Accuracy of CPAP predicted from anthropometric and polysomnographic indices

Polysomnographic evaluation of obstructive sleep apnea treatment with fixed pressure CPAP determined by formula

PURPOSE

The use of continuous positive airway pressure (CPAP) is one of the therapeutic modalities for obstructive sleep apnea (OSA). Manual titration polysomnography and the 90th or 95th percentiles of pressure titrated by automatic CPAP (APAP) are the current standard for determining fixed pressure. Pressures programmed at an arbitrary fixed value, or at preset values based on body mass index (BMI) or by predictive formulas, are presented as alternative forms. This study aimed to evaluate the residual apnea-hypopnea index (r-AHI) in polysomnography with CPAP therapy using pressure determined by formula and assess its feasibility to start treatment.

METHODS

Patients referred for CPAP therapy were followed up in three outpatient assessments and underwent polysomnography study with pressure CPAP obtained by formula.

RESULTS

The study sample consisted of 80 patients, 41 women; age 58.6 ± 11.3 years, BMI 34.1 ± 7.5 kg/m² and cervical circumference 42.0 ± 4.2 cm. Most patients (74%) had severe OSA and Epworth sleepiness scale (ESS) of 12.0 ± 5.7 points. The calculated average pressure was 7.8 ± 2.1 cmH₂O. Polysomnography studies showed an r-AHI of 6.1 ± 5.2 events/h and reduction of 84% from baseline AHI. The r-AHI in the REM-supine was 8.4 ± 9.9 events/h. At 30- and 120-day follow-up assessment, adherence to CPAP was 78% and 75% and the ESS score was 6.9 and 6.1 points, respectively.

CONCLUSION

Results suggest that a formula provides an effective initial pressure in the majority of patients (73%). This simplified approach appears to be a viable alternative, with reductions in waiting lists and time from diagnosis to initiation of therapy.

Using craniofacial characteristics to predict optimum airway pressure in obstructive sleep apnea treatment

Introduction

Manual titration is the gold standard to determinate optimal continuous positive airway pressure, and the prediction of the optimal pressure is important to avoid delays in prescribing a continuous positive airway pressure treatment.

Objective

To verify whether anthropometric, polysomnographic, cephalometric, and upper airway clinical assessments can predict the optimal continuous positive airway pressure setting for obstructive sleep apnea patients.

Methods

Fifty men between 25 and 65 years, with body mass indexes of less than or equal to 35 kg/m² were selected. All patients had baseline polysomnography followed by cephalometric and otolaryngological clinical assessments. On a second night, titration polysomnography was carried out to establish the optimal pressure.

Results

The average age of the patients was 43 ± 12.3 years, with a mean body mass index of 27.1 ± 3.4 kg/m² and an apnea–hypopnea index of 17.8 ± 10.5 events per hour. Smaller mandibular length (p = 0.03), smaller atlas–jaw distance (p = 0.03), and the presence of a Mallampati III and IV (p = 0.02) were predictors for higher continuous positive airway pressure. The formula for the optimal continuous positive airway pressure was: 17.244 − (0.133 × jaw length) + (0.969 × Mallampati III and IV classification) − (0.926 × atlas–jaw distance).

Conclusion

In a sample of male patients with mild-to-moderate obstructive sleep apnea, the optimal continuous positive airway pressure was predicted using the mandibular length, atlas–jaw distance and Mallampati classification.

A new predictive model for continuous positive airway pressure in the treatment of obstructive sleep apnea

BACKGROUND

Numerous mathematical formulas have been developed to determine continuous positive airway pressure (CPAP) without an in-laboratory titration study. Recent studies have shown that style of CPAP mask can affect the optimal pressure requirement. However, none of the current models take mask style into account. Therefore, the goal of this study was to develop new predictive models of CPAP that take into account the style of mask interface.

METHODS

Data from 200 subjects with attended CPAP titrations during overnight polysomnograms using nasal masks and 132 subjects using oronasal masks were randomized and split into either a model development or validation group. Predictive models were then created in each model development group and the accuracy of the models was then tested in the model validation groups.

