Analysis of influencing factors of secondary coagulopathy in patients with severe acute pancreatitis and establishment of prediction model
- VernacularTitle:老年重症急性胰腺炎患者继发凝血功能障碍影响因素分析及预测模型建立
- Author:
Yi ZHANG
1
;
Hehua YU
1
;
Tianpeng XU
1
Author Information
- Publication Type:Journal Article
- Keywords: pancreatitis; blood coagulation disorders; Logistic models; aged; severe acute pancreatitis; prediction model
- From: Tianjin Medical Journal 2025;53(11):1180-1185
- CountryChina
- Language:Chinese
- Abstract: Objective To investigate the influencing factors of secondary coagulation dysfunction in patients with severe acute pancreatitis(SAP)and establish the prediction model.Methods A total of 298 SAP patients in our hospital from July 2021 to July 2024 were consecutively selected,and those with secondary coagulation dysfunction were included in the observation group,while those without secondary coagulation dysfunction were included in the control group.Multivariate Logistic regression analysis was employed to investigate the risk factors for secondary coagulation dysfunction in patients with SAP and to establish a multivariate joint prediction model.The receiver operating characteristic curve(ROC)and decision curve were used to evaluate the predictive value of the multivariate joint prediction model for secondary coagulation dysfunction in SAP patients.Results The incidence of secondary coagulation dysfunction in SAP patients was 32.21%.The activated partial thromboplastin time(APTT),prothrombin time(PT)and fibrinogen(FIB)were longer in the observation group than those in the control group(P<0.05).Acute physiological and chronic health score Ⅱ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,D-dimer,C-reactive protein and albumin were independent influencing factors of secondary coagulation dysfunction in SAP patients(P<0.05).The regression equation model was established according to the results of multivariate analysis screened variables,logit(P)=-24.747+0.363×APACHEⅡ score+0.952×SOFA score-0.449×albumin+1.768×D-dimer+1.004×C-reactive protein,with a good fit.Regression model predicted that the AUC value of secondary coagulation dysfunction in SAP patients was 0.937.Decision curve analysis results showed that the Logistic regression model could achieve the maximum clinical benefit when the threshold probability was in the range of 0.06 to 0.97.Conclusion A comprehensive assessment of influencing factors can comprehensively evaluate the condition of patients and identify patients at high risk of coagulation disorders at an early stage.
