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

Turn off MathJax

Article Contents

Abstract

References

Relative Articles

Article Navigation > Journal of Kunming Medical University > 2022 > 43(11): 44-51

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

PDF( 777 KB)

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

FullText(HTML)

References(19)

References

[1]	Reinhart K,Daniels R,Kissoon N,et al. Recognizing sepsis as a global health priority - WHO resolution[J]. N Engl J Med,2017,377(5):414-417. doi: 10.1056/NEJMp1707170
[2]	Preiser J C,van Zanten A R,Berger M M,et al. Metabolic and nutritional support of critically ill patients:Consensus and controversies[J]. Crit Care,2015,19(1):35-46. doi: 10.1186/s13054-015-0737-8
[3]	Wischmeyer P E. Nutrition therapy in sepsis[J]. Crit Care Clin,2018,4(1):107-125.
[4]	McKnight C L,Newberry C,Sarav M,et al. Refeeding syndrome in the critically ill:A literature review and clinician’s guide[J]. Curr Gastroenterol Rep,2019,21(11):1-7.
[5]	Brozek J,Chapman C B,Keys A. Drastic food restriction:effect on cardiovascular dynamics dynamics in normotensive and hypertensive conditions[J]. J Am Med Assoc,1948,137(18):1569. doi: 10.1001/jama.1948.02890520001001
[6]	Da Silva J S V,Seres D,Sabino K et al. ASPEN Consensus recommendations for refeeing syndrome[J]. Nutr Clin Pract,2020,35(2):178-195. doi: 10.1002/ncp.10474
[7]	National Collaborating Centre for Acute Care ( UK). Nutrition support for adults:oral nutrition support,enteral tube feeding and parenteral nutrition[J]. London:National Collaborating Centre for Acute Care (UK),2006,63(3):342-350.
[8]	Wong,Gabriel J Y,Pang,et al. Refeeding hypophosphatemia in patients receiving parenteral nutrition:Prevalence,risk factors,and predicting its occurrence[J]. Nutr Clin Pract,2021,36(3):679-688. doi: 10.1002/ncp.10559
[9]	Luke R G,Galla J H. It is chloride depletion alkalosis,not contraction alkalosis[J]. J Am Soc Nephrol,2012,23:204-207. doi: 10.1681/ASN.2011070720
[10]	于恺英,刘俐惠,石汉平. 营养状况是基本生命体征[J]. 肿瘤代谢与营养电子杂志,2019,6(4):391-396. doi: 10.16689/j.cnki.cn11-9349/r.2019.04.001
[11]	沈丽达,龙庭凤,赵艳芳,等. 胃癌患者营养风险筛查和营养支持治疗调查分析[J]. 昆明医科大学学报,2014,35(10):86-90. doi: 10.3969/j.issn.1003-4706.2014.10.024
[12]	Datta,Debapriya,Foley,et al. Can creatinine height index predict weaning and survival outcomes in patients on prolonged mechanical ventilation after critical illness?[J]. J Intensive Care Med,2018,33(2):104-110. doi: 10.1177/0885066616648133
[13]	Blaser A R, van Zanten R H. Electrolyte disorders during the initiation of nutrition therapy in the ICU[J]. Curr Opin Clin Nutr Metab Care, 202）, 24（2）: 151-158.
[14]	Tongyoo S,Viarasilpa T,Permpikul C. Serum potassium levels and outcomes in critically ill patients in the medical intensive care unit[J]. J Int Med Res,2018,46:1254-1262. doi: 10.1177/0300060517744427
[15]	Friedli N,Baumann J,Hummel R,et al. Refeeding syndrome is associated with increased mortality in malnourished medical inpa- tients:secondary analysis of a randomized trial[J]. Medicine (Baltimore),2020,99(1):e18506.
[16]	Deutsche,Gesellschaftfür,ErnährungÖGfE,et al. Gesellschaft für Ernährungsforschung (2015) Referenzwerte für die Nährstoffffzufuhr[J]. Umschau Buchverlag,Neustadtan derWeinstraße,2015,64(12):975-976.
[17]	Collie J T B,Greaves R F,Jones O A H,et al. Vitamin B1 in critically ill patients:needs and challenges[J]. Clin Chem Lab Med,2017,55(11):1652-1668.
[18]	Zhang Q,Zhao G,Yang N L,et al. Fasting blood glucose levels in patients with different types of diseases[J]. Prog Mol Biol Transl Sci,2019,162:277-292.
[19]	See K C. Glycemic targets in critically ill adults:A mini-review[J]. World J Diabetes,2021,12(10):1719-1730. doi: 10.4239/wjd.v12.i10.1719

Relative Articles(20)

