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Keywords

hypertrophic scar, pathologic scarring, prediction, prevention, risk, scarring, thyroidectomy, wound healing

 

Authors

  1. Xie, Hang MS
  2. Xiang, Ying BN
  3. Yang, E. MM
  4. Zhang, HengShu MM

ABSTRACT

OBJECTIVE: To identify the risk factors of hypertrophic scarring (HS) after thyroidectomy and construct a risk prediction model.

 

METHODS: From November 2018 to March 2019, the clinical data of patients undergoing thyroidectomy were collected for retrospective analysis. According to the occurrence of HS, the patients were divided into an HS group and a non-HS group. Univariate analysis and binary logistic regression analysis were conducted to explore the independent risk factors for HS. Receiver operating characteristic analysis was also carried out.

 

RESULTS: In this sample, 121 of 385 patients developed HS, an incidence of 31.4%. Univariate analysis showed significant differences in sex, age, postoperative infection, history of abnormal wound healing, history of pathologic scar, family history of pathologic scar, and scar prevention measures between the two groups (P < .05). Binary logistic regression analysis indicated that age 45 years or younger (odds ratio [OR], 1.815), history of abnormal wound healing (OR, 4.247), history of pathologic scarring (OR, 9.840), family history of pathologic scarring (OR, 5.708), and absence of preventive scar measures (OR, 5.566) were independent factors for HS after thyroidectomy. The area under the receiver operating characteristic curve was 0.837. When the optimal diagnostic cutoff value was 0.206, the sensitivity was 0.661, and the specificity was 0.932.

 

CONCLUSIONS: The development of HS after thyroidectomy is related to many factors, and the proposed risk prediction model based on the combined risk factors shows a good predictive value for postoperative HS. When researchers consider the prevention and treatment of scarring in patients at risk, the incidence of HS in different populations can provide theoretical support for clinical decision-making.