RESULTS

The correlation between our new oronasal model and laboratory determined optimal CPAP was significant, r = 0.61, p < 0.001. Our nasal formula was also significantly related to laboratory determined optimal CPAP, r = 0.35, p < 0.001. The oronasal model created in our study significantly outperformed the original CPAP predictive model developed by Miljeteig and Hoffstein, z = 1.99, p < 0.05. The predictive performance of our new nasal model did not differ significantly from Miljeteig and Hoffstein's original model, z = -0.16, p < 0.90. The best predictors for the nasal mask group were AHI, lowest SaO2, and neck size, whereas the top predictors in the oronasal group were AHI and lowest SaO2.

CONCLUSION

Our data show that predictive models of CPAP that take into account mask style can significantly improve the formula's accuracy. Most of the past models likely focused on model development with nasal masks (mask style used for model development was not typically reported in previous investigations) and are not well suited for patients using an oronasal interface. Our new oronasal CPAP prediction equation produced significantly improved performance compared to the well-known Miljeteig and Hoffstein formula in patients titrated on CPAP with an oronasal mask and was also significantly related to laboratory determined optimal CPAP.

Mathematical Equations to Predict Positive Airway Pressures for Obstructive Sleep Apnea: A Systematic Review

Objective. To systematically review the international literature for mathematical equations used to predict effective pressures for positive airway pressure (PAP) devices. Methods. Google Scholar, PubMed, Scopus, Embase, Web of Science, CINAHL, and The Cochrane Library were searched through June 27, 2015. The PRISMA statement was followed. There was no language limitation. Results. 709 articles were screened, fifty were downloaded, and twenty-six studies presented equations that met the inclusion and exclusion criteria. In total, there were 4,436 patients in the development phases and 3,489 patients in the validation phases. Studies performed multiple linear regressions analyses as part of the equation(s) development and included the following variables: physical characteristics, polysomnography data, behavioral characteristics, and miscellaneous characteristics, which were all predictive to a variable extent. Of the published variables, body mass index (BMI) and mean oxygen saturation are the most heavily weighted, while BMI (eighteen studies), apnea-hypopnea index (seventeen studies), and neck circumference (eleven studies) were the variables most frequently used in the mathematical equations. Ten studies were from Asian countries and sixteen were from non-Asian countries. Conclusion. This systematic review identified twenty-six unique studies reporting mathematical equations which are summarized. Overall, BMI and mean oxygen saturation are the most heavily weighted.

Predictive equations for CPAP titration in OSAS patients

BACKGROUND

Continuous positive airway pressure (CPAP) is the elective treatment of obstructive sleep apnea. The therapeutic level of CPAP is generally established by manual titration or an auto CPAP device, but an alternative way involves the use of predictive formulas. The aim of the present study was to test the difference between mathematical equations and CPAP or auto CPAP in terms of therapeutic pressure.

METHODS

A retrospective analysis of 197 subjects with a diagnosis of obstructive sleep apnea needing a CPAP treatment was performed. The patients were divided into two groups: the first one included patients who had received CPAP after manual titration and the second one included patients who had received auto CPAP titration. The therapeutic CPAP pressure was then compared to the pressure calculated by three different equations: Eq. A by Stradling, Eq. B by Sériès, and Eq. C by Hoffstein.

RESULTS

One hundred ninety-seven patients were included in the study, 110 were titrated by auto CPAP and 87 by manual titration. There was a positive correlation between the pressure defined by the three equations and both titration methods, but each equation usually gave a higher pressure with patients needing CPAP <8 and lower for patients needing CPAP >11. Equation C normally gave a lower result than the other two equations.

CONCLUSIONS

Manual or auto CPAP titration remains the best way to define the appropriate CPAP. However, predictive formulas can be useful if used with caution and always after verifying the real efficacy, particularly for patients needing higher pressure.

Utility of formulas predicting the optimal nasal continuous positive airway pressure in a Greek population

BACKGROUND

There have been reports that optimal CPAP pressure can be predicted from a previously derived formula, with the Hoffstein formula being the most accurate and accepted in the literature so far. However, the validation of this predictive model has not been applied in different clinical settings. Our aim was to compare both the Hoffstein prediction formula and a newly derived formula to the CPAP pressure setting assessed during a formal CPAP titration study.

