脓毒症患者肠内喂养不耐受风险预测模型构建

杨羚; 张莉; 彭虎; 辜新; 于湘友

doi:10.16766/j.cnki.issn.1674-4152.003408

脓毒症患者肠内喂养不耐受风险预测模型构建

doi: 10.16766/j.cnki.issn.1674-4152.003408

杨羚^{1, 2},
张莉^{1, 3, ,},
彭虎¹,
辜新¹,
于湘友⁴

1.
新疆医科大学护理学院，新疆乌鲁木齐 830054
2.
新疆医科大学第一附属医院心血管中心重症监护室，新疆乌鲁木齐 830054
3.
新疆医科大学第一附属医院护理部
4.
新疆医科大学第一附属医院重症医学中心

基金项目:

国家自然科学基金项目 82160360

吴阶平医学基金会临床科研专项资助基金项目 320.6750.2023-02-3

详细信息

通讯作者:
张莉，E-mail：121449072@qq.com

中图分类号: R631 R459.3
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- 文章访问数: 109
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出版历程
- 收稿日期: 2023-11-28
- 网络出版日期: 2024-05-27

Development of a prediction model for enteral feeding intolerance in patients with sepsis

YANG Ling^{1, 2},
ZHANG Li^{1, 3
, ,},
PENG Hu¹,
GU Xin¹,
YU Xiangyou⁴

1.
School of Nursing, Xinjiang Medical University, Urumqi, Xinjiang 830054, China

摘要

摘要: 目的探讨脓毒症患者肠内喂养不耐受(EFI)的危险因素，了解脓毒症患者EFI的发生情况。构建脓毒症患者发生EFI的风险预测模型，便于高危人群的筛查，以防控EFI的发生。方法采用方便抽样法选取2022年1月—2023年6月新疆某三级甲等医院重症医学科收治的行肠内营养治疗的脓毒症患者作为研究对象，以患者是否发生EFI分为喂养耐受组和喂养不耐受组，对患者的一般资料、临床资料、喂养不耐受相关资料进行单因素分析，应用LASSO回归解决各变量间的共线性问题，将LASSO回归分析结果中的变量纳入logistic回归分析，得出脓毒症患者发生EFI的危险因素，并基于此构建脓毒症患者EFI风险预测模型，绘制列线图、ROC曲线、决策曲线、校准曲线，对预测模型的诊断效能进行评价及验证。结果共纳入199例行肠内营养治疗的脓毒症患者，其中不耐受组93例，耐受组106例，EFI发生率为46.7%。多因素分析显示序贯性器官功能衰竭(SOFA)评分、营养风险筛查2002(NRS-2002)评分、镇痛剂、ICU住院时间是脓毒症患者发生EFI的独立影响因素。ROC曲线分析显示，脓毒症患者发生EFI的曲线下面积为0.885(95% CI：0.833~0.933)，最大约登指数为0.689，敏感度为0.849，特异度为0.839。结论 SOFA评分、NRS-2002评分、镇痛剂、ICU住院时间是脓毒症患者发生EFI的影响因素，基于此构建的风险预测模型，经验证后发现该模型可较准确地预测脓毒症患者发生EFI的风险，有助于临床医护人员对患者进行科学、个体化的肠内营养治疗。
- 脓毒症 /
- 肠内喂养不耐受 /
- 预测模型
Abstract: Objective To explore the risk factors of enteral feeding intolerance (EFI) in sepsis patients and to understand the occurrence of EFI in sepsis patients. To construct a risk prediction model for the occurrence of EFI in sepsis patients to facilitate the screening of high-risk groups to prevent and control the occurrence of EFI. Methods Convenience sampling method was used to select sepsis patients admitted to the Department of Critical Care Medicine of a tertiary-level hospital in Xinjiang who underwent enteral nutrition therapy from January 2022 to June 2023 as study subjects and were divided into the feeding tolerance group and feeding intolerance group by whether or not EFI occurred in the patients, and a one-way analysis was carried out on the patients ' general, clinical, and feeding intolerance-related data, and LASSO regression was applied to solve the covariance problem among variables, then the variables in the results of LASSO regression analysis were included in logistic regression analysis to derive the risk factors for EFI in patients with sepsis, on the basis of which we constructed a prediction model for the risk of EFI in patients with sepsis, and plotted a Nomogram, ROC curve, decision curve analysis, and calibration curve to evaluate and validate the diagnostic efficacy of the prediction model. Results A total of 199 sepsis patients with enteral nutrition therapy were included, including 93 cases in the intolerant group and 106 cases in the tolerant group, and the incidence rate of EFI was 46.7%. Multifactorial analysis showed that sequential organ failure assessment (SOFA) score, nutritional risk screening (NRS-2002) score, analgesics, and ICU length of stay were independent risk factors for the occurrence of EFI in patients with sepsis. The area under the ROC curve was 0.885 (95% CI: 0.833-0.933), and the maximal Youden index was 0.689, with a sensitivity of 0.849, and a specificity of 0.839. Conclusion SOFA score, NRS-2002 score, analgesics, and ICU length of stay are risk factors for EFI in septic patients. The risk prediction model constructed in this study based on this was validated and found to be a more accurate predictor of the risk of EFI in patients with sepsis, which can help clinical healthcare professionals provide scientific and individualized enteral nutrition therapy to patients.
- Sepsis /
- Enteral feeding intolerance /
- Prediction model

