Risk Prediction Model for Elderly Differentiated Thyroid Cancer Based on Combined Sleep Quality Assessment and...

Area of Science:

• Endocrinology
• Oncology
• Diagnostic Imaging

Background:

• Distinguishing benign from malignant thyroid nodules in the elderly is challenging.
• Sleep quality has not been extensively studied as a diagnostic factor for thyroid nodules.
• Developing accurate risk prediction models is crucial for guiding clinical management.

Purpose of the Study:

• To investigate the diagnostic value of sleep quality in differentiating benign and malignant thyroid nodules in the elderly.
• To construct and validate a risk prediction model integrating sleep quality and ultrasound findings.
• To provide a basis for clinical treatment decisions in elderly thyroid cancer patients.

Main Methods:

• Collected clinical data, Pittsburgh Sleep Quality Index (PSQI) scores, and multimodal ultrasound data from elderly patients.
• Utilized postoperative pathological results as the gold standard for diagnosis.
• Employed binary logistic regression and ROC curve analysis to identify risk factors and validate the prediction model.

Main Results:

• Identified independent risk factors including TPOAB positivity, daytime dysfunction, PSQI > 7, irregular nodule shape, calcification, blood flow, high elasticity, and low contrast enhancement.
• The combined prediction model achieved an AUC of 0.860, outperforming models using only ultrasound or ultrasound with TPOAB.
• The nomogram-based model demonstrated excellent discrimination, calibration, and clinical utility upon validation.

Conclusions:

• Integrating sleep quality assessment with multimodal ultrasound enhances the preoperative diagnosis of thyroid nodules in the elderly.
• The developed risk prediction model, presented as a nomogram, offers an intuitive and reliable tool for clinical decision-making.
• This approach may significantly improve preoperative diagnostic accuracy for elderly patients with thyroid nodules.