Comparative Efficacy and Safety of Wakefulness-Promoting Agents for Excessive Daytime Sleepiness in Patients With Obstructive Sleep Apnea

Pitre, Tyler; Mah, Jasmine; Roberts, Sarah; Desai, Kairavi; Gu, Yusing; Ryan, Clodagh M.; Busse, Jason W.; Zeraatkar, Dena

doi:10.7326/m22-3473

Cited by 11 publications

(3 citation statements)

References 49 publications

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“…In response to these limitations, various life scales, such as the ESS, Quebec sleep questionnaire, and the OSA-18 scale for children, have been used in clinical settings to complement polysomnography. The ESS, comprising subjective sleepiness questions in eight conditions, is widely used despite its subjective nature, as its reliability and validity have been established (Pitre et al, 2023 ). However, the advent of computer machine learning models has revolutionized disease prediction by integrating and analyzing both subjective and objective indicators.…”

Section: Discussionmentioning

confidence: 99%

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Liu,

Geng,

Shen

et al. 2024

Front. Big Data

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ObjectiveTo develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.MethodsWe conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.ResultsAmong the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.ConclusionMachine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

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Section: Discussionmentioning

confidence: 99%

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Liu,

Geng,

Shen

et al. 2024

Front. Big Data

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“…A recent network meta-analysis (14 RCTs) compared modafinil, armodafinil, pitolisant, and solriamfetol for EDS in both CPAPtreated or untreated OSA (Pitre et al, 2023). Despite the limitations related to this kind of statistical model, the authors highlighted the greater efficacy of solriamfetol, and a higher risk of treatment discontinuation with modafinil and armodafinil at 4 weeks of treatment.…”

Section: Other Targetsmentioning

confidence: 99%

Obstructive sleep apnea phenotypes eligible for pharmacological treatment

Bruyneel

2023

Front. Sleep

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Obstructive sleep apnea (OSA) is a common disorder. Its prevalence is increasing worldwide, partially due to increasing rates of obesity, and OSA has a well-documented impact on physical health (increased risk of cardiovascular and metabolic disorders) and mental health, as well as major socioeconomic implications. Although continuous positive airway pressure treatment (CPAP) remains the primary therapeutic intervention for moderate to severe OSA, other treatment strategies such as weight loss, positional therapy, mandibular advancement devices (MAD), surgical treatment, myofunctional therapy of upper airways (UA) muscles and hypoglossal nerve stimulation are increasingly used. Recently, several trials have demonstrated the clinical potential for various pharmacological treatments that aim to improve UA muscle dysfunction, loop gain, or excessive daytime sleepiness. In line with the highly heterogeneous clinical picture of OSA, recent identification of different clinical phenotypes has been documented. Comorbidities, incident cardiovascular risk, and response to CPAP may vary significantly among phenotypes. With this in mind, the purpose of this review is to summarize the data on OSA phenotypes that may respond to pharmacological approaches.

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“…We piloted the prompts using 15 trials drawn from systematic reviews previously performed by our own team and refined the prompts by iterative discussion and input by the co-authors (23)(24)(25). All prompts asked ChatGPT to judge risk of bias for all RoB 2.0 domains (bias due to randomization, deviation from intended intervention, missing outcome data, measurement of outcome, and selective reporting) as low risk of bias, some concerns, or high risk of bias-consistent with RoB 2.0 guidance (5).…”

Section: Chatgpt Promptsmentioning

confidence: 99%

ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: A methods study

Pitre,

Jassal,

Talukdar

et al. 2023

Preprint

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BackgroundThe assessment of risk of bias is a critical component of systematic review methods. Assessing risk of bias, however, can be time- and resource-intensive. AI-based solutions may increase efficiency and reduce burden.ObjectiveTo evaluate the reliability of ChatGPT for performing risk of bias assessments of randomized trials.MethodsWe sampled recently published Cochrane systematic reviews of medical interventions (up to October 2023) that included randomized controlled trials and assessed risk of bias using the Cochrane-endorsed revised risk of bias tool for randomized trials (RoB 2.0). From each eligible review, we collected data on the risk of bias assessments for the first three reported outcomes. Using ChatGPT-4, we assessed the risk of bias for the same outcomes using three different prompts: a minimal prompt including limited instructions, a maximal prompt with extensive instructions, and an optimized prompt that was designed to yield the best risk of bias judgments. The agreement between ChatGPT’s assessments and those of the systematic reviewers was quantified using weighted kappa statistics.ResultsWe included 34 systematic reviews with 157 unique trials. We found the agreement between ChatGPT and systematic review authors for assessment of overall risk of bias to be 0.16 (95% CI: 0.01 to 0.3) for the maximal ChatGPT prompt, 0.17 (95% CI: 0.02 to 0.32) for the optimized prompt, and 0.11 (95% CI: -0.04 to 0.27) for the minimal prompt. For the optimized prompt, agreement ranged between 0.11 (95% CI: -0.11 to 0.33) to 0.29 (95% CI: 0.14 to 0.44) across risk of bias domains, with the lowest agreement for the deviations from the intended intervention domain and the highest agreement for the missing outcome data domain.ConclusionOur results suggest that ChatGPT and systematic reviewers only have “slight” to “fair” agreement in risk of bias judgments for randomized trials. ChatGPT is currently unable to reliably assess risk of bias of randomized trials. We recommend systematic reviewers avoid using ChatGPT to perform risk of bias assessments.

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Comparative Efficacy and Safety of Wakefulness-Promoting Agents for Excessive Daytime Sleepiness in Patients With Obstructive Sleep Apnea

Cited by 11 publications

References 49 publications

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Obstructive sleep apnea phenotypes eligible for pharmacological treatment

ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: A methods study

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