Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy.
| Autor: | Gabbay FH; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland.; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland., Wynn GH; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland., Georg MW; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland.; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland., Gildea SM; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts., Kennedy CJ; