基于炎症因子、肺超声和<b>CT</b>评分系统的急性胰腺炎预后不良列线图预测模型的构建

任夏; 叶叶; 刘罗杰; 徐晓丹; 张岩

doi:10.12449/JCH250417

基于炎症因子、肺超声和CT评分系统的急性胰腺炎预后不良列线图预测模型的构建

DOI: 10.12449/JCH250417

任夏¹,
叶叶^a,
刘罗杰^a,
徐晓丹^a,
张岩^b, , ,

a.
苏州大学附属常熟医院（常熟市第一人民医院）消化内科，江苏常熟 215500
b.
苏州大学附属常熟医院（常熟市第一人民医院）重症医学科，江苏常熟 215500

基金项目:

苏州市科技发展计划项目 (SLT2023006);

常熟市科技发展计划重点项目 (CSWS202209);

中华国际医学交流基金会呼吸疾病专项项目 (Z-2014-08-2309-1)

伦理学声明：本研究方案于2024年4月26日经由常熟市第一人民医院伦理委员会审核批准，批号：L2024013。

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：任夏、刘罗杰负责研究设计，统计学分析，论文撰写；任夏、叶叶负责数据收集，数据筛选与整理；徐晓丹负责研究过程的实施及提供经费支持；张岩负责研究设计，指导撰写文章。

详细信息

通信作者:
张岩， zy_rocky @163.com （ORCID： 0009-0007-9651-299X）

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出版历程
- 收稿日期: 2024-08-28
- 录用日期: 2024-09-24
- 出版日期: 2025-04-25

Establishment of a nomogram prediction model for poor prognosis of acute pancreatitis based on inflammatory factors， lung ultrasound， and CT scores

Xia REN¹,
Ye YE^a,
Luojie LIU^a,
Xiaodan XU^a,
Yan ZHANG^{b
, ,
,}

a.
Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University & Changshu First People’s Hospital，Changshu，Jiangsu 215500，China
b.
Department of Critical Care Medicine, Changshu Hospital Affiliated to Soochow University & Changshu First People’s Hospital，Changshu，Jiangsu 215500，China

Research funding:

Science and Technology Planning Project of Suzhou (SLT2023006);

Key Project of the Changshu Science and Technology Development Program (CSWS202209);

Respiratory Disease Special Project of the China International Medical Foundation (Z-2014-08-2309-1)

More Information

Corresponding author: ZHANG Yan， zy_rocky@163.com （ORCID： 0009-0007-9651-299X）

摘要

摘要: 目的本研究旨在通过分析急性胰腺炎（AP）患者的炎症因子、肺超声评分及CT评分系统，识别AP预后不良的独立危险因素，并构建列线图预测模型，为临床早期干预提供依据。方法选取2021年1月—2023年10月苏州大学附属常熟医院收治的409例AP患者为研究对象，使用简单随机抽样法以7∶3分为建模组（n=288）和验证组（n=121）。各组依据转归情况分为预后不良组与预后良好组。于入院72 h内检测患者C反应蛋白（CRP）、降钙素原（PCT）、IL-6、IL-10、TNF-α水平，并在入院48～72 h评估肺超声（LUS）评分、改良CT严重指数（MCTSI）评分和胰腺外炎症CT（EPIC）评分。符合正态分布的计量资料组间比较采用成组t检验；非正态分布的计量资料组间比较采用Mann-Whitney U秩和检验。计数资料组间比较采用χ²检验。使用LASSO回归筛选变量并纳入多因素Logistic回归模型，分析AP预后不良的独立危险因素，构建列线图预测模型，采用受试者操作特征曲线（ROC曲线）和校准曲线评估列线图模型的区分度和拟合优度，决策曲线分析评价预测模型的临床适用性。结果 288例建模组AP患者中，预后不良组33例（11.46%），预后良好组255例（88.54%）；121例验证组AP患者中，预后不良组13例（10.74%），预后良好组108例（89.26%）。建模组中，与预后良好组相比，预后不良组CRP（Z=3.607）、IL-6（Z=4.189）、TNF-α（t=2.584）水平，以及LUS评分（t=8.075）、MCTSI评分（t=5.929）、EPIC评分（t=8.626）均较高（P值均<0.05）；多因素Logistic回归分析显示，CRP（OR=3.592，95%CI：1.272～10.138）、IL-6（OR=4.225，95%CI：1.468～12.156）、TNF-α（OR=3.540，95%CI：1.205～10.401）、LUS评分（OR=7.094，95%CI：2.398～20.986）、MCTSI评分（OR=7.612，95%CI：2.832～20.462）及EPIC评分（OR=11.915，95%CI：4.007～35.432）是AP患者发生预后不良的独立危险因素（P值均<0.05）。依据以上6项，建立列线图预测模型，ROC曲线下面积（AUC）为0.924（95%CI：0.883～0.964）；最佳截断值的约登指数为0.670，灵敏度为0.909，特异度为0.761；校准曲线显示建模组及验证组的预测结果与观察结果之间均具有良好的一致性；决策曲线分析提示预测模型具有临床有效性。结论基于CRP、IL-6、TNF-α、LUS评分、MCTSI评分及EPIC评分构建的AP患者预后不良的风险预测列线图模型有较好的预测效能，可为AP患者临床早期强化治疗方案提供重要策略指导。
- 胰腺炎 /
- C反应蛋白质 /
- 白细胞介素类 /
- 肺超声 /
- 列线图
Abstract: Objective To investigate the independent risk factors for poor prognosis in patients with acute pancreatitis （AP） by analyzing inflammatory factors， lung ultrasound （LUS） scores， and CT scores， to establish a nomogram prediction model， and to provide a basis for early clinical intervention. Methods A total of 409 patients with AP who were admitted to Changshu Hospital Affiliated to Soochow University from January 2021 to October 2023 were enrolled as subjects， and they were divided into modeling group with 288 patients and validation group with 121 patients using the simple random sampling method at a ratio of 7∶3. According to the prognosis， each group was further divided into poor prognosis group and good prognosis group. The levels of C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， interleukin-10 （IL-10）， and tumor necrosis factor-α （TNF-α） were measured for both groups within 72 hours after admission， and LUS scores， modified CT severity index （MCTSI）， and extrapancreatic inflammation on computed tomography （EPIC） scores were assessed within 48‍ ‍—‍ ‍72 hours after admission. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A LASSO regression analysis was used to screen for the variables that were included in the multivariate logistic regression model to identify the independent risk factors for the poor prognosis of AP， and then a nomogram prediction model was established. The receiver operating characteristic （ROC） curve and the calibration curve were used to assess the discriminatory ability and goodness of fit of the nomogram model， and a decision curve analysis was used to assess the clinical applicability of the model. Results Among the 288 patients with AP in the modeling group， there were 33 （11.46%） in the poor prognosis group and 255 （88.54%） in the good prognosis group； among the 121 patients with AP in the validation group， there were 13 （10.74%） in the poor prognosis group and 108 （89.26%） in the good prognosis group. Compared with the good prognosis group， the poor prognosis group had significantly higher levels of CRP （Z=3.607， P<0.05）， IL-6 （Z=4.189， P<0.05）， and TNF-α （t=2.584， P<0.05）， and significantly higher scores of LUS （t=8.075， P<0.05）， MCTSI （t=5.929， P<0.05）， and EPIC （t=8.626， P<0.05）. The multivariate logistic regression analysis showed that CRP （odds ratio ［OR］=3.592， 95% confidence interval ［CI］： 1.272‍ ‍—‍ ‍10.138， P<0.05）， IL-6 （OR=4.225， 95%CI： 1.468‍ ‍—‍ ‍12.156， P<0.05）， TNF-α （OR=3.540， 95%CI： 1.205‍ ‍—‍ ‍10.401， P<0.05）， LUS （OR=7.094， 95%CI： 2.398‍ ‍—‍ ‍20.986， P<0.05）， MCTSI （OR=7.612， 95%CI： 2.832‍ ‍—‍ ‍20.462， P<0.05）， and EPIC （OR=11.915， 95%CI： 4.007‍ ‍—‍ ‍35.432， P<0.05） were independent risk factor for poor prognosis in patients with AP. A nomogram prediction model was established based on the above 6 indicators， which had an area under the ROC curve of 0.924 （95%CI： 0.883‍ ‍—‍ ‍0.964）， and the Youden index for the optimal cut-off value was 0.670， with a sensitivity of 0.909 and a specificity of 0.761. The calibration curve showed good consistency between the predicted and observed results in both the modeling group and the validation group. The decision curve analysis showed that the predictive model had certain clinical effectiveness. Conclusion The nomogram model for predicting the risk of poor prognosis in AP patients based on CRP， IL-6， TNF-α， LUS score， MCTSI score， and EPIC score has relatively good predictive performance and can provide important strategic guidance for developing early intensified treatment regimens for AP patients in clinical practice.
- Pancreatitis /
- C-Reactive Protein /
- Interleukins /
- Lung Ultrasound /
- Nomograms

HTML全文

注： a，临床特征系数图；b，交叉验证图。

图 1 LASSO回归筛选变量

Figure 1. Selection of potential predictors by LASSO regression

下载: 全尺寸图片幻灯片

图 2 AP患者发生预后不良的风险列线图预测模型

Figure 2. Nomogram model for predicting poor prognosis in patients with AP

下载: 全尺寸图片幻灯片

注： a，建模组；b，验证组。

图 3 AP患者发生预后不良的风险列线图的ROC曲线验证图

Figure 3. ROC curve of nomogram model for predicting poor prognosis in patients with AP

下载: 全尺寸图片幻灯片

注： a，建模组；b，验证组。

图 4 AP患者发生预后不良的风险列线图的校正曲线验证图

Figure 4. Calibration curve of nomogram model for predicting poor prognosis in patients with AP

