Early Distraction for Mild to Moderate Unilateral Craniofacial Microsomia: Long-Term Follow-Up, Outcomes, and Recommendations

Background: There is controversy regarding the treatment of young patients with unilateral craniofacial microsomia and moderate dysmorphism. The relative indication for mandibular distraction in such patients poses several questions: Is it deleterious in the context of craniofacial growth and appearance? This study was designed to address these questions. Methods: A retrospective review of patients undergoing mandibular distraction by a single surgeon between 1989 and 2010 was conducted. Patients with "moderate" unilateral craniofacial microsomia (as defined by Pruzansky type I or IIa mandibles) and follow-up until craniofacial skeletal maturity were included for analysis. Patients were divided into two cohorts: satisfactory and unsatisfactory results based on photographic aesthetic evaluation by independent blinded observers at the initial presentation and at the age of skeletal maturity. Clinical variables were analyzed to detect predictors for satisfactory distraction. Results: Nineteen patients were included for analysis. The average age at distraction was 68.2 months and the average age at follow-up was 19.55 years. Thirteen patients (68.4 percent) had Pruzansky type IIA and six patients (31.6 percent) had Pruzansky type I mandibles. Twelve patients (63.2 percent) had satisfactory outcomes, whereas seven patients (36.8 percent) had unsatisfactory outcomes. Comparing the two cohorts, patients with satisfactory outcomes had distraction at an earlier age (56.4 months versus 89.8 months; p = 0.07) and a greater percentage overcorrection from craniofacial midline (41.7 percent versus 1.8 percent; p = 0.003). Conclusion: Mandibular distraction is successful in patients with mild to moderate dysmorphism, provided that there is a comprehensive clinical program emphasizing adequate mandibular bone stock, proper vector selection, planned overcorrection, and comprehensive orthodontic management. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Developing the aesthetic postoperative complication score (APeCS) for detecting major morbidity in racial aesthetic surgery

Background:Facial aesthetic surgery encompasses a variety of procedures with complication rates that are difficult to estimate due to a lack of published data. Objectives:We sought to estimate major complication rates in patients undergoing facial aesthetic procedures and develop a risk assessment tool to stratify patients. Methods:We utilized the Tracking Operation and Outcomes for Plastic Surgeons (TOPS) database from 2003-2018. The analytic database included major facial aesthetic procedures. Univariate analysis and a backward stepwise multivariate regression model identified risk factors for major complications. Regression coefficients were used to create the score. Area under receiver operating characteristic (ROC) curves and sensitivity analyses were used to measure performance robustness. Results:A total of 38,569 patients were identified. The major complication rate was 1.2% (460). The regression model identified risk factors including over three concomitant surgeries, BMI ≥25, ASA class ≥2, current/former smoker status, and age ≥45 as the variables fit for risk prediction (n = 13,004; AUC: 0.68, SE: 0.013, [0.62-0.67]). Each of the five variables counts for one point except over three concomitant surgeries counting for two, giving a score range from 0-6. Sensitivity analysis showed the cutoff point of ≥3 to best balance sensitivity and specificity, 58% and 66%, respectively. At this cutoff, 65% of cases were correctly classified as a major complication. Conclusions:We developed an acceptable risk prediction score with a cutoff value of ≥3 associated with correctly classifying approximately 65% of those at risk for major morbidity when undergoing face and neck aesthetic surgery

Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data

Depression is a serious mental illness. The current best guideline in depression treatment is closely monitoring patients and adjusting treatment as needed. Close monitoring of patients through physician-administered follow-ups or self-administered questionnaires, however, is difficult in clinical settings due to high cost, lack of trained professionals, and burden to the patients. Sensory data collected from mobile devices has been shown to provide a promising direction for long-term monitoring of depression symptoms. Most existing studies in this direction, however, focus on depression detection; the few studies that are on predicting changes in depression are not in clinical settings. In this paper, we investigate using one type of sensory data, sleep data, collected from wearables to predict improvement of depression symptoms over time after a patient initiates a new pharmacological treatment. We apply sleep trend filtering to noisy sleep sensory data to extract high-level sleep characteristics and develop a family of machine learning models that use simple sleep features (mean and variation of sleep duration) to predict symptom improvement. Our results show that using such simple sleep features can already lead to validation F1 score up to 0.68, indicating that using sensory data for predicting depression improvement during treatment is a promising direction