Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score.

Objective: To use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML).

Methods: In 1130 patients with moderate or severe AS, we used bootstrap lasso regression (BLR), an ML method, to identify echocardiographic and clinical features important for predicting the combined outcome of all-cause mortality or aortic valve replacement (AVR) within 5 years after the initial echocardiogram. A separate hold out set, from a different centre (n=540), was used to test the generality of the model.

Antigen vs RT-PCR Tests for Screening Quarantined Students in Florida During the COVID-19 Pandemic SARS-CoV-2 Delta Variant Surge.

This diagnostic/prognostic study compares the results of antigen vs real-time reverse transcription–polymerase chain reaction tests among quarantined students 5 days after exposure to SARS-CoV-2 during the surge of Delta variant cases in the COVID-19 pandemic.

SARS-CoV-2 Positivity on or After 9 Days Among Quarantined Student Contacts of Confirmed Cases.

This study describes coronavirus test positivity rates among elementary, middle, and high school student contacts of confirmed COVID-19 cases in a Florida county where schools required a negative test on day 9 before return to school on day 10.

Identifying unreliable predictions in clinical risk models.

The ability to identify patients who are likely to have an adverse outcome is an essential component of good clinical care. Therefore, predictive risk stratification models play an important role in clinical decision making. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model's performance on large patient datasets using standard statistical measures of success (e.

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) - a Machine Learning method for selecting important variables - to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome.

Machine Learning Improves Risk Stratification After Acute Coronary Syndrome.

The accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as Artificial Neural Networks (ANN), form an attractive platform to build risk metrics because they can easily incorporate disparate pieces of data, yielding classifiers with improved performance.

Sustainability of school-located influenza vaccination programs in Florida.

Background: School-located influenza vaccination (SLIV) programs are a promising strategy for increasing vaccination coverage among schoolchildren. However, questions of economic sustainability have dampened enthusiasm for this approach in the United States. We evaluated SLIV sustainability of a health department led, county-wide SLIV program in Alachua County, Florida.

School-located influenza vaccination reduces community risk for influenza and influenza-like illness emergency care visits.

Background: School-located influenza vaccination (SLIV) programs can substantially enhance the sub-optimal coverage achieved under existing delivery strategies. Randomized SLIV trials have shown these programs reduce laboratory-confirmed influenza among both vaccinated and unvaccinated children. This work explores the effectiveness of a SLIV program in reducing the community risk of influenza and influenza-like illness (ILI) associated emergency care visits.

Implementing a community-supported school-based influenza immunization program.

School-based influenza immunization programs are increasingly recognized as a key component of community-based efforts to control annual influenza epidemics. Computer modeling suggests that immunizing 70% of schoolchildren could protect an entire community from the flu. Most of the school-based influenza immunization programs described in the literature have had support from industry or federal grants.