Abstract
BackgroundNo existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing’s disease (CD) after transsphenoidal surgery.PurposeThe aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery.MethodsA total of 419 patients with CD from Peking Union Medical College Hospital were enrolled between January 2014 and August 2020. The EMR of the patients were embedded and transformed into low-dimensional dense vectors that can be included in four ML-based models together with structured features. The area under the curve (AUC) of receiver operating characteristic curves was used to evaluate the performance of the models.ResultsThe overall remission rate of the 419 patients was 75.7%. From the results of logistic multivariate analysis, operation (p < 0.001), invasion of cavernous sinus from MRI (p = 0.046), and ACTH (p = 0.024) were strongly correlated with IR. The AUC values for the four ML-based models ranged from 0.686 to 0.793. The highest AUC value (0.793) was for logistic regression when 11 structured features and “individual conclusions of the case by doctor” were included.ConclusionAn ML-based model was developed using both structured and unstructured features (after being processed using a word embedding method) as input to preoperatively predict postoperative IR.
Highlights
Pituitary corticotroph adenoma is called Cushing’s disease (CD)
The inclusion criteria were as follows: [1] manifestations of Cushing’s syndrome; [2] positive result on magnetic resonance imaging (MRI) or negative result on MRI, but CD was strongly suspected according to manifestations; [3] ruling out the possibility of ectopic adrenocorticotropic hormone (ACTH) syndrome; and [4] plasma cortisol level (8:00 a.m.) > 22.3 mg/dl or 24-h urine free cortisol (UFC) level > 103.5 mg
A total of 392/419 participants had histological confirmation of CD, and the diagnosis of CD was based on synthesized evidence that included MRI results, clinical manifestations, results of the low-dose dexamethasone suppression test (LDDST) and high-dose dexamethasone suppression test (HDDST), and pathological results
Summary
Pituitary corticotroph adenoma is called Cushing’s disease (CD). It accounts for the majority of Cushing’s syndrome cases [1, 2]. The first-line treatment method is transsphenoidal surgery (TSS) according to a consensus statement [4]. Immediate remission (IR) is important for both patients and surgeons. No existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing’s disease (CD) after transsphenoidal surgery. Purpose: The aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery
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