title: Cost minimization using an artificial neural network sleep apnea prediction tool for sleep studies.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  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.
authors: 1 Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Ohio State University Wexner Medical Center, Columbus, Ohio.
authors: Rahel A
authors: RA
authors: Teferra
authors: Brydon J B
authors: BJ
authors: Grant
authors: Jesse W
authors: JW
authors: Mindel
authors: Tauseef A
authors: TA
authors: Siddiqi
authors: Imran H
authors: IH
authors: Iftikhar
authors: Fatima
authors: F
authors: Ajaz
authors: Jose P
authors: JP
authors: Aliling
authors: Meena S
authors: MS
authors: Khan
authors: Stephen P
authors: SP
authors: Hoffmann
authors: Ulysses J
authors: UJ
authors: Magalang
mesh_terms: D000368:Aged; D015331:Cohort Studies; D017046:Cost Savings; D005260:Female; D017048:Health Care Costs; D006699:Home Care Services / Q000191:economics; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017286:Polysomnography / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000191:economics*
keywords: clinical prediction rule; cost analysis; neural network models; sleep-disordered breathing