Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy

• Published in: Journal of clinical sleep medicine Vol. 19; no. 8; pp. 1399 - 1410
• Main Authors: Gabbay, Frances H, Wynn, Gary H, Georg, Matthew W, Gildea, Sarah M, Kennedy, Chris J, King, Andrew J, Sampson, Nancy A, Ursano, Robert J, Stein, Murray B, Wagner, James R, Kessler, Ronald C, Capaldi, Vincent F
• Format: Journal Article
• Language: English
• Published: United States American Academy of Sleep Medicine 01-08-2023
• Subjects: Humans; Machine Learning; Military Personnel; ROC Curve; Scientific Investigations; Sleep Initiation and Maintenance Disorders - drug therapy; military; machine learning; pharmacotherapy; treatment response; insomnia; personalized medicine
• ISSN: 1550-9389; 1550-9397; 1550-9397
• DOI: 10.5664/jcsm.10574

Although many military personnel with insomnia are treated with prescription medication, little reliable guidance exists to identify patients most likely to respond. As a first step toward personalized care for insomnia, we present results of a machine-learning model to predict response to insomnia medication.
The sample comprised n = 4,738 nondeployed US Army soldiers treated with insomnia medication and followed 6-12 weeks after initiating treatment. All patients had moderate-severe baseline scores on the Insomnia Severity Index (ISI) and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble machine-learning model was developed in a 70% training sample to predict clinically significant ISI improvement, defined as reduction of at least 2 standard deviations on the baseline ISI distribution. Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample.
21.3% of patients had clinically significant ISI improvement. Model test sample area under the receiver operating characteristic curve (standard error) was 0.63 (0.02). Among the 30% of patients with the highest predicted probabilities of improvement, 32.5.% had clinically significant symptom improvement vs 16.6% in the 70% sample predicted to be least likely to improve (χ
= 37.1,
< .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity.
Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment, but parallel models will be needed for alternative treatments before such a system is of optimal value.
Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy.
. 2023;19(8):1399-1410.