PubMed Health⌕ Search

Biomedical subjects

Bertrand Herer

Publications and source records attributed to Bertrand Herer.

6 recordsLinked to original sources
PubMed2003

A case report of an obesity hypoventilation syndrome associated with obstructive sleep apnea due to a carotid body paraganglioma.

BACKGROUND AND PURPOSE: To the best of our knowledge, the association between an obstructive sleep apnea syndrome (OSAS) due to a neck mass and an obesity hypoventilation syndrome (OHS) has not been reported. PATIENTS AND METHODS: We report the case of a patient with obesity hypoventilation syndrome (OHS) in whom OSAS caused by a carotid body paraganglioma contributed to recurrent bouts of severe alveolar hypoventilation. RESULTS AND CONCLUSIONS: The complete surgical excision of the paraganglioma resulted in the cure of the OSAS and contributed to a clear improvement of the clinical symptoms of OHS.

Aged↗
PubMed2002

Prediction of obstructive sleep apnea by OxiFlow in overweight patients.

OBJECTIVES: The aim of our study was to assess the diagnostic characteristics of the OxiFlow (OF) device that combines oximetry with recording of thermistor airflow. METHODS: In patients referred to the sleep laboratory of an obesity clinic apnea-hypopnea index (AHI, events h(-1)) was calculated both by a full-night polysomnography (PSG) and OF on a separate night. Fifty-six patients were studied, of whom 49 had OSA defined as an AHI> or =15 events h(-1). RESULTS: There was an underestimation of AHI by OF when assessed by the Bland-Altman plot. Sensitivity (Se), specificity (Sp), positive (PPV) and negative (NPV) predictive values for OF-AHI thresholds (10, 15 and 20 events h(-1)), taking PSG as a gold standard with a fixed PSG-AHI threshold of 15 events h(-1), were evaluated in two groups of patients with intermediate (group A, n=18, OSA prevalence=72.2%) and high (group B, n=38, OSA prevalence=94.7%) clinical probability of OSA. Se and PPV ranged respectively from 0.77 to 0.85 and from 0.73 to 0.77 (group A); from 0.74 to 0.97 and from 0.94 to 0.98 (group B). Sp and NPV ranged respectively from 0.20 to 0.40 and from 0.33 to 0.40 (group A); from 0.50 to 0.83 and from 0.21 to 0.67 (group B). Likelihood ratios (LRs) for a positive OF result ranged from 1.06 to 1.28 (group A) and from 1.83 to 4.42 (group B). CONCLUSIONS: We conclude that in a population with a high OSA prevalence, we have found a low agreement between PSG-AHI and OF-AHI and an underestimation of AHI by OF. The LRs of OF as a diagnostic test were of low significance, precluding its usefulness in generating significant shifts in pretest to posttest probability of OSA.

Journal Article↗
PubMed2002

Prospective testing of two models based on clinical and oximetric variables for prediction of obstructive sleep apnea.

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.

Female↗