These differences have clinical relevance, with significantly more patients crossing potential diagnostic thresholds for OSA and moderate-severe OSA with the AL compared to Comp. Oximetry is a key signal measured during polysomnography and is usually considered the most robust and reproducible signal. As a result, oximetry is the cornerstone of type 4, and many type 3, limited-channel home sleep test devices. However, previous literature assessing the performance characteristics of individual oximeters has demonstrated that due to differences in internal signal processing, there is significant variability in the SpO 2 values obtained during simulated OSA.
To date, the only previous study that directly compared ODI between devices is that by Zou et al. Similar to the current study, they demonstrated that despite good correlation there was poor agreement between devices. These findings are of importance to clinicians.
This finding appears to partly contradict the findings of a previous study by Ward and colleagues. However, there are several important differences between their study and ours.
This is likely due to the different denominator used to calculate the AHI monitoring time for the AL and sleep time for the Comp , leading to a systematically lower AHI for the AL, particularly for patients with poor sleep efficiency. In our study, based on the calculated odds ratio, a patient has 2.
There are three potential reasons why oximeters could give different ODI values: 1 oximeter acquisition and internal processing, 2 patient factors related to the use of different fingers, or 3 algorithm differences in the way desaturation events are defined.
Pulse oximeters rely on light-emitting diodes, light sensors, and the differing light absorption characteristics of oxygenated and deoxygenated hemoglobin.
These include: light and wavelength measurement characteristics, artefact rejection, sampling frequency, signal averaging times, accuracy and reproducibility. All of these may influence both the baseline SpO 2 and the temporal characteristics of any change in SpO 2. AHI is known to vary significantly with different signal averaging times. Poor per-fusion reduces the pulsatile flow in the finger and hence the accuracy of measured SpO 2. All of these factors may lead to important differences in ODI between oximeters and future research is needed to address how some of these factors differ between devices.
A key strength of our study is that our results demonstrate that the differences in ODI between the AL and the Comp are likely due to oximeter acquisition and processing factors, not patient-related factors or ODI calculation algorithms. We exported the oximeter data from the AL into the Comp software.
These results mean that there was no significant effect of the ODI calculation algorithm to explain the variability seen in our primary analysis, rather that the data including any prealgorithm processing was the difference. If random patient effects such as finger tissue perfusion were to be the cause, one would expect minimal bias as the variability would extend in either direction, but wide limits of agreement although note that we did not use a formal randomization process in selecting fingers for pulse oximetry monitoring.
Moreover, oximeter processing factors are the likely explanation for our results as the processing parameters between the AL and the Comp are known to be different. The AL samples and records SpO 2 data at 1 Hz and uses a 3 second signal averaging time to create the final output value. In contrast, the Comp samples SpO 2 every heartbeat. The most recent seven samples are used to calculate the SpO 2 output value; the highest and lowest values in that block of data are excluded and the remaining five data points are averaged to provide the final SpO 2 data point.
In addition, how each device determines and rejects artefact as compared to a valid signal is not known, but each is unlikely to deal with artefact in the same way. Our study has a few limitations. There is no universally accepted gold standard for either oximeter processing or ODI algorithm calculation, and there is not even an accepted standard for manual scoring of ODI.
The most important limitation, however, is that our results relate only to these two devices and their respective software, and results cannot be generalized to other oximeters and sleep diagnostic systems. Nevertheless, the important take-home message from our study is that ODI values cannot be directly compared between patients, unless one is comparing data acquired with the same oximeter and software.
Clinicians need to know how an oximeter processes SpO 2 data and how an ODI is calculated, and whether there is a bias for one oximeter to overestimate or underestimate an ODI compared to the other. It is also important that other comparative studies such as ours are performed with other manufacturers' equipment and software, and hopefully this study will stimulate further research into the area.
The differences are large enough to significantly affect diagnostic thresholds for OSA and, in particular, moderate-severe OSA. The differences are likely the result of signal processing rather than patient factors or manufacturer algorithms for scoring desaturations.
Caution is advised when comparing ODI between patients or when performing posttreatment reassessment in the same patient, unless the same oximeter and software algorithm have been used.
