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SLEEP MEDICINE: Oliver Senn, Thomas Brack, Erich W. Russi, and Konrad E. Bloch A Continuous Positive Airway Pressure Trial as a Novel Approach to the Diagnosis of the Obstructive Sleep Apnea Syndrome Chest 2006; 129: 67-75 [Abstract] [Full text] [PDF]

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Therapeutic Trial of Continuous Positive Airway Pressure to diagnose Obstructive Sleep Apnea

Waseem Sharieff   (5 March 2006)

Therapeutic Trial of Continuous Positive Airway Pressure to diagnose Obstructive Sleep Apnea 5 March 2006
Waseem Sharieff, Clinical Epidemiologist Department of Health Policy, Managment and Evaluation, University of Toronto, Toronto ON, Canada Send letter to journal: Re: Therapeutic Trial of Continuous Positive Airway Pressure to diagnose Obstructive Sleep Apnea doc.sharieff{at}utoronto.ca Waseem Sharieff	Dear Editor, I applaud Dr Bloch and colleagues for taking a lead in evaluating the utility of a therapeutic trial of continuous positive airway pressure (CPAP) in the diagnosis of obstructive sleep apnea (OSA).(1) Dr Collop has already commented on some of the issues.(2) I have the following additional comments: Sleep disorders are not uncommon, majority of which is OSA.(3) However, OSA unlike other diseases such as cancer can not be diagnosed by a tissue biopsy. Thus, the absence of a ‘gold standard’ to determine the ‘true’ disease status makes it difficult to calibrate any test for OSA diagnosis. Traditionally, polysomnography (PSG) in an attended setting (sleep laboratory) has been used as a ‘reference standard’ for the diagnosis of OSA. PSG measures several sleep parameters, one of which is the apnea-hyponea index (AHI). AHI has been widely used to diagnose OSA, although with different cut off levels – the basis of which is unclear. With the evolution of technology, portable devices have emerged that measure more or less the same sleep parameters in attended or unattended settings. These devices if equally accurate may reduce the dependency on sleep laboratories. However, it has been difficult to demonstrate agreement between test conducted by a new device and that conducted in a sleep laboratory.(4) Several researchers have used correlation coefficients or sensitivity and specificity. A few have used Bland and Altman plots. And, some have used receiver operating characteristics (ROC) curves. All these approaches have pitfalls. Correlation coefficients do not measure agreement. Sensitivity and specificity are not helpful when the ‘true’ disease status is unknown. Bland and Altman plots measure agreement but are helpful when a range of clinical equivalence is known. ROC curves are generated using logistic regression with the true disease status as the dependent variable and test values as the independent variable. Thus, each value of the test is used as a cut point to measure sensitivity and specificity, which are then plotted on an x-y plane. The cut point which maximizes both sensitivity and specificity is chosen as the cut off level to discriminate between disease and no-disease states.(5) In the absence of a gold standard to determine the true disease status, ROC curves are of minimal value. Therefore, use of a therapeutic trial may provide a gold standard for calibrating AHI (and other sleep parameters) measured in attended and unattended settings to diagnose OSA. Bloch et al., presented ROC curves (figure 3 of reference 1), but they used PSG as the reference standard, and used all the cut points of the response scale (1-15) to generate ROC curves. Since, values < 4 were predefined as negative test results, these ROC curves are ambiguous. Reworking from results of Bloch et al., which used a cut off level of AHI > 10 to diagnose OSA, the sensitivity and specificity of PSG correspond to 97% and 78% respectively, using the therapeutic trial as the gold standard. Following this, I suggest to establish a cut off level of a given sleep parameter in attended and unattended settings using ROC curves by comparing its values with the true disease status determined by a therapeutic trial, and not by the current reference standard. The estimated sensitivity and specificity can be used across populations if the tests are conducted in standardized conditions. Indeed, pre-test probability (prevalence) of the disease does not affect sensitivity and specificity of the test, but affect the post-test probability (positive predictive value (PPV)) of the test. Using Bayes’ theorem, one may use the estimated sensitivity and specificity of the test, and prevalence estimate of own setting, to calculate PPV.(5) PPV in this case can also be interpreted as the probability that the patient would improve with CPAP therapy – this would be helpful in clinical decision making. The use of therapeutic trial to establish diagnosis is not new. One application is for iron deficiency anemia where the gold standard is bone marrow biopsy. As bone marrow biopsy is very invasive, multiple markers (some combination of haemoglobin, serum iron, serum transferrin receptor, total iron binding capacity, serum ferritin) are often used at considerable cost. Alternatively, a therapeutic trial of iron supplementation that results in an increment of haemoglobin by 10 g/L in two weeks can be used to establish the diagnosis.(6) It is time to move towards a more clinically meaningful diagnostic test in sleep medicine. Waseem Sharieff MD PhD Dept of Health Policy, Management and Evaluation University of Toronto Toronto ON Doc.sharieff@utoronto.ca References 1. Senn O, Brack T, Russi EW, Bloch KE. A continuous positive airway pressure trial as a novel approach to the diagnosis of the obstructive sleep apnea syndrome. Chest 2006;129(1):67-75. 2. Collop NA. Blue light special on CPAP, aisle 11.Chest 2006;129(1):6-7. 3. Redline S, Young T. Epidemiology and natural history of obstructive sleep apnea. Ear Nose Throat J 1993;72(1):20-1,24-6. 4. Centers for Medicare and Medicaid Services. Decision memo for continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (OSA) (CAG-00093R). Available: http://www.cms.hhs.gov/mcd/viewtrackingsheet.asp?id=110. (accessed Feb 16 2006). 5. Streiner D, Norman G. PDQ Epidemiology. 2nd ed. Hamilton ON. B.C. Decker Inc, 1998. 6. Lanzkowsky P. Problems in diagnosis of iron deficiency anemia. Pediatr Ann 1985;14(9):618,622-3,627.