Cost minimization using an artificial neural network sleep apnea prediction tool for sleep studies

Abstract

Rationale: More than a million polysomnograms (PSGs) are performed annually in the United States to diagnose obstructive sleep apnea (OSA). Third-party payers now advocate a home sleep test (HST), rather than an in-laboratory PSG, as the diagnostic study for OSA regardless of clinical probability, but the economic benefit of this approach is not known.

Objectives: We determined the diagnostic performance of OSA prediction tools including the newly developed OSUNet, based on an artificial neural network, and performed a cost-minimization analysis when the prediction tools are used to identify patients who should undergo HST.

Methods: The OSUNet was trained to predict the presence of OSA in a derivation group of patients who underwent an in-laboratory PSG (n = 383). Validation group 1 consisted of in-laboratory PSG patients (n = 149). The network was trained further in 33 patients who underwent HST and then was validated in a separate group of 100 HST patients (validation group 2). Likelihood ratios (LRs) were compared with two previously published prediction tools. The total costs from the use of the three prediction tools and the third-party approach within a clinical algorithm were compared.

Measurements and main results: The OSUNet had a higher +LR in all groups compared with the STOP-BANG and the modified neck circumference (MNC) prediction tools. The +LRs for STOP-BANG, MNC, and OSUNet in validation group 1 were 1.1 (1.0-1.2), 1.3 (1.1-1.5), and 2.1 (1.4-3.1); and in validation group 2 they were 1.4 (1.1-1.7), 1.7 (1.3-2.2), and 3.4 (1.8-6.1), respectively. With an OSA prevalence less than 52%, the use of all three clinical prediction tools resulted in cost savings compared with the third-party approach.

Conclusions: The routine requirement of an HST to diagnose OSA regardless of clinical probability is more costly compared with the use of OSA clinical prediction tools that identify patients who should undergo this procedure when OSA is expected to be present in less than half of the population. With OSA prevalence less than 40%, the OSUNet offers the greatest savings, which are substantial when the number of sleep studies done annually is considered.

Keywords: clinical prediction rule; cost analysis; neural network models; sleep-disordered breathing.

Figure 1.

Clinical algorithm for sleep studies. (A) The total cost of the third-party approach wherein all patients undergo a home sleep test (HST) as the initial procedure and (B) the costs of the approaches using the three prediction tools were calculated on the basis of the algorithm shown. In (B), patients identified as having an increased risk of having obstructive sleep apnea (OSA) by the prediction tool go on to have an HST as the initial diagnostic procedure whereas those without an increased risk have an in-laboratory polysomnogram (PSG) as the initial diagnostic procedure.

Figure 2.

Comparison of positive likelihood ratios of clinical prediction tools. (A) Derivation group, (B) validation group 1, and (C) validation group 2. The error bars represent 95% confidence intervals. MNC = modified neck circumference; STOP-BANG, Snoring, Tiredness, being Observed to stop breathing during sleep, high blood Pressure, Body mass index greater than 35 kg/m², Age greater than 50 years, Neck circumference greater than 40 cm, and male Gender.

Figure 3.

Cost savings from the use of prediction tools using an apnea–hypopnea index (AHI) of at least 15/hour to define the presence of obstructive sleep apnea (OSA). (A) Savings per 1,000 patients using the OSUNet to select patients to undergo a home sleep test (HST) as the initial diagnostic procedure according to prevalence of OSA. (B) Savings per 1,000 patients using the modified neck circumference (MNC) to select patients to undergo HST as the initial diagnostic procedure according to prevalence of OSA. (C) Savings per 1,000 patients using the STOP-BANG to select patients to undergo HST as the initial diagnostic procedure according to prevalence of OSA. The dashed lines represent the corresponding 95% confidence intervals. STOP-BANG, Snoring, Tiredness, being Observed to stop breathing during sleep, high blood Pressure, Body mass index greater than 35 kg/m², Age greater than 50 years, Neck circumference greater than 40 cm, and male Gender.

Figure 4.

Cost savings from the use of prediction tools using an apnea–hypopnea index (AHI) of at least 5/hour to define the presence of obstructive sleep apnea (OSA). (A) Savings per 1,000 patients using the OSUNet to select patients to undergo a home sleep test (HST) as the initial diagnostic procedure according to prevalence of OSA. (B) Savings per 1,000 patients using the modified neck circumference (MNC) to select patients to undergo HST as the initial diagnostic procedure according to prevalence of OSA. (C) Savings per 1,000 patients using the STOP-BANG to select patients to undergo HST as the initial diagnostic procedure according to prevalence of OSA. The dashed lines represent the corresponding 95% confidence intervals. STOP-BANG, Snoring, Tiredness, being Observed to stop breathing during sleep, high blood Pressure, Body mass index greater than 35 kg/m², Age greater than 50 years, Neck circumference greater than 40 cm, and male Gender.