Relative Articles

[1]	Yuanzhen WANG, Hongyan WEI, Renhai TIAN, Yongzhen Chen, Danqing XU, Yingyuan ZHANG, Lixian CHANG, Chunyun LIU, Li LIU. Establishment and Evaluation of a Risk Prediction Model for Chronic Liver Failure Complicated by Primary Hepatocellular Carcinoma Before Intervention. Journal of Kunming Medical University, 2025, 46(3): 1-9.
[2]	Jun DENG, Jun WANG, Xi WANG, Change GAO, Xiao CHEN, Mingxia SHI. Prediction Model and Its Value of IrAEs Based on Peripheral Blood Markers. Journal of Kunming Medical University, 2025, 46(): 1-10.
[3]	Congxin LI, Haidong YUE, Pengxi ZHU, Guangxian HUANG, Lingjie MU, Yanan PENG, Yijie WANG, Yang YANG. Correlation Analysis between Different Vitamin D3 Levels and Immune Inflammatory Indicators in Elderly Patients with Sepsis. Journal of Kunming Medical University, 2025, 46(2): 51-58. doi: 10.12259/j.issn.2095-610X.S20250208
[4]	Dongyan LIU, Yan LIU, Zhiming REN, Feng WANG, Yong WANG. Analysis of Risk Factors for Chemotherapy Induced Myelosuppression and Construction of Prediction Models for Myelosuppression Based on Logistic Regression Analysis in Cancer Patients. Journal of Kunming Medical University, 2025, 46(): 1-8.
[5]	Huijuan ZENG, Bo TIAN, Hongling YUAN, Jie HE, Guanxi LI, Guojia RU, Min XU, Dong ZHAN. Predictive Modeling of Chronic Kidney Disease with Hypertension or Diabetes Based on Machine Learning Algorithms. Journal of Kunming Medical University, 2024, 45(3): 99-105. doi: 10.12259/j.issn.2095-610X.S20240315
[6]	Junjie NIU, Wenjuan JI, Zhuaizhuai YU. Correlation between Gut Microbiota，Serum ET，PCT Levels with the Severity and Prognosis of Sepsis. Journal of Kunming Medical University, 2024, 45(4): 140-145. doi: 10.12259/j.issn.2095-610X.S20240420
[7]	Yanmei JI, Wenjun LI, Qingyun LI, Ni GUO, Ni MENG, Dan ZHOU, Qiuyu LI, Xingfang JIN. The Analysis of Related Factors of Cognitive Impairment after the Acute Ischemic Stroke and Construction of Nomogram Model. Journal of Kunming Medical University, 2024, 45(5): 73-81. doi: 10.12259/j.issn.2095-610X.S20240511
[8]	Xing LIU, Weixian LI, Xiang ZHU, Mengxing LIU, Na LI, Jiawei XIA, Le ZHANG, Yan WU, Shenghao LI. Construction of a Nomogram Model of Tsutsugamushi Disease Complicated with Sepsis Based on Lasso Regression. Journal of Kunming Medical University, 2024, 45(9): 35-41. doi: 10.12259/j.issn.2095-610X.S20240906
[9]	Saiqiong ZHANG, Wuquan LI, Qingjiang CHEN. Clinical Efficacy of Continuous Renal Replacement Therapy in Severe Burn Sepsis. Journal of Kunming Medical University, 2023, 44(11): 94-99. doi: 10.12259/j.issn.2095-610X.S20231114
[10]	Linlin XUE, Binghan LI, Chunyun LIU, Weikun LI, Lixian CHANG, Huimin LI, Yanwei QI, Li LIU. Establishment and Evaluation of a Nomogram for Predicting Potential Death in Patients with Hepatitis C Cirrhosis and Sepsis in General Wards. Journal of Kunming Medical University, 2023, 44(6): 113-119. doi: 10.12259/j.issn.2095-610X.S20230630
[11]	Wenzhuo LI, Li YANG, Jing XIA. Efficacy of Xinmailong Injection on Septic Cardiomyopathy. Journal of Kunming Medical University, 2022, 43(12): 111-116. doi: 10.12259/j.issn.2095-610X.S20221221
[12]	Min ZHOU, Zhihui MA, Jiayan LI, Jianhua FAN, Ling LIN, Tingying YU, Huifang ZHANG, Li LIU. Predictive Model of Risk Factors for the Recurrence of Liver Cirrhosis with Pleural Effusion. Journal of Kunming Medical University, 2022, 43(5): 149-154. doi: 10.12259/j.issn.2095-610X.S20220524
[13]	Guimei ZHANG, Shu CHEN, Yunhua SONG, Yang WU, Hongyuan ZHOU. Risk Factors of Readmission in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease and Establishment of Risk Prediction Model. Journal of Kunming Medical University, 2022, 43(8): 184-190. doi: 10.12259/j.issn.2095-610X.S20220830
[14]	Jing-rong DAI, Jie LI, Xu HE, Yang LI, Yan LI. Risk Factors Analysis and Risk Prediction Model Construction of Depression in Inpatients of Geriatrics Department of a Hospital in Yunnan. Journal of Kunming Medical University, 2021, 42(11): 20-26. doi: 10.12259/j.issn.2095-610X.S20211104
[15]	Liu Ya Ming , Xu Mian , Yan Yue Xin , Zhou Feng Gao , Xu Cheng , Zhao Kun , Jiang Guo Yun , Wu Yu, Liu Rong . . Journal of Kunming Medical University, 2019, 40(06): 16-22.
[16]	Teng Yan . Effect of Hemoperfusion on Inflammation Factors in Patients with Sepsis. Journal of Kunming Medical University,
[17]	Wang Jin . . Journal of Kunming Medical University,
[18]	Xu Ying . . Journal of Kunming Medical University,
[19]	Miao Yu Lan . . Journal of Kunming Medical University,
[20]	Miao Yu Lan . . Journal of Kunming Medical University,