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图 1 基于Lambda模型的相关危险因素筛选

注：A为基于10倍交叉验证法确定交叉验证误差最优lambda值为0.083 205 97；B为7个筛选变量的回归系数。

Figure 1. Screening of relevant risk factors based on the Lambda model

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图 2 脓毒症患者发生EFI的列线图

Figure 2. Nomogram depicting EFI occurrence in septic patients

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图 3 脓毒症患者肠内喂养不耐受预测模型的ROC曲线和校准曲线

Figure 3. ROC curves and calibration curves for the EFI prediction model in patients with sepsis

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图 4 脓毒症患者肠内喂养不耐受预测模型的临床决策曲线

Figure 4. Decision curve analysis for the EFI prediction model in patients with sepsis

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表 1 2组脓毒症患者一般资料比较

Table 1. Comparison of clinical data between two groups of sepsis patients

项目	耐受组(n=106)	不耐受组(n=93)	统计量	P值	项目	耐受组(n=106)	不耐受组(n=93)	统计量	P值
性别[例(%)]			0.895^a	0.344	镇痛剂[例(%)]			43.983^a	< 0.001
男性	75(70.8)	59(63.4)			否	75(70.8)	22(23.7)
女性	31(29.2)	34(36.6)			是	31(29.2)	71(76.3)
年龄(x±s, 岁)	59.0±17.2	76.2±10.1	8.693^b	< 0.001	使用抗菌药物种类[例(%)]			0.103^a	0.748
基础疾病[例(%)]			0.178^a	0.672	< 2	22(20.8)	22(23.7)
无	17(16.0)	12(12.9)			≥2	84(79.2)	71(76.3)
有	89(84.0)	81(87.1)			抑酸剂[例(%)]			0.211^a	0.646
NRS-2002评分[例(%)]			59.938^a	< 0.001	否	40(37.7)	39(41.9)
< 3	75(70.8)	14(15.1)			是	66(62.3)	54(58.1)
≥3	31(29.2)	79(84.9)			营养液类型[例(%)]			1.507^a	0.471
mNUTRIC评分[例(%)]			8.200^a	0.007	短肽类	69(65.1)	68(73.1)
< 5	33(31.1)	13(14.0)			整蛋白类	33(31.1)	22(23.7)
≥5	73(68.9)	80(86.0)			短肽+整蛋白	4(3.8)	3(3.2)
APACHEⅡ评分[例(%)]			0.026^a	0.873	血清白蛋白[例(%)]			2.192^a	0.146
< 20	57(53.8)	52(55.9)			≥35 g/L	45(42.5)	30(32.3)
≥20	49(46.2)	41(44.1)			< 35 g/L	61(57.5)	63(67.7)
SOFA评分[M(P₂₅, P₇₅), 分]	7(5, 12)	12(10, 15)	-6.799^c	< 0.001	血红蛋白(x±s, g/L)	103.6±32.9	108.1±27.9	1.036^b	0.307
CRRT[例(%)]			4.271^a	0.040	白细胞计数[M(P₂₅, P₇₅)，×10⁹/L]	13.3(10.5, 16.9)	13.1(9.4, 16.9)	-0.419^c	0.675
否	70(66.0)	48(51.6)			ICU住院时间[M(P₂₅, P₇₅)，d]	12(7, 17)	14(9, 22)	-2.777^c	0.005
是	36(34.0)	45(48.4)			机械通气时间[M(P₂₅, P₇₅)，d]	11(7, 15)	11(8, 17)	-0.983^c	0.325
注：^a为χ²值，^b为t值, ^c为Z值。

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表 2 变量赋值情况

Table 2. Variable assignment

变量	赋值方法
年龄	以实际值赋值
SOFA评分	以实际值赋值
ICU住院时间	以实际值赋值
NRS-2002评分	＜3分=0，≥3分=1
mNUTRIC评分	＜5分=0，≥5分=1
CRRT	否=0，是=1
镇痛剂	否=0，是=1

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表 3 脓毒症患者肠内喂养不耐受危险因素的logistic多因素回归分析

Table 3. Logistic multivariable regression analysis of risk factors for enteral feeding intolerance

预测变量	B	SE	Waldχ²	P值	OR值	95% CI
NRS-2002评分	2.104	0.437	23.142	< 0.001	8.199	3.479~19.322
SOFA评分	0.191	0.048	15.880	< 0.001	1.210	1.102~1.329
镇痛剂	0.948	0.414	5.236	0.022	2.580	1.146~5.810
ICU住院时间	0.044	0.019	5.510	0.019	1.045	1.007~1.085

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