---
title: "Estimating AHI Uncertainty"
excerpt: "Obstructive sleep apnea (OSA), marked by reduced or paused breathing during sleep, affects over 10% of the population and is tied to numerous comorbidities. Diagnosis and treatment decisions rely on the apnea–hypopnea index (AHI), yet it is treated as an exact point estimate without accounting for statistical uncertainty. In this work, we quantify that uncertainty using non-parametric bootstrap and theoretical Poisson approaches applied to data from over 2,000 participants in the MESA cohort, revealing that variability is substantial relative to clinical thresholds. Incorporating uncertainty and additional patient data is essential for more accurate diagnosis and treatment decisions. <br/><br/> <img src='/images/AHI.png'>
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For more details, check the online toolbox [here](https://prerau.bwh.harvard.edu/sleep-spindle-dynamics-toolbox/), which is in companion to the paper:<br/><br/>
**Shuqiang Chen**, Mingjian He, Uri T. Eden, Michael J. Prerau. [Individualized temporal patterns drive human sleep spindle timing](https://www.pnas.org/doi/10.1073/pnas.2405276121). Proc Natl Acad Sci U S A (2025);122(2):e2405276121. doi: 10.1073/pnas.2405276121.  "
collection: portfolio
---

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