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Emergency craniotomy in patients with traumatic brain injury poses a significant risk for surgical site infections (SSIs). Understanding the risk factors and pathogenic characteristics of SSIs in this context is crucial for improving outcomes. This comprehensive retrospective analysis spanned from February 2020 to February 2023 at our institution. We included 25 patients with SSIs post-emergency craniotomy and a control group of 50 patients without SSIs. Data on various potential risk factors were collected, including demographic information, preoperative conditions, and intraoperative details. The BACT/ALERT3D Automated Bacterial Culture and Detection System was utilized for rapid bacterial pathogen identification. Statistical analyses included univariate and multivariate logistic regression to identify significant risk factors for SSIs. The study identified Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus as the most prevalent pathogens in SSIs. Significant risk factors for SSIs included the lack of preoperative antibiotic use, postoperative drainage tube placement, diabetes mellitus, and the incorporation of invasive procedures, all of which showed a significant association with SSIs in the univariate analysis. The multivariate analysis further highlighted the protective effect of preoperative antibiotics and the increased risks associated with anaemia, diabetes mellitus, postoperative drainage tube placement, and the incorporation of invasive procedures. Our research underscores the critical role of factors like insufficient preoperative antibiotics, postoperative drainage, invasive techniques, anaemia, and diabetes mellitus in elevating the risk of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy. Enhanced focus on these areas is essential for improving surgical outcomes.
The current methods for the prediction of mortality and amputation for inpatients with diabetic foot (DF) use only conventional, simple variables, which limits their performance. Here, we used a random survival forest (RSF) model and multicomponent variables to improve the prediction of mortality and amputation for these patients. We performed a retrospective cohort study of 175 inpatients with DF who were recruited between 2014 and 2021. Thirty-one predictors in six categories were considered as potential covariates. Seventy percent (n = 122) of the participants were randomly selected to constitute a training set, and 30% (n = 53) were assigned to a testing set. The RSF model was used to screen appropriate variables for their value as predictors of 2-year all-cause mortality and amputation, and a multicomponent prediction model was established. Model performance was evaluated using the area under the curve (AUC) and the Hosmer–Lemeshow test. The AUCs were compared using the Delong test. Seventeen variables were selected to predict mortality and 23 were selected to predict amputation. Uric acid and alanine transaminase were the top two most useful variables for the prediction of mortality, whereas urine protein and platelet were the top variables for the prediction of amputation. The AUCs were 0.913 and 0.851 for the prediction of mortality for the training and testing sets, respectively; and the equivalent AUCs were 0.963 and 0.893 for the prediction of amputation. There were no significant differences between the AUCs for the training and testing sets for both the mortality and amputation models. These models showed a good degree of fit. Thus, the RSF model can predict mortality and amputation in inpatients with DF. This multicomponent prediction model could help clinicians consider predictors of different dimensions to effectively prevent DF from clinical outcomes .
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