the number of patients at high risk with relevant risk threshold, and the blue dotted line indicates that patients with death that are truly positive. This curve showed that the model had a better predictive ability for high-risk death patients with a range of threshold probability.
Figure 6
Figure 6
Decision curve analysis (DCA) for the nomogram in the training dataset (A) and validation dataset (B).
See this image and copyright information in PMC

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