METHODS

We prospectively studied 1,111 patients (871 males/240 females) with obstructive sleep apnea hypopnea syndrome (OSAHS) undergoing a CPAP titration procedure. In this large population sample, we tested the Hoffstein formula, utilizing body mass index (BMI), neck circumference and apnea/hypopnea index (AHI), and we compared it with our new formula that included not only AHI and BMI but also smoking history and gender adjustment.

RESULTS

We found that using the Hoffstein prediction formula, successful prediction (predicted CPAP pressure within ±2 cm H(2)O compared to the finally assessed optimum CPAP pressure during titration) was accomplished in 873 patients (79%), with significant correlation between CPAP predicted pressure (CPAPpred(1)) and the optimum CPAP pressure (CPAPopt) [r = 0.364, p < 0.001]. With the new formula, including smoking history and gender adjustment, successful prediction was accomplished in 1,057 patients (95%), with significant correlation between CPAP predicted pressure (CPAPpred(2)) and the CPAPopt (r = 0.392, p < 0.001). However, there was a highly significant correlation between the two formulas (r = 0.918, p < 0.001).

CONCLUSIONS

We conclude that the level of CPAP necessary to abolish sleep apnea can be successfully predicted from both equations, using common clinical measurements and prediction formulas that may be useful in calculating the starting pressure for initiating CPAP titration. It may also be possible to shorten CPAP titration and perhaps in selected cases to combine it with the initial diagnostic study.

Predicting optimal CPAP by neural network reduces titration failure: a randomized study

PURPOSE

Continuous positive airway pressure (CPAP) is considered the standard therapy for obstructive sleep apnea syndrome. In the absence of standard protocol, CPAP titration may be unsuccessful. The purpose of this study was to test the hypothesis that application of an artificial neural network (ANN) to CPAP titration would achieve an optimal CPAP pressure within a shorter time interval and would lead to a decrease in CPAP titration failure.

METHODS

One hundred fifteen patients were randomized 1:1 to either conventional CPAP titration (n = 58) or to an ANN-guided CPAP titration (n = 57). Both groups were assessed for time to optimal CPAP pressure, for titration failure, and for CPAP compliance therapy.

RESULTS

Patients in the ANN-guided CPAP titration arm were able to achieve optimal CPAP at a shorter time interval compared to the conventional group (198.7 +/- 143.8 min versus 284.0 +/- 126.5 min) (p < 0.001). There was also a lower titration failure in patients randomized to the ANN-guided CPAP titration arm (16%) compared to the conventional arm (36%) (p = 0.02). Compliance with treatment did not differ across the two arms.

CONCLUSIONS

The use of ANN for guiding CPAP titration may be superior to the conventional method in maximizing the time to achieve optimal CPAP and in reducing CPAP titration failure.

Predicting effective continuous positive airway pressure in sleep apnea using an artificial neural network

BACKGROUND

Mathematical formulas have been less than adequate in assessing the optimal continuous positive airway pressure (CPAP) level in patients with obstructive sleep apnea (OSA). The objectives of the study were (1) to develop an artificial neural network (ANN) using demographic and anthropometric information to predict optimal CPAP level based on an overnight titration study and (2) to compare the predicted pressures derived from the ANN to the pressures computed from a previously described regression equation.

METHODS

A general regression neural network was used to develop the predictive model. The derivation cohort included 311 consecutive patients who underwent CPAP titration at a University-affiliated Sleep Center. The model was validated subsequently on 98 participants from a private sleep laboratory.

RESULTS

The correlation coefficients between the optimal pressure determined by the titration study and the predicted pressure by the ANN were 0.86 (95% confidence interval [CI] 0.83-0.88; p<0.001) for the derivation cohort and 0.85 (95% CI 0.78-0.9; p<0.001) for the validation cohort, respectively. Whereas there was no significant difference between the optimal pressure obtained during overnight polysomnography and the predicted pressure estimated by the ANN (p=0.4), the estimated pressure derived from the regression equation underestimated the optimal pressure in both the derivation and the validation group, respectively.

CONCLUSION

The optimal CPAP level predicted by the ANN provides a more accurate assessment of the pressure derived from the historic regression equation.

