Abstract
Study Objectives: 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. Methods: 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. Results: 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 (x21 = 37.1, P < .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity. Conclusions: 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.
Original language | English |
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Pages (from-to) | 1399-1410 |
Number of pages | 12 |
Journal | Journal of Clinical Sleep Medicine |
Volume | 19 |
Issue number | 8 |
DOIs | |
State | Published - 1 Aug 2023 |
Keywords
- insomnia
- machine learning
- military
- personalized medicine
- pharmacotherapy
- treatment response