Characterizes respiratory instability metrics in a clinical sleep cohort, indicating the need for improved assessment tools.
Introduction Respiratory instability during sleep reflects irregular ventilatory control and may contribute to sleep fragmentation. Traditional sleep apnea metrics, like the apnea-hypopnea index, count discrete events but miss continuous breathing physiology. We developed two novel respiratory-instability markers. Self-Similarity (SS) represents the percentage of respiratory epochs with self-similar patterns, where < 10% indicates acceptable stability. The Breathing Stability Index (BSI) represents the median breath-to-breath stability across the night, where < 0.5 indicates stable breathing and >1.5 indicates marked instability. We characterized their demographic patterns across a large clinical dataset. Methods We analyzed 24,596 polysomnograms from Beth Israel Deaconess Medical Center (BIDMC) and Massachusetts General Hospital (MGH) (age 55.7±16.7 years, 55.7% male), including diagnostic (n=13,579), split-night (n=5,185), and titration (n=5,832) studies. SS and BSI were computed from respiratory envelopes derived from effort belts, allowing for breath-to-breath amplitude comparison. Sleep measures included N1/N2/N3/REM percentages, sleep efficiency, and sleep fragmentation index (SFI). We examined distributions by study type, demographic associations using correlations and t-tests, and sex and site differences using Cohen's d. Results The mean SS was 5.2±6.9% (range 0-86%) and the mean BSI was 1.06±0.48 (range 0.2-4.8). Instability above clinical thresholds (SS≥10%) was evident in all study types, with the highest seen in split night studies: 11% of diagnostic studies (mean BSI=1.00), 11% of titration studies (mean BSI=1.02), and 34% of split-night studies (with 11% exceeding 20%; mean BSI=1.22). Both metrics increased with age (r=0.17, p< 0.001) in steady, monotonic increments. Men exhibited higher instability (SS d=0.40; BSI d=0.19) than women. MGH showed higher SS (d=0.42) but slightly lower BSI (d=-0.10) than BIDMC. The two metrics showed moderate intercorrelation (r=0.43), and moderate correlation with N1% (r=0.24) and SFI (SS: r=0.16; BSI: r=0.23), indicating related but non-redundant sleep-instability information. Conclusion This large dataset confirmed a high prevalence of SS and warrants further clinical attention. The moderate correlation between SS and BSI (r=0.43) confirms they provide complementary insights into breathing control that are otherwise missed by discrete event-based metrics. These measures are now integrated into a larger multicomponent analysis suite to support clinical decision making at BIDMC by enabling more comprehensive assessment of sleep-disordered breathing phenotypes. Support (if any) 1R01HL161253-01A1 R01AG073410
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