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World Journal of Methodology logoLink to World Journal of Methodology
. 2026 Jun 20;16(2):114192. doi: 10.5662/wjm.v16.i2.114192

Obstructive sleep apnea: Proposed scoring system

Arkiath Veettil Raveendran 1, Jothydev Kesavadev 2, Banshi Saboo 3, Sunny George 4
PMCID: PMC13121997  PMID: 42058798

Abstract

Obstructive sleep apnea is a common yet under-recognized condition. 80% of sleep apnea cases go undiagnosed, and it is a common contributor to sudden death. Therefore, a simple and sensitive screening tool is the need of the hour. We developed a simple, more convenient, and user-friendly 3-question screening tool to identify individuals at risk of obstructive sleep apnea.

Keywords: Obstructive sleep apnea, Screening tool, Diagnosis, Epworth Sleepiness Scale, STOP-BANG, Cardiovascular disease


Core Tip: Individuals with obstructive sleep apnea have an increased risk of hypertension, cardiovascular events, premature death, pulmonary hypertension, atrial fibrillation, neurocognitive dysfunction, awakening headache, motor vehicle accidents, and poor quality of life. However, the majority remain undiagnosed and untreated, leading to an increased risk of multiple health complications. Therefore, early identification is crucial, highlighting the importance of a screening tool that is both sensitive and user-friendly to identify people at risk. Our newly proposed screening tool for obstructive sleep apnea is expected to be highly beneficial for practicing clinicians worldwide, and the self-assessment version for the general public.

INTRODUCTION

Obstructive sleep apnea (OSA) is a common yet under-recognized condition[1]. It is characterized by partial or complete airway collapse, resulting in desaturation or arousals from sleep. It is associated with loud, disruptive snoring and apnea during sleep, leading to fragmented, non-restorative sleep and excessive daytime sleepiness. People with OSA have an increased risk of hypertension, cardiovascular events, premature death, pulmonary hypertension, atrial fibrillation, neurocognitive dysfunction, awakening headache, motor vehicle accidents, and poor quality of life[2]. Although OSA is common in obese individuals, it is also observed in non-obese individuals, contrary to popular belief. Various structural or anatomical features predispose individuals to OSA and are particularly important in non-obese individuals. It includes innate anatomic variations (facial elongation, posterior facial compression), retrognathia, macroglossia, micrognathia, mandibular hypoplasia, structural alteration of the skull, etc.[3]. Enlarged tonsils and adenoids are common causes of OSA in children. According to the American Academy of Sleep Medicine, OSA affects 12% of United States adults, yet 80% remain undiagnosed[4]. Several screening tools are available to identify people who are at risk of OSA, like STOP, STOP-BANG, Epworth Sleepiness Scale[5-8], the 4-Variable screening tool, Obstructive Sleep Apnea Syndrome Scale, GOAL, and No-Apnea[9-11]. Newer screening tools using machine learning based questionnaire, home sleep apnea tests, and the use of smartphone/wearable algorithms are the recent additions[12-14]. However, a screening tool that is simple, more convenient, and user-friendly is the need of the hour.

PROPOSED SCORING SYSTEM FOR OSA

Of 80% of sleep apnea cases go undiagnosed, and this is a common cause of sudden death. Therefore, a simple and sensitive screening tool is an urgent necessity. With this background, we designed a 3-question (SSG) screening tool to identify individuals at risk of OSA. The abbreviation SSG stands for major manifestations of OSA-snore (snore loudly), sleepiness (excessive daytime sleepiness), and gasp (gasp for air or choke while asleep), respectively (Figure 1). A positive response to the screening questions indicates a high probability of OSA. For individuals who screen positive, the second stage involves assessing additional risk factors using a second set of questionnaires, which enhances the tool’s positive predictive value. Additionally, a modified, simpler version of the questionnaire (SSG-self) is available as a self-assessment tool for the public to screen the individual’s risk of having OSA. The proposed SSG screening tool is presented in Table 1. Each question carries a score of 0 to 5, where 0 indicates “Never” and 5 denotes “Always”.

Figure 1.

Figure 1

Major manifestations of obstructive sleep apnea and its pathophysiological basis.

Table 1.

