Development and Validation of a Prediction Model for Thyroid Dysfunction in Patients During Immunotherapy

Objective

This study was designed to develop and validate a predictive model for assessing the risk of thyroid toxicity following treatment with immune checkpoint inhibitors.

Methods

A retrospective analysis was conducted on a cohort of 586 patients diagnosed with malignant tumors who received programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Logistic regression analyses were performed on the training set to identify risk factors of thyroid dysfunction, and a nomogram was developed based on these findings. Internal validation was performed using K-fold cross-validation on the validation set. The performance of the nomogram was assessed in terms of discrimination and calibration. Additionally, decision curve analysis was utilized to demonstrate the decision efficiency of the model.

Results

Our clinical prediction model consisted of 4 independent predictors of thyroid immune-related adverse events, namely baseline thyrotropin (TSH, OR = 1.427, 95%CI:1.163-1.876), baseline thyroglobulin antibody (TgAb, OR = 1.105, 95%CI:1.035-1.180), baseline thyroid peroxidase antibody (TPOAb, OR = 1.172, 95%CI:1.110-1.237), and baseline platelet count (platelet, OR = 1.004, 95%CI:1.000-1.007). The developed nomogram achieved excellent discrimination with an area under the curve of 0.863 (95%CI: 0.817-0.909) and 0.885 (95%CI: 0.827-0.944) in the training and internal validation cohorts respectively. Calibration curves exhibited a good fit, and the decision curve indicated favorable clinical benefits.

Conclusion

The proposed nomogram serves as an effective and intuitive tool for predicting the risk of thyroid immune-related adverse events, facilitating clinicians making individualized decisions based on patient-specific information.