脓毒症相关肝损伤预后分析及基于机器学习方法的预测模型建立

赵云; 蒋伟; 郑瑞强; 於江泉

doi:10.7619/jcmp.20244867

脓毒症相关肝损伤预后分析及基于机器学习方法的预测模型建立

赵云^{1, 2},
蒋伟¹,
郑瑞强³,
於江泉^3,

1.
扬州大学医学院, 江苏扬州, 225200
2.
泰州市中医院重症医学科, 江苏泰州, 225300
3.
扬州大学附属苏北人民医院重症医学科, 江苏扬州, 225200

基金项目:

国家临床重点专科建设单位 176(2022)

扬州市卫生健康委员会科研重点项目 2023-1-0

详细信息

通讯作者:
於江泉

中图分类号: R575.3;R459.7;R319
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出版历程
- 收稿日期: 2024-10-15
- 修回日期: 2024-12-23
- 刊出日期: 2025-04-14

Prognostic analysis of sepsis-related liver injury and development of a prediction model based on machine learning method

1.
School of Medicine of Yangzhou University, Yangzhou, Jiangsu, 225200
2.
Department of Critical Care Medicine, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, Jiangsu, 225300
3.
Department of Critical Care Medicine, the Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, Jiangsu, 225200

摘要

摘要:
目的
分析脓毒症相关肝损伤(SRLI)患者的预后, 并使用8种机器学习方法建立脓毒症患者入住ICU后发生SRLI的预测模型。
方法
纳入MIMIC-IV数据库中满足脓毒症诊断标准且无肝脏、胆系基础疾病的患者。根据肝酶≥5倍正常值上限(ULN)或胆红素≥2.0 mg/dL将患者分为SRLI组和非SRLI组。采用卡方检验、多因素Logistics回归分析及倾向性评分匹配法分析2组患者死亡风险。采用8种机器学习算法[Logistics回归、分类回归树(CART)、随机森林(RF)、支持向量机(SVM)、K-近邻(K-NN)、朴素贝叶斯(NBM)、极端梯度提升(XGBoost)、梯度提升树(GBDT)]构建SRLI预测模型并进行验证。
结果
卡方检验(P < 0.001)、多因素Logistics回归分析(P < 0.05)、倾向性评分匹配分析后Log-rank (P < 0.05)均显示SRLI增加患者死亡风险。SRLI预测模型中, RF算法的曲线下面积(AUC)最高为0.866, 其后依次是GBDT (AUC=0.862)、Logistics回归(AUC=0.859)、SVM (AUC=0.837)、NBM (AUC=0.830)、CART (AUC=0.771)、XGBoost (AUC=0.764)、K-NN (AUC=0.722)。
结论
SRLI增加患者死亡风险。RF算法构建预测模型有较高的诊断价值。
- 脓毒症 /
- 脓毒症相关肝损伤 /
- 机器学习算法 /
- 预测模型 /
- 分类回归树 /
- 随机森林 /
- 支持向量机 /
- 朴素贝叶斯
Abstract:
Objective
To analyze the prognosis of patients with sepsis-related liver injury (SRLI) and establish a prediction model for the occurrence of SRLI after ICU admission in sepsis patients using eight machine learning methods.
Methods
Patients who met the sepsis diagnostic criteria and had no underlying liver or biliary diseases were included from the MIMIC-IV database, and were classified into SRLI and non-SRLI groups based on liver enzymes ≥5 times the upper limit of normal (ULN) or bilirubin ≥2.0mg/dL. Chi-square test, multivariate Logistic regression analysis, and propensity score matching were used to analyze the mortality risk between the two groups. Eight machine learning algorithms[Logistic regression, classification and regression tree (CART), random forest (RF), support vector machine (SVM), K-nearest neighbors (K-NN), naive Bayes method (NBM), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT)]were employed to construct and validate the SRLI prediction model.
Results
The chi-square test (P < 0.001), multivariate Logistic regression analysis (P < 0.05), and log-rank test after propensity score matching (P < 0.05) all indicated that SRLI increased the mortality risk of patients. Among the SRLI prediction models, RF algorithm had the highest area under the curve (AUC), with its value of 0.866, followed by GBDT (AUC=0.862), Logistic regression (AUC=0.859), SVM (AUC=0.837), NBM (AUC=0.830), CART (AUC=0.771), XGBoost (AUC=0.764), and K-NN (AUC=0.722).
Conclusion
SRLI increases the mortality risk of patients. The prediction model constructed using the RF algorithm has high diagnostic value.
- sepsis /
- sepsis-related liver injury /
- machine learning algorithms /
- prediction model /
- classification and regression tree /
- random forest /
- support vector machine /
- naive Bayes method

