Services on Demand
Journal
Article
Indicators
- Cited by SciELO
- Access statistics
Related links
- Cited by Google
- Similars in SciELO
- Similars in Google
Share
Revista Ciencias de la Salud
Print version ISSN 1692-7273On-line version ISSN 2145-4507
Abstract
MADRID IDARRAGA, Yarly Katery et al. Modeling of the Time it Takes for a Patient with Differentiated Thyroid Cancer to Present the First Relapse. Rev. Cienc. Salud [online]. 2022, vol.20, n.2, pp.49-62. Epub Aug 20, 2023. ISSN 1692-7273. https://doi.org/10.12804/revistas.urosario.edu.co/revsalud/a.9684.
Introduction:
Between all thyroid carcinomas, the differentiated are predominant. According to the Colombian Association of Endocrinology, the relapse rate can be up to 30%, especially in patients older than 45 years old and with aggressive tumor characteristics. In this investigation, the time that elapses between the initial surgical treatment and the first relapse of the disease was estimated.
Materials and methods:
A data file was taken with the records of 469 patients with differentiated thyroid cancer (CDT) treated in a specialized clinic of fourth level of complexity IV in the city of Bogotá (Colombia). Data were collected between January 1997 and December 2012 and were statistically analyzed using parametric and non-parametric models to obtain survival curves and risk.
Results:
With the non-parametric method, it is evident that in 8.5 years 75% of the patients will not have presented the first relapse in CDT. While applying the parametric method 50% of patients who do not have a postreatment thyroglobulin or one less than or equal to 1 ng/mL and a tumour size less than or equal to 2 cm, their estimated time of First relapse was 29.2 years.
Conclusions:
Disease-free time and the risk of relapse for patients with cdt is affected by the presence of a tumor size greater than 2 cm at the time of consultation and levels of thyroglobulin greater than 1 ng/mL, recorded at the end of the treatment.
Keywords : Differentiated thyroid cancer; first relapse; survival; risk; shape-dependent survival models.