RESUMEN
Body Dysmorphic Disorder (BDD) is a psychiatric condition involving a preoccupation with physical appearance disproportionate to physical findings, which are often absent altogether. Previously published data has estimated its prevalence at 11.3-11.9% approximately, across various medical specialties. No recent systematic reviews strictly related to dermatology clinics and the prevalence of BDD have been published. The goal of the review was to gather a pooled prevalence of BDD in outpatient dermatology clinics around the world and further underline the importance of its recognition and appropriate treatment. Twenty-one articles tackling BDD in outpatient cosmetic and general dermatology clinics were selected. Studies were graded based on the Newcastle-Ottawa Scale (NOS) and the Statistical Package for the Social Sciences software (SPSS) was used to a calculate a mean for the pooled prevalence, yielding a weighted mean prevalence of 12.5% among general dermatology patients and 25.01% among cosmetic dermatology patients. The mean prevalence of BDD among general dermatology patients fell within previously reported numbers. It was, however, markedly higher than previously reported in cosmetic dermatology patients, which we postulate could be due to dermatologists being at the forefront of non-invasive cosmetic procedures. As such, we conclude that given the high prevalence of BDD among dermatology patients, we highlight the importance of keeping a high index of suspicion of BDD among dermatologists, ways to uncover it in a clinical setting, and additional data showcasing the importance of psychiatric treatment of these patients for better outcomes, all while avoiding unnecessary interventions.
RESUMEN
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes. OBJECTIVES: To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. METHODS: We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness. RESULTS: Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/µL) (34.8%). CONCLUSIONS: Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed.