Analysis of influencing factors for splenomegaly secondary to acute pancreatitis and construc-tion of nomogram prediction model
10.3760/cma.j.cn115610-20240221-00121
- VernacularTitle:急性胰腺炎继发脾大的影响因素分析及列线图预测模型构建
- Author:
Bohan HUANG
1
;
Feng CAO
;
Yixuan DING
;
Ang LI
;
Tao LUO
;
Xiaohui WANG
;
Chongchong GAO
;
Zhe WANG
;
Chao ZHANG
;
Fei LI
Author Information
1. 首都医科大学宣武医院普通外科 首都医科大学急性胰腺炎临床诊疗与研究中心，北京100053
- Keywords:
Acute pancreatitis;
Splenomegaly;
Risk factors;
Nomogram;
Prediction model
- From:
Chinese Journal of Digestive Surgery
2024;23(5):712-719
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the influencing factors for splenomegaly secondary to acute pancreatitis (AP) and construction of a nomogram prediction model.Methods:The retrospective case-control study was conducted. The clinicopathological data of 180 patients with AP who were admitted to Xuanwu Hospital of Capital Medical University from December 2017 to December 2021 were collected. There were 124 males and 56 females, aged (49±15) years. Among them, 60 AP patients who developed secondary splenomegaly were taken as the case group, including 48 males and 12 females, aged (47±13)years, and the rest of 120 cases of AP without secondary splenomegaly were taken as the control group, including 76 males and 44 females, aged (50±16)years. Observation indicators: (1) occurrence and clinical characteristics of splenomegaly secondary to AP; (2) influencing factors for splenomegaly secondary to AP; (3) construction and evaluation of a nomogram prediction model for splenomegaly secondary to AP. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was analyzed using the rank sum test. Count data were represented as absolute numbers, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. The univariate analysis was performed using statistical methods appropriate to the data type. The optimal cut-off value was determined by the receiver operating characteristic curves. Multivariate analysis was conducted using the Logistic regression model with forward method. Based on the results of the multivariate analysis, a nomogram prediction model was constructed. The receiver operating characteristic curve was drawn, and the discrimination was evaluated using the area under curve. The consistency of the nomogram prediction model was evaluated using calibration curve, and its clinical benefit was evaluated using decision curve. Results:(1) Occurrence and clinical characteristics of splenomegaly secondary to AP. The first detection time of 60 patients with splenomegaly secondary to AP was 60(30,120)days after the onset of AP. Cases with persistent respiratory dysfunction, multiple organ failure, severity of illness as mild or moderately severe/severe, pancreatic and/or peripancreatic infection, surgery were 19, 17, 4, 56, 37, 32 for 60 patients with splenomegaly secondary to AP, versus 16, 19, 43, 77, 39, 29 for 120 patients without splenomegaly secondary to AP, respectively, showing significant differences in the above indicators between the two groups ( χ2=8.58, 3.91, 17.64, 13.95, 15.19, P<0.05). (2) Influencing factors for splenomegaly secondary to AP. Resuts of multivariate analysis showed that white blood cell count <5.775×10?/L within 24 hours of AP onset, revised computed tomography (CT) severity index >7 in 3-7 days after onset and the presence of local complications were independent risk factors influencing the splenomegaly secondary to AP ( odds ratio=3.85, 2.86, 6.40, 95% confidence interval as 1.68-8.85, 1.18-6.95, 1.56-26.35, P<0.05). (4) Construction and evaluation of a nomogram prediction model for splenomegaly secondary to AP. The nomogram prediction model was constructed based on white blood cell count within 24 hours of AP onset, revised CT severity index in 3-7 days after onset and local complications. The area under the receiver operating characteristic curve of the nomogram prediction model was 0.76 (95% confidence interval as 0.69-0.83, P<0.05), with a sensitivity of 0.87 and a specificity of 0.55. The calibration curve demonstrated consistency between the predicted rate from the nomogram prediction model and the actually observed rate. The decision curve analysis indicated that the nomogram prediction model had favorable clinical practicability. Conclusions:Patients with AP who develop secondary splenomegaly tend to have a higher severity of illness than those develop no secondary splenomegaly. White blood cell count <5.775×10?/L within 24 hours of AP onset, revised CT severity index >7 in 3-7 days after onset and presence of local complications are independent risk factors influencing splenomegaly secondary to AP, and its nomogram prediction model can predict incidence rate of splenomegaly secondary to AP.
