Correlation of fitzpatrick skin type and iris color with tumor size in 823 patients with uveal melanoma.

PURPOSE: To determine the correlation of Fitzpatrick Skin Type (FST) and iris color with tumor size (tumor thickness and basal diameter) in patients with uveal melanoma. DESIGN: Retrospective Cohort METHODS: Retrospective cohort from a single ocular oncology center of 823 patients with uveal melanoma and documented FST, iris color, and tumor size. Patients were classified by FST (type I, II, and III-V) and iris color (blue, green, and brown) on the basis of external facial photography. There were no FST type VI patients. Tumor thickness was classified into small [< 3 millimeter (mm)], medium (3.1-8.0 mm), or large (> 8.0 mm), and basal diameter into small (< 10 mm), medium (10.1-15 mm) or large (> 15 mm). The correlation of FST and iris color with tumor thickness and basal diameter was evaluated using the Kruskal-Wallis H test. RESULTS: The FST classification was type I (n = 92, 11%), type II (n = 643, 78%), or III-V (n = 88, 11%), and iris color was blue (n = 472, 57%), green (n = 102, 12%), or brown (n = 249, 30%). A comparison of FST revealed differences in mean tumor thickness (P = 0.04) and basal diameter (P = 0.006). Iris color showed no difference for mean tumor thickness (P = 0.41) or basal diameter (P = 0.48). There was a statistically significant difference with brown iris color relative to FST III-V for mean tumor thickness (P = 0.003) and basal diameter (P = 0.001) but no difference with blue or green iris color (P > 0.05). CONCLUSIONS: Iris color alone showed no difference in tumor size, but those with brown iris color and FST type III-V demonstrated larger tumor thickness and basal diameter.

Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study.

BACKGROUND: The identification of an appropriate rhythm management strategy for patients diagnosed with atrial fibrillation (AF) remains a major challenge for providers. Although clinical trials have identified subgroups of patients in whom a rate- or rhythm-control strategy might be indicated to improve outcomes, the wide range of presentations and risk factors among patients presenting with AF makes such approaches challenging. The strength of electronic health records is the ability to build in logic to guide management decisions, such that the system can automatically identify patients in whom a rhythm-control strategy is more likely and can promote efficient referrals to specialists. However, like any clinical decision support tool, there is a balance between interpretability and accurate prediction. OBJECTIVE: This study aims to create an electronic health record-based prediction tool to guide patient referral to specialists for rhythm-control management by comparing different machine learning algorithms. METHODS: We compared machine learning models of increasing complexity and used up to 50,845 variables to predict the rhythm-control strategy in 42,022 patients within the University of Colorado Health system at the time of AF diagnosis. Models were evaluated on the basis of their classification accuracy, defined by the F1 score and other metrics, and interpretability, captured by inspection of the relative importance of each predictor. RESULTS: We found that age was by far the strongest single predictor of a rhythm-control strategy but that greater accuracy could be achieved with more complex models incorporating neural networks and more predictors for each participant. We determined that the impact of better prediction models was notable primarily in the rate of inappropriate referrals for rhythm-control, in which more complex models provided an average of 20% fewer inappropriate referrals than simpler, more interpretable models. CONCLUSIONS: We conclude that any health care system seeking to incorporate algorithms to guide rhythm management for patients with AF will need to address this trade-off between prediction accuracy and model interpretability.

Range of motion of the healthy pediatric elbow: cross-sectional study of a large population.

To evaluate normative data on elbow range of motion (ROM) in the pediatric population. The passive ROM of 1361 healthy pediatric elbows was measured using a small goniometer calibrated in 1° increments, and recorded. The mean amount of flexion, extension, and arc of motion was 142°, -11°, and 153°, respectively. Our data indicated no meaningful correlation between patient sex, age, or weight and the amount of flexion, extension, or the total arc of motion. It appears that elbow ROM in this population is not affected by sex, age, or weight. Normative data are critical when setting the goals of a particular therapeutic approach.