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Purpose:

Patients with acromegaly have an increased risk of sleep apnea, but reported prevalence rates vary largely. Here we aimed to evaluate the sleep apnea prevalence in a large national cohort of patients with acromegaly, to examine possible risk factors, and to assess the proportion of patients diagnosed with sleep apnea prior to acromegaly diagnosis.

Methods: Cross-sectional multicenter study of 259 Swedish patients with acromegaly. At patients' follow-up visits at the endocrine outpatient clinics of all seven university hospitals in Sweden, questionnaires were completed to assess previous sleep apnea diagnosis and treatment, cardiovascular diseases, smoking habits, anthropometric data, and S-IGF-1 levels. Daytime sleepiness was evaluated using the Epworth Sleepiness Scale. Patients suspected to have undiagnosed sleep apnea were referred for sleep apnea investigations.

Results: Of the 259 participants, 75 (29%) were diagnosed with sleep apnea before the study start. In 43 (57%) of these patients, sleep apnea had been diagnosed before the diagnosis of acromegaly. After clinical assessment and sleep studies, sleep apnea was diagnosed in an additional 20 patients, yielding a total sleep apnea prevalence of 37%. Higher sleep apnea risk was associated with higher BMI, waist circumference, and index finger circumference. Sleep apnea was more frequent among patients with S-IGF-1 levels in the highest quartile.

Conclusion: Sleep apnea is common among patients with acromegaly, and is often diagnosed prior to their acromegaly diagnosis. These results support early screening for sleep apnea in patients with acromegaly and awareness for acromegaly in patients with sleep apnea.

Objective: Carpal tunnel syndrome (CTS) is common in patients with acromegaly, with a reported prevalence of 19–64%.We studied CTS in a large national cohort of patients with acromegaly and the temporal relationship between thetwo diagnoses.

Design: Retrospective, nationwide, cohort study including patients diagnosed with acromegaly in Sweden, 2005–2017,identified in the Swedish Healthcare Registries.

Methods: CTS (diagnosis and surgery in specialised healthcare) was analysed from 8.5 years before the diagnosis ofacromegaly until death or end of the study. Standardised incidence ratios (SIRs) with 95% CIs were calculated for CTSwith the Swedish population as reference.

Results: The analysis included 556 patients with acromegaly (50% women) diagnosed at mean (s.d.) age 50.1 (15.0)years. During the study period, 48 patients were diagnosed with CTS and 41 patients underwent at least one CTSsurgery. In the latter group, 35 (85%) were operated for CTS before the acromegaly diagnosis; mean interval (range)2.2 (0.3–8.5) years and the SIR for having CTS surgery before the diagnosis of acromegaly was 6.6 (4.8–8.9). Womenwith acromegaly had a higher risk for CTS than men (hazard ratio: 2.5, 95% CI: 1.3–4.7).

Conclusions: Patients with acromegaly had a 6-fold higher incidence for CTS surgery before the diagnosis of acromegalycompared with the general population. The majority of patients with both diagnoses were diagnosed with CTS priorto acromegaly. Increased awareness of signs of acromegaly in patients with CTS might help to shorten the diagnosticdelay in acromegaly, especially in women.

Context: There is a considerable diagnostic delay in acromegaly, contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly.

Objective: Our aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning.

Methods: Voice recordings from patients with acromegaly and matched controls were collected using a mobile phone at Swedish university hospitals. Anthropometric and clinical data and the Voice Handicap Index (VHI) were assessed. Digital voice analysis of a sustained and stable vowel [a] resulted in 3274 parameters, which were used for training of machine learning models classifying the speaker as “acromegaly” or “control.” The machine learning models were trained with 76% of the data and the remaining 24% was used to assess their performance. For comparison, voice recordings of 50 pairs of participants were assessed by 12 experienced endocrinologists.

Results: We included 151 Swedish patients with acromegaly (13% biochemically active and 10% newly diagnosed) and 139 matched controls. The machine learning model identified patients with acromegaly more accurately (area under the receiver operating curve [ROC AUC] 0.84) than experienced endocrinologists (ROC AUC 0.69). Self-reported voice problems were more pronounced in patients with acromegaly than matched controls (median VHI 6 vs 2, P < .01) with higher prevalence of clinically significant voice handicap (VHI ≥20: 22.5% vs 3.6%).

Conclusion: Digital voice analysis can identify patients with acromegaly from short voice recordings with high accuracy. Patients with acromegaly experience more voice disorders than matched controls.

Background. There is a substantial delay in the diagnosis of acromegaly contributing to increased morbidity. Analysis of face images using machine learning to identify acromegaly has been investigated with impressive results but for clinical utilization there is a need for reassessment in different, large acromegaly cohorts using new methodological insights of machine learning development. 

Methods. Video recordings from patients with acromegaly and matched controls were collected at all Swedish University hospitals. Facial pictures from different angles were extracted. Clinical data regarding disease course and status were collected. Machine learning models based on four pre-trained deep neural networks (FaRL and 3 ImageNet models: ResNet50, InceptionV2 and DenseNet121) were trained to distinguish photographs of patients with acromegaly from controls. The models were compared to the performance of 12 experienced endocrinologists. 

Results. The FaRL based model matched the diagnostic precision of human experts (ROC AUC 0.89, balanced accuracy 0.89 for both) but had a higher sensitivity (0.82 vs 0.66) and comparable specificity (0.87 vs 0.93). An ensemble of the ImageNet models was inferior to FaRL (ROC AUC 0.85, sensitivity 0.75 and specificity 0.80). All models and human experts showed a higher sensitivity in the classification of males compared to females. There was a high level of agreement between FaRL, ImageNet ensemble, and the experts for true negatives (76%) but lower agreement for true positives (55%). 

Conclusions. The FaRL based model shows the same accuracy as expert endocrinologists in acromegaly identification by face photographs, but with a higher sensitivity. This supports that digital face analysis can be useful in acromegaly detection, but further studies are needed for validation and evaluation in a clinical setting.