The medical picture of acromegaly in acromedic, a retrospective database of electronic health records (EHR) from patients with confirmed acromegaly diagnoses across a consortium of four hospital systems in the USA

Background: Understanding of acromegaly disease management is hampered in the US by lack of a national registry. Information from payer databases is limited by need for validation of an acromegaly ICD- 9-CM diagnosis code, such as documentation of acro-appropriate interventional therapies or drugs as confirmation of disease. Objective: To describe medical management in a population with validated acromegaly. Subjects & Methods: Inpatient and outpatient EHR were used to create a database of de-identified patients who had been assigned the Acromegaly and Gigantism ICD-9 code of 253.0 and/or an appropriate pituitary surgery code in the course of their treatment at one of 4 hospital systems from 2003 or 2008 through 2013. Data related to diagnosis, procedures, lab studies, medications, and demographics were extracted. Here we report data from cases of confirmed acromegaly validated by chart review. Results: In our 2016 ENDO abstract we reported on 722 patients who met original basic inclusion criteria. Subsequent review confirmed the diagnosis of acromegaly in the medical record of 367 - roughly half the original cohort. This lack of precision is due in part to an inherent limitation of ICD-9 coding, with the diagnosis code used in circumstances where a condition is being evaluated or ruled out. In the validated cohort, 88% had the acromegaly ICD-9 code in their records and 12% did not. 54% were coded for benign neoplasm of the pituitary gland and 20% were coded for panhypopituitarism; not necessarily independent findings. Women (53%) and men were represented approximately equally. As anticipated, acromegaly primarily affected a middle aged demographic with a bell-curve distribution that centered on those who were 41-50 years old at presentation. 31% of patients had a pituitary surgery and 4% received radiotherapy during the years covered. Lab data was available for 88% of the patients. Of these, 72% had at least one IGF-1 value suggesting that disease activity was actively monitored. 53% of patients had drug records. Of these, 42% were prescribed a drug indicated for acromegaly. The most prevalent coded comorbidities were: hypertension (37%), hyperlipidemia (26%), acquired hypothyroidism (26%), type 2 diabetes mellitus (24%), arthralgias (21%), depressive disorders (20%), headache (20%), and esophageal reflux or GERD (20%). GERD, which is not uncommon in the broad US population, was previously unrecognized as an acromegaly comorbidity. 15% were obese or morbidly obese and 14% had sleep apnea. Conclusions: AcroMEDIC is the first US multi-site retrospective study of acromegaly that includes clinical data. Key learnings include that coded diagnoses for rare conditions require verification. The majority in this cohort had evidence of medical management at some level. Most comorbidities identified here are known to increase with age and obesity and are commonly associated with acromegaly