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 1LR in all groups compared with the STOP-BANG and the modified neck circumference (MNC) prediction tools. The 1LRs 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.

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?

“…As a preprocessing step first, all cases with missing data were excluded. Second, BMI was categorized as normal (20.0 ≤ BMI < 25), overweight (25.0 ≤ BMI < 30.0), and obese (BMI ≥ 30) according to the World (14) 127 42154 5113 410 313 (4) 87 (29) 153 (50) 51 (17) 259 (85) 45 (15) 51 (17) 111 (36) 96 (32) 46 (15) 103 (34) Health Organization guidelines [23]. Age was categorized as young adult (18-35 years), middle-aged adult (36-55 years) and older adult (56 years and older) [24].…”

“…Since there is no widely approved predefined classification of neck circumference, it was categorized in four equally sized groupings using the quintile values of their distribution. A cut-off score of 10 was used for the Epworth sleepiness scale (ESS), with categorization as normal (0-9) and abnormal (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) [25].…”

“…Several strategies have been proposed and investigated for the detection of obstructive sleep apnea diagnoses from nonpolysomnographic features to decrease the number of PSG studies [4][5][6][7][8][9][10][11][12][13][14]. In those studies, the clinical features that are associated with the OSA have been combined using statistical modeling or machine learning techniques into a clinical prediction model.…”

The Use of Artificial Neural Networks in Clinical Medicine

“…There are several studies that addressed this problem, since additional tests and procedures do not always result in definitive diagnosis or termination of illness. There are several studies that suggest the use of ANN models in reducing overall costs for proper treatment of clinical conditions (Walczak, 2000;Liew, 2007, Abbod, 2011Teferra, 2014).…”

Obesity, Orthopaedics, and Outcomes

“…Other screening tools, such as the multivariable apnea prediction 15 and OSUNet, 16 treat BMI and age as continuous variables and may offer greater utility. 3 Therefore, a binary, highthreshold value for BMI limits the discriminatory power of this key risk factor.…”