Robotics & Machine Learning Daily News2024,Issue(Feb.26) :41-42.

Icahn School of Medicine at Mount Sinai Reports Findings in Obstructive Sleep Apnea [Heterogeneous Effects of CPAP in Non- Sleepy OSA on CVD Outcomes: Post-hoc Machine Learning Analysis of the ISAACC Trial (ECSACT Study)]

Robotics & Machine Learning Daily News2024,Issue(Feb.26) :41-42.

Icahn School of Medicine at Mount Sinai Reports Findings in Obstructive Sleep Apnea [Heterogeneous Effects of CPAP in Non- Sleepy OSA on CVD Outcomes: Post-hoc Machine Learning Analysis of the ISAACC Trial (ECSACT Study)]

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Abstract

New research on Respiratory Tract Diseases and Conditions - Obstructive Sleep Apnea is the subject of a report. According to news reporting from New York City, New York, by NewsRx journalists, research stated, “Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with obstructive sleep apnea (OSA) have been largely neutral. However, given OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects.” The news correspondents obtained a quote from the research from the Icahn School of Medicine at Mount Sinai, “Apply a novel data-drive approach to identify non-sleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the ISAACC study. Participants were randomly partitioned into two datasets. One for training (70%) our machine learning model and a second (30%) for validation of significant findings. Model-based recursive partitioning was applied to identify subgroups with heterogeneous treatment effects. Survival analysis was conducted to compare treatment (CPAP versus usual care [UC]) outcomes within subgroups. A total of 1,224 non-sleepy OSA participants were included. Of fifty-five features entered into our model only two appeared in the final model (i.e., average OSA event duration and hypercholesterolemia). Among participants at or below the model-derived average event duration threshold (19.5 seconds), CPAP was protective for a composite of CVD events (training Hazard Ratio [HR] 0.46, p=0.002). For those with longer event duration (>19.5 seconds), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared to UC (training HR 2.24, p=0.011). The point estimate for this harmful signal was also replicated in the testing dataset (HR 1.83, p=0.118). We discovered subgroups of non-sleepy OSA participants within the ISAACC study with heterogeneous effects of CPAP.”

Key words

New York City/New York/United States/North and Central America/Cardiovascular Diseases and Conditions/Cyborgs/Dyslipidemias/Emerging Technologies/Health and Medicine/Hypercholesterolemia/Hyperlipidemias/Lipid Metabolism Disorders/Machine Learning/Nutritional and Metabolic Diseases and Conditions/Obstructive Sleep Apnea/Respiratory Tract Diseases and Conditions/Sleep Apnea

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2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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