STUDY OBJECTIVES: To evaluate the utility of the odds ratio product (ORP) in differentiating comorbid insomnia and sleep apnea (COMISA) from obstructive sleep apnea (OSA) and chronic insomnia (CIN).
METHODS: We retrospectively analyzed 9750 patients in four groups: (1) 1152 controls, (2) 2395 with CIN, (3) 2297 with OSA, and (4) 3906 with COMISA. CIN was defined as difficulty initiating/maintaining sleep with daytime fatigue/sleepiness occurring "often"/"always." OSA was defined as an apnea-hypopnea index ≥5 on polysomnography. ORP, computed every 3 s from polysomnography, was analyzed alongside sleep metrics, comorbidities, and sleep habits. Associations were assessed using univariate multinomial logistic regression, followed by stepwise regression to identify independent predictors of COMISA versus OSA or CIN. Machine learning models classified COMISA, OSA, and CIN as distinct clinical groups.
RESULTS: ORP-derived features showed stronger associations with COMISA than traditional sleep metrics (except N3 latency). Independent objective predictors of COMISA included male sex (OR = 1.31, 95% CI = [1.16, 1.47]), BMI (1.27, [1.25, 1.29]), N3 latency (1.21, [1.13, 1.29]), age (1.17, [1.16, 1.19]), peak ORP during spontaneous arousals (1.12, [1.01, 1.25]), and time in ORP decile 7 (1.10, [1.07, 1.13]). Subjective predictors included depression, hypertension, allergy, headache, sleep aid/alcohol use, sleepiness, and lower sleep duration. Machine learning achieved overall accuracy of 61.2 per cent (p<.05), with sensitivity of 71 per cent for COMISA, 65 per cent for OSA, and 43 per cent for CIN.
CONCLUSIONS: ORP is a promising objective marker for COMISA, distinguishing it from OSA more effectively than sleep metrics but separating COMISA from CIN poorly. Statement of Significance This study highlights the potential of the odds ratio product (ORP) as a novel objective marker to complement self-report questionnaires for identifying comorbid insomnia and sleep apnea (COMISA). Our findings demonstrate that ORP, combined with demographics and self-reported comorbidities and sleep habits, can distinguish COMISA from obstructive sleep apnea more effectively than traditional polysomnography metrics. These findings may refine the current understanding of COMISA from a research perspective and, in the future, may have clinical implications for improving the diagnosis and management of the disorder, which is often underrecognized in clinical patients who are evaluated for sleep-disordered breathing. Future studies should explore how ORP can be integrated into clinical sleep assessments and diagnostic strategies.
