Sleep disorders in low- and middle-income countries: a call for action

Utility of the psychomotor vigilance task in screening for obstructive sleep apnoea

PURPOSE

The study aimed to assess the performance of the PVT in patients with suspected OSA, evaluate its role in population screening for OSA.

METHODS

The NoSAS, STOP-Bang, ESS scores and PVT tests were performed after suspected OSA patients' admission, followed by PSG. Then we compared the PVT results, calculated the sensitivity, specificity and ROC curve of PVT, and analyzed the accuracy of STOP-Bang and NoSAS questionnaire combined with PVT in predicting OSA.

RESULTS

A total of 308 patients were divided into four groups based on AHI: primary snoring (2.74 ± 1.4 events/h, n = 37); mild OSA (9.96 ± 3.25 events/h, n = 65); moderate OSA (22.41 ± 4.48 events/h, n = 76); and, severe OSA (59.42 ± 18.37 events/h, n = 130). There were significant differences in PVT lapses (p < 0.001) and reaction time (RT, p = 0.03) among the four groups. The PVT lapses and RT were positively correlated with AHI (p < 0.001) and ODI (p < 0.001), and negatively correlated with LSpO2 (p < 0.001). When diagnosing OSA (AHI ≥ 5 events/h), the AUCs of PVT, ESS, STOP-Bang, and NoSAS were 0.679, 0.579, 0.727, and 0.653, respectively; the AUCs of STOP-Bang and NoSAS combined with PVT increased. After combined PVT, the diagnostic specificity of STOP-Bang and NoSAS at nodes with AHI ≥ 5, ≥ 15 and ≥ 30 events/h increased to varying degrees.

CONCLUSION

Patients with OSA exhibited impairment in the PVT, and the combination of the PVT and STOP-Bang or NoSAS scores can improve the diagnostic efficacy and specificity for OSA.

Performance of artificial intelligence chatbots in sleep medicine certification board exams: ChatGPT versus Google Bard

PURPOSE

To conduct a comparative performance evaluation of GPT-3.5, GPT-4 and Google Bard in self-assessment questions at the level of the American Sleep Medicine Certification Board Exam.

METHODS

A total of 301 text-based single-best-answer multiple choice questions with four answer options each, across 10 categories, were included in the study and transcribed as inputs for GPT-3.5, GPT-4 and Google Bard. The first output responses generated were selected and matched for answer accuracy against the gold-standard answer provided by the American Academy of Sleep Medicine for each question. A global score of 80% and above is required by human sleep medicine specialists to pass each exam category.

RESULTS

GPT-4 successfully achieved the pass mark of 80% or above in five of the 10 exam categories, including the Normal Sleep and Variants Self-Assessment Exam (2021), Circadian Rhythm Sleep-Wake Disorders Self-Assessment Exam (2021), Insomnia Self-Assessment Exam (2022), Parasomnias Self-Assessment Exam (2022) and the Sleep-Related Movements Self-Assessment Exam (2023). GPT-4 demonstrated superior performance in all exam categories and achieved a higher overall score of 68.1% when compared against both GPT-3.5 (46.8%) and Google Bard (45.5%), which was statistically significant (p value < 0.001). There was no significant difference in the overall score performance between GPT-3.5 and Google Bard.

CONCLUSIONS

Otolaryngologists and sleep medicine physicians have a crucial role through agile and robust research to ensure the next generation AI chatbots are built safely and responsibly.

Characterization of sleep apnea among a sample of adults from Samoa

Sleep apnea is a public health concern around the world, but little research has been dedicated to examining this issue in low- and middle-income countries, including Samoa. Using data collected through the Soifua Manuia ("Good Health") study, which aimed to investigate the impact of the body mass index (BMI)-associated genetic variant rs373863828 in CREB3 Regulatory Factor ( CREBRF ) on metabolic traits in Samoan adults, we examined the sample prevalence and characteristics of sleep apnea using data collected with a validated home sleep apnea device (WatchPAT, Itamar). A total of 330 participants (sampled to overrepresent the obesity-risk allele of interest) had sleep data available. Participants (53.3% female) had a mean (SD) age of 52.0 (9.9) years and BMI of 35.5 (7.5) kg/m 2 and 36.3% of the sample had type 2 diabetes. Based on the 3% and 4% apnea hypopnea indices (AHI) and the 4% oxygen desaturation index (ODI), descriptive analyses revealed that many participants had potentially actionable sleep apnea defined as >5 events/hr (87.9%, 68.5%, and 71.2%, respectively) or clinically actionable sleep apnea defined as ≥15 events/hr (54.9%, 31.5%, and 34.5%, respectively). Sleep apnea was more severe in men; for example, clinically actionable sleep apnea (≥15) based on the AHI 3% definition was observed in 61.7% of men and 48.9% of women. Correction for non-representational sampling related to the CREBRF obesity-risk allele resulted in only slightly lower estimates. Across the AHI 3%, AHI 4%, and ODI 4%, multiple linear regression revealed associations between a greater number of events/hr and higher age, male sex, higher body mass index, higher abdominal-hip circumference ratio, and geographic region of residence. Our study identified a much higher frequency of sleep apnea in Samoa compared with published data from other studies, but similar predictors. Continued research addressing generalizability of these findings, as well as a specific focus on diagnosis and affordable and equitable access to treatment, is needed to alleviate the burden of sleep apnea in Samoa and around the world.