Investigation of risk factors and predictive model development for the progression of incarcerated inguinal hernia to strangulation

PURPOSE: Inguinal incarcerated hernia is a common disease in hernia surgery. This study was conducted to collect risk factors for strangulated inguinal hernia and to construct a predictive model to assist surgeons in decision-making. METHODS: We conducted a retrospective analysis of the clinical data from patients diagnosed with incarcerated inguinal hernias in the Department of Gastrointestinal Surgery, Huai'an Hospital Affiliated to Xuzhou Medical University, from January 2020 to January 2023. The independent risk factors for the progression of incarcerated inguinal hernias to strangulation were screened in the modeling group using univariate and logistic regression analyses and were used for construction of the nomogram prediction model. RESULTS: Tenderness in the inguinal region (OR = 9.164, 95% CI = 2.540 ~ 33.066), intestinal obstruction (OR = 6.781, 95% CI = 1.568 ~ 29.330), elevated CRP (OR = 1.023, 95% CI = 1.009 ~ 1.037), and elevated neutrophils (OR = 1.253, 95% CI = 1.073 ~ 1.463) were identified as independent risk factors for the progression of incarcerated inguinal hernias to strangulation, while elevated prealbumin (OR = 0.992, 95% CI = 0.986 ~ 0.998) was identified as an independent protective factor. A risk prediction model for the progression of incarcerated inguinal hernias to strangulation was constructed based on these five observational indicators. The model's discrimination was tested by the ROC curve and showed AUC = 0.906, 95% CI = 0.851 ~ 0.962, and P < 0.05. The calibration curve showed good agreement with the ideal curve. The clinical decision curves showed that the model had good clinical utility. CONCLUSION: Tenderness in the inguinal region, combined intestinal obstruction, elevated CRP, and elevated neutrophils are independent risk factors for strangulation in incarcerated inguinal hernias, while elevated prealbumin is an independent protective factor. The constructed nomogram prediction model exhibits high sensitivity, specificity, and clinical utility.