Construction of risk prediction model of enteral feeding intolerance in severe neurosurgical patients based on machine learning algorithm

YU Shanzhao*; BAO Yiping; ZHAO Ling; YOU Hui

doi:10.16766/j.cnki.issn.1674-4152.003916

Volume 23 Issue 3

Mar. 2025

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Article Navigation > Chinese Journal of General Practice > 2025 > 23(3): 414-416

YU Shanzhao*, BAO Yiping, ZHAO Ling, YOU Hui. Construction of risk prediction model of enteral feeding intolerance in severe neurosurgical patients based on machine learning algorithm[J]. Chinese Journal of General Practice, 2025, 23(3): 414-416. doi: 10.16766/j.cnki.issn.1674-4152.003916

Citation:

YU Shanzhao*, BAO Yiping, ZHAO Ling, YOU Hui. Construction of risk prediction model of enteral feeding intolerance in severe neurosurgical patients based on machine learning algorithm[J]. Chinese Journal of General Practice, 2025, 23(3): 414-416. doi: 10.16766/j.cnki.issn.1674-4152.003916

Citation:

YU Shanzhao*, BAO Yiping, ZHAO Ling, YOU Hui. Construction of risk prediction model of enteral feeding intolerance in severe neurosurgical patients based on machine learning algorithm[J]. Chinese Journal of General Practice, 2025, 23(3): 414-416. doi: 10.16766/j.cnki.issn.1674-4152.003916

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Construction of risk prediction model of enteral feeding intolerance in severe neurosurgical patients based on machine learning algorithm

doi: 10.16766/j.cnki.issn.1674-4152.003916

YU Shanzhao*¹
, BAO Yiping^{2
,
,}
, ZHAO Ling³
, YOU Hui⁴

1.
^*Department of Nursing, Affiliated Hospital of Shaoxing University of Arts and Sciences, Shaoxing, Zhejiang 312000, China

Funds:

2023KY1240

Received Date: 2024-11-26
Available Online: 2025-05-14

Abstract

Abstract

Objective Severe patients are prone to gastrointestinal motor dysfunction when experiencing stress. Among the various clinical manifestations of this condition, feeding intolerance (FI) is a common occurrence. Prolonged FI increases the risk of a poor prognosis. The objective of this study is to develop a risk prediction tool for FI in adult neurosurgical patients with severe symptoms. The tool will be constructed using a machine learning algorithm to predict enteral nutrition feeding intolerance. The tool will help to reduce the incidence of FI and improve the prognosis of patients. Methods A retrospective analysis of the clinical data of 396 patients with severe neurological conditions in the intensive care unit of the Affiliated Hospital of Shaoxing University of Arts and Sciences from January 2018 to May 2024 was conducted. The study aimed to identify the risk factors for FI in neurosurgical critical patients Three machine learning algorithms were employed to construct an FI risk prediction model. The 396 neonatal intensive care unit (NICU) patients were randomly divided into a training set (n=272) and a validation set (n=124) according to a 7∶3 ratio. Machine learning algorithms were used to construct a prediction model in the training set, and the sensitivity and accuracy of the model were evaluated using the validation set. Results The incidence of FI in NICU patients was 35.86% (142/396). A subsequent analysis of the influencing factors revealed that a history of diabetes, the use of mechanical ventilation, the use of vasoactive drugs, an albumin level of < 35 g/L, and low blood potassium concentration were independent risk factors for FI in NICU patients. All three models demonstrated good accuracy and sensitivity. The Kappa values of all three models exceeded 0.80, and their negative and positive predictive values were greater than 0.90. The logistic regression analysis model exhibited superior predictive power. Conclusion The FI prediction performance of the three models is satisfactory, and the FI prediction model of the logistic regression analysis model has more advantages, which can effectively identify the risk of FI in neurosurgical critical patients. This is conducive for predicting and identifying patients with high risk of FI.
- Machine learning algorithms,
- Critically ill neurosurgical patients,
- Enteral nutrition,
- Feeding intolerance,
- Risk prediction model

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References(21)

