ArticleFrontiers in endocrinology2023
Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: construction of a dynamic predictive model.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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8 authors at 2 institutions in 1 country.
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Abstract
Objective: The aim of this study was to investigate the relationships and predictive value of preoperative peripheral blood inflammatory markers as a means by which to assess risk for patients with ≤ 1 cm papillary thyroid carcinomas (PTCs). In addition, a preoperative risk stratification predictive model was constructed and validated. Methods: Clinical and pathologic data, as well as preoperative blood specimens, were collected from patients who underwent initial thyroid cancer surgery at the Hangzhou First People's Hospital, from January 2014 to January 2023. Risk assessment was performed based on postoperative pathology according to the 2015 ATA guidelines for recurrence risk stratification. Using univariate analysis and multivariate logistic regression, we identified independent risk factors associated with risk stratification. A predictive model was established and its discriminative and calibration abilities were validated. An independent validation dataset was used to verify the model, and the model was deployed as an online calculator. Results: A total of 1326 patients were included in the study, with 1047 cases (79.0%) classified as low risk and 279 cases (21.0%) classified as intermediate to high risk. The modeling group consisted of 981 cases, through univariate analysis and multivariate logistic regression analysis, preoperative blood Neutrophil/Lymphocyte Ratio (NLR), gender, tumor diameter, and multifocality were identified as independent risk factors that distinguished between low and intermediate to high risk patients with ≤ 1 cm PTCs. The clinical predictive model exhibited an AUC of 0.785, specificity of 70.6%, and sensitivity of 75.8%. For the independent validation group of 345 patients, the AUC was 0.813, specificity was 83.8%, and sensitivity was 70.4%. The calibration curve and clinical decision curve indicate that the model demonstrates excellent calibration performance. Conclusion: A dynamic clinical predictive model based on preoperative blood NLR and clinical information for patients with ≤ 1 cm PTCs was established. The model is useful for preoperative risk assessment of patients with ≤ 1 cm PTCs.
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