Observational studyNeurourology and urodynamics2026
Deciphering Sleepless Nights: Nocturia Analyzed Through a CART-Based Predictive Model in Patients With Insomnia. Retrospective Data From the TUCSON Study.
Observational study in Neurourology and urodynamics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05404828 (TUCSON Study), which is not on this map. Not yet cited in PubMed.
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The trial behind it
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TUCSON Study: Tackling Underlying Causes of Sleep Related Nocturia
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6 authors.
Funding
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Abstract
aimsInsomnia and nocturia are prevalent, highly comorbid conditions and share a mutual connection. We aim to further elucidate the link between both in order to improve sleep and eventually overall health. The objective of this analysis was to construct a predictive model for nocturia, using retrospective clinical and polysomnographic data from patients with severe nocturia in line with the TUCSON project: Tackling Underlying Causes Of Sleep Related Nocturia (NCT05404828).
methodsRetrospective data was collected from all adult patients consulting a third line center for insomnia between 2019 and 2021. Data on demographics, medical history, nocturia, insomnia severity index (ISI), Pittsburgh Sleep Quality Index (PSQI), polysomnographic sleep parameters and First Uninterrupted Sleep Period (FUSP) were collected. Potentially significant variables related to nocturia were incorporated into a Classification and Regression Trees (CART) model to predict nocturia using the R "rpart" package.
resultsA total of 170 patients presenting with insomnia were analyzed, including 106 without and 64 with nocturia. The median age was 45 years (IQR 31-55), and 58.2% were women. In exploratory CART analyses, FUSP, apnea-hypopnea index (AHI), and REM sleep percentage were identified as potential predictors of nocturia. The predictive model's accuracy was 73.08%, with a sensitivity of 79.41%, specificity of 61.11%, and an ROC area of 0.82. However, logistic regression outperformed CART in internal validation (cross-validated AUC 0.76 vs. 0.52; bootstrap AUC 0.76 vs. 0.64), and both models showed poor generalization on a held-out test set.
conclusionThis study is the first to investigate clinical and polysomnographic predictors of nocturia within a select insomnia population. While exploratory CART modeling allowed the identification of potential non-linear relationships and clinically relevant subgroups, its predictive performance was limited and inferior to logistic regression after internal validation. Although FUSP was not consistently retained across resampled models, its role as a potential contributor to nocturia remains of interest and warrants further investigation in larger, well-defined cohorts.
trial registrationClinicalTrials.gov identifier: NCT05404828.
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