Prognostic factors of biochemical remission after transsphenoidal surgery for acromegaly: a structured review

N Agrawal, AG Ioachimescu - Pituitary, 2020 - Springer
N Agrawal, AG Ioachimescu
Pituitary, 2020Springer
Purpose Biochemical control is the main determinant of survival, clinical manifestations and
comorbidities in acromegaly. Transsphenoidal selective adenomectomy (TSA) is the initial
treatment of choice with reported biochemical remission rates varying between 32 and 85%.
Understanding the limiting factors is essential for identification of patients who require
medical treatment. Methods We reviewed the English literature published in
Medline/Pubmed until Dec 31, 2019 to identify eligible studies that described outcomes of …
Purpose
Biochemical control is the main determinant of survival, clinical manifestations and comorbidities in acromegaly. Transsphenoidal selective adenomectomy (TSA) is the initial treatment of choice with reported biochemical remission rates varying between 32 and 85%. Understanding the limiting factors is essential for identification of patients who require medical treatment.
Methods
We reviewed the English literature published in Medline/Pubmed until Dec 31, 2019 to identify eligible studies that described outcomes of TSA as primary therapy and performed analyses to determine the main predictors of remission.
Results
Most publications reported single-institution, retrospective studies. The following preoperative parameters were consistently associated with lower remission rates: cavernous sinus invasion by imaging, larger tumor size and higher GH levels. Young age and preoperative IGF-1 levels were predictive in some studies. When controlled for covariates, the best single preoperative predictor was cavernous sinus invasion, followed by preoperative GH levels. Conversely, low GH level in the first few days postoperatively was a robust predictor of durable remission. The influence of tumor histology (sparsely granular pattern, co-expression of prolactin and proliferation markers) on surgical remission remains to be established. Few studies developed predictive models that yielded much higher predictive values than individual parameters.
Conclusion
Surgical outcome prognostication systems could be further generated by machine learning algorithms in order to support development and implementation of personalized care in patients with acromegaly.
Springer
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