Risk Factors Analysis and Prediction Model Establishment of Refeeding Syndrome in ICU Patients with Sepsis

Hong WANG; Dexing YANG; Qiang WANG; Weiyu ZHOU; Jiefu TANG; Zhenfang WANG; Kai FU; Shengzhe LIU; Rong LIU

doi:10.12259/j.issn.2095-610X.S20221102

Volume 43 Issue 11

Nov. 2022

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Hong WANG, Dexing YANG, Qiang WANG, Weiyu ZHOU, Jiefu TANG, Zhenfang WANG, Kai FU, Shengzhe LIU, Rong LIU. Risk Factors Analysis and Prediction Model Establishment of Refeeding Syndrome in ICU Patients with Sepsis[J]. Journal of Kunming Medical University, 2022, 43(11): 44-51. doi: 10.12259/j.issn.2095-610X.S20221102

Citation:

Hong WANG, Dexing YANG, Qiang WANG, Weiyu ZHOU, Jiefu TANG, Zhenfang WANG, Kai FU, Shengzhe LIU, Rong LIU. Risk Factors Analysis and Prediction Model Establishment of Refeeding Syndrome in ICU Patients with Sepsis[J]. Journal of Kunming Medical University, 2022, 43(11): 44-51. doi: 10.12259/j.issn.2095-610X.S20221102

Citation:

Hong WANG, Dexing YANG, Qiang WANG, Weiyu ZHOU, Jiefu TANG, Zhenfang WANG, Kai FU, Shengzhe LIU, Rong LIU. Risk Factors Analysis and Prediction Model Establishment of Refeeding Syndrome in ICU Patients with Sepsis[J]. Journal of Kunming Medical University, 2022, 43(11): 44-51. doi: 10.12259/j.issn.2095-610X.S20221102

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Risk Factors Analysis and Prediction Model Establishment of Refeeding Syndrome in ICU Patients with Sepsis

doi: 10.12259/j.issn.2095-610X.S20221102

1.
Dept. of ICU，Yuxi People’s Hospital，Yuxi Yunnan 653100
2.
Dept. of Internal Emergency，The 1st People’s Hospital of Yunnan，Kunming Yunnan 650034
3.
Dept. of ICU，The 1st Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650032，China

Received Date: 2022-04-04
Publish Date: 2022-11-25

Abstract

Abstract

Objective To explore the relevant risk factors for Refeeding Syndrome （RFS） in ICU patients with sepsis, and to establish a prediction model of RFS based on the selected risk factors. Methods The clinical data of sepsis patients admitted to the ICU of the First Affiliated Hospital of Kunming Medical University from November 2020 to January 2022 were studied retrospectively. Finally, 202 patients were selected according to the inclusion and exclusion criteria. According to the diagnosis criteria of ASPEN Consensus Recommendations （2020） for RFS, the patients were divided into two groups: RFS group （n = 141） and non-RFS group （n = 61） on the basis of whether occur of RFS. The differences of gender, body mass index （BMI）, nutrition risk screening, nutritional way, related critical score, intraabdominal pressure （IAP）, relevant laboratory tests, biochemical indexes and pharmacy were compared between the two groups. The independent risk factors of RFS in ICU patients with sepsis were screened by single factor and multivariate logistic regression analysis, and a prediction model was established according to the results of the analysis. The predictive value of the prediction model for RFS in ICU patients with sepsis was evaluated by drawing the receiver operating characteristic curve （ROC） of the subjects. Results After analyzing the relevant data of 202 sepsis patients, the results of monofactor logistic regression analysis showed: Body mass index （BMI）, albumin （ALB）, prealbumin （PA）, creatinine-height index （CHI）, serum sodium （Na+）, serum potassium （K+）, serum magnesium （Mg2+）, serum phosphorus （P- ）, interleukin-6 （IL-6）, fasting blood glucose （FBG）, vitamin B1 （VitB1）, glycosylated hemoglobin （HbA1c）, use of diuretics, use of insulin and other indicators were significantly different （P < 0.05）. And multivariate analysis showed that the use of diuretics, BMI, CHI, serum K+, FBG, and VitB1 were independent risk factors for RFS in ICU sepsis patients. Conclusions Based on the independent risk factors in the multivariate screening, the prediction model expression is established as: L = 1.39×diuretics + 0.15×BMI - 0.14×CHI + 0.75×K⁺-0.16×FBG+0.78×VitB1-2.94. The prediction model established by regression analysis has strong consistency, the joint prediction model has better predictive value.
- Sepsis,
- Refeeding Syndrome,
- Risk factor,
- Prediction model

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References(19)

References

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