DING Jiali, LIU Xiaoguang, SHI Tian, MA Qiang, QI Yajie, LI Yuping, YU Hailong, LU Guangyu. 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
Citation: DING Jiali, LIU Xiaoguang, SHI Tian, MA Qiang, QI Yajie, LI Yuping, YU Hailong, LU Guangyu. 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

Construction of a risk prediction model for enteral nutrition feeding intolerance in patients with severe cerebral hemorrhage based on machine learning algorithms

More Information
  • Received Date: January 24, 2024
  • Revised Date: March 21, 2024
  • Available Online: June 28, 2024
  • Objective 

    To construct and validate a risk prediction model for enteral nutrition feeding intolerance (FI) in patients with severe cerebral hemorrhage based on machine learning algorithms.

    Methods 

    The clinical data of 485 patients with cerebral hemorrhage admitted to the neurological intensive care unit of Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2020 to December 2022 were retrospectively analyzed. The patients were randomly divided into training set (n=339) and validation set (n=146) in a 7 to 3 ratio. Five machine learning algorithms were used to construct FI risk prediction models. The receiver operating characteristic (ROC) curve was plotted, and the model with the best predictive performance was selected based on the area under the curve (AUC). A nomogram model was constructed based on the optimal model. The calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical net benefit of the nomogram model.

    Results 

    The incidence of enteral nutrition FI in patients with severe cerebral hemorrhage was 38.4%(186/485). Among the five machine learning algorithm models, the Logistic regression model had the best predictive performance(AUC=0.88). The analysis results of the Logistic regression model showed that the use of diuretics, mechanical ventilation, Glasgow Coma Scale score ≤5, vasoactive drugs, and albumin level<35 g/L were risk factors for enteral nutrition FI in patients with severe cerebral hemorrhage. A nomogram model was further constructed based on these five risk factors. The calibration curve analysis showed that the calibration curve fitted well with the ideal curve, indicating a high calibration degree of the nomogram model. The DCA results showed that when the threshold probability was 5% to 73%, the application of the nomogram model for screening could clinically benefit patients.

    Conclusion 

    The construction of a nomogram model for predicting the risk of enteral nutrition FI in patients with severe cerebral hemorrhage based on machine learning methods can help to early screen high-risk patients for enteral nutrition FI and timely formulate preventive measures, thereby reducing the incidence of enteral nutrition FI in patients with severe cerebral hemorrhage.

