Predictors of postoperative biochemical remission in acromegaly

Abstract

OBJECTIVE Acromegaly is a rare neuroendocrine condition that can lead to significant morbidity; therefore, large studies are invaluable for understanding the disease burden. Despite the vast population size in China, studies on acromegaly remain sparse. In this report, we aimed to investigate clinical characteristics and predictors of biochemical remission after surgery for acromegaly. METHODS A retrospective nationwide study was conducted using patient-reported data from the China Acromegaly Patient Association (CAPA) from 1998 to 2018. Univariate analyses were performed using Wilcoxon rank-sum tests, two-sample t-tests, and chi-squared tests. Using the purposeful selection method, multivariate logistic regression analysis was employed to determine independent predictors of biochemical remission at 3 months in patients after surgery. RESULTS In total, we identified 575 patients (mean age: 37.3 years; 59% female). The most common clinical features were enlarged nose and prognathism (97%) and overgrowth of extremities (93%). The majority of the cohort comprised of macroadenomas (n=479, 83%), and invasive tumors with a Knosp score of 3-4 (n=353, 61%). Ninety-five percent of patients were treated first with surgery and 38.3% exhibited biochemical remission at 3-months postoperatively. The following independent predictors of biochemical remission were identified: preoperative growth hormone (GH) levels between 12 and 28 μg/L [odds ratio (OR)=0.61; 95% confidence interval (CI), 0.39-0.96; p=0.031], preoperative GH levels >28 μg/L (OR=0.56; 95% CI, 0.35-0.90; p=0.016), macroadenoma (OR=0.57; 95% CI, 0.33-0.97; p=0.041), giant adenomas (OR=0.17; 95% CI, 0.06-0.44; p=0.0005), Knosp score 3-4 (OR=0.39; 95% CI, 0.25-0.59; p<0.0001), and preoperative medication usage (OR=2.16; 95% CI, 1.38-3.39; p=0.0008). CONCLUSIONS In this nationwide study spanning over two decades, we highlight that higher preoperative GH levels, large tumor size, and greater extent of tumor invasiveness are associated with a lower likelihood of biochemical remission at 3-months after surgery, while preoperative medical therapy increases the chance of remission.

Preoperative medical treatments and surgical approaches for acromegaly: A systematic review

“…The overall remission rate in the TSS group was 37.6% versus 21.4% in the transcranial group. Yet this was statistically insignificant ( p > .05) 22 …”

“…It was interesting to see that 10 authors concluded that high basal preoperative GH levels significantly reduced remission but only two authors found the same regarding basal preoperative IGF1 levels. Furthermore, Yao 2021 found that even GH levels >28 μg/L were high enough to significantly alter prognosis 22 . On the other hand, Anik 2017 found that >60 μg/L is considered to be “high” enough 27 …”

Diagnosis and Treatment of Acromegaly: An Update

“…3 Some studies have reported that preoperative medical therapy with somatostatin receptor ligands (SRLs) may improve surgical remission rates. 3,39,40 However, methodologic concerns and low remission rates among the groups of patients who underwent surgery without preoperative medical therapy have raised concerns about the generalizability of some of these studies. 41,42 Select patients may also be candidates for primary medical therapy with SRLs, including those whose tumors do not compress the optic apparatus and are unlikely to be cured by surgery (because of tumor extension into the cavernous sinuses or clivus) and those who decline surgery.…”

Automatic Detection for Acromegaly Using Hand Photographs: A Deep-Learning Approach

“…Recent research showed that detecting acromegaly from facial photographs by machine learning is plausible [19], [22], [23], [28]- [30], which focused on the use of facial photographs helping diagnosis by doctors or self-diagnosis by acromegaly patients. In our previous work, facial and limb changes were the earliest symptoms in early stage of acromegaly [17]. Different from previous work using facial images, we try to identify a person who has acromegaly from hand photos due to the privacy protection of facial ones.…”

“…According to our previous research, facial and limb changes in patients with acromegaly occurred at early stage [17]. Machine learning assisted diagnosis of acromegaly from facial photographs had been proved feasible in recent years.…”