Nocturnal oximetry for the diagnosis of the sleep apnoea hypopnoea syndrome: a method to reduce the number of polysomnographies?

Comparison of the value of the STOP-BANG questionnaire with oxygen desaturation index in screening obstructive sleep apnea in Germany

PURPOSE

Despite polysomnography being the gold standard method of diagnosing obstructive sleep apnea (OSA), it is time-consuming and has long waiting lists. Alternative methods including questionnaires and portable sleep devices have been developed to increase the speed of diagnosis. However, most questionnaires such as the STOP-BANG questionnaire (SBQ) are limited due to low specificity. This study evaluated the value of SBQ to screen for OSA and compared it with the oxygen desaturation index (ODI) and their combination.

METHODS

This retrospective study included patients who completed the SBQ and underwent a night at the sleep lab or home sleep testing. The ODI was extracted from these sleep study reports. The combination of SBQ with ODI and their individual scores were compared with apnea-hypopnea index (AHI) in terms of their accuracy in diagnosing OSA. Sensitivity, specificity, and area under the curve (AUC) for different severities of OSA were calculated and compared.

RESULTS

Among 132 patients, SBQ showed a sensitivity of 0.9 and a specificity of 0.3 to screen for OSA. As the severity of OSA increased, the sensitivity increased whilst specificity decreased for both measurements. ODI achieved an increased specificity of 0.8 and could correctly diagnose OSA 86% of the time which was better than SBQ's 60%. For all severities of OSA, ODI alone displayed a larger AUC than SBQ and similar AUC to their combination.

CONCLUSION

ODI produced a higher specificity and AUC than SBQ. Furthermore, ODI combined with SBQ failed to increase diagnostic value. Therefore, ODI may be the preferred way to initially screen patients for OSA as an easy-to-use alternative compared to SBQ.

Nocturnal respiratory abnormalities among ward-level postoperative patients as detected by the Capnostream 20p monitor: A blinded observational study

PURPOSE

This prospective observational study aimed to establish the frequency of postoperative nocturnal respiratory abnormalities among patients undergoing major surgery who received ward-level care. These abnormalities may have implications for postoperative pulmonary complications (PPCs).

METHODS

Eligible patients underwent blinded noninvasive continuous capnography with pulse oximetry using the Capnostream™ 20p monitor over the first postoperative night. All patients received oxygen supplementation and patient-controlled opioid analgesia. The primary outcome was the number of prolonged apnea events (PAEs), defined as end-tidal carbon dioxide (EtCO2) ≤5 mmHg for 30-120 seconds or EtCO2 ≤5 mmHg for >120 seconds with oxygen saturation (SpO2) <85%. Secondary outcomes were the proportion of recorded time that physiological indices were aberrant, including the apnea index (AI), oxygen desaturation index (ODI), integrated pulmonary index (IPI), and SpO2. Exploratory analysis was conducted to assess the associations between PAEs, PPCs, and pre-defined factors.

RESULTS

Among 125 patients who had sufficient data for analysis, a total of 1800 PAEs occurred in 67 (53.4%) patients. The highest quartile accounted for 89.1% of all events. Amongst patients who experienced any PAEs, the median (IQR) number of PAE/patient was four (2-12). As proportions of recorded time (median (IQR)), AI, ODI, and IPI were aberrant for 12.4% (0-43.2%), 19.1% (2.0-57.1%), and 11.5% (3.1-33.3%) respectively. Only age, ARISCAT, and opioid consumption/kg were associated with PPCs.

CONCLUSIONS

PAE and aberrant indices were frequently detected on the first postoperative night. However, they did not correlate with PPCs. Future research should investigate the significance of detected aberrations.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

Accuracy and usability of AcuPebble SA100 for automated diagnosis of obstructive sleep apnoea in the home environment setting: an evaluation study

OBJECTIVES

Obstructive sleep apnoea (OSA) is a heavily underdiagnosed condition, which can lead to significant multimorbidity. Underdiagnosis is often secondary to limitations in existing diagnostic methods. We conducted a diagnostic accuracy and usability study, to evaluate the efficacy of a novel, low-cost, small, wearable medical device, AcuPebble_SA100, for automated diagnosis of OSA in the home environment.

SETTINGS

Patients were recruited to a standard OSA diagnostic pathway in an UK hospital. They were trained on the use of type-III-cardiorespiratory polygraphy, which they took to use at home. They were also given AcuPebble_SA100; but they were not trained on how to use it.

