Correlation of broad-spectrum antibiotic utilization patterns with invasive candidiasis in critically ill patients and development of an early prediction model

Invasive candidiasis (IC) remains an infection with high incidence and mortality rates in the ICU setting, particularly among patients treated with broad-spectrum antibiotics. This study aims to investigate the association between detailed antibiotic usage profiles and the occurrence of IC, and to develop an IC-predictive model specialized for patients who received broad-spectrum antibiotics. We retrospectively collected detailed information on antibiotic categories, treatment duration, combination therapies and other clinical data of enrolled patients. Univariate and multivariate logistic regression analyses were performed to identify risk factors for IC and to construct a nomogram model. We analyzed 1,260 patients treated with broad-spectrum antibiotics and 877 without. After adjusting for IC-related risk factors using propensity score matching (PSM) and inverse probability of treatment weighting (IPTW), broad-spectrum antibiotics remained an independent risk factor for IC. Among patients receiving antibiotic monotherapy, lipopeptides, glycopeptides and oxazolidinones were the top three antibiotic classes associated with an increased risk of IC. The duration of antibiotic therapy showed a positive correlation with IC risk. Combination therapy significantly increased the risk of IC (odds ratio [OR] = 2.341, 95% confidence interval [CI]: 1.316-4.162), with the combination of beta-lactams/beta-lactamase inhibitors and glycopeptides showing the highest IC risk. Based on univariate and multivariate regression analyses, we developed an IC risk nomogram specific to patients receiving broad-spectrum antibiotics, including smoking history, sepsis, continuous renal replacement therapy (CRRT), prognostic nutritional index (PNI), use of beta-lactams/beta-lactamase inhibitors and plasma (1,3)-β-D-glucan (BDG) positivity. The model demonstrated good predictive performance with an area under the curve (AUC) of 0.863 (95% CI: 0.806-0.920) in the training dataset and 0.784 (95% CI: 0.685-0.883) in the validation dataset. Decision curve analysis (DCA) and clinical impact curve (CIC) analysis demonstrated favorable clinical benefits of the model. Our findings suggest that specific antibiotic profiles-type, duration, and combination-were significantly associated with IC. Furthermore, we developed a nomogram to predict IC risk among patients treated with broad-spectrum antibiotics, which showed good predictive performance and potential clinical utility.