Predicting Infection Risk in Acute Compartment Syndrome: A Nomogram Model Based on Admission Blood Indicators

JianJun Song,1 YueJun Liu,2 Meng Yang,3 Yan Li,1 YueYue Hu4 1Emergency ICU, Gynecology Department, Affiliated Hospital of Hebei University of Engineering, Handan, Hebei, People’s Republic of China; 2Department of Gynecology, Gynecology Department, Affiliated Hospital of Hebei University of Engineering, Handan, Hebei, People’s Republic of China; 3Department of Obstetrics and Gynaecology, Mian County People’s Hospital, Handan, Hebei, People’s Republic of China; 4Emergency Department, Gynecology Department, Affiliated Hospital of Hebei University of Engineering, Handan, Hebei, People’s Republic of ChinaCorrespondence: YueYue Hu, Emergency Department, Gynecology Department, Affiliated Hospital of Hebei University of Engineering, Handan, Hebei, People’s Republic of China, Email [email protected]: Acute compartment syndrome (ACS) is a serious complication after tibial fracture and it commonly needs fasciotomy, which may affect 20.4% of patients. However, the predictors of infection remain debated. Our purpose aims to explore the role of admission blood indicators in infection in ACS patients.Methods: We collected clinical data on ACS patients between Jan. 2015 and Jan 2025. According to whether ACS patients suffer from infection or not, they were divided into two groups. We copy with these data by R language software.Results: Based on univariate analysis, we found that time from injury to admission, time from injury to surgery, and numerous admission blood indicators were relevant to ACS, but logistic regression analysis showed that neutrophil (NEU), white blood cell (WBC), C-reactive protein (CRP) and time from injury to surgery (all p< 0.0001) were predictors for infection in ACS patients. Our nomogram prediction model with 0.995 in AUC with good consistency and good clinical practicality.Conclusion: We found that the levels of NEU, WBC, CRP and time from injury to surgery were predictors for infection in ACS patients. Our nomogram prediction model can efficiently predict infection in ACS patients.Keywords: acute compartment syndrome, infection, admission blood indicators, nomogram prediction model, ACS