PARTICIPANTS

182 consecutive patients had been referred for OSA diagnosis in which 150 successfully completed the study.

PRIMARY OUTCOME MEASURES

Efficacy of AcuPebble_SA100 for automated diagnosis of moderate-severe-OSA against cardiorespiratory polygraphy (sensitivity/specificity/likelihood ratios/predictive values) and validation of usability by patients themselves in their home environment.

RESULTS

After returning the systems, two expert clinicians, blinded to AcuPebble_SA100's output, manually scored the cardiorespiratory polygraphy signals to reach a diagnosis. AcuPebble_SA100 generated automated diagnosis corresponding to four, typically followed, diagnostic criteria: Apnoea Hypopnoea Index (AHI) using 3% as criteria for oxygen desaturation; Oxygen Desaturation Index (ODI) for 3% and 4% desaturation criteria and AHI using 4% as desaturation criteria. In all cases, AcuPebble_SA100 matched the experts' diagnosis with positive and negative likelihood ratios over 10 and below 0.1, respectively. Comparing against the current American Academy of Sleep Medicine's AHI-based criteria demonstrated 95.33% accuracy (95% CI (90·62% to 98·10%)), 96.84% specificity (95% CI (91·05% to 99·34%)), 92.73% sensitivity (95% CI (82·41% to 97·98%)), 94.4% positive-predictive value (95% CI (84·78% to 98·11%)) and 95.83% negative-predictive value (95% CI (89·94% to 98·34%)). All patients used AcuPebble_SA100 correctly. Over 97% reported a strong preference for AcuPebble_SA100 over cardiorespiratory polygraphy.

CONCLUSIONS

These results validate the efficacy of AcuPebble_SA100 as an automated diagnosis alternative to cardiorespiratory polygraphy; also demonstrating that AcuPebble_SA100 can be used by patients without requiring human training/assistance. This opens the doors for more efficient patient pathways for OSA diagnosis.

TRIAL REGISTRATION NUMBER

NCT03544086; ClinicalTrials.gov.

Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes

BACKGROUND

Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes.

METHODS

A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models.

RESULTS

ODI was strongly correlated with apnea-hypopnea index (AHI) (r_s = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917-0.994), 0.962 (95%CI 0.943-0.981), and 0.976 (95%CI 0.956-0.996) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. We identified the cutoff of ODI 5/h, 15/h, and 25/h, as having important predictive value for AHI ≥5/h, ≥15/h, and ≥30/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904-0.978), 0.969 (95%CI 0.969-0.991), and 0.949 (95%CI 0.915-0.983) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. The sensitivity of ODI ≥5/h, ≥15/h, and ≥25/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively.

CONCLUSIONS

ODI is a sensitive and specific tool to predict SDB in patients with diabetes.

Usefulness of Home Overnight Pulse Oximetry in Patients with Suspected Sleep-Disordered Breathing

Methods

Prospective study conducted in a university hospital. Subjects with a clinical suspicion of SAHS were included. All of them underwent home polygraphy and oximetry on the same night. A correlation was made between the apnea-hypopnea index (AHI) and the oximetry variables. The variable with the highest diagnostic value was calculated using the area under the curve (AUC), and the best cut-off point for discriminating between patients with SAHS and severe SAHS was identified.

Results

One hundred and four subjects were included; 73 were men (70%); mean age was 52 ± 10.1 years; body mass index was 30 ± 4.1, and AHI = 29 ± 23.2/h. A correlation was observed between the AHI and oximetry variables, particularly ODI3 (r = 0.850; P < 0.001) and ODI4 (r = 0.912; P < 0.001). For an AHI ≥ 10/h, the ODI3 had an AUC = 0.941 (95% confidence interval (CI) = 0.899–0.982) and the ODI4, an AUC = 0.984 (95% CI = 0.964–1), with the ODI4 having the best cut-off point (5.4/h). Similarly, for an AHI ≥ 30/h, the ODI4 had an AUC = 0.922 (95% CI = 0.859–0.986), with the best cut-off point being 10.5/h.

Conclusion

Nocturnal oximetry is useful for diagnosing and evaluating the severity of SAHS. The ODI4 variable was most closely correlated with AHI for both diagnosis.

Feasibility of Single Channel Oximetry for Mass Screening of Obstructive Sleep Apnea

BACKGROUND

The growing awareness for the high prevalence of obstructive sleep apnea (OSA) coupled with the dramatic proportion of undiagnosed individuals motivates the elaboration of a simple but accurate screening test. This study assesses, for the first time, the performance of oximetry combined with demographic information as a screening tool for identifying OSA in a representative (i.e. non-referred) population sample.

