Affiliation:
1. Department of Critical Care Medicine State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College Beijing China
Abstract
ABSTRACTThis study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra‐abdominal candidiasis (IAC) in septic patients. A prospective cohort study of 633 consecutive patients diagnosed with sepsis and intra‐abdominal infection (IAI) was performed. We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI. A machine‐learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. A nomogram model was constructed, and the discrimination, calibration, and clinical effectiveness of the model were verified. High‐dose corticosteroids receipt, the CD4+T/CD8+ T ratio, total parenteral nutrition, gastrointestinal perforation, (1,3)‐β‐D‐glucan (BDG) positivity and broad‐spectrum antibiotics receipt were independent predictors of IAC. Using the above parameters to establish a nomogram, the area under the curve (AUC) values of the nomogram in the derivation and validation cohorts were 0.822 (95% CI 0.777–0.868) and 0.808 (95% CI 0.739–0.876), respectively. The AUC in the derivation cohort was greater than the Candida score [0.822 (95% CI 0.777–0.868) vs. 0.521 (95% CI 0.478–0.563), p < 0.001]. The calibration curve showed good predictive values and observed values of the nomogram; the Decision Curve Analysis (DCA) results showed that the nomogram had high clinical value. In conclusion, we established a nomogram based on the CD4+/CD8+ T‐cell ratio and clinical risk factors that can help clinical physicians quickly rule out IAC or identify patients at greater risk for IAC at the onset of infection.