Supplements(0)

Cited By

Cited by

Periodical cited type(18)

1.	邵彤，赵月月，马洁，许丹丹，郭洁. 肠内营养支持脓毒血症患儿再喂养综合征发生现状及其影响因素. 河南医学研究. 2025(04): 607-610 .
2.	苏燕，徐九云，雷海露，刘晓蓓. 老年危重症患者发生再喂养综合征的危险因素回归方程的构建及干预措施分析. 实用临床医药杂志. 2024(01): 123-128 .
3.	崔建伟，赵炳朕，苏海英. 重症监护病房重症肠内营养支持患者并发再喂养综合征的相关危险因素分析. 基层医学论坛. 2024(05): 48-51 .
4.	张静，李丹，林朝霞，秦秀香. 危重症肠内营养患者再喂养综合征现状及影响因素分析. 中外医学研究. 2024(08): 147-151 .
5.	伋可欣，王玲华. 营养支持病人发生再喂养综合征预测模型的系统评价. 循证护理. 2024(07): 1169-1175 .
6.	韩欢欢，荆婵，曲瑞杰. 基于风险预测的干预模式在重症监护室肝癌合并再喂养综合征患者中的应用效果. 癌症进展. 2024(07): 754-757 .
7.	孟莹莹，刘玉平，梁蒙蒙. 重症监护患者再喂养综合征发生率及危险因素的Meta分析. 军事护理. 2024(06): 98-102 .
8.	千雪艳，曲瑞杰，荆婵. 基于营养评估的序贯护理模式在ICU患者并发再喂养综合征中的应用. 黑龙江医药科学. 2024(03): 119-120 .
9.	何红梅，喻芳，林丽华，舒芳. 急性胰腺炎患者再喂养综合征风险预测模型的构建及护理启示. 中华护理杂志. 2024(13): 1608-1615 .
10.	张聪聪，李佳倩，赵明曦，兰元梅，陈天超，冯悦颖. 营养不良患者再喂养综合征的危险因素分析. 中外医疗. 2024(29): 37-40 .
11.	杨茂凡，周会兰，陈柯宇，高杰，戴思琪. 危重病人再喂养综合征风险预测模型的研究进展. 护理研究. 2024(22): 4057-4060 .
12.	邹小翠，毛孝容，王利学，杨小娟，文青. 重症患者再喂养综合征发生率及影响因素的Meta分析. 中华护理杂志. 2024(21): 2640-2648 .
13.	顾丽娟，张继美，尹春艳，李佳. 营养不良患者再喂养综合征的危险因素分析. 中外医疗. 2024(35): 57-60+69 .
14.	陈梦琳，陈云霞，孟晓冰. ICU肠内营养患者再喂养综合征危险因素分析及列线图预测模型构建. 齐鲁护理杂志. 2023(19): 85-88 .
15.	王润秋，李秀川. 重症病人再喂养综合征研究的范围综述. 循证护理. 2023(23): 4242-4248 .
16.	张婧，何芸，朱梦莉，童丽. 重症急性胰腺炎患者再喂养综合征的危险因素及预后. 临床与病理杂志. 2023(09): 1664-1669 .
17.	周文蕾，李兴祥. 肠内营养干预脓毒症患者CRRT相关指标观察. 中华养生保健. 2023(13): 55-59 .
18.	段晓红. 1例应用营养治疗方案治疗再喂养综合征的病例分析. 中国社区医师. 2023(36): 16-18 .