Clinical Prediction Model of Obstructive Sleep Apnea at Mansoura University Hospitals

Completed

• Conditions: Osa Syndrome

• Registration Number: NCT06326580
• Lead Sponsor: Mansoura University

Brief Summary

Obstructive sleep apnea syndrome (OSAS)is a sleep breathing disorder manifested by complete apnea or partial hypopnea obstruction of the upper airway, which often remains undiagnosed and untreated (Kuczynski, W., 2019). These episodes, which should be more than 5 per hour and last at least 10 s, can lead to a sleep fragmentation and hypoxia (Huon, L.-K.A., 2017). OSAS predominantly affects 26% of individuals between 30 and 70 years in the U.S (apnea hypopnea index ≥5 events per hour) (Schwartz, M., 2018).

Obstructive sleep apnea it is increasingly recognized as an independent risk factor for cardiac, neurologic, and perioperative morbidities. Yet this disorder remains undiagnosed in a substantial portion of our population. It is imperative for all physicians to remain vigilant in identifying patients with signs and symptoms consistent with OSA (Park, J. G., 2011).

The test of hypothesis is to design a clinical prediction model of obstructive sleep apnea from collected data of the patients having symptoms of obstructive sleep apnea and the results of their sleep study

Detailed Description

Aim of study:

To identify standard clinical parameters that may predict the presence of obstructive sleep apnea (OSA) among Egyptian patients in sleep study unit, ChestDepartment Mansoura University Hospital, Egypt Study Design :Retrospective study The study will include the patients who visited sleep outpatient clinic of sleep disordered breathing unit of Chest Department, Mansoura University from 2004 to 2022 Inclusion criteria

1. Patients aged \>18 years old

2. presented with symptoms suggestive of OSA like excessive daytime sleepiness, snoring, nocturnal chocking and witnessed apnea Exclusion criteria :Patients with long-term use of medications known to affect sleep integrity,Current alcohol or drug abuse, History of psychosis ,Central sleep apnea syndromes ,Previous OSA surgery ,Other sleep disorders

METHODS:

Study Design: this is retrospective study The recorded data of all patients which include all their symptoms and comorbidities as well as their use of any regular medications will be collected.

1. Physical examination:

* All recorded vital data, height, weight, and body mass index (BMI) of the patients will be collected

* The probability of OSA using the STOP-BANG and Berlinequestionnaire in all the patients will be collected

* The calculated Epworth Sleepiness Scale (ESS) for each patient will be assessed.

2. Sleep Study (PSG) Diagnostic polysomnogram that had been done for all the patients for diagnostic purpose will be assessed.

Study Timeline

• Study Start: 2022-10-20
• Primary Completion: 2023-10-20
• Study Completion: 2024-02-20
• Status Verified: 2024-03
• Study First Submitted: 2024-03-14
• Study First Submitted QC: 2024-03-20
• Last Update Submitted: 2024-03-20
• Study First Posted: 2024-03-22
• Last Update Posted: 2024-03-22

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1129

Inclusion Criteria

1. Patients aged >18 years old
2. presented with symptoms suggestive of OSA like excessive daytime sleepiness, snoring, nocturnal chocking and witnessed apnea

Exclusion Criteria

1. Patients with long-term use of medications known to affect sleep integrity Current
2. alcohol or drug abuse
3. History of psychosis
4. Central sleep apnea syndromes
5. Previous OSA surgery
6. Other sleep disorders

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The recorded data of all patients which include all their symptoms and comorbidities as well as their use of any regular medications will be collected.	1 year	The recorded data of all patients which include all their symptoms and comorbidities as well as their use of any regular medications will be collected.

Trial Locations

Locations (1)

Mansoura University; 🇪🇬 Mansoura, Dakhlia, Egypt