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Clinical Investigations: SLEEP |

Prospective Testing of Two Models Based on Clinical and Oximetric Variables for Prediction of Obstructive Sleep Apnea*

Nicolas Roche, MD; Bertrand Herer, MD; Catherine Roig, MD; Gérard Huchon, MD, FCCP
Author and Funding Information

*From the Service de Pneumologie et Réanimation (Drs. Roche and Huchon), Hôpital de l’Hôtel Dieu, Paris; and Service de Pneumologie (Drs. Herer and Roig), Centre Médical de Forcilles, Férolles-Attilly, France.

Correspondence to: Nicolas Roche, MD, Service de Pneumologie et Réanimation, Hôpital de l’Hôtel-Dieu, 1, place du Parvis Notre-Dame, F-75181 Paris Cedex 04, France; e-mail: roche_nicolas@hotmail.com



Chest. 2002;121(3):747-752. doi:10.1378/chest.121.3.747
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Study objective: To test the validity of two models for prediction of obstructive sleep apnea syndrome (OSAS) before polysomnography.

Design: Prospective study.

Setting: Sleep laboratory in an obesity clinic.

Patients: Data from two populations were analyzed: the first (group 1) included 102 consecutive overweight patients referred to our laboratory by an obesity clinic between May 1992 and November 1994, and was used to develop the prediction models. The second (group 2) included 108 consecutive new patients referred to our laboratory by the same obesity clinic between February 1997 and September 1998, and was used to test the prediction models.

Measurements and results: Models were developed using a clinical score, pulmonary function tests, arterial blood gas tensions, and nocturnal pulse oximetry. OSAS was defined by an apnea-hypopnea index (AHI) > 15 events per hour, as measured by full-night polysomnography. Step-by-step multiple linear regression analysis (MLR) was used to provide an equation for calculation of predicted AHI, while logistic regression analysis (LR) provided an equation for calculation of the probability (P′) of having OSAS. Characteristics of groups 1 and 2 were similar except for the prevalence of OSAS, which was higher in group 2 (74% vs 39% in group 1). The negative predictive value (NPV) of the MLR model dropped from 82.9% in group 1 to 36.7% in group 2. In parallel, the NPV of a P′ < 0.25 according to LR decreased from 78.6% in group 1 to 23.5% in group 2.

Conclusion: Our results emphasize the need for systematic prospective testing of mathematical predictive models in OSAS, since their diagnostic characteristics may differ markedly between populations, even when the setting and mode of recruitment remain unchanged.

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