METHODS

A polysomnography (PSG) clinical database of 887 individuals from a representative population sample of São Paulo's city (Brazil) was used. Using features derived from the oxygen saturation signal during sleep periods and demographic information, a logistic regression model (termed OxyDOSA) was trained to distinguish between non-OSA and OSA individuals (mild, moderate, and severe). The OxyDOSA model performance was assessed against the PSG-based diagnosis of OSA (AASM 2017) and compared to the NoSAS and STOP-BANG questionnaires.

FINDINGS

The OxyDOSA model had mean AUROC = 0.94 ± 0.02, Se = 0.87 ± 0.04 and Sp = 0.85 ± 0.03. In particular, it did not miss any of the 75 severe OSA individuals. In comparison, the NoSAS questionnaire had AUROC = 0.83 ± 0.03, and missed 23/75 severe OSA individuals. The STOP-BANG had AUROC = 0.77 ± 0.04 and missed 14/75 severe OSA individuals.

INTERPRETATION

We provide strong evidence on a representative population sample that oximetry biomarkers combined with few demographic information, the OxyDOSA model, is an effective screening tool for OSA. Our results suggest that sleep questionnaires should be used with caution for OSA screening as they fail to identify many moderate and even some severe cases. The OxyDOSA model will need to be further validated on data recorded using overnight portable oximetry.

Predicting Severe Sleep Apnea in Patients with Complaints: Pulse Oximetry and Body Mass Index

Objective

An adequate evaluation combined with an easily accessible test would be a useful way to direct the appropriate patients to sleep centers in circumstances with a limited opportunity for polysomnography (PSG). For this reason, it is necessary to use a screening method prior to PSG evaluation. The aim of the present study was to investigate whether the use of body mass index (BMI) and pulse oximetry is sufficient to predict the severity of obstructive sleep apnea syndrome (OSAS) without PSG.

Methods

A total of 956 patients who were admitted to a tertiary referral center with complaints of witnessed apnea, excessive daytime sleepiness, and previously performed PSG were included in the study. Data of PSG (included pulse oximetry) and BMI were investigated for the determination of cut-off points for parameters in the patients.

Results

Based on the presence of severe OSAS, the cut-off points were ≥31.7 kg/m² for BMI, <81% for minimum oxygen saturation (Min O₂), and ≥14.1 min for sleep time with oxygen saturation <90% (ST₉₀). Severe OSAS risk was found to be higher in patients with BMI ≥31.7 kg/m², ST₉₀ ≥14.1 min, and Min O₂ ≤81% than in those without (OR: 37.173; 95% CI: 22.465-61.510, p=0.001). Specificity and accuracy were 94.85% and 72.49%, respectively, when all three cut-off scores were provided.

Conclusion

The appropriate cut-off values obtained from combining BMI and pulse oximetry data can provide accurate results for predicting the severity of OSAS.

High Interrater Reliability of Overnight Pulse Oximetry Interpretation Among Inexperienced Physicians Using a Structured Template

STUDY OBJECTIVES

To assess the interrater reliability and accuracy of overnight pulse oximetry (OPO) interpretations by pulmonary fellows using a comprehensive structured template and after a brief educational session.

METHODS

Using the template, four pulmonary and critical care (PCC) fellows interpreted OPO saturation waveforms and parameters extracted from 50 adult consecutive in-laboratory sleep studies. The template included three saturation parameters (mean saturation, oxygen desaturation index [ODI], and cumulative desaturation time) and description of the saturation waveform. A scoring system was proposed combining waveform characteristics and ODI to determine the suspicion for moderate to severe sleep apnea. Waveform description and mean saturation determined the suspicion for cardiopulmonary disease (CPD). Cumulative desaturation time determined need for oxygen prescription. Apnea-hypopnea index was extracted from the sleep study results.

RESULTS

The overall interrater reliability for final recommendations (sleep apnea suspicion, presence of CPD, and oxygen prescription) was high (kappa = .81, 95% confidence interval [CI] .76-.88). Good agreement was noted in CPD evaluation and suspicion of moderate to severe sleep apnea (kappa = .70, 95% CI .46-.86 and kappa = .65, 95% CI .56-.77 respectively). The interrater reliability for oxygen prescription was in an excellent range (kappa = .98, 95% CI .91-1.00). The accuracy of a high sleep apnea suspicion score in detecting apnea-hypopnea index ≥ 15 events/h ranged from 88.0% to 94.0% (sensitivity 91.3% to 95.7%, specificity 81.5% to 92.6%). Desaturations due to CPD were identified by 75% of the raters as desaturations due to conditions other than sleep apnea.

