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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
Deciphering Sleepless Nights: Nocturia Analyzed Through a CART-Based Predictive Model in Patients With Insomnia.
Irina Verbakel1, George Boukheir1, Donald Bliwise2
1Department of Urology, Ghent University Hospital, Ghent, Belgium.
Neurourology and Urodynamics
|June 8, 2026
Summary
This study explored predictors of nocturia in insomnia patients. First Uninterrupted Sleep Period (FUSP), AHI, and REM sleep showed potential, but models had limited predictive accuracy.
Area of Science:
- Sleep Medicine
- Clinical Research
- Predictive Modeling
Background:
- Insomnia and nocturia are common, comorbid conditions.
- Understanding their link can improve sleep and overall health.
- The TUCSON project (NCT05404828) investigates causes of sleep-related nocturia.
Purpose of the Study:
- To build a predictive model for nocturia.
- Utilize retrospective clinical and polysomnographic data.
- Identify predictors of nocturia in severe cases.
Main Methods:
- Retrospective analysis of adult insomnia patients (2019-2021).
- Collected data: demographics, medical history, nocturia, sleep indices (ISI, PSQI), polysomnography, First Uninterrupted Sleep Period (FUSP).
- Used Classification and Regression Trees (CART) for predictive modeling.
Main Results:
- 170 insomnia patients analyzed (64 with nocturia).
- CART identified FUSP, apnea-hypopnea index (AHI), and REM sleep % as potential nocturia predictors.
- Model accuracy: 73.08%; logistic regression showed better internal validation but poor generalization.
Conclusions:
- First study on clinical/polysomnographic nocturia predictors in insomnia.
- CART identified potential non-linear relationships but had limited predictive power.
- FUSP's role in nocturia warrants further investigation in larger cohorts.
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