Shane Landry and Dr. Anthony Turton has received consultancy fees from Compumedics Ltd. The other authors have indicated no financial conflicts of interest. The authors thank all the patients who participated in this study and the sleep scientists at Monash Health. Read article at publisher's site DOI : J Clin Sleep Med , 16 4 , 01 Apr COPD , 17 1 , 22 Jan Nat Sci Sleep , , 12 Jul Kapoor M. J Clin Sleep Med , 15 3 , 15 Mar J Clin Sleep Med , 14 12 , 15 Dec To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.
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Coloriage v. How It Works. Download PDF Tutorial. Subscribe to News. Learn More. Recovery from acute respiratory failure ARF is usually determined by normalized arterial blood gases ABGs , but the prevalence of persistent exercise-induced desaturation EID and dyspnea is still unknown.
Patients underwent a 6-min walking test 6MWT before discharge from hospital. We recorded dyspnea score and heart rate during 6MWT. Patients underwent a LU scan and scores for each explored area were summed to give a total LU score.
These patients had significantly higher dyspnea and heart rate compared to non-desaturators. LU may be useful to identify patients at risk who could benefit from a rehabilitation program. However, more than half of these patients still complain of breathlessness even long after discharge from hospital 1 but it is not known if this is associated with oxygen desaturation during exercise.
Conversely, we have a lot of evidence showing that lung ultrasound LU findings correlate strongly with CT images. This is a prospective cohort study enrolling consecutive patients ready to be discharged after hospitalization for COVID pneumonia between April 15 and May 30, The study protocol was approved by the Hospital Ethics Committee n.
Inclusion criteria were: confirmed SARS-CoV-2 infection as detected by real-time reverse-transcriptase polymerase chain reaction RT-PCR from a nasopharyngeal swab; admission to our sub-acute unit for care stabilization after an acute respiratory failure ARF episode; and recovery from pneumonia as determined by normalization of ABGs i. Exclusion criteria were neurological or orthopedic disease chronic or new onset , which could have compromised the execution of a walking test, and use of long-term oxygen therapy before hospitalization.
The staff operating in the two wards was adequately trained and protected with filtering facepiece class 3 FFP3 or FFP2 masks, double non-sterile gloves, long-sleeved water-resistant gowns or protective clothing, and goggles or face shield when visiting the patients. Demographic and clinical data were also collected. The approach used was the standard sequence of ultrasound scans in 14 anatomic chest landmarks. For comparison of categorical and binary variables, the chi-square test was used.
Differences between desaturators and non-desaturators were defined using unpaired t-test. The optimal threshold was defined as the point closest to the top-left part of the plot with perfect sensitivity or specificity. Seventy consecutive patients admitted to our sub-acute unit after ARF due to COVID pneumonia who met the enrolment criteria were included in this study. Mean age was Demographic and clinical data are reported in Table 1.
Arterial blood gases at discharge showed a mean PaO 2 of The mean alveolar-arterial difference of oxygen A-aO 2 was Desaturators had a significantly higher length of stay LOS both in the acute and subacute units. No differences were observed between desaturators and non-desaturators in terms of PaO 2 and A-aO 2 at rest or prevalence of comorbidities cardiovascular, COPD, and diabetes. Desaturators had a significantly higher LU score than non-desaturators From the ROC curve Fig.
Patient demographic and clinical characteristics for the whole group and according to presence or not of exercise-induced desaturation. ROC curve of LU scores based on the whole sample. The point indicates the LU score corresponding to the optimal threshold specificity, sensitivity. Our study showed that normal values of arterial PaO 2 at rest cannot predict the persistence of EID, which may be one of the most important causes of persistent effort dyspnea after recovery from COVID pneumonia.
To our knowledge, no one has investigated the persistence of oxygen desaturation during exercise with a standardized walking test. In our cohort of patients discharged with normal gas exchange values, almost half were desaturators at 6MWT. To overcome a possible bias represented by hypocapnia, still persistent in some patients and leading to a higher PaO 2 value at ABGs, we considered also the A-aO 2 value that did not differ between the two groups.
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