HTML全文

图 1 患者筛选流程

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图 2 研究流程图

SRLI: 脓毒症相关性肝损伤; ICU: 重症监护室; CART: 分类与回归树模型; RF: 随机森林模型; SVM: 支持向量机模型; K-NN: K-近邻模型; NBM: 朴素贝叶斯模型; XGBoost: 极端梯度提升模型; GBDT: 梯度提升树模型。

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图 3 倾向性匹配分析后生存曲线

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图 4 8种机器学习模型的ROC曲线

RF: 随机森林模型; GBDT: 梯度提升树模型; SVM: 支持向量机模型; NBM: 朴素贝叶斯模型; CART: 分类与回归树模型; XGBoost: 极端梯度提升模型; K-NN: K-近邻模型。

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图 5 机器学习模型预测SRLI的DCA曲线

RF: 随机森林模型; GBDT: 梯度提升树模型; SVM: 支持向量机模型; NBM: 朴素贝叶斯模型; CART: 分类与回归树模型; XGBoost: 极端梯度提升模型; K-NN: K-近邻模型。

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图 6 随机森林模型预测SRLI前12项特征的重要性得分

Change_TBIL: 入ICU 24 h内总胆红素改变值; Change_AST: 入ICU 24 h内谷草转氨酶改变值; Change_ALT: 入ICU 24 h内谷丙转氨酶改变值; TBIL_ad: 入ICU时胆红素值; PLT: 血小板; ALT_ad: 入ICU时谷丙转氨酶; SAPS-Ⅲ: 简化急性; 生理功能评分AST_ad: 入ICU时谷草转氨酶; Ventil: 机械通气; Vaso-time: 血管活性药物使用时间; pH: 酸碱度; Albumin: 白蛋白。

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表 1 SRLI组与非SRLI组基线资料比较[n(%)][M(Q_L, Q_U)]

因素	非SRLI组(n=6 559)		SRLI组(n=2 080)	t/χ²/Z	P
男性	3 600(54.89)		1 254(60.29)	19.00	< 0.001
年龄/岁	70.27(58.04, 81.27)		65.83(52.88, 77.14)	46.68	< 0.001
BMI/(kg/m²)	26.87(23.30, 31.87)		27.57(23.95, 32.41)	52.82	< 0.001
GCS评分/分	14.00(9.00, 15.00)		13.00(7.00, 15.00)	48.70	< 0.001
MBP/mmHg	58.00(51.00, 64.00)		56.00(47.00, 63.00)	48.14	< 0.001
心率/(次/min)	105.00(91.00, 120.00)		112.00(97.00, 128.00)	55.82	< 0.001
呼吸频率/(次/min)	28.00(24.00, 32.50)		29.00(25.00, 34.00)	54.03	< 0.001
体温/℃	36.44(36.11, 36.72)		36.40(35.72, 36.72)	48.31	< 0.001
SpO₂/%	92.00(90.00, 95.00)		92.00(88.00, 94.00)	48.71	< 0.001
SOFA/分	3.00(2.00, 4.00)		4.00(2.00, 6.00)	59.02	< 0.001
SAPS-Ⅲ/分	51.00(38.00, 67.00)		67.00(48.00, 91.00)	60.77	< 0.001