下载: 全尺寸图片幻灯片

图 5 AP患者发生预后不良的风险列线图的临床决策曲线

Figure 5. DCA curve of nomogram model for predicting poor prognosis in patients with AP

下载: 全尺寸图片幻灯片

表 1 建模组与验证组AP患者基本资料比较

Table 1. Comparison of baseline characteristics between the modeling and validation group of AP patients

指标	建模组（n=288）	验证组（n=121）	统计值	P值
性别［例（%）］			χ²=1.273	0.259
男	186（64.58）	71（58.68）
女	102（35.42）	50（41.32）
年龄（岁）	53±17	55±18	t=0.745	0.457
病因［例（%）］			χ²=1.077	0.783
胆管疾病	148（51.39）	60（49.59）
高脂血症	114（39.58）	46（38.02）
酒精性	21（7.29）	12（9.91）
其他	5（1.74）	3（2.48）
严重程度［例（%）］			χ²=0.193	0.908
轻症	176（61.11）	74（61.16）
中度重症	96（33.33）	39（32.23）
重症	16（5.56）	8（6.61）
BMI（kg/m²）	22.16±3.41	21.49±3.57	t=1.767	0.078
CRP（mg/L）	70.15（39.65～94.80）	70.30（39.70～104.70）	Z=0.066	0.947
PCT（ng/mL）	0.35（0.19～0.73）	0.32（0.15～0.87）	Z=0.960	0.337
IL-6（pg/mL）	77.60（43.85～120.00）	76.10（44.75～112.35）	Z=0.021	0.984
IL-10（pg/mL）	31.80（25.20～38.90）	32.90（26.40～38.80）	Z=0.637	0.524
TNF-α（pg/mL）	88.43±33.01	84.71±34.03	t=1.029	0.304
LUS评分（分）	8.70±3.10	8.55±2.67	t=0.468	0.640
MCTSI评分（分）	3.85±1.88	3.97±1.92	t=0.584	0.560
EPIC评分（分）	3.17±1.69	3.10±1.68	t=0.369	0.712

下载: 导出CSV

表 2 建模组AP患者中预后不良组与预后良好组临床资料比较

Table 2. Comparison of clinical characteristics between the poor and good prognosis groups in the modeling group of AP patients

指标	预后不良组（n=33）	预后良好组（n=255）	统计值	P值
性别［例（%）］			χ²=0.071	0.790
男	22（66.67）	164（64.31）
女	11（33.33）	91（35.69）
年龄（岁）	50±18	54±17	t=0.914	0.329
病因［例（%）］			χ²=2.240	0.524
胆管疾病	20（60.61）	128（50.20）
高脂血症	10（30.30）	104（40.78）
酒精性	3（9.09）	18（7.06）
其他	0（0）	5（1.96）
BMI（kg/m²）	21.97±3.46	22.18±3.41	t=0.804	0.745
CRP（mg/L）	108.30（55.45～187.75）	69.50（38.60～91.40）	Z=3.607	<0.001
PCT（ng/mL）	0.48（0.23～1.99）	0.35（0.19～0.50）	Z=1.811	0.070
IL-6（pg/mL）	132.00（79.40～203.95）	72.70（41.80～112.30）	Z=4.189	<0.001
IL-10（pg/mL）	29.90（23.55～35.70）	32.00（25.30～39.10）	Z=1.092	0.275
TNF-α（pg/mL）	108.27±48.66	85.86±29.68	t=2.584	0.014
LUS评分（分）	13.48±3.74	8.09±2.40	t=8.075	<0.001
MCTSI评分（分）	5.58±1.71	3.62±1.79	t=5.929	<0.001
EPIC评分（分）	5.30±1.55	2.89±1.51	t=8.626	<0.001

下载: 导出CSV

表 3 预测AP患者发生预后不良的多因素Logistic分析

Table 3. Multivariate logistic analysis of factors for predicting poor prognosis in AP patients

因素	回归系数	标准误	Wald值	P值	OR	95%CI
CRP（≤70.15 mg/L=0；>70.15 mg/L=1）	1.279	0.529	5.832	0.016	3.592	1.272～10.138
IL-6（≤77.60 pg/mL=0；>77.60 pg/mL=1）	1.441	0.539	7.142	0.008	4.225	1.468～12.156
TNF-α（≤83.85 pg/mL=0；>83.85 pg/mL=1）	1.264	0.550	5.286	0.021	3.540	1.205～10.401
LUS评分（≤8分=0；>8分=1）	1.959	0.553	12.537	<0.001	7.094	2.398～20.986
MCTSI评分（≤4分=0；>4分=1）	2.030	0.505	16.185	<0.001	7.612	2.832～20.462
EPIC评分（≤3分=0；>3分=1）	2.478	0.556	19.857	<0.001	11.915	4.007～35.432

下载: 导出CSV

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