CONCLUSIONS

A structured template for OPO interpretation can produce a high interrater agreement and good accuracy, and make it a reliable clinical tool.

COMMENTARY

A commentary on this article appears in this issue on page 497.

Comparison of two home sleep testing devices with different strategies for diagnosis of OSA

PURPOSE

Home sleep testing devices are being widely used in diagnosis/screening for obstructive sleep apnea (OSA). We examined differences in OSA metrics obtained from two devices with divergent home monitoring strategies, the Apnea Risk Evaluation System (ARES™, multiple signals plus forehead reflectance oximetry) and the Nonin WristOx₂™ (single channel finger transmission pulse oximeter), compared to differences from night-night variability of OSA.

METHODS

One hundred fifty-two male/26 female subjects (BMI = 30.3 ± 5.6 kg/m², age = 52.5 ± 8.9 years) were recruited without regard to OSA symptoms and simultaneously wore both ARES™ and Nonin WristOx₂™ for two nights (n = 351 nights). Automated analysis of the WristOx₂ yielded oxygen desaturation index (ODI_Ox2, ≥4% O2 dips/h), and automated analysis with manual editing of ARES™ yielded AHI4_ARES (apneas + hypopneas with ≥4% O2 dips/h) and RDI_ARES (apneas + hypopneas with ≥4% O2 dips/h or arousal surrogates). Baseline awake oxygen saturation, percent time < 90% O2 saturation (%time < 90%O2Sat), and O2 signal loss were compared between the two methods.

RESULTS

Correlation between AHI4_ARES and ODI_Ox2 was high (ICC = 0.9, 95% CI = 0.87-0.92, p < 0.001, bias ± SD = 0.7 ± 6.1 events/h). Agreement values for OSA diagnosis (77-85%) between devices were similar to those seen from night-to-night variability of OSA using a single device. Awake baseline O2 saturation was significantly higher in the ARES™ (96.2 ± 1.6%) than WristOx₂™ (92.2 ± 2.1%, p < 0.01). There was a significantly lower %time < 90%O2Sat reported by the ARES™ compared to WristOx₂ (median (IQR) 0.5 (0.0, 2.6) vs. 2.1 (0.3, 9.7), p < 0.001), and the correlation was low (ICC = 0.2).

CONCLUSIONS

OSA severity metrics predominantly dependent on change in oxygen saturation and metrics used in diagnosis of OSA (AHI4 and ODI) correlated well across devices tested. However, differences in cumulative oxygen desaturation measures (i.e., %time < 90%O2Sat) between the devices suggest that caution is needed when interpreting this metric particularly in populations likely to have significant hypoxia.

Oxygen Desaturation Index Differs Significantly Between Types of Sleep Software

STUDY OBJECTIVES

The aim of this study was to compare the oxygen desaturation index (ODI) generated by two different sleep software systems.

METHODS

Participants undergoing diagnostic polysomnography for suspected obstructive sleep apnea underwent simultaneous oximetry recording using the ResMed ApneaLink Plus device (AL) and Compumedics Profusion PSG3 system (Comp). The ODI was calculated by the algorithms in the respective software of each system. To determine if differences were due to algorithm or recording devices, the Comp software was also used to generate ODI values using oximetry data from the AL.

RESULTS

In 106 participants, there was good correlation but poor agreement in the ODI generated by the two systems. AL ODI values tended to be higher than Comp ODI values, but with significant variability. For ODI4%, bias was 4.4 events/h (95% limits of agreement -5.8 to 14.6 events/h). There was excellent correlation and agreement when the same oximetry raw data was analyzed by both systems. For ODI4%, bias was 0.03 events/h (95% limits of agreement -2.7 to 2.8 events/h). Similar results were evident when the ODI3% was used.

CONCLUSIONS

There is a clinically significant difference in ODI values generated by the two systems, likely due to device signal processing, rather than difference in ODI calculation algorithms.

