Browsing by Subject "TIA"

Now showing 1 - 3 of 3
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    Clinical Decision Support for Patient Cases with Asymptomatic Carotid Artery Stenosis Using AI Models and Electronic Medical Records
    (MDPI, 2025-02-06) Madison, Mackenzie; Luo, Xiao; Silvey, Jackson; Brenner, Robert; Gannamaneni, Kartik; Sawchuk, Alan P.; Surgery, School of Medicine
    An artificial intelligence (AI) analysis of electronic medical records (EMRs) was performed to analyze the differences between patients with carotid stenosis who developed symptomatic disease and those who remained asymptomatic. The EMRs of 872 patients who underwent a carotid endarterectomy between 2009 and 2022 were analyzed with AI. This included 408 patients who had carotid intervention for symptomatic carotid disease and 464 patients for asymptomatic, >70% stenosis. By analyzing the EMRs, the Support Vector Machine achieved the highest sensitivity at 0.626 for predicting which of these patients would go on to develop a stroke or TIA. Random Forest had the highest specificity at 0.906. The risk for stroke in patients with carotid stenosis was a balance between optimum medical treatment and the underlying disease processes. Risk factors for developing symptomatic carotid disease included elevated glucose, chronic kidney disease, hyperlipidemia, and current or recent smoking, while protective factors included cardiovascular agents, antihypertensives, and beta blockers. An AI review of EMRs can help determine which patients with carotid stenosis are more likely to develop a stroke to assist with decision making as to whether to proceed with intervention or to demonstrate and encourage reduced stroke risk with risk factor modification.
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    Development, Validation, and Assessment of an Ischemic Stroke or Transient Ischemic Attack-Specific Prediction Tool for Obstructive Sleep Apnea
    (Elsevier, 2017-08) Sico, Jason J.; Yaggi, H. Klar; Ofner, Susan; Concato, John; Austin, Charles; Ferguson, Jared; Qin, Li; Tobias, Lauren; Taylor, Stanley; Vaz Fragoso, Carlos A.; McLain, Vincent; Williams, Linda S.; Bravata, Dawn M.; Biostatistics, School of Public Health
    BACKGROUND: Screening instruments for obstructive sleep apnea (OSA), as used routinely to guide clinicians regarding patient referral for polysomnography (PSG), rely heavily on symptomatology. We sought to develop and validate a cerebrovascular disease-specific OSA prediction model less reliant on symptomatology, and to compare its performance with commonly used screening instruments within a population with ischemic stroke or transient ischemic attack (TIA). METHODS: Using data on demographic factors, anthropometric measurements, medical history, stroke severity, sleep questionnaires, and PSG from 2 independently derived, multisite, randomized trials that enrolled patients with stroke or TIA, we developed and validated a model to predict the presence of OSA (i.e., Apnea-Hypopnea Index ≥5 events per hour). Model performance was compared with that of the Berlin Questionnaire, Epworth Sleepiness Scale (ESS), the Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index, Age, Neck circumference, and Gender instrument, and the Sleep Apnea Clinical Score. RESULTS: The new SLEEP Inventory (Sex, Left heart failure, ESS, Enlarged neck, weight [in Pounds], Insulin resistance/diabetes, and National Institutes of Health Stroke Scale) performed modestly better than other instruments in identifying patients with OSA, showing reasonable discrimination in the development (c-statistic .732) and validation (c-statistic .731) study populations, and having the highest negative predictive value of all in struments. CONCLUSIONS: Clinicians should be aware of these limitations in OSA screening instruments when making decisions about referral for PSG. The high negative predictive value of the SLEEP INventory may be useful in determining and prioritizing patients with stroke or TIA least in need of overnight PSG.
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    Reducing cardiovascular risk through treatment of obstructive sleep apnea: 2 methodological approaches
    (Elsevier, 2016-02) Yaggi, Klar; Mittleman, Murray A.; Bravata, Dawn M.; Concato, John; Ware, James; Stoney, Catherine M.; Redline, Susan; Department of Medicine, IU School of Medicine
    Obstructive sleep apnea (OSA) significantly impacts cardiovascular health, demonstrated by observational investigations showing an independently increased risk of ischemic heart disease, diabetes, hypertension, congestive heart failure, acute coronary syndrome, stroke, cardiovascular mortality, and all-cause mortality. Positive airway pressure (PAP), a medical therapy for sleep apnea, reverses airway obstruction and may help reduce cardiovascular risk. Prior to planning large phase III randomized controlled trials to test the impact of PAP on cardiovascular outcomes, several gaps in knowledge need to be addressed. This article describes 2 independent studies that worked collaboratively to fill these gaps. The populations, design features, and relative benefits/challenges of the 2 studies (SleepTight and BestAIR) are described. Both studies were encouraged to have multidisciplinary teams with expertise in behavioral interventions to improve PAP compliance. Both studies provide key information that will be useful to the research community in future large-scale, event-driven, randomized trials to evaluate the efficacy and/or effectiveness of strategies to identify and treat significant OSA for decreasing risk of major adverse cardiovascular events in high-risk patients.