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Development and validation of a predictive model for complications in robotic thyroid surgery
Yuhan Zhang1,2, Shuai Xue2, Jie Luo1
1Department of General Surgery, Panzhihua Central Hospital, Panzhihua, 617000, Sichuan, China.
Updates in Surgery
|August 13, 2026
Summary
A new nomogram predicts complications in robotic thyroid surgery (RTS) before surgeons complete their learning curve. Key predictors include BMI, Hashimoto
Area of Science:
- Surgical Innovation
- Medical Informatics
- Oncology
Background:
- Robotic thyroid surgery (RTS) offers cosmetic benefits and minimal invasiveness.
- Complications can arise before surgeons master RTS, impacting patient safety.
- Predictive tools are needed to identify high-risk patients preoperatively.
Purpose of the Study:
- To develop and validate a predictive model for complications in robotic thyroid surgery (RTS).
- To identify independent predictors of complications in RTS patients.
- To aid clinicians in preoperative risk assessment and decision-making.
Main Methods:
- Retrospective analysis of 236 RTS cases (Jan 2020 - Dec 2022).
- Data split into training (n=165) and validation (n=71) sets.
- Multivariate regression analysis to identify independent predictors; nomogram construction and validation.
Main Results:
- Overall complication rate was 29.24% (69/236 patients).
- Independent predictors identified: BMI (OR=3.52), Hashimoto's thyroiditis (OR=1.69), and tumor size (OR=2.37).
- The nomogram demonstrated good predictive accuracy (C-index=0.8574 in validation set).
Conclusions:
- A validated nomogram effectively predicts complications in robotic thyroid surgery (RTS) before learning curve completion.
- The model incorporates BMI, Hashimoto's thyroiditis, and tumor size.
- This tool can enhance preoperative counseling and surgical decision-making to mitigate risks.
