Background: Invasive fungal infections (IFI) represent a significant contributor to mortality among sepsis patients in the Intensive Care Unit (ICU). Early diagnosis of IFI is challenging, and currently, there are no predictive tools for identifying sepsis patients who may develop IFI. Our study aims to develop a predictive scoring system to assess the risk of IFI in patients with sepsis admitted to the ICU.
Methods: A retrospective collection of data from a total of 549 patients was conducted. Data-driven, clinically knowledge-driven, and decision tree models were used to identify predictive variables for risk of IFI in ICU patients with sepsis. Demographic data, vital signs, laboratory values, comorbidities, medication use, and clinical outcomes were all collected. The optimal model was selected based on model performance and clinical utility to establish a risk score.
Results: Among adult patients with sepsis admitted to the ICU, 127 patients (23.1%) developed IFI. The final data-driven model included four predictive factors, the clinically knowledge-driven model included three predictive factors, and the decision tree model included two. Based on the good performance and clinical utility of the clinically knowledge-driven model, it was chosen as the optimal risk scoring model (C-statistics: 0.79 (95% confidence interval (CI): 0.75-0.83); Hosmer-Lemeshow (H-L) test P = 0.884). The ICU sepsis patient invasive fungal infection risk (AMI) score, created based on the clinically knowledge-driven model, includes mechanical ventilation, application of immunosuppressants, and the types of antibiotics used. The C-statistics for this risk score was 0.79 (95% CI:0.75-0.84) with good calibration (H-L test P = 0.992 and see calibration curve: Fig. 2). Moreover, in terms of clinical utility, the decision curve analysis for AMI showed a favorable net benefit.
Conclusions: The application of the AMI score can effectively distinguish whether ICU sepsis patients will develop IFI, which is beneficial for clinicians to formulate targeted and timely preventive and treatment measures based on the risk of IFI.
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