The determinants of therapeutic levels of continuous positive airway pressure in elderly sleep apnea patients

We have examined the role of age on the continuous positive airway pressure (CPAP) levels required to treat two groups of elderly (n=70) and young (n=70) sleep apneic patients, matched for disease severity (apnea/hypopnea index), body mass index and neck circumference. Elderly patients required lower CPAP levels compared to young [mean (sd): 6.9(1.9)cm H(2)O and 9.4(3.5)cm H(2)O, respectively; P<0.0001]. To investigate this finding, we studied the effects of CPAP and its components (inspiratory and expiratory positive airway pressure) on lung volume and upper airway resistance in two groups of elderly [n=9, age 71.7(3.3) years] and young [n=9, age 36.7(4.4)] patients with sleep apnea during wakefulness. CPAP produced a greater decrease in airway resistance (P=0.009) and a greater increase in lung volume (P=0.008) in the elderly compared to young patients. We conclude that both the greater lung inflation and the greater direct splinting of the upper airway contributed to the lower CPAP level required by the elderly. Ageing may be an important determinant of therapeutic CPAP levels in clinical practice, especially in older sleep apneic patients.

The use of a predicted CPAP equation improves CPAP titration success

Titration of continuous positive airway pressure (CPAP) is performed to determine the CPAP setting to prescribe for an individual patient. A prediction equation has been published that could be used to improve the success rate of CPAP titrations. The goals of this study were: (1) to test the hypothesis that the use of the prediction equation would achieve a higher rate of successful CPAP titrations; (2) to validate the equation as an accurate predictor of the prescribed CPAP setting and determine the factors that influence the accuracy of the prediction equation. A total of 224 patients underwent CPAP titration prior to using the equation, with a starting pressure of 5 cm H(2)O. A total of 192 patients underwent CPAP titration using the equation-predicted CPAP level as the starting pressure (median starting pressure of 8 cm H(2)O [interquartile range 7, 10 cm H(2)O]). The percentage of successful studies, as defined by a 50% decrease in the apnea-hypopnea index (AHI) and a final AHI < or =10 cm H(2)O, increased from 50% to 68% (p<0.001), while the number of patients who were prescribed a CPAP level that had not been tested decreased from 22% to 5% (p<0.001). The equation was not accurate in predicting the prescribed level of CPAP, with only 30.8% of the patients with a prescribed pressure < or =3 cm H(2)O of the predicted pressure. Female gender was the only predictor of a prescribed pressure < or =3 cm H(2)O from the predicted pressure (odds ratio 3.45, 95% confidence intervals 1.67, 7.13, p<0.001). A CPAP prediction equation modestly increases the rate of successful CPAP titrations by increasing the starting pressure of the titration. The equation does not accurately predict the prescribed CPAP level, reaffirming the need for a titration study to determine the optimal prescribed level in a given patient.

Predicting effective continuous positive airway pressure

PURPOSE

The purpose of this study was to compare the pressure required to abolish apneas as predicted from a previously derived algorithm (Ppred) with the true effective pressure (Peff) determined during a continuous positive airway pressure (CPAP) titration study.

SETTING

Sleep clinic of a university hospital.

METHODS

We prospectively studied 329 patients with sleep apnea undergoing CPAP titration. The following protocol was employed. Titration began at a pressure (Ppred) calculated from a previously derived equation based on body mass index, neck circumference, and apnea/hypopnea index (AHI). If AHI at Ppred was > 10, the pressure was increased in steps of 1 cm H(2)O until AHI became < 10. If, on the other hand, AHI at Ppred was < 10, the pressure was reduced in increments of 1 cm H(2)O until AHI became > 10. The lowest pressure that abolishes sleep apnea is defined as the Peff. Paired t tests, linear correlation, and distribution of (Ppred - Peff) were used to compare Peff and Ppred.

RESULTS

Successful titration was accomplished in 276 patients (84%). Mean Ppred was similar to mean Peff (8.1 +/- 2.2 vs 8.1 +/- 2.6 cm H(2)O, respectively). There was a significant correlation between these two pressures (r = 0.73; p = 0.0001). Examination of the distribution of (Peff - Ppred) revealed that in 63% of patients, Ppred was within +/- 1 cm H(2)O of Peff; in 83% of patients, the two measures were within +/- 2 cm H(2)O; and in 95%, within +/- 3 cm H(2)O.

CONCLUSION

We conclude that pressure predicted from an algorithm based on simple anthropometric and sleep variables constitutes a good starting point for CPAP titration, allowing the optimum pressure to be achieved with only a few incremental changes.