METHODS

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See the online supplement for additional detail regarding subject selection, diagnostic studies, and data analysis.

Two-stage Strategy

We sorted in-laboratory subjects on the basis of high, intermediate, or low multivariable predictions (29). Two parameters were defined to categorize participant scores into three groups: “upper bound” separated the high predictions from the intermediate, and “lower bound” separated the intermediate from the low. High scorers were predicted to have OSA, with subsequent review of their polysomnogram to assess this prediction. Intermediate scorers would undergo oximetry; if the ODI equaled or exceeded a third parameter value (the ODI threshold), they would be predicted to have OSA and undergo polysomnography. Those with low multivariable prediction or an ODI less than the ODI threshold would be predicted not to have OSA (Figure 1)

Figure 1. The two-stage strategy for prediction of apnea, which combines use of a multivariable prediction index and oximetry. Stage I consists of administering the symptom questionnaire and calculating the multivariable apnea prediction on the basis of a symptom score, body mass index (BMI), age, and sex. On the basis of the multivariable apnea prediction, drivers undergo a sleep study if the score is high, or no further testing if the score is low. The intermediate group undergoes oximetry, with subsequent overnight sleep study if the desaturation index is high, and no further testing if the desaturation index is low.

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DISCUSSION

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Identifying commercial drivers with severe apnea, defined as an AHI of 30 events per hour or more, was our primary goal because this cut point is associated with marked sleepiness and impaired task performance in our studies (9). In addition, patients with severe sleep apnea derive benefits from positive airway pressure therapy (10) including reduction in crashes (12) and improved driving performance as measured by a driving simulator (13).

In this regard, our analysis shows that a two-stage strategy combining symptoms and BMI with oximetry performed very well, with 91% sensitivity and specificity, and yielded the best negative likelihood ratio, 0.10. Applying a standard Bayesian nomogram (35), if this strategy predicts low risk for severe apnea, then given our pretest probability of 4.7%, the likelihood of having severe apnea is below 0.5%. This excludes severe apnea with high confidence (36). This result is particularly useful, because confirmatory polysomnography testing is expensive and often inaccessible. In addition, the strategy predicted that oximetry is not necessary in 31% of our sample, nor is polysomnography necessary in 86%. The false-positive rate was 8.9%, the false-negative rate was 0.5%, and the negative predictive value was 99%. Oximetry applied to every subject maintained the negative predictive value at 99%, but is more expensive and less convenient than the two-stage strategy, and did not improve the negative likelihood ratio. Thus, to exclude severe apnea, the two-stage strategy that we proposed previously (29) is optimal for this sample.

All of our strategies performed relatively worse in the prediction of any apnea, compared with severe apnea (see Table 3). Again, symptoms alone had little value in this population, with a negative likelihood of 0.71, and oximetry offered minimal additional predictive advantage to the two-stage strategy, with a negative likelihood of 0.29.

For our two-stage strategy, we chose multivariable prediction, which combined symptoms with BMI, rather than using BMI alone. Although a BMI of less than 25 kg/m² was successful in excluding all but 3 cases of apnea, only 46 subjects (20% of the weighted sample) met this criterion, whereas 88 (38%) drivers had a multivariable prediction of less than 0.3. Thus, addition of symptoms was more useful in this setting than use of BMI alone, because more subjects could potentially be prevented from requiring oximetry.

Evaluating these strategies in this group of commercial drivers is likely to be important for public health reasons, as U.S. accident reports indicate that in 2001, large trucks were involved in 429,000 crashes. Nearly 5,000 of these crashes were fatal, responsible for 12% of all traffic deaths, whereas an additional 130,000 victims suffered nonfatal injuries. Commercial crashes are also expensive, costing on average $75,637 per crash and $3.54 million per fatal crash (37). Sleepiness has been shown to account for 31–41% of major crashes of commercial vehicles (38, 39). Although we know little about the role of OSA in crashes in commercial vehicles, studies of passenger cars have shown increased crash risk in drivers with apnea (5–7) and that effective treatment of OSA reduces such risk (12).

Our strategies assess the risk of OSA, rather than OSA syndrome, in which the presence of sleep-disordered breathing is accompanied by subjective sleepiness. We did not limit our analysis to those drivers who reported sleepiness in our study. Such sleepiness may itself be subject to reporting bias, particularly in an occupational setting. Therefore, we propose that enrollment of drivers regardless of the report of sleepiness remains one of the study's strengths.

Without taking sleepiness into account, the proportion of OSA in our population was similar to that reported by Young and coworkers, who reported that 24% of middle-aged men had an AHI of at least 5 events per hour and 9.1% had an AHI of at least 15 events per hour (1). Stoohs and coworkers (40) reported a much higher value of 78% with an oxyhemoglobin desaturation index at or exceeding 5 per hour, and 10% with oxyhemoglobin desaturation index at or exceeding 30 per hour. In our population, we note that 29% of the weighted sample had an oxyhemoglobin desaturation index of 5 or more per hour, and 3% had an oxyhemoglobin desaturation index of 30 or more per hour. Diagnostic ascertainment methodologies differed between these studies; similar to Young and coworkers, we used full sleep study data, whereas Stoohs and coworkers used a less rigorous definition of snoring with 3% desaturation. Because electroencephalogram and airflow data were not recorded during this study, desaturations without airflow limitation could be scored and raise the reported prevalence considerably (41), particularly because this group had a high prevalence (44%) of smoking. This rate is higher than the smoking rate we report in our sample (see Table 1). Pulmonary function data, were they available, might explain a high prevalence of desaturation. In addition, the subjects were recruited from a single employer, rather than being drawn from a more representative, community-based population.

