Establishment and Validation of a Machine‐Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra‐Abdominal Candidiasis in Septic Patients
ABSTRACT This 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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Clinical and Translational Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/cts.70140 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | ABSTRACT This 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. |
|---|---|
| ISSN: | 1752-8054 1752-8062 |