Why the study?
Night-to-night variability in PSG causes diagnostic uncertainty in OSA, but multi-metric evaluations using closely spaced nights remain limited, and the comparative stability of AHI definitions, hypoxic burden, and threshold calibration remains unclear.
What is the night-to-night variability across PSG-derived metrics, and how do AHI scoring definitions influence diagnostic stability in OSA?
Observational (n=147)
What is the night-to-night variability across PSG-derived metrics, and how do AHI scoring definitions influence diagnostic stability in OSA?
Absolute Event Rate: 29.9% vs 21.2%
AHI 4% criteria for OSA diagnosis show greater night-to-night classification disagreement compared to AHI 3%/arousal criteria, driven by threshold differences.
AHI 4% criteria may increase night-to-night diagnostic variability in OSA; leaves open standardization of scoring rules for reliable classification.
BACKGROUND: Night-to-night variability (NtNV) in polysomnography (PSG) contributes to diagnostic uncertainty in obstructive sleep apnea (OSA), yet multi-metric evaluations using closely spaced PSG nights-particularly in moderate-to-severe disease-remain limited. The comparative stability of apnea-hypopnea index (AHI) definitions, hypoxic burden (HB), and threshold calibration remains unclear. RESEARCH QUESTION: What is the NtNV across PSG-derived metrics, and how do AHI scoring definitions and threshold calibration influence diagnostic stability in OSA? STUDY DESIGN AND METHODS: We performed a retrospective analysis of a prospective study including 147 participants with prior diagnosis or high pretest likelihood of moderate-to-severe OSA who underwent two PSGs within 10 days. NtNV was quantified across 20 PSG-derived metrics. A normalized NtNV matrix was analyzed using PCA followed by unsupervised k-means clustering to identify data-driven variability-pattern groups. Diagnostic stability was compared across AHI definitions (3%/arousal vs. 4%) and HB risk categories. Statistical calibration models derived AHI 4% thresholds aligned with AHI 3%/arousal severity cutpoints. In the PCA followed by k-means analysis, respiratory event frequency metrics contributed most strongly and separated participants into lower- and higher-respiratory-variability pattern groups. AHI 4% showed higher classification disagreement than AHI 3%/arousal in short-interval (29.9% vs. 21.2% overall; 14.3% vs. 5.4% at the moderate-to-severe threshold) and longitudinal comparisons (45.9% vs. 31.1%; 20.9% vs. 8.2%). HB showed low inter-night disagreement (11.8%). Calibration models aligned AHI 4% thresholds of 6.1-6.9 and 18.4-22.3 events/h with AHI 3%/arousal cutpoints of 15 and 30 events/h. INTERPRETATION: Positional, autonomic, and sleep architecture metrics showed the highest NtNV; respiratory event frequency metrics were intermediate and oxygenation most stable. Greater classification disagreement with AHI 4% was threshold-driven, with implications for hypopnea scoring, and payer policy in OSA diagnosis.
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Alavi et al. (2026) conducted an observational in Obstructive sleep apnea (OSA) (n=147). AHI 4% scoring definition vs. AHI 3%/arousal scoring definition was evaluated on Overall classification disagreement in short-interval comparisons.
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