Trends in anthropometry and severity of sleep-disordered breathing over two decades of diagnostic sleep studies in an Australian adult sleep laboratory.

Objective: To document trends in subject demographics, anthropometry and sleep disorder severity over 21 years of diagnostic sleep studies.

Design, Participants And Setting: A retrospective observational study of consecutive subjects undergoing initial diagnostic polysomnography for investigation of possible sleep disorders in a university-affiliated tertiary public metropolitan hospital in the Hunter New England region of New South Wales between 1987 and 2007.

Main Outcome Measures: Body weight, body mass index (BMI) and severity of sleep-related breathing disorders (apnoea-hypopnoea index [AHI]).

Prediction of sleep-disordered breathing by unattended overnight oximetry.

Between January 1994 and July 1997, 793 patients suspected of having sleep-disordered breathing had unattended overnight oximetry in their homes followed by laboratory polysomnography. From the oximetry data we extracted cumulative percentage time at SaO2 < 90% (CT90) and a saturation variability index (delta Index, the sum of the differences between successive readings divided by the number of readings - 1). CT90 was weakly correlated with polysomnographic apnea/hypopnea index (AHI).

A comparison of clinical assessment and home oximetry in the diagnosis of obstructive sleep apnea.

In order to determine whether measurement of arterial oxygen saturation (SaO2) could identify patients with obstructive sleep apnea (OSA), 98 consecutive patients referred for assessment of snoring and/or daytime somnolence were assessed clinically and then underwent both unsupervised oximetry in their homes and formal polysomnography. Clinical assessment identified patients with an apnea+hypopnea index (AHI) > or = 15 events per hour with a sensitivity of 79% and a specificity of 50%. Home oximetry analyzed by counting the number of arterial oxygen desaturations recorded was inferior to clinical assessment.

Estimation of the probability of disturbed breathing during sleep before a sleep study.

We have investigated the ability of a statistical model developed from clinical data and questionnaire responses to predict disturbance of breathing during sleep. Data from 100 consecutive patients referred for sleep study for suspected sleep apnea were used to develop the model using logistic regression analysis. For each subject, the model predicted the probability of having an apnea-hypopnea index (AHI) greater than 15; this probability was compared with the AHI measured from sleep study.