Literature DB >> 26599791

Development and Validation of a Morphologic Obstructive Sleep Apnea Prediction Score: The DES-OSA Score.

Eric Deflandre1, Stephanie Degey, Jean-Francois Brichant, Robert Poirrier, Vincent Bonhomme.   

Abstract

BACKGROUND: Obstructive sleep apnea (OSA) is a common and underdiagnosed entity that favors perioperative morbidity. Several anatomical characteristics predispose to OSA. We developed a new clinical score that would detect OSA based on the patient's morphologic characteristics only.
METHODS: Patients (n = 149) scheduled for an overnight polysomnography were included. Their morphologic metrics were compared, and combinations of them were tested for their ability to predict at least mild, moderate-to-severe, or severe OSA, as defined by an apnea-hypopnea index (AHI) >5, >15, or >30 events/h. This ability was calculated using Cohen κ coefficient and prediction probability.
RESULTS: The score with best prediction abilities (DES-OSA score) considered 5 variables: Mallampati score, distance between the thyroid and the chin, body mass index, neck circumference, and sex. Those variables were weighted by 1, 2, or 3 points. DES-OSA score >5, 6, and 7 were associated with increased probability of an AHI >5, >15, or >30 events/h, respectively, and those thresholds had the best Cohen κ coefficient, sensitivities, and specificities. Receiver operating characteristic curve analysis revealed that the area under the curve was 0.832 (95% confidence interval [CI], 0.762-0.902), 0.805 (95% CI, 0.734-0.876), and 0.834 (95% CI, 0.757-0.911) for DES-OSA at predicting an AHI >5, >15, and >30 events/h, respectively. With the aforementioned thresholds, corresponding sensitivities (95% CI) were 82.7% (74.5-88.7), 77.1% (66.9-84.9), and 75% (61.0-85.1), and specificities (95% CI) were 72.4% (54.0-85.4), 73.2% (60.3-83.1), and 76.9% (67.2-84.4). Validation of DES-OSA performance in an independent sample yielded highly similar results.
CONCLUSIONS: DES-OSA is a simple score for detecting OSA patients. Its originality relies on its morphologic nature. Derived from a European population, it may prove useful in a preoperative setting, but it has still to be compared with other screening tools in a general surgical population and in other ethnic groups.

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Mesh:

Year:  2016        PMID: 26599791     DOI: 10.1213/ANE.0000000000001089

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  9 in total

1.  OBSTRUCTIVE SLEEP APNEA AND OFFICE-BASED SURGERY.

Authors:  Steven Ganzberg
Journal:  Anesth Prog       Date:  2016

2.  Author's response to Letter-to-the-editor regarding "Are the Epworth Sleepiness Scale and Stop-Bang Model effective at predicting the severity of obstructive Sleep Apnoea (OSA); in particular OSA requiring treatment?"

Authors:  Binita Panchasara; Alan J Poots; Gary Davies
Journal:  Eur Arch Otorhinolaryngol       Date:  2018-01-24       Impact factor: 2.503

3.  Pre-Operative Ability of Clinical Scores to Predict Obstructive Sleep Apnea (OSA) Severity in Susceptible Surgical Patients.

Authors:  E Deflandre; S Degey; J-F Brichant; A-F Donneau; R Frognier; R Poirrier; V Bonhomme
Journal:  Obes Surg       Date:  2017-03       Impact factor: 4.129

4.  A Global Comparison of Anatomic Risk Factors and Their Relationship to Obstructive Sleep Apnea Severity in Clinical Samples.

Authors:  Kate Sutherland; Brendan T Keenan; Lia Bittencourt; Ning-Hung Chen; Thorarinn Gislason; Sarah Leinwand; Ulysses J Magalang; Greg Maislin; Diego R Mazzotti; Nigel McArdle; Jesse Mindel; Allan I Pack; Thomas Penzel; Bhajan Singh; Sergio Tufik; Richard J Schwab; Peter A Cistulli
Journal:  J Clin Sleep Med       Date:  2019-04-15       Impact factor: 4.062

5.  Are the Epworth Sleepiness Scale and Stop-Bang model effective at predicting the severity of obstructive sleep apnoea (OSA); in particular OSA requiring treatment?

Authors:  Binita Panchasara; Alan J Poots; Gary Davies
Journal:  Eur Arch Otorhinolaryngol       Date:  2017-08-30       Impact factor: 2.503

Review 6.  Should the diagnosis and management of OSA move into general practice?

Authors:  Monique Suárez; Jeisson Osorio; Marta Torres; Josep M Montserrat
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7.  Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes.

Authors:  Christina H Shin; Stephanie D Grabitz; Fanny P Timm; Noomi Mueller; Khushi Chhangani; Karim Ladha; Scott Devine; Tobias Kurth; Matthias Eikermann
Journal:  BMC Anesthesiol       Date:  2017-05-30       Impact factor: 2.217

8.  Comparison of clinical scores in their ability to detect hypoxemic severe OSA patients.

Authors:  Eric Deflandre; Nicolas Piette; Vincent Bonhomme; Stephanie Degey; Laurent Cambron; Robert Poirrier; Jean-Francois Brichant; Jean Joris
Journal:  PLoS One       Date:  2018-05-07       Impact factor: 3.240

9.  Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score.

Authors:  Naima Laharnar; Sebastian Herberger; Lisa-Kristin Prochnow; Ning-Hung Chen; Peter A Cistulli; Allan I Pack; Richard Schwab; Brendan T Keenan; Diego R Mazzotti; Ingo Fietze; Thomas Penzel
Journal:  Nat Sci Sleep       Date:  2021-11-09
  9 in total

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