Risk Factors for Systemic Inflammatory Response Syndrome After Percutaneous Transhepatic Cholangioscopic Lithotripsy

Abstract

Background There is a lack of validated predictive models for the occurrence of systemic inflammatory response syndrome (SIRS) after percutaneous transhepatic cholangioscopic lithotripsy (PTCSL) for the treatment of hepatolithiasis. This is the first study to estimate the incidence of SIRS after PTCSL. Methods A retrospective analysis of 284 PTCSL sessions for the treatment of hepatolithiasis at our institution between January 2019 and January 2023 was performed. The development of SIRS after PTCSL was the primary study endpoint. Independent risk factors for SIRS after PTCSL were identified using univariate and multivariate logistic regression analyses. A nomogram prediction model was constructed using these independent risk factors, and the predictive value was assessed using receiver operating characteristic (ROC) curves. Results The incidence of SIRS after PTCSL was 20.77%. According to multivariate analysis, the number of PTCSL sessions (odds ratio [OR]=0.399, 95% confidence interval [CI]=0.202–0.786, p=0.008), stone location (OR=2.194, 95% CI=1.107–4.347, p=0.024), intraoperative use of norepinephrine (OR=0.301, 95% CI=0.131–0.689, p=0.004), intraoperative puncture (OR=3.476, 95% CI=1.749–6.906, P<0.001), preoperative gamma-glutamyltransferase (OR=1.002, 95% CI=1.001–1.004, p=0.009), and preoperative total lymphocyte count (OR=1.820, 95% CI=1.110–2.985, p=0.018) were found to be independent risk factors for the development of SIRS after PTCSL. These six independent risk factors were used to construct a nomogram prediction model, which showed satisfactory accuracy with an area under the ROC curve of 0.776 (95% CI: 0.702–0.850). Conclusion The number of PTCSL sessions, stone location, intraoperative use of norepinephrine, intraoperative puncture, preoperative gamma-glutamyltransferase, and preoperative total lymphocyte count may predict the occurrence of SIRS after PTCSL. This prediction model may help clinicians identify high-risk patients in advance.