Ultrasonographic Detection of Airway Obstruction in a Model of Obstructive Sleep Apnea

Purpose Obstructive sleep apnea (OSA) is a common clinical disorder characterized by repetitive airway obstruction during sleep. The gold standard for diagnosis of OSA, polysomnogram (PSG), cannot anatomically localize obstruction. Precise identification of obstruction has potential to improve outcomes following surgery. Current diagnostic modalities that provide this information require anesthesia, involve ionizing radiation or disrupt sleep. To mitigate these problems, we conceived that ultrasound (US) technology may be adapted (i) to detect, quantify and localize airway obstruction and (ii) for translational application to home-based testing for OSA. Materials and Methods Segmental airway collapse was induced in 4 fresh cadavers by application of negative pressure. Following visualization of airway obstruction, a rotary US probe was used to acquire transcervical images of the airway before and after induction of obstruction. These images (n=800) were analyzed offline using image processing algorithms. Results Our results show that the non-obstructed airway consistently demonstrated the presence of a US air-tissue interface. Importantly, automated detection of the air-tissue interface strongly correlated with manual measurements. The algorithm correctly detected an air-tissue interface in 90% of the US images while incorrectly detecting it in 20% (area under the curve=0.91). Conclusion The non-invasive detection of airway obstruction using US represents a major step in expanding OSA diagnostics beyond PSG. The preliminary data obtained from our model could spur further research in non-invasive localization of obstruction. US offers the benefit of precise localization of the site of obstruction, with potential for improving outcomes in surgical management.

The role of compact polysomnography/polygraphy in sleep breathing disorder patients' management

While managing obstructive sleep apnea (OSA) or suspicious OSA patients, the otorhinolaryngologist frequently has to deal with patients undergoing repeated polysomnography (PSG) or portable monitoring (PM) and, on the other hand, should be confident about the quality and consistency of the polysomnographic diagnosis. The main polysomnographic traces compressed in a unique epoch, defined as compact PSG/PM (CP), which should be reported in all PSG/PM report, could represent an efficient tool to confirm the quality of PSG/PM diagnosis and to recognize the sleep breathing disorders (SBD): OSA, no-OSA SBD and overlap of OSA with no-OSA SBD. In this study, a synthetic and clear guided iconography and an easy decision-making algorithm based on desaturation patterns (phasic, prolonged and overlap desaturation patterns) identifiable on the CP are suggested for a quick check of the quality of PSG/PM diagnosis and to achieve an improvement in the patient's clinical management.

During economic crisis can sleep questionnaires improve the value of oximetry for assessing sleep apnea?

BACKGROUND

The diagnosis of obstructive sleep apnea/hypopnea syndrome (OSAHS) is essential but polysomnography (PSG) is expensive and time consuming. Oximetry has been used as a less expensive indicator of OSAHS. The aim of the study was to evaluate the clinical utility of the combination of oximetry with four different questionnaires: Stop, Stop Bang (S-B), Berlin questionnaire (BQ), Epworth Sleepiness Scale (ESS) in order to identify patients at risk for OSAHS compared with in-laboratory PSG.

METHODS

Patients visiting a sleep clinic were prospectively studied. They completed Stop, S-B, BQ and ESS. Home oximetry and in laboratory PSG were performed within 3-20 days.

RESULTS

A total of 204 patients were included in the study (77.5% males, mean age 51.8±13.8 years, BMI 32.8±6.2 kg/m², SaO₂% awake 95.7±2). S-B had the highest sensitivity (Se) (97.5%) and negative predictive value (NPV) (62.5%) but the lowest specificity (Sp) (9%), whereas ESS had the best Sp (75%) and positive predictive values (PPV) (81.4%). The predictive values of questionnaires improved as the severity of OSAHS worsened. The predictive values of oximetry were high for severe but low for mild and moderate OSAHS. For that oximetry was combined with different sleep questionnaires in different OSAHS severity groups, but with no improvement in the predictive values.

CONCLUSIONS

Oximetry may be used as a tool for identifying severe OSAHS. For mild and moderate disease the combination of questionnaires did not improve the diagnostic accuracy and especially for symptomatic patients with negative results, the need of PSG is essential.

Cognitive Event-Related Potentials in Patients With Adenoid Hypertrophy: A Case-Control Pilot Study

PURPOSE

Children with adenoid hypertrophy commonly have sleep-disordered breathing. Sleep-disordered breathing is associated with various neurocognitive problems. The aim of this study was to assess the cognitive function in those patients using cognitive event-related potentials.