Proposed 3-question screening tool for obstructive sleep apnea

Screening questions
Never
Slight chance
Moderate chance
High chance
Almost always
Always
Score
1. Do you snore loudly while sleeping? 0 1 2 3 4 5
2. Do you experience excessive daytime sleepiness? 0 1 2 3 4 5
3. Has anyone/bed partner told you that you gasp for air or choke while asleep? 0 4 6 8 10 12
Total score

A score of greater than 6 on the SSG screening tool indicate high probability of OSA. If an individual is staying alone or feedback from bed partners/roommates is unavailable, consider the first two questions to calculate the score. In such a situation, if the score from the two questions is greater than 4, it also indicates a high probability of OSA. A simplified version (SSG-self) of the above questionnaire has been prepared for the use of the public (Table 2). In the self-assessment questionnaire, a score of more than 2 indicate high probability of OSA (Table 2). If a response to question number 3 is unavailable, a score of 1 or more indicate high probability of OSA. The tool can be used by individuals without any medical background and is suitable for population-level screening. Individuals whose screening score is above the cut-off are at risk of OSA and should undergo risk factor (SOHNA) screening (Table 3). The abbreviation SOHNA stands for sex, obesity, hypertension, neck circumference, and anatomical abnormalities. A score of 2 or more on the SOHNA score increases the risk of OSA.

Table 2.

Proposed self-assessment 3-question screening tool for obstructive sleep apnea

Self-assessment questions
No
Yes
1. Do you snore loudly while sleeping? 0 1
2. Do you experience excessive daytime sleepiness? 0 1
3. Has anyone told you that you gasp for air or choke during sleep? 0 2
Total score

Table 3.

Risk factors screening tool for obstructive sleep apnea

Risk factors
No
Yes
Male sex 0 1
BMI > 35 kg/m2 or presence of obesity 0 2
High blood pressure or under treatment for hypertension 0 1
Short, thick neck or enlarged neck circumference (men: > 43 cm, 17 in; women: > 37 cm, 15 in) 0 2
Any anatomical abnormalities predisposing to OSA 0 3
Total score

BMI: Body mass index; OSA: Obstructive sleep apnea.

In this SSG screening tool, we are assessing whether symptoms suggestive of OSA are present or not, and if present, should go to the second stage questionnaire (SOHNA) to assess risk factors (Link to online SSG OSA Screening Calculator https://ssgosacalculator.netlify.app/). The SSG screening questions are not influenced by ethnicity, age, or body habits, as these are questions regarding the symptoms of OSA, which are universally present. So, it can be applied to any population with the same cut-off and scores. It is easy to use the SSG screening tool even in a busy clinic, as it involves only 3 simple questions. The SOHNA component cut-off values can vary depending upon the ethnic group, body habit, etc. In short, the SSG screening tool can be applied to any population group.

CONCLUSION

Our simple, easy-to-use screening tool for OSA is expected to be beneficial for practicing clinicians worldwide, and the self-assessment version enables the public to easily identify the presence of OSA as a cause for their clinical condition. Individuals with high scores should be referred for clinical and laboratory evaluation at centers equipped to diagnose and manage OSA. Our tool may also aid researchers in defining new standards for the diagnosis and treatment of OSA. It also helps to monitor treatment response, which will reduce the expenses of diagnosis and follow-up of individuals with OSA.

Footnotes

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

Peer review: Externally peer reviewed.

Peer-review model: Single blind

Corresponding Author's Membership in Professional Societies: Research Society for the Study of Diabetes in India, 6266.

Specialty type: Medical laboratory technology

Country of origin: India

Peer-review report’s classification

Scientific quality: Grade C

Novelty: Grade B

Creativity or innovation: Grade B

Scientific significance: Grade C

P-Reviewer: Leonan-Silva B, DDS, Professor, Researcher, Brazil S-Editor: Bai SR L-Editor: A P-Editor: Zhang YL

Contributor Information

Arkiath Veettil Raveendran, Department of Internal Medicine, Govt Medical College, Kozhikode 673010, Kerala, India. raveendranav@yahoo.co.in.

Jothydev Kesavadev, Department of Diabetes, Jothydev’s Diabetes and Research Center, Thiruvananthapuram 695032, Kerala, India.

Banshi Saboo, Department of Diabetology, Dia Care Hormone Clinic, Ahmedabad 380015, Gujarāt, India.

Sunny George, Department of Pulmonary Medicine, Government Medical College, Kochi 683106, Kerala, India.

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