Analysis of preoperative influential factors and construction of a predictive nomogram of difficult thyroidectomy

Author:

Dong Meng,Song Jun-Long,Hu Lin-Lin,Hong Chen-Chen,Nie Xin-Yue,Wang Zhong,Liao Shi-Chong,Yao Feng

Abstract

Abstract Objective To explore the preoperative influential factors of difficult thyroidectomy and establish a preoperative nomogram for predicting the difficulty of thyroidectomy. Methods A total of 753 patients who underwent total thyroidectomy with central lymph node dissection between January 2018 and December 2021 were retrospectively enrolled in this study and randomly divided into training and validation groups at a ratio of 8:2. In both subgroups, the patients were divided into difficult thyroidectomy and nondifficult thyroidectomy groups based on the operation time. Patient age, sex, body mass index (BMI), thyroid ultrasound, thyroid function, preoperative fine needle aspiration (FNA), postoperative complications and other data were collected. Logistic regression analysis was performed to identify the predictors of difficult thyroidectomy, and a nomogram predicting surgical difficulty was created. Results Multivariate logistic regression analysis demonstrated that male sex (OR = 2.138, 95% CI 1.055–4.336, p = 0.035), age (OR = 0.954, 95% CI 0.932–0.976, p < 0.001), BMI (OR = 1.233, 95% CI 1.106–1.375, p < 0.001), thyroid volume (OR = 1.177, 95% CI 1.104–1.254, p < 0.001) and TPO-Ab (OR = 1.001, 95% CI 1.001–1.002, p = 0.001) were independent risk factors for difficult thyroidectomy. The nomogram model incorporating the above predictors performed well in both the training and validation sets. A higher postoperative complication rate was found in the difficult thyroidectomy group than in the nondifficult thyroidectomy group. Conclusion This study identified independent risk factors for difficult thyroidectomy and created a predictive nomogram for difficult thyroidectomy. This nomogram may help to objectively and individually predict surgical difficulty before surgery and provide optimal treatment.

Funder

Fundamental Research Funds for the Central Universities

the Natural Science Foundation of Hubei Province

Publisher

Springer Science and Business Media LLC

Subject

General Medicine,Surgery

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