Department of Dermatology Clinic, Qinhuangdao Jungong Hospital, Qinhuangdao, 066000, China
Objective: This study aims to analyze the risk factors of sternal incision infection (SWI) based on large sample clinical data, and construct a nomogram prediction model with independent risk factors, in order to provide prediction tools for clinical treatment of SWI. In addition, based on hybrid reality technology (MR), it explores a comprehensive plastic surgery diagnosis and treatment strategy with early diagnosis, classified diagnosis and treatment, thorough debridement and timely reconstruction, in order to comprehensively improve the cure rate of SWI patients.
Methods: The cardiovascular surgery patients who underwent median sternotomy (MSI) in our hospital from June 2010 to June 2021 were retrospectively analyzed (23182 cases). Based on whether there was postoperative infection, they were divided into the control group and the observation group. The risk factors of SWI were analyzed by univariate and multivariate logistic regression model. At the same time, taking SWI patients admitted to our hospital from June 2019 to June 2021 for plastic surgery diagnosis and treatment as clinical data (35 cases), the SWI patients were classified for diagnosis and treatment in combination with diagnostic criteria, including superficial sternal incision infection (sswi) and deep sternal incision infection (dswi), and the clinical efficacy was evaluated according to the patient's hospitalization time, incision recovery, cure rate and other indicators.
Results: In the analysis of risk factors, univariate Logistic regression analysis showed that age, BMI, obesity, two operations, hypertension, diabetes, ICU time, cardiogenic shock, myocardial infarction, respiratory tract infection, renal failure, hypoproteinemia, angina pectoris and hyperlipidemia were statistically different (P<0.05). Multivariate Logistic regression analysis showed that BMI, two operations, diabetes and ICU time were independent risk factors for SWI patients. The c-test parameter of the nomogram prediction model is 0.705 (95% CI: 0.746-0.803), the calibration curve is consistent with the ideal curve, and the p-value of Hosmer-Lemeshow goodness of fit test is 0.538. At the level of plastic surgery diagnosis and treatment, the average hospital stay of 27 patients with sswi was 57 days, and the surgical incision healing and cure rate of 25 patients was 92.6%; The average length of stay of 8 patients with dswi was 32 days. The surgical incision healed and the cure rate was 100%.
Conclusion: The nomogram prediction model based on independent risk factors of SWI patients is both reliable and predictive, and then has guiding significance for early screening and clinical intervention of SWI high-risk groups; At the same time, comprehensive plastic surgery can improve the wound healing and clinical cure rate of SWI patients, and then assist the preoperative planning and intraoperative guidance of MSI in SWI patients.
Sternal incision infection, Risk factors, Nomogram prediction model, Plastic surgery diagnosis and treatment, SSWI, DSWI.