References

[1]	赵玲玲. 住院危重患者早期肠内营养水平的相关因素研究[J]. 医药论坛杂志, 2020, 41(9): 128-130. ZHAO L L. Study on the related factors of early enteral nutrition level in critically ill hospitalized patients[J]. Journal of Medical Forum, 2020, 41(9): 128-130.
[2]	陶维玲, 张红, 唐冬梅. 护士驱动下MDT管理模式在降低肠内营养患者喂养中断发生率中的应用[J]. 现代医药卫生, 2024, 40(14): 2422-2426. doi: 10.3969/j.issn.1009-5519.2024.14.020 TAO W L, ZHANG H, TANG D M. Application of nurse-driven MDT management model in reducing the occurrence of feeding interruption in patients with enteral nutrition[J]. Journal of Modern Medicine & Health, 2024, 40(14): 2422-2426. doi: 10.3969/j.issn.1009-5519.2024.14.020
[3]	卜黎静, 程飞儿, 张爱琴, 等. 危重症患者肠内营养喂养不耐受风险预测模型的构建及验证[J]. 中华护理杂志, 2024, 59(15): 1877-1883. BU L J, CHENG F E, ZHANG A Q, et al. Development and validation of a prediction model for enteral feeding intolerance in critically ill patients[J]. Chinese Journal of Nursing, 2024, 59(15): 1877-1883.
[4]	徐建英, 饶美霞. 微生态制剂的序贯性肠内营养对重型颅脑损伤患者营养状况的影响[J]. 浙江临床医学, 2022, 24(12): 1815-1817. doi: 10.3969/j.issn.1008-7664.2022.12.026 XU J Y, RAO M X. Effect of sequential enteral nutrition with probiotics on nutritional status of patients with severe craniocerebral injury[J]. Zhejiang Clinical Medical Journal, 2022, 24(12): 1815-1817. doi: 10.3969/j.issn.1008-7664.2022.12.026
[5]	范小宁, 孙盼盼, 靳玉萍, 等. 护士主导的肠内营养喂养策略在神经重症患者中的应用[J]. 中华全科医学, 2022, 20(12): 2151-2155. doi: 10.16766/j.cnki.issn.1674-4152.002791 FAN X N, SUN P P, JIN Y P, et al. Application of nurse-led enteral nutritional feeding strategies in neurological intensive care unit patients[J]. Chinese Journal of General Practice, 2022, 20(12): 2151-2155. doi: 10.16766/j.cnki.issn.1674-4152.002791
[6]	SINGER P, ROBINSON E, RAPHAELI O. The future of artificial intelligence in clinical nutrition[J]. Curr Opin Clin Nutr Metab Care, 2024, 27(2): 200-206. doi: 10.1097/MCO.0000000000000977
[7]	四川大学华西循证护理中心, 中华护理学会护理管理专业委员会, 中华医学会神经外科学分会. 中国卒中肠内营养护理指南[J]. 中国循证医学杂志, 2021, 21(6): 628-641. Evidence-based Nursing Center, West China Hospital, Sichuan University; Nursing Management Professional Committee of Chinese Nursing Association; Chinese Neurosurgical Society, Chinese Medical Association. Nursing practice guideline for enteral nutrition in patients with stroke[J]. Chinese Journal of Evidence-based Medicine, 2021, 21(6): 628-641.
[8]	钱传云. 2018ESPEN重症临床营养指南解读[J]. 中华重症医学电子杂志(网络版), 2019, 5(4): 384. QIAN C Y. Interpretation of 2018 ESPEN Clinical nutrition Guidelines for Severe diseases[J]. Chinese Journal of Critical Care & Intensive Care Medicine (Electronic Edition), 2019, 5(4): 384.
[9]	丁佳莉, 刘晓光, 史甜, 等. 基于机器学习算法的重症脑出血患者肠内营养喂养不耐受风险预测模型构建[J]. 实用临床医药杂志, 2024, 28(12): 1-6. doi: 10.7619/jcmp.20240467 DING J L, LIU X G, SHI T, et al. Construction of a risk prediction model for enteral nutrition feeding intolerance in patients with severe cerebral hemorrhage based on machine learning algorithms[J]. Journal of Clinical Medicine in Practice, 2024, 28(12): 1-6. doi: 10.7619/jcmp.20240467
[10]	李亚, 宋玉敏, 刘乐乐, 等. 基于前馈控制的早期肠内营养干预在重症患者中的应用[J]. 海南医学, 2023, 34(13): 1938-1941. doi: 10.3969/j.issn.1003-6350.2023.13.026 LI Y, SONG Y M, LIU L L, et al. Application of early enteral nutrition intervention based on feedforward control in critically ill patients[J]. Hainan Medical Journal, 2023, 34(13): 1938-1941. doi: 10.3969/j.issn.1003-6350.2023.13.026
[11]	张丹. 间歇鼻饲不同泵注时间在重症监护治疗病房机械通气患者肠内营养中的临床效果[J]. 中国临床医生杂志, 2021, 49(7): 819-821. doi: 10.3969/j.issn.2095-8552.2021.07.019 ZHANG D. Clinical effect of intermittent nasal feeding with different pumping time on enteral nutrition in patients with mechanical ventilation in intensive care unit[J]. Chinese Journal For Clinicians, 2021, 49(7): 819-821. doi: 10.3969/j.issn.2095-8552.2021.07.019
