Response to Letter to the Editor: “Predictors of Postoperative Diabetes Insipidus Following Endoscopic Resection of Pituitary Adenomas”

Nayak, Pratima; Prevedello, Daniel M.; Kirschner, Lawrence S.; Ghalib, Luma

doi:10.1210/js.2019-00174

Cited by 1 publication

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References 3 publications

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“…While current literature extensively highlights the effectiveness and safety of endoscopic endonasal surgery, predictive factors for patient outcomes remain elusive, and would be useful to help guide postoperative adjuvant treatment including medical and radiation-based therapy. Studies have proposed the use of presenting patient symptoms including vision loss [16], tumor characteristics including size and invasion [9,[17][18][19], and pre-and postoperative endocrine lab values [9,20] to predict long-term hormonal remission and tumor control. A recent systematic review of endoscopic pituitary surgery outcomes found older age to be predictive of postoperative complications, but suggested the need for more comprehensive data [21].…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas

et al. 2021

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Purpose Functional pituitary adenomas (FPAs) cause severe neuro-endocrinopathies including Cushing's disease (CD) and acromegaly. While many are effectively cured following FPA resection, some encounter disease recurrence/progression or hormonal non-remission requiring adjuvant treatment. Identification of risk factors for suboptimal postoperative outcomes may guide initiation of adjuvant multimodal therapies. Methods Patients undergoing endonasal transsphenoidal resection for CD, acromegaly, and mammosomatotroph adenomas between 1992 and 2019 were identified. Good outcomes were defined as hormonal remission without imaging/biochemical evidence of disease recurrence/progression, while suboptimal outcomes were defined as hormonal non-remission or MRI evidence of recurrence/progression despite adjuvant treatment. Multivariate regression modeling and multilayered neural networks (NN) were implemented. The training sets randomly sampled 60% of all FPA patients, and validation/testing sets were 20% samples each. Results 348 patients with mean age of 41.7 years were identified. Eighty-one patients (23.3%) reported suboptimal outcomes. Variables predictive of suboptimal outcomes included: Requirement for additional surgery in patients who previously had surgery and continue to have functionally active tumor (p = 0.0069; OR = 1.51, 95%CI 1.12-2.04), Preoperative visual deficit not improved after surgery (p = 0.0033; OR = 1.12, 95%CI 1.04-1.20), Transient diabetes insipidus (p = 0.013; OR = 1.27, 95%CI 1.05-1.52), Higher MIB-1/Ki-67 labeling index (p = 0.038; OR = 1.08, 95%CI 1.01-1.15), and preoperative low cortisol axis (p = 0.040; OR = 2.72, 95%CI 1.06-7.01). The NN had overall accuracy of 87.1%, sensitivity of 89.5%, specificity of 76.9%, positive predictive value of 94.4%, and negative predictive value of 62.5%. NNs for all FPAs were more robust than for CD or acromegaly/mammosomatotroph alone. Conclusion We demonstrate capability of predicting suboptimal postoperative outcomes with high accuracy. NNs may aid in stratifying patients for risk of suboptimal outcomes, thereby guiding implementation of adjuvant treatment in high-risk patients.

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