肺癌患者发生甲状腺免疫相关不良事件预测模型的构建与验证

Background  In recent yearsimmune checkpoint inhibitorsICIs have shown remarkable efficacy in lung cancer treatmentbut their immune-related adverse eventsirAEs have also received widespread attention. Thyroid injury is the most common endocrine irAEsso it is of great clinical value to construct a prediction model. Objective  To establish a prediction model of thyroid irAE in lung cancer patients receiving ICI therapy. Methods  A total of 243 lung cancer patients treated with ICI in Hebei Provincial People's Hospital from January 2020 to March 2024 were retrospectively included as study subjectsand randomly divided into training set169 cases and validation set74 cases according to a ratio of 7 3. According to thyroid functionthe training set was divided into thyroid irAE group71 cases and thyroid non-irAE group98 cases. General data and laboratory test indicators of the patients were collectedand subgroup analysis was performed according to Common Terminology Criteria for Adverse EventsCTCAE. Univariate analysis was performed to screen variablesand multivariate logistic regression was used to analyze the independent influencing factors of thyroid irAE. Variance inflation factor was used to evaluate multicollinearity among the predictors. A thyroid irAE nomogram model was constructed based on the results of multivariate logistic regression analysis. The receiver operating characteristicROC curve of thyroid irAE was drawnand the area under ROC curveAUC was calculated. The model was validated internally by Bootstrap self-sampling methodand the model efficacy was evaluated by Hosmer Lemeshow testdecision curve analysisDCAand clinical impact curveCIC. Kaplan-Meier analysis was used to compare the cumulative incidence of thyroid irAE in each risk stratification. Multiple logistic regression was used to analyze the influencing factors of thyroid irAE in patients with different CTCAE grades. Results  Among 169 patients with lung cancer13881.66% were males and 3118.34% were females. The median age was 66 years607171 cases42.01% in thyroid irAE group and 98 cases57.99% in thyroid non-irAE group. There were significant differences in ICI treatment cycleKi-67tumor size and FT3 between the two groupsP<0.05. Multivariate Logistic regression analysis showed that TSHOR=1.63695%CI=1.070-2.503P=0.023FT3OR=6.86895%CI=2.812-16.776P<0.001tumor sizeOR=0.96595%CI=0.942-0.989P=0.004Ki-67OR=1.02895%CI=1.008-1.048P=0.005 and CYFRA21-1OR=1.05095%CI=1.016-1.085P=0.003 were the independent influencing factors for thyroid irAE in lung cancer patientsP0.05. The calibration curve showed that the predicted probability of thyroid irAE was in good agreement with the observed value. The DCA curve shows that the model can provide a good clinical net benefit in the probability range of 0 to 95%. The CIC curve showed that with the increase of the model thresholdthe predicted occurrence of thyroid irAE was consistent with the actual diagnosis resultsindicating that the model had good clinical practicability. Based on the optimal probability threshold of thyroid irAE prediction modelPr value 0.474243 patients with lung cancer were divided into high-risk group 9438.68%and low-risk group 14961.32%. Kakaplan Meier analysis showed that the cumulative incidence of thyroid irAE in high-risk group was significantly higher than that in low-risk group at the 6th treatment cycle2 =28.15P<0.001and the incidence risk in high-risk group was 2.63 times higher than that in low-risk groupHR=2.6395%CI=1.770-3.905P<0.001. The results of multivariate logistic analysis of influencing factors of thyroid irAE at different CTCAE grades showed that FT3OR=5.51395%CI=1.846-16.465P=0.002tumor sizeOR=0.96395%CI=0.928-0.999P=0.044 were the independent influencing factors of thyroid irAE in CTCAE 2 subgroups. TSHFT3tumor sizeKi-67 and CYFRA21-1 were the independent influencing factors of thyroid irAE in CTCAE grade 1 subgroupP<0.05suggesting that the model has more predictive advantages for thyroid irAE of CTCAE grade 1. Conclusion  In this studythyroid irAE prediction model was established based on TSHFT3tumor sizeKi-67 and CYFRA21-1. Active monitoring of thyroid function in patients with prediction probability 47.4% is helpful to reduce the risk of immunotoxicity and improve the quality of life.