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ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R
Volume 39 Issue 6
Jun.  2023
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Article Contents

Influencing factors for persistent inflammation, immunosuppression, and catabolism syndrome in patients with severe acute pancreatitis and establishment of a predictive model

DOI: 10.3969/j.issn.1001-5256.2023.06.019
Research funding:

National Natural Science Foundation of China (81970558)

More Information
  • Corresponding author: TANG Guodu, tguodu02@126.com (ORCID:0000-0002-2849-3244)
  • Received Date: 2022-10-24
  • Accepted Date: 2022-11-28
  • Published Date: 2023-06-20
  •   Objective  To investigate the influencing factors for persistent inflammation, immunosuppression, and catabolism syndrome (PICS) in patients with severe acute pancreatitis(SAP), and to establish a predictive model.  Methods  A retrospective analysis was performed for the clinical data of 163 patients who were admitted to the intensive care unit and the emergency intensive care unit due to SAP in The First Affiliated Hospital of Guangxi Medical University from May 2012 to May 2022, and according to the diagnostic criteria for PICS, these patients were divided into PICS group (65 SAP patients with PICS) and non-PICS group (98 SAP patients without PICS). The independent-samples t test was used for comparison of normally distributed continuous data between two groups, and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between two groups; the chi-square test or the Fisher's exact test was used for comparison of categorical data between two groups. Variance inflation factor and correlation matrix heatmap were used to evaluate multicollinearity between variables, and Lasso regression and multivariate logistic regression were used to identify independent risk factors and establish a nomogram predictive model. The receiver operating characteristic (ROC) curve, the calibration curve, and the Hosmer-Lemeshow goodness-of-fit test were used for the internal validation of the model, and the decision curve was used to evaluate the clinical practicability of the model.  Results  The univariate analysis showed that there were significant differences between the PICS group and the non-PICS group in mean arterial pressure, hemoglobin, hematocrit (HCT), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), blood urea nitrogen, creatinine, Glasgow coma score, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, Sequential Organ Failure Assessment (SOFA) score, mechanical ventilation, acute respiratory distress syndrome, acute kidney injury (AKI), acute liver injury, hypovolemic shock, sepsis, intra-abdominal hypertension, intra-abdominal hemorrhage, and multiple organ dysfunction syndrome (all P < 0.05). The Lasso regression analysis showed that related predictive variables included PLR, HCT, APACHE Ⅱ, SOFA, mechanical ventilation, AKI, hypovolemic shock, and intra-abdominal hypertension, and the multivariate logistic regression analysis showed that PLR (odds ratio [OR]=1.006, P < 0.05), mechanical ventilation (OR=4.324, P < 0.05), AKI (OR=3.432, P < 0.05), and hypovolemic shock (OR= 6.910, P < 0.05) were independent risk factors for PICS in patients with SAP. Model fitting was performed for the above factors, and bootstrap internal validation showed that the nomogram model had an area under the ROC curve of 0.874 (95% confidence interval: 0.822-0.925); the calibration curve of the model was close to the reference curve, and the Hosmer-Lemeshow goodness-of-fit test showed that the model was well fitted (χ2=8.895, P=0.351). The decision curve analysis showed that the predictive model had good clinical practicability.  Conclusion  PLR, mechanical ventilation, AKI, and hypovolemic shock are independent risk factors for PICS in patients with SAP, and the nomogram model established has good discriminatory ability, calibration, and clinical practicability.

     

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