Effect of recording duration on the diagnostic accuracy of out-of-center sleep testing for obstructive sleep apnea

Yield of overnight pulse oximetry in screening commercial drivers for obstructive sleep apnea

PURPOSE

To assess the efficacy of overnight pulse oximetry in screening male commercial drivers (CDs) for obstructive sleep apnea (OSA).

METHODS

Consecutive male CDs undergoing their annual scheduled occupational health visit were enrolled from ten transportation facilities. All subjects underwent a home sleep apnea test (HSAT) to determine the Respiratory Event Index (REI). Oxygen desaturation indices (ODIs) below the 3% and 4% thresholds were computed using the built-in HSAT pulse oximeter. We then assessed the association between ODI values and the presence of OSA (defined as an REI ≥ 5 events/hour) as well as moderate to severe OSA (REI ≥ 15 events/hour).

RESULTS

Of 331 CDs recruited, 278 (84%) completed the study protocol and 53 subjects were excluded due to inadequate HSAT quality. The included and excluded subjects were comparable in demographics and clinical characteristics. The included CDs had a median age of 49 years (interquartile range (IQR) = 15 years) and a median body mass index of 27 kg/m2 (IQR = 5 kg/m2). One hundred ninety-nine (72%) CDs had OSA, of which 48 (17%) were with moderate OSA and 45 (16%) with severe OSA. The ODI3 and ODI4 receiving operating characteristic curve value were 0.95 for predicting OSA and 0.98-0.96 for predicting moderate to severe OSA.

CONCLUSION

Overnight oxygen oximetry may be an effective means to screen CDs for OSA.

Screening for obstructive sleep apnea in patients with cancer - a machine learning approach

Background

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in patients with cancer. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk.

Study Design and Methods

This is a retrospective study of patients with cancer at a single tertiary cancer institution who underwent a home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross-validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT.

Results

From a training dataset of 249 patients, kernel principal component analysis (PCA) extracted eight components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA + RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA + RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set.

Conclusions

The PCA + RF ML model had the highest accuracy in screening for OSA in patients with cancer. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.

Evaluating an under-mattress sleep monitor compared to a peripheral arterial tonometry home sleep apnea test device in the diagnosis of obstructive sleep apnea

STUDY OBJECTIVES

To evaluate whether or not the apnea-hypopnea index (AHI) from a peripheral arterial tonometry (PAT) home sleep apnea test (HSAT) is equivalent to the AHI provided by the mean of one, three, or seven nights from the Withings Sleep Analyzer (WSA) under-mattress device.

METHODS

We prospectively enrolled patients with suspected OSA in whom a PAT-HSAT was ordered. Eligible patients used the WSA for seven to nine nights. PAT data were scored using the device's intrinsic machine learning algorithms to arrive at the AHI using both 3% and 4% desaturation criteria for hypopnea estimations (PAT3%-AHI and PAT4%-AHI, respectively). These were then compared with the WSA-estimated AHI (WSA-AHI).

RESULTS

Of 61 patients enrolled, 35 completed the study with valid PAT and WSA data. Of the 35 completers 16 (46%) had at least moderately severe OSA (PAT3%-AHI ≥ 15). The seven-night mean WSA-AHI was 2.13 (95%CI =  - 0.88, 5.14) less than the PAT3%-AHI, but 5.64 (95%CI = 2.54, 8.73) greater than the PAT4%-AHI. The accuracy and area under the receiver operating curve (AUC) using the PAT3%-AHI ≥ 15 were 77% and 0.87 and for PAT4%-AHI ≥ 15 were 77% and 0.85, respectively. The one-, three-, or seven-night WSA-AHI were not equivalent to either the 3% or 4% PAT-AHI (equivalency threshold of ± 2.5 using the two one-sided t-test method).

CONCLUSIONS

The WSA derives estimates of the AHI unobtrusively over many nights, which may prove to be a valuable clinical tool. However, the WSA-AHI over- or underestimates the PAT-AHI in clinical use, and the appropriate use of the WSA in clinical practice will require further evaluation.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04778748.

Documentation of polysomnographic and home sleep apnea test interpretations: room for improvement?

STUDY OBJECTIVES

Obstructive sleep apnea (OSA), a heterogeneous disorder with many different presentations, is diagnosed with sleep studies. In standard clinical practice, test data are reviewed and scored, and interpretations are documented. Little standardization exists regarding what should be included in interpretations. We aimed to determine how consistently the documented interpretation included references to study quality parameters and accepted disease phenotypes.

