Screening for Obstructive Sleep Apnea in Commercial Drivers Using EKG-Derived Respiratory Power Index

Abstract

Study objectives: Obstructive sleep apnea (OSA) is common in commercial motor vehicle operators (CMVOs); however, polysomnography (PSG), the gold-standard diagnostic test, is expensive and inconvenient for screening. OSA is associated with changes in heart rate and voltage on electrocardiography (EKG). We evaluated the utility of EKG parameters in identifying CMVOs at greater risk for sleepiness-related crashes (apnea-hypopnea index [AHI] ≥ 30 events/h).

Methods: In this prospective study of CMVOs, we performed EKGs with concurrent PSG, and calculated the respiratory power index (RPI) on EKG, a surrogate for AHI calculated from PSG. We evaluated the utility of two-stage predictive models using simple clinical measures (age, body mass index [BMI], neck circumference, Epworth Sleepiness Scale score, and the Multi-Variable Apnea Prediction [MVAP] score) in the first stage, followed by RPI in a subset as the second-stage. We assessed area under the receiver operating characteristic curve (AUC), sensitivity, and negative posttest probability (NPTP) for this two-stage approach and for RPI alone.

Results: The best-performing model used the MVAP, which combines BMI, age, and sex with three OSA symptoms, in the first stage, followed by RPI in the second. The model yielded an estimated (95% confidence interval) AUC of 0.883 (0.767-0.924), sensitivity of 0.917 (0.706-0.962), and NPTP of 0.034 (0.015-0.133). Predictive characteristics were similar using a model with only BMI as the first-stage screen.

Conclusions: A two-stage model that combines BMI or the MVAP score in the first stage, with EKG in the second, had robust discriminatory power to identify severe OSA in CMVOs.

Keywords: EKG; OSA; commercial motor vehicle drivers; electrocardiography; obstructive sleep apnea; occupational driving; respiratory power index; screening; surrogate measure for apnea hypopnea index; truck drivers.

Figure 1. Exemplary illustration of the methods behind the electrocardiographically derived respiratory power index (RPI).

The nighttime electrocardiograph recordings are preprocessed, including detection of fiduciary points and an estimation of signal-to-noise ratio (SNR), and are cut into segments of limited length (4–10 minutes) and high SNR (A). Segments of insufficient quality are discarded. Multiple embeddings of respiration into the ECG are used to derive electrocardiographically derived respiration (EDR) signals based on, e.g., the amplitudes of the peaks of the r-, p-, and t-wave (B), the respiratory sinus arrhythmia (RSA) (D). The spectrograms calculated from these signals (C,E) are normalized and averaged to amplify the common, ie, respiration based, component. In this case the RSA is not dominant as a source of modulation of the heart rate variability and shows mostly uncorrelated components that are reduced in the averaging process. The averaged spectrum is then analyzed to derive an estimate for the instantaneous respiratory frequency and masked to further reduce nonrespiration-related power in the spectrum (F). The power at each time-step is then calculated together with two levels used in the selection of events (G). The green line is the maximum level that determines the extent of an event if it lasts longer than a minimum amount of time and falls below the red level at least once. Time spans that indicate an event are underlaid blue on all time series. The amount of detected events, in relation to the analyzed time, is the RPI.