Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study

Abstract

Purpose The purpose of this report was to identify effective indicators capable of predicting bacterial infection during the early stages of diabetic ketoacidosis (DKA) and to establish a diagnostic model suitable for clinical application. Methods This was a retrospective cross-sectional study. Between February 2018 and May 2023, Yuhuangding Hospital admitted 101 DKA patients, of whom 45 were diagnosed with bacterial infections. A confirmed bacterial infection was defined as documented bacteriological evidence in any bacterial sample. Clinical parameters and biological markers (including cortisol, C-reactive protein (CRP), procalcitonin, etc.) were recorded during the initial DKA phase. Multivariate regression analysis was employed to construct a diagnostic model. Results CRP (OR = 1.014, 95% CI: 1.002–1.026, p = 0.017) and cortisol (OR = 1.007, 95% CI: 1.002–1.012, p = 0.003) were found to have an independent association with bacterial infection in DKA patients. The area under the receiver operating characteristic curve (AUC) for CRP in identifying bacterial infection was 0.855 (95% CI, 0.771–0.917), with a sensitivity of 76.1% and a specificity of 83.6%. The AUC for cortisol in identifying bacterial infection was 0.847 (95% CI, 0.761–0.911), with a sensitivity of 71.7% and a specificity of 89.1%. A joint diagnostic model based on cortisol and CRP was developed through multifactor regression analysis. The AUC of this diagnostic model was 0.930 (95% CI, 0.862–0.972), resulting in a sensitivity of 93.5% and a specificity of 80.0%. Conclusion CRP and cortisol are early indicators of bacterial infection in DKA patients. Furthermore, based on their combination, the regression diagnostic model exhibits enhanced diagnostic performance.