METHODS

Twenty-three patients with moderate to severe adenoid hypertrophy were compared with 20 healthy controls. The intelligence quotient was performed for all study participants. The latencies of the N200, P300 peaks and the amplitudes of the N200/P300 components of event-related potentials were recorded. The above variables were measured at baseline for both patients and control groups and 2 months after adenoidectomy for the patient group.

RESULTS

There was no significant difference between patients and controls regarding full intelligence quotient scales. P300 latency was significantly prolonged in patient group compared with the healthy controls. Moreover, postoperative P300 latency was significantly reduced compared with the preoperative P300 latency. Postoperative P300 latency was not statistically different from healthy controls' data.

CONCLUSIONS

P300 latency delay may reflect some sort of cognitive impairment in patients with adenoid hypertrophy. This delay was reversible after adenoidectomy. Event-related potentials may help for assessment of cognitive functions in patients with adenoid hypertrophy.

Diagnostic accuracy of a mathematical model to predict apnea-hypopnea index using nighttime pulse oximetry

The intent of this study is to develop a predictive model to convert an oxygen desaturation index (ODI) to an apnea-hypopnea index (AHI). This model will then be compared to actual AHI to determine its precision. One thousand four hundred and sixty-seven subjects given polysomnograms with concurrent pulse oximetry between April 14, 2010, and February 7, 2012, were divided into model development (n = 733) and verification groups (n = 734) in order to develop a predictive model of AHI using ODI. Quadratic regression was used for model development. The coefficient of determination (r(2)) between the actual AHI and the predicted AHI (PredAHI) was 0.80 (r = 0.90), which was significant at a p < 0.001. The areas under the receiver operating characteristic curve ranged from 0.96 for AHI thresholds of ≥ 10 and ≥ 15/h to 0.97 for thresholds of ≥ 5 and ≥ 30/h. The algorithm described in this paper provides a convenient and accurate way to convert ODI to a predicted AHI. This tool makes it easier for clinicians to understand oximetry data in the context of traditional measures of sleep apnea.

Sleep apnea in patients reporting insomnia or restless legs symptoms

OBJECTIVE

Insomnia and restless legs syndrome (RLS) are defined by self-reported symptoms, and polysomnography (PSG) is not routinely indicated. Occult obstructive sleep apnea (OSA), common even in asymptomatic adults, may complicate management of patients presenting with insomnia or restless legs. To this end, we investigated objective sleep apnea metrics in a large retrospective cohort according to self-reported symptom profiles.

METHODS

We compared sleep apnea findings in patients referred to our center according to self-reported symptoms associated with insomnia, sleep apnea, and restless legs. The cohort included over 1900 adults who underwent diagnostic (n = 1418) or split-night (n = 504) PSGs and completed a symptom and medical history questionnaire.

RESULTS

More than 30% of patients who did not endorse any OSA symptoms, but did endorse insomnia or restless legs symptoms, were found to have OSA based on apnea-hypopnea index (AHI) >5 during overnight laboratory testing. Regression models of the full cohort showed that the risk of OSA was related, as expected, to older age, male sex, elevated body mass index, and presence of OSA symptoms. The presence of insomnia symptoms did not alter the risk of OSA. The presence of restless legs symptoms showed a small odds ratio for lowered OSA risk.

CONCLUSIONS

Objective evidence of OSA occurs similarly in those with insomnia or restless legs symptoms, even among those without self-reported OSA symptoms. Providers should be aware of the potential for occult OSA in populations with insomnia and restless legs, which may complicate their management in addition to presenting an independent medical risk itself.

Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome

The relationship between sleep apnoea-hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea-hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.

Automated detection of sleep apnea in infants using minimally invasive sensors

To address the difficult and necessity of early detection of sleep apnea hypopnea syndrome in infants, we present a study into the effectiveness of pulse oximetry as a minimally invasive means of automated diagnosis of sleep apnea in infants. Overnight polysomnogram data from 328 infants were used to extract time-domain based oximetry features and scored arousal data for each subject. These records were then used to determine apnea events and to train a classifier model based on linear discriminants. Performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 68% was achieved, with a specificity of 68.6% and a sensitivity of 55.9%.

Automated detection of sleep apnea in infants: A multi-modal approach

This study explores the use and applicability of two minimally invasive sensors, electrocardiogram (ECG) and pulse oximetry, in addressing the high costs and difficulty associated with the early detection of sleep apnea hypopnea syndrome in infants. An existing dataset of 396 scored overnight polysomnography recordings were used to train and test a linear discriminants classifier. The dataset contained data from healthy infants, infants diagnosed with sleep apnea, infants with siblings who had died from sudden infant death syndrome (SIDS) and pre-term infants. Features were extracted from the ECG and pulse-oximetry data and used to train the classifier. The performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 66.7% was achieved, with a specificity of 67.0% and a sensitivity of 58.1%. Although the performance of the system is not yet at the level required for clinical use, this work forms an important step in demonstrating the validity and potential for such low-cost and minimally invasive diagnostic systems.