  • [1]
    KASE C S, HANLEY D F. Intracerebral hemorrhage: advances in emergency care[J]. Neurol Clin, 2021, 39(2): 405-418. doi: 10.1016/j.ncl.2021.02.002
    [2]
    聂丹丹. 脑梗死患者肠内营养喂养不耐受的影响因素[J]. 医疗装备, 2023, 36(8): 140-142. https://www.cnki.com.cn/Article/CJFDTOTAL-YLZB202308045.htm
    [3]
    PREISER J C, ARABI Y M, BERGER M M, et al. A guide to enteral nutrition in intensive care units: 10 expert tips for the daily practice[J]. Crit Care, 2021, 25(1): 424. doi: 10.1186/s13054-021-03847-4
    [4]
    ZHU W, JIANG Y, LI J P. Intermittent versus continuous tube feeding in patients with hemorrhagic stroke: a randomized controlled clinical trial[J]. Eur J Clin Nutr, 2020, 74(10): 1420-1427. doi: 10.1038/s41430-020-0579-6
    [5]
    祝娟, 李红叶, 龚艳黎. 间歇性与持续性肠内营养对ICU脑出血患者喂养不耐受的影响比较[J]. 现代中西医结合杂志, 2022, 31(23): 3347-3350, 3358. doi: 10.3969/j.issn.1008-8849.2022.23.027
    [6]
    QIU C F, CHEN C X, ZHANG W X, et al. Fat-modified enteral formula improves feeding tolerance in critically ill patients: a multicenter, single-blind, randomized controlled trial[J]. JPEN J Parenter Enteral Nutr, 2017, 41(5): 785-795. doi: 10.1177/0148607115601858
    [7]
    LIU Y T, LI Y P, HUANG Y J, et al. Prediction of catheter-associated urinary tract infections among neurosurgical intensive care patients: a decision tree analysis[J]. World Neurosurg, 2023, 170: 123-132. doi: 10.1016/j.wneu.2022.11.046
    [8]
    李育平, 裴云龙, 朱磊, 等. 重型颅脑创伤术后中枢神经系统感染风险预测模型的构建[J]. 中华神经外科杂志, 2022, 38(8): 837-842. doi: 10.3760/cma.j.cn112050-20210909-00449
    [9]
    REINTAM BLASER A, MALBRAIN M L N G, STARKOPF J, et al. Gastrointestinal function in intensive care patients: terminology, definitions and management. Recommendations of the ESICM Working Group on Abdominal Problems[J]. Intensive Care Med, 2012, 38(3): 384-394. doi: 10.1007/s00134-011-2459-y
    [10]
    潘宜波, 王琳, 陈霞, 等. 神经外科重症患者肠内营养喂养不耐受动态列线图预测模型的构建与应用[J]. 护士进修杂志, 2023, 38(21): 1921-1926. https://www.cnki.com.cn/Article/CJFDTOTAL-FSJX202321001.htm
    [11]
    李炜, 杨富, 王晓平, 等. 神经重症患者肠内营养喂养不耐受风险预警模型的构建[J]. 中国神经免疫学和神经病学杂志, 2022, 29(5): 398-403. doi: 10.3969/j.issn.1006-2963.2022.05.010
    [12]
    WANG Y Q, LI Y H, WANG H M, et al. Development and validation of a nomogram for predicting enteral feeding intolerance in critically ill patients(NOFI): mixed retrospective and prospective cohort study[J]. Clin Nutr, 2023, 42(12): 2293-2301. doi: 10.1016/j.clnu.2023.10.003
    [13]
    张慧杰, 刘莉, 马莎莎, 等. 脑出血患者便秘风险预测模型的构建与验证[J]. 中国实用护理杂志, 2023, 39(29): 2285-2291. doi: 10.3760/cma.j.cn211501-20230425-01017
    [14]
    刘佳欣, 朱艳华, 程培霞, 等. 重症脑卒中患者肠内营养喂养不耐受风险列线图模型的构建与验证[J]. 护士进修杂志, 2023, 38(12): 1069-1073, 1102. https://www.cnki.com.cn/Article/CJFDTOTAL-FSJX202312003.htm
    [15]
    于娣, 龙玲, 赵鹤龄. 血流动力学不稳定重症患者肠内营养的耐受性与安全性[J]. 中华急诊医学杂志, 2016, 25(1): 113-116. https://cdmd.cnki.com.cn/Article/CDMD-10089-1015338051.htm
    [16]
    BRENNAN C A, OSEI-BONSU P, MCCLENAGHAN R E, et al. Vasoactive agents in acute mesenteric ischaemia in critical care. A systematic review[J]. F1000Research, 2021, 10: 453. doi: 10.12688/f1000research.52782.2
    [17]
    刘佳欣. 个体化预测重症脑卒中患者肠内营养喂养不耐受风险的可视化列线图预警模型的构建[D]. 济南: 山东中医药大学, 2022.
    [18]
    LI H, YANG Z Y, TIAN F. Risk factors associated with intolerance to enteral nutrition in moderately severe acute pancreatitis: a retrospective study of 568 patients[J]. Saudi J Gastroenterol, 2019, 25(6): 362-368. doi: 10.4103/sjg.SJG_550_18
    [19]
    ROBBA C, POOLE D, MCNETT M, et al. Mechanical ventilation in patients with acute brain injury: recommendations of the European Society of Intensive Care Medicine consensus[J]. Intensive Care Med, 2020, 46(12): 2397-2410. doi: 10.1007/s00134-020-06283-0
    [20]
    周乾晓, 冯灵, 汪锐, 等. 脑卒中患者肠内营养不耐受的研究进展[J]. 实用医院临床杂志, 2020, 17(5): 248-251. https://www.cnki.com.cn/Article/CJFDTOTAL-YYLC202005076.htm
    [21]
    杨婷, 李玉. 急性脑梗死患者肠内营养喂养不耐受相关因素分析[J]. 临床医药实践, 2023, 32(5): 377-381. https://www.cnki.com.cn/Article/CJFDTOTAL-SXLC202305015.htm
    [22]
    梁天英, 梁本禧, 陈晓燕. 重型颅脑损伤患者肠内营养喂养不耐受现状及影响因素分析[J]. 中西医结合护理: 中英文, 2021, 7(10): 184-186. https://www.cnki.com.cn/Article/CJFDTOTAL-ZXHL202110056.htm
    [23]
    刘桂英, 张艳艳. 危重患者肠内营养喂养不耐受危险因素的meta分析[J]. 中国医药科学, 2022, 12(5): 48-52. https://www.cnki.com.cn/Article/CJFDTOTAL-GYKX202205011.htm
    [24]
    苏小平. 危重患者早期肠内营养喂养不耐受风险预测模型的构建与验证[D]. 苏州: 苏州大学, 2022.
    [25]
    苏小平, 徐静娟, 赵亚东, 等. 危重患者早期肠内营养喂养不耐受风险预测模型的构建[J]. 护理学报, 2022, 29(17): 47-51. https://www.cnki.com.cn/Article/CJFDTOTAL-NFHL202217009.htm
    [26]
    FERRER R, MATEU X, MASEDA E, et al. Non-oncotic properties of albumin. A multidisciplinary vision about the implications for critically ill patients[J]. Expert Rev Clin Pharmacol, 2018, 11(2): 125-137.
    [27]
    邹圣强, 朱小芳, 乔瑶, 等. ICU脓毒症患者肠内营养喂养不耐受的危险因素调查[J]. 中华灾害救援医学, 2017, 5(9): 498-501. https://www.cnki.com.cn/Article/CJFDTOTAL-JYZH201709008.htm
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