Objective To explore the key factors contributing to post traumatic stress disorder (PTSD) in patients with papillary thyroid carcinoma (PTC) and to construct a risk prediction model. Methods A total of 1022 PTC patients admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2020 to June 2025 were selected and divided into a modeling group (n=715) and a validation group (n=307) at a ratio of 7∶3. The modeling group was further divided into the PTSD group (n=171) and the non-PTSD group (n=544) based on the civilian version of the PTSD Checklist (PCL-C) score and clinical diagnosis by psychiatrists. The difference indicators were included in KNN, XGBoost, and Logistic regression to evaluate the optimal model. Based on the optimal model, variables were selected and included in the CatBoost model to rank the importance of the variables, and a nomogram prediction model was constructed. Internal validation was conducted using the Bootstrap method. Results There were significant differences in age, gender, marital status, education level, monthly income, duration of illness, cancer grade, and lateral cervical lymph node metastasis between the two groups (P<0.05). Compared with XGBoost and Logistic regression, the KNN model had the best performance. Based on this, the following six important factors were selected: age, marital status, education level, monthly income, duration of illness, and cancer grade. The CatBoost model ranked the importance of the factors as marital status > cancer grade > age > duration of illness > education level > monthly income. A nomogram was constructed, and the Hosmer-Lemeshow test showed χ2=6.811, P=0.557. The area under the receiver operating characteristic (ROC) curve (AUC) for the modeling group and validation group was 0.778 (95%CI=0.733-0.822) and 0.698 (95%CI=0.611-0.784), respectively. The calibration curve showed good consistency with the actual risk. Conclusion The occurrence of PTSD is related to age, marital status, education level, monthly income, duration of illness, and cancer grade. The constructed nomogram model has good discrimination and calibration.
Key words
Papillary thyroid carcinoma /
Post-traumatic stress disorder /
Machine learning algorithm /
Influencing factors /
Receiver operating characteristic curve /
Nomogram /
Prediction model /
Internal validation
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