A clinical prediction formula for apnea-hypopnea index

Objectives. There are many studies regarding unnecessary polysomnography (PSG) when obstructive sleep apnea syndrome (OSAS) is suspected. In order to reduce unnecessary PSG, this study aims to predict the apnea-hypopnea index (AHI) via simple clinical data for patients who complain of OSAS symptoms. Method. Demographic, anthropometric, physical examination and laboratory data of a total of 390 patients (290 men, average age 50 ± 11) who were subject to diagnostic PSG were obtained and evaluated retrospectively. The relationship between these data and the PSG results was analyzed. A multivariate linear regression analysis was performed step by step to identify independent AHI predictors. Results. Useful parameters were found in this analysis in terms of body mass index (BMI), waist circumference (WC), neck circumference (NC), oxygen saturation measured by pulse oximetry (SpO₂), and tonsil size (TS) to predict the AHI. The formula derived from these parameters was the predicted AHI = (0.797 × BMI) + (2.286 × NC) − (1.272 × SpO₂) + (5.114 × TS) + (0.314 × WC). Conclusion. This study showed a strong correlation between AHI score and indicators of obesity. This formula, in terms of predicting the AHI for patients who complain about snoring, witnessed apneas, and excessive daytime sleepiness, may be used to predict OSAS prior to PSG and prevent unnecessary PSG.

Screening of obstructive sleep apnea with empirical mode decomposition of pulse oximetry

Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic detection methods that allows the screening for this syndrome, reducing the need of the expensive polysomnography based studies. In this paper a novel recognition method based on the empirical mode decomposition of the pulse oximetry signal is proposed. The desaturations produce a very specific wave pattern that is extracted in the modes of the decomposition. Using this information, a detector based on properly selected thresholds and a set of simple rules is built. The oxygen desaturation index constructed from these detections produces a detector for obstructive sleep apnea-hypopnea syndrome with high sensitivity (0.838) and specificity (0.855) and yields better results than standard desaturation detection approaches.

The Relation of the Obstruction Site on Muller's Maneuver with BMI, Neck Circumference and PSG Findings in OSAS

We investigated relation of the site of obstruction detected on Muller maneuver (MM) with polysomnography (PSG) and physical examination findings. Data of 703 patients admitted to Ankara Numune Education and Research Hospital Sleep Laboratory between 2008 and 2013 were analyzed retrospectively; 394 patients with apnea-hypopnea indexes (AHI) ≥5/h were included. Site of collapse on MM was determined at retrolingual level at anteroposterior (M1) and lateral-lateral (M2) directions; at retropalatal level at anteroposterior (M3) and lateral-lateral (M4) directions. There were 125 (31.7 %) females and 269 (68.3 %) males. BMI had significant positive correlations with M2 (p < 0.001) and M4 (p = 0.002) scores, ESS (p = 0.013) and AHI (p = 0.001). AHI had significant positive correlations with ESS (p = 0.003), M2 (p < 0.001), M3 (p = 0.037) and M4 (p < 0.001) scores and NC (p = 0.001). Minimum oxygen saturation had significant reverse correlations with M1 (p = 0.046), M2 (p < 0.001), M3 (p = 0.003), M4 (p < 0.001), AHI (p < 0.001), ESS (p = 0.003) and BMI (p = 0.001). In OSAS patients, increased BMI, NC and AHI are correlated with lateral pharyngeal wall (LPW) collapse in retropalatal and retrolingual levels on MM. LPW collapse is more valuable to predict OSAS compared to anteroposterior collapse. LPW collapse on MM may be used as a selection criterion for ordering PSG. Further studies are needed to better clarify importance of LPW in OSAS surgery.

Predicting postoperative pulmonary complications in high-risk populations

PURPOSE OF REVIEW

Our objective is to describe prediction models for surgical patients who have suspected obstructive sleep apnea (OSA) at risk for postoperative respiratory complications and for surgical patients at risk for postoperative acute respiratory distress syndrome (ARDS).