METHODS

This study was performed at a single academic center in January 2021. From the literature, we formulated a list of test and titration quality criteria and OSA phenotypes that should be reflected in study interpretations, including total recording time, total sleep time, positionality, and supine rapid eye movement (REM) sleep during titration. We retrospectively reviewed the documentation of sleep studies to determine how often these factors were reflected in interpretation reports or clinical notes.

RESULTS

Of 134 patients in the study, 81 were diagnosed with OSA. A finding of inadequate total recording time during polysomnography or total sleep time on home sleep apnea testing was most often not documented. Positionality of OSA was not documented in 33% of applicable studies. The absence of supine REM sleep during positive airway pressure titration was not mentioned in 15% of interpretations.

CONCLUSIONS

The documentation of quality concerns and clinically important OSA phenotypes in interpretations was inconsistent. Documentation of meaningful test quality information and sleep apnea phenotypes might be improved with report standardization or by developing enhanced data displays.

CITATION

Herberts MB, Morgenthaler TI. Documentation of polysomnographic and home sleep apnea test interpretations: room for improvement? J Clin Sleep Med. 2023;19(6):1043-1049.

Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters

BACKGROUND AND OBJECTIVE

The diagnosis of obstructive sleep apnea (OSA) relies on polysomnography which is time-consuming and expensive. We therefore aimed to develop two simple, non-invasive models to screen adults for OSA.

METHODS

The effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants).

RESULTS

Based on the development set, the best BMI cut-off value for diagnosing OSA was 26.45 kg/m², with an area under the curve (AUC) of 0.7213 (95% confidence interval (CI), 0.6861-0.7566), a sensitivity of 57% and a specificity of 78%. Through forward conditional logistic regression analysis using a stepwise selection model developed from observed data, seven clinical variables were evaluated as independent predictors of OSA: age, BMI, sex, Epworth Sleepiness Scale score, witnessed apnoeas, dry mouth and arrhythmias. With this new model, the AUC was 0.7991 (95% CI, 0.7668-0.8314) for diagnosing OSA (sensitivity, 75%; specificity, 71%). The results were confirmed using the validation set. A nomogram for predicting OSA was generated based on this new model using statistical software.

CONCLUSIONS

BMI can be used as an indicator to screen for OSA in the community. We created an internally validated, highly distinguishable, visual and parsimonious prediction model comprising BMI and other parameters that can be used to identify patients with OSA among outpatients. Use of this prediction model may help to improve clinical decision-making.

Poor performance of screening questionnaires for obstructive sleep apnea in male commercial drivers

PURPOSE

Screening commercial drivers (CDs) for obstructive sleep apnea (OSA) reduces the risk of motor vehicle accidents. We evaluated the accuracy of standard OSA questionnaires in a cohort of CDs.

STUDY DESIGN AND METHODS

We enrolled consecutive male CDs at 10 discrete transportation companies during their yearly scheduled occupational health visit. The CDs had their anthropometric measures taken; completed the Berlin, STOP, STOP-BANG, OSAS-TTI, SACS, EUROSAS, and ARES questionnaires; and underwent a home sleep apnea test (HSAT) for the determination of their respiratory events index (REI). We assessed the questionnaires' ability to predict OSA (REI ≥ 5 events/h) and moderate-to-severe OSA (REI ≥ 15 events/h).

RESULTS

Among 315 CDs recruited, 243 (77%) completed the study protocol, while 72 subjects were excluded for inadequate HSAT quality. The demographics and clinical data were comparable in both the included and excluded subjects. The included CDs had a median age of 50 years (interquartile range (IQR) 25-70) and a mean body mass index of 27 ± 4 kg/m². One hundred and seventy-one subjects (71%) had OSA, and 68 (28%) had moderate-to-severe OSA. A receiver operating characteristic curve of the questionnaires were 0.51-0.71 for predicting OSA and 0.51-0.66 for moderate-to-severe OSA. The STOP-BANG questionnaire had an unsatisfactory positive predictive value, while all of the other questionnaires had an inadequate negative predictive value.

CONCLUSIONS

Standard OSA questionnaires are not suited for screening among CDs. The use of the HSAT could provide an objective evaluation of for OSA in this special population.