[12]	SINGER P, ROBINSON E, RAPHAELI O. Gastrointestinal failure, big data and intensive care[J]. Curr Opin Clin Nutr Metab Care, 2023, 26(5): 476-481. doi: 10.1097/MCO.0000000000000961
[13]	SONG J F, LI Z Y, YAO G J, et al. Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis[J]. PLoS One, 2022, 17(8): e0273383. DOI: 10.1371/journal.pone.0273383.
[14]	吕玉颖, 曹志新, 钟晖, 等. 早期肠内营养治疗对重症呼吸衰竭患者营养状况及肺功能的影响[J]. 中国食物与营养, 2024, 30(5): 84-88. doi: 10.3969/j.issn.1006-9577.2024.05.014 LYU Y Y, CAO Z X, ZHONG H, et al. Effect of early enteral nutrition therapy on nutritional status and lung function in patients with severe respiratory failure[J]. Food and Nutrition in China, 2024, 30(5): 84-88. doi: 10.3969/j.issn.1006-9577.2024.05.014
[15]	陈翻享, 莫绮君, 方笑媚. 通降胃气法干预结合床旁超声监测对机械通气重症患者早期肠内营养喂养不耐受的影响[J]. 现代消化及介入诊疗, 2024, 29(5): 578-582. CHEN F X, MO Q J, FANG X M. Effect of regulating gastric qi combined with bedside ultrasound monitoring on early enteral nutritional feeding intolerance in patients with severe mechanical ventilation[J]. Modern Interventional Diagnosis and Treatment in Gastroenterology, 2024, 29(5): 578-582.
[16]	周甜, 李贞, 王猛霞, 等. 信息化肠内营养耐受性动态管理干预在急诊重症监护室患者中的应用效果[J]. 中国社区医师, 2024, 40(13): 111-113. ZHOU T, LI Z, WANG M X, et al. Application Effect of Information-Based Dynamic Management Intervention for Enteral Nutrition Tolerance in Emergency Intensive Care Unit Patients[J]. Chinese Community Doctors, 2024, 40(13): 111-113.
[17]	杨茂凡, 周会兰, 陈柯宇, 等. ICU患者肠内营养喂养不耐受风险预测模型的系统评价[J]. 护士进修杂志, 2024, 39(14): 1512-1517. YANG M F, ZHOU H L, CHEN K Y, et al. Systematicevaluation of risk prediction models for enteral feeding intolerance in ICU patients[J]. Journal of Nurses Training, 2024, 39(14): 1512-1517.
[18]	赵海霞, 肖婷, 胡敏, 等. 基于肠内营养耐受性评估表的早期肠内营养支持对重症监护病房患者疾病治疗的价值研究[J]. 陕西医学杂志, 2024, 53(5): 637-640. doi: 10.3969/j.issn.1000-7377.2024.05.013 ZHAO H X, XIAO T, HU M, et al. Value of early enteral nutrition support therapy for patients in intensive care unit based on enteral nutrition tolerance assessment sheet[J]. Shaanxi Medical Journal, 2024, 53(5): 637-640. doi: 10.3969/j.issn.1000-7377.2024.05.013
[19]	李红玲, 王昌成, 葛晓璐, 等. 重症胰腺炎患者早期肠内营养治疗前后肠黏膜屏障的变化及喂养不耐受危险因素分析[J]. 临床和实验医学杂志, 2024, 23(8): 809-812. doi: 10.3969/j.issn.1671-4695.2024.08.007 LI H L, WANG C C, GE X L, et al. Changes of intestinal mucosal barrier and risk factors of feeding intolerance in patients with severe pancreatitis before and after eraly enteral nutrition treatment[J]. Journal of Clinical and Experimental Medicine, 2024, 23(8): 809-812. doi: 10.3969/j.issn.1671-4695.2024.08.007
[20]	陈小颉, 段霞, 郑微艳, 等. 肠内营养病人喂养不耐受风险预测模型的系统评价[J]. 肠外与肠内营养, 2024, 31(2): 107-113. CHEN X J, DUAN X, ZHENG W Y, et al. A systematic review of risk prediction models for feeding intolerance in patients receiving enteral nutrition[J]. Parenteral & Enteral Nutrition, 2024, 31(2): 107-113.
[21]	LONGATO E, ACCIAROLI G, FACCHINETTI A, et al. Simple linear support vector machine classifier can distinguish impaired glucose tolerance versus type 2 diabetes using a reduced set of CGM-based glycemic variability indices[J]. J Diabetes Sci Technol, 2020, 14(2): 297-302. doi: 10.1177/1932296819838856

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Construction of risk prediction model of enteral feeding intolerance in severe neurosurgical patients based on machine learning algorithm

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