Predictive model for increased postoperative drainage volume in patients with papillary thyroid carcinoma: a retrospective cohort study.

Background: Papillary thyroid carcinoma (PTC) is the predominant type of thyroid cancer, with a rapidly increasing incidence worldwide. Surgery is the cornerstone of treatment for PTC. Postoperative placement of a negative-pressure drainage tube is commonly performed to prevent hematoma, seroma, and chyle leak. However, the timing for drain removal relies largely on the surgeon's own clinical judgment. There is no reliable model to help identify patients who face a higher risk of increased postoperative drainage output volume (DOV). Excessive DOV may lead to a prolonged hospital stay, increased patient discomfort, and a higher risk of infection. Therefore, we aim to develop a reliable predictive tool for postoperative DOV in patients with PTC. Methods: This retrospective study enrolled 171 patients with PTC who underwent surgery between July 2024 and May 2026. These patients were randomly assigned to training (n=120) and validation (n=51) cohorts in a 7:3 ratio. Univariable and multivariable logistic regression analyses were performed in the training cohort to identify independent risk factors for postoperative DOV in patients with PTC. A nomogram was constructed based on the independent risk factors. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were utilized to assess the predictive performance of the nomogram. Results: Patients were divided into high-output (HO, n=60) and low-output (LO, n=60) groups based on the median postoperative DOV. Univariable analysis revealed statistically significant differences between the groups in surgical time, number of tumors, number of central lymph node (LN) metastases (LNM), total number of LNs dissected, total number of LNM, scope of surgery, thyroid capsular invasion, and Hashimoto's thyroiditis (HT). Multivariable logistic regression analysis revealed that the total number of LNs dissected [odds ratio (OR) =1.125, 95% confidence interval (CI): 1.022-1.239, P=0.02], scope of surgery (OR =6.544, 95% CI: 1.445-29.64, P=0.02), and thyroid capsular invasion (OR =6.507, 95% CI: 1.63-25.975, P=0.008) were independent risk factors for increased postoperative DOV. The nomogram constructed based on these independent risk factors showed good discrimination in the internal validation cohort, with an area under the ROC curve (AUC) of 0.823 (95% CI: 0.746-0.901) in the training cohort and 0.849 (95% CI: 0.737-0.960) in the validation cohort. The calibration curve showed good agreement between the nomogram-predicted outcomes and the observed outcomes. DCA further confirmed its favorable clinical utility. Conclusions: We developed and validated a nomogram that integrates the total number of LNs dissected, scope of surgery, and thyroid capsular invasion. This model effectively predicts the likelihood of increased postoperative DOV and provides clinically useful guidance for drain management and recovery in patients with PTC. Nevertheless, these findings warrant further confirmation through large-scale, multicentre external validation studies before clinical implementation.