RECENT FINDINGS

Because of the increased rate of severe perioperative respiratory complications in patients with OSA, the American Society of Anesthesiologists issued practice guidelines for perioperative management. When OSA is diagnosed preoperatively, the rate of postoperative pulmonary complications is low and not associated with OSA severity. However, OSA continues to be an important risk because a substantial proportion of patients in the contemporary surgical population have undiagnosed OSA. Strategies based on preoperative and immediate postoperative clinical signs and symptoms can help identify patients with a high likelihood of OSA, postoperative desaturations, and pulmonary complications. ARDS is another serious postoperative complication associated with high mortality rate and limited treatment options, and its prevention is critical. Practice changes have led to a dramatic reduction in ARDS incidence. A recently developed prediction model can help identify high-risk patients.

SUMMARY

Evidence is emerging that early identification of modifiable risk factors and implementation of 'protective' management strategies may lead to reduction of severe postoperative pulmonary complications.

Evaluating Innovative In-Ear Pulse Oximetry for Unobtrusive Cardiovascular and Pulmonary Monitoring During Sleep

Homecare is healthcare based on the principle "outpatient before inpatient," with the aim of moving at least some care-delivery to the home. But reliable determination of vital signs at home requires new, smart sensors, which can be used by the patients themselves. We present a novel pulse oximetry sensor worn in the ear channel. It was previously shown that measurement of heart rate, arterial oxygen saturation and related respiratory information can be performed with reliable accuracy under laboratory conditions. The present study explores the clinical feasibility of the sensor system for cardiovascular monitoring during sleep, with the aim to diagnose sleep apnea. For this, human trials were performed in a sleep laboratory including patients with a clinical suspicion of sleep apnea. Besides a general analysis of the sensor's signal quality during sleep, the evaluation focuses on heart rate dynamics and time-variant oxygen saturation. In addition, several methods to derive respiration rate from photoplethysmographic signals are examined and discussed. Results from the in-ear sensor are compared with standard polysomnography monitoring and demonstrate that this novel system allows long-term nocturnal measurement of heart rate, oxygen saturation and respiratory rate with sufficient accuracy.

Treatment outcomes and compliance according to obesity in patients with obstructive sleep apnea

The objective of this study was to assess treatment outcomes and compliance according to obesity among groups of patients with obstructive sleep apnea (OSA) receiving different treatments. A total of 297 patients with OSA treated between 2006 and 2009 underwent pre- and post-treatment polysomnography. One hundred and fifty-one patients were treated with continuous airway positive pressure (CPAP), 76 with mandible advancement device (MAD), and 70 with oropharyngeal surgery. All patients were classified according to obesity. Treatment success rate and compliance of CPAP were analyzed according to obesity. For each treatment modality, the overall treatment success rate was not significantly different between obese and non-obese patients. However, the oxygen desaturation index was different in patients who were treated with MAD and surgery. Additionally, obese patients with severe OSA showed an unfavorable response to CPAP treatment. For CPAP compliance, obese patients showed a tendency to be highly compliant with CPAP treatment at 12 months than non-obese patients. This study showed that obesity might be a factor in determining the success or failure of treatment. Additionally, obesity may be a predictive factor to determine CPAP compliance.

Probabilistic neural network approach for the detection of SAHS from overnight pulse oximetry

Diagnosis of sleep apnea hypopnoea syndrome (SAHS) depends on the apnea-hypopnea index determined by the standard in-laboratory overnight polysomnography (PSG). PSG is a costly, labor intensive and, at times, inaccessible approach. Because of the high demand, the need for timely diagnosis and the associated costs, novel methods for SAHS detection are required. In this study, a novel multivariate system is proposed for SAHS detection from the analysis of overnight blood oxygen saturation (SpO2). 115 subjects with SAHS suspicion were studied. A starting set of 17 time domain, stochastic, frequency-domain and nonlinear features were initially computed from SpO2 recordings. Sequential forward feature selection and a probabilistic neural network with leave-one-out cross-validation were applied. Oxygen desaturations below a 4 % threshold within 30 s (ODI430), restorations of 4 % within 10 s (RES4), median value (Sat50), SD1 Poincaré descriptor and the relative power in the 0.013-0.067 Hz frequency band (PSD15/75) formed the optimum features subset. 92.4 % sensitivity and 95.9 % specificity were achieved. Results significantly outperformed the univariate and multivariate approaches reported in literature. The outcome is a simple cost-effective tool that could be used as an alternative or supplementary method in a domiciliary approach to early diagnosis of SAHS.