Murphree, Dennis H

Abstract Background Perianal draining tunnels in hidradenitis suppurativa (HS) and perianal fistulizing inflammatory bowel disease (IBD) present diagnostic and management dilemmas. Methods We conducted a retrospective chart review of patients with perianal disease evaluated at Mayo Clinic from January 1, 1998, through July 31, 2021. Patients' demographic and clinical data were extracted, and 28 clinical features were collected. After experimenting with several machine learning techniques, random forests were used to select the 15 most important clinical features to construct the diagnostic prediction model to distinguish perianal HS from fistulizing perianal IBD. Results A total of 263 patients were included (98 with HS, 100 with IBD, and 65 with both IBD and HS). Patients with HS had a higher mean body mass index, a higher smoking rate, and more commonly showed cutaneous manifestations of tunnels and comedones, while fistulas, abscesses, induration, anal tags, ulcers, and anal fissures were more common in patients with IBD. In addition to having lesions in the perianal area, patients with IBD often had lesions in the buttocks and perineum, while those with HS had additional lesions in the axillae and groin. Among the statistically significant features, the 15 most important were identified by random forest: fistula , tunnel , digestive symptom , knife‐cut ulcer , perineum , body mass index , age , axilla , abscess , tags , smoking , groin , genital cutaneous edema , erythema , and bilateral/unilateral . Conclusions The results of this study may help differentiate perianal lesions, especially perineal HS and fistulizing perineal IBD, and provide promise for a better therapeutic outcome.

Abstract Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50). Conclusion An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.