因素	分类	非SRLI组(n=6 559)	SRLI组(n=2 080)	t/χ²/Z	P
24 h液体量/mL		1 820.41(927.39, 3 009.11)	2 777.71(1 452.49, 4 445.51)	59.17	< 0.001
机械通气		3 215(49.02)	1 422(68.37)	237.00	< 0.001
VDI/(μg/min)		0(0, 0.10)	0.10(0, 0.50)	59.22	< 0.001
休克		1 390(21.19)	965(46.39)	505.00	< 0.001
CHF		2 101(32.03)	748(35.96)	11.00	0.001
COPD		1 776(27.08)	491(23.60)	9.70	0.002
CRD		1 654(25.22)	423(20.34)	20.00	< 0.001
肿瘤		978(14.91)	329(15.82)	0.94	0.332
糖尿病		2 105(32.09)	546(26.25)	25.00	< 0.001
Charlson共病指数		5.00(3.00, 8.00)	6.00(4.00, 8.00)	48.53	< 0.001
感染源	肺部	1 403(21.39)	454(21.83)	12.00	0.014
	腹腔	373(5.69)	110(5.29)
	泌尿系	1 001(15.26)	256(12.31)
	皮肤软组织	59(0.90)	21(1.01)
	其他	3 723(56.76)	1 239(59.57)
实验室检查	乳酸/(mmol/L)	1.70(1.20, 2.80)	3.05(1.70, 5.93)	62.28	< 0.001
	pH	7.35(7.28, 7.41)	7.28(7.18, 7.37)	42.29	< 0.001
	p_a(O₂)/mmHg	80.00(63.00, 109.00)	75.00(60.00, 98.00)	48.00	< 0.001
	p_a(CO₂)/mmHg	43.00(37.00, 50.00)	45.00(38.00, 54.00)	53.58	< 0.001
	HCT/%	30.40(25.90, 34.90)	29.40(24.30, 34.73)	48.78	< 0.001
	血红蛋白/(g/dL)	10.00(8.50, 11.55)	9.70(8.00, 11.50)	49.24	< 0.001
	血小板/(×10⁹/L)	181.00(129.00, 249.00)	141.00(87.00, 203.00)	43.10	< 0.001
	白细胞/(×10⁹/L)	13.60(9.70, 18.70)	15.80(10.60, 21.40)	54.75	< 0.001
	白蛋白/(g/L)	3.20(2.70, 3.60)	3.00(2.50, 3.50)	45.77	< 0.001
	尿素氮/(mmol/L)	25.00(16.00, 41.00)	29.00(18.00, 46.25)	54.24	< 0.001
	肌酐/(mg/dL)	1.20(0.90, 1.90)	1.50(1.00, 2.40)	56.20	< 0.001
	PT/s	14.20(12.70, 16.60)	15.80(13.60, 20.73)	58.45	< 0.001
	APTT/s	31.80(27.70, 41.60)	37.20(30.30, 65.13)	58.35	< 0.001
	ALT-ad/(U/L)	21.00(14.00, 34.00)	70.00(28.00, 190.25)	68.57	< 0.001
	AST-ad/(U/L)	29.00(20.00, 45.00)	130.00(45.00, 301.00)	70.48	< 0.001
	TBIL-ad/(mg/dL)	0.50(0.30, 0.80)	0.90(0.50, 1.90)	63.39	< 0.001
SRLI: 脓毒症相关肝损伤; BMI: 体质量指数; GCS: 格拉斯哥昏迷评分; MBP: 平均动脉压; SpO₂: 血氧饱和度; SOFA: 序贯器官衰竭评分; SAPS-Ⅲ: 简化急性生理功能评分; VDI: 血管活性药物强度; CHF: 慢性心功能不全; COPD: 慢性阻塞性肺疾病; CRD: 慢性肾功能不全; pH: 酸碱度; p_a(O₂): 动脉血氧分压; p_a(CO₂): 动脉血二氧化碳分压; HCT: 红细胞压积; PT: 凝血酶原时间; APTT: 活化部分凝血活酶时间; ALT-ad: 入ICU时谷丙转氨酶; AST-ad: 入ICU时谷草转氨酶; TBIL-ad: 入ICU时总胆红素。1 mmHg=0.133 kPa。

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表 2 SRLI组与非SRLI组患者预后比较[n(%)][M(Q_L, Q_U)]

预后指标	非SRLI组(n=6 559)	SRLI组(n=2 080)	t/χ²/Z	P
急性肾损伤	4 585(69.90)	1 726(82.98)	137.00	< 0.001
机械通气时间/d	0(0, 1.50)	1.20(0, 4.04)	58.99	< 0.001
血管活性药物使用时间/h	0(0, 12.92)	7.52(0, 49.67)	58.66	< 0.001
住院时间/d	7.80(4.78, 13.54)	10.10(5.17, 19.07)	55.13	< 0.001
入住ICU时间/d	2.97(1.71, 5.86)	4.33(2.16, 9.57)	56.89	< 0.001
28 d死亡	1 316(20.06)	681(32.74)	142.00	< 0.001
住院死亡	1 003(15.29)	651(31.30)	260.00	< 0.001

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表 3 机器学习模型预测效能

模型	AUC	准确度	精确度	召回率	F1得分
RF	0.866	0.842	0.950	0.388	0.551
GBDT	0.862	0.843	0.816	0.483	0.607
Logistics	0.859	0.855	0.852	0.510	0.638
SVM	0.837	0.830	0.776	0.449	0.569
NBM	0.830	0.809	0.973	0.245	0.391
CART	0.771	0.814	0.694	0.463	0.555
XGBoost	0.764	0.809	0.688	0.435	0.533
K-NN	0.722	0.772	0.610	0.245	0.350
RF: 随机森林模型; GBDT: 梯度提升树模型; SVM: 支持向量机模型; NBM: 朴素贝叶斯模型; CART: 分类与回归树模型; XGBoost: 极端梯度提升模型; K-NN: K-近邻模型。

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