We address whether this difference in proportion could be an indication of differential participation in our study—not merely on the basis of a given predictor, but rather on both the predictor and apnea status simultaneously (e.g., greater participation among obese subjects who also have OSA, versus obese subjects without OSA). Although this bias may limit generalizability, more serious is the potential threat to internal validity. To mitigate this bias, then, given the value of any predictor, participation must be independent of apnea status.

Specific design strengths limit, but do not eliminate, the likelihood of such bias in our study: prospective data collection, objective disease ascertainment, and blinded scoring of results. We also conducted a nonresponder analysis to assess the likelihood of such bias, were it to occur. Because we performed sleep studies only after questionnaire administration, reporting symptoms on the basis of a priori knowledge of apnea status was unlikely. In addition, an overnight sleep study is the objective standard for apnea diagnosis. Moreover, technicians who scored the sleep studies had no knowledge of any other screening test result. Finally, our comparison of age, sex, and sociodemographic factors as assessed by ZIP code between responders, nonresponders, and in-laboratory subjects yielded no statistically significant differences. Indeed, the age and sex distributions were nearly identical (data not shown). Although this comparability does not guarantee the absence of participation bias, we are encouraged that responders and nonresponders were similar in the variables we assessed. The availability of BMI data in nonresponders would have further strengthened our analysis. Despite these limitations, this is the first and largest scale study to address screening for OSA in any high-risk population, where public safety remains a distinct concern.

We also note that we validated our strategy in the same cohort in which it was derived, which may artificially inflate its predictive value, a phenomenon known as regression toward the mean (42). Developing the strategy in a subset of our population and validating it in another would have been a stronger approach. However, this split-sample approach is not viable in this study because of insufficient numbers of subjects with severe apnea.

Oximetry was conducted concurrently with polysomnography in our study, raising the question of whether the two tests could be scored independently. However, our scorer of oximetry data had no prior knowledge of sleep study data. In addition, rescoring of a random 10% sample of oximetry tracings by a second as well as by the original interpreter showed no significant differences in scores. Test–retest and interrater reliabilities were high, with intraclass correlation coefficients of 99 and 97%, respectively. Performing oximetry and sleep studies together in the laboratory also ensured that the driver was sleeping in an identical position and was in an identical stage of sleep for both studies. Future studies need to evaluate oximetry done independently, and also at home.

Although we chose oximetry for Stage II of our two-stage strategy, we also considered the selection of multivariable prediction for the first stage of this strategy, rather than BMI. Although BMI may be a simpler alternative, multivariable prediction incorporates other information in addition to BMI—including age, sex, and symptom frequency. The predictive utility of these additional variables becomes most important when the subject is not obese (21). Thus, we expect our algorithm to have incremental value among populations with lower prevalence of obesity compared with using BMI as the Stage I screen. Even in this relatively obese population, however, multivariable apnea prediction provided substantial improvement in discriminatory power over BMI alone. Moreover, the optimal cut points we selected for BMI and multivariable prediction, which take into account prevalence and misclassification rates, show that multivariable prediction excluded a much larger proportion of drivers from requiring further testing as compared with BMI alone.

Our study raises the question of whether commercial drivers should be screened routinely for severe OSA, perhaps during preemployment physical examination. Our study is the first step toward addressing this question; the two-stage screening strategy we propose is optimal and highly accurate in excluding severe apnea. Before instituting routine screening, however, we propose that additional data be acquired. A case-control study similar to that done among drivers of passenger cars (7) should be conducted first to evaluate the role of OSA as a risk factor for crashes of commercial vehicles, particularly those involving injury or death. A finding that an AHI of or exceeding 30 events per hour is a risk factor for crashes would strengthen our conclusion that the two-stage strategy would be a reasonable approach to screen commercial drivers for this disorder. However, if further studies indicate that an AHI below this level is a more appropriate cutoff, then the screening strategy to be adopted will require further refinement.

Second, controlled, randomized trials should assess whether drivers identified by screening will use and benefit from therapy. The costs of screening need to be weighed against the costs of outcomes without screening. Confirming treatment benefits at acceptable economic costs would provide justification for institution of a routine screening program in this occupational setting.

We conclude that among our community-based sample of commercial drivers, a two-stage screening algorithm that incorporates questionnaire data in all, followed by oximetry in a subset, is useful in excluding drivers with severe sleep apnea, a group that may be at risk for fall-asleep accidents judged by off-road deviations in driving simulators (4).