Chest
Volume 124, Issue 5, November 2003, Pages 1694-1701
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Clinical Investigations
SLEEP AND BREATHING
Prediction of the Apnea-Hypopnea Index From Overnight Pulse Oximetry*

https://doi.org/10.1378/chest.124.5.1694Get rights and content

Study objectives

To compare the relative usefulness of the different indexes derived from pulse oximetry in the diagnosis of obstructive sleep apnea (OSA), and to determine if a combination of these indexes improves the prediction of the apnea-hypopnea index (AHI) measured by polysomnography.

Design

Prediction model developed from 224 patients, validated prospectively in 101 patients from the same center (group 1) and in 191 patients from a different sleep center (group 2).

Setting

Two independent sleep clinics run by university sleep specialists.

Participants

Patients who underwent polysomnography for suspicion of OSA.

Interventions

The following indexes were calculated from pulse oximetry recordings performed simultaneously during polysomnography: (1) Δ index, the average of the absolute differences of oxygen saturation between successive 12-s intervals; (2) desaturation events per hour to 2%, 3%, and 4% levels; and (3) cumulative time spent below 90%, 88%, 86%, 84%, 82%, and 80% saturation.

Measurements and results

The best predictor was the Δ index, although desaturation events provided similar levels of diagnostic accuracy. An aggregation of multivariate models using combination of indexes reduced the prediction error (r2 = 0.70) significantly (p < 0.05) compared to using the Δ index alone (r2 = 0.60). The proportion of subjects from the validation groups within 95% confidence interval (CI) of the derivation group was 90% (95% CI, 83 to 95%) and 91% (95% CI, 86 to 95%) for groups 1 and 2, respectively. The overall likelihood ratios for the aggregated model in all patient groups were 4.2 (95% CI, 3.3 to 15.3), 3.4 (95% CI, 2.7 to 4.3), 3.0 (95% CI, 2.2 to 4.1), and 6.7 (95% CI, 4.9 to 9.2) for normal (AHI < 5/h), mild (AHI 5 to < 15/h), moderate (AHI 15 to < 30/h), and severe (AHI ≥ 30/h) disease, respectively.

Conclusions

The Δ index and oxygen desaturation indexes provided similar levels of diagnostic accuracy. The combination of indexes improved the precision of the predicted AHI and may offer a potentially simpler alternative to polysomnography.

Section snippets

Patient Population

Five hundred sixteen patients suspected of having OSA were enrolled into this prospective study. Patients were recruited from two independent sleep clinics in Buffalo, NY: the Associated Sleep Center (ASC) and the Buffalo Veterans Affairs Medical Center (VAMC) Sleep Center. The eligibility criteria were all patients who underwent overnight polysomnography for suspected sleep apnea. The exclusion criteria were age < 18 years; oxygen supplementation was used during the sleep study, or CPAP

Patient Characteristics

A total of 224 patients were entered into the derivation group. Another 101 patients were enrolled in validation group 1, and 191 patients were enrolled in validation group 2. Therefore, a total of 516 patients were included in the analysis. The patient characteristics of the derivation and two validation groups are shown in Table 1. All groups have similar body mass index and AHI. The patients in validation group 2 were significantly older compared to the derivation group and validation group

Discussion

The major findings of this study are as follows: (1) among the different oximetry indexes, the Δ index was the best predictor of the presence of OSA, although desaturation events provided similar levels of diagnostic accuracy; (2) the Δ index had good sensitivity but low specificity; (3) a bootstrap aggregation of models involving a combination of all the oximetry indexes (compared to using the Δ index alone) improved the precision of the prediction of the AHI; and (4) the prediction model

Multivariate Prediction Models

We used multivariate adaptive regression splines (MARS) to develop prediction models.15 The splines used in this study consisted of one or more of a series of linear segments joined at adjacent ends that could be fitted to nonlinear data. MARS is a multivariate nonparametric procedure that builds flexible regression-like models using exhaustive search techniques to test the necessity of different predictors. Interactions between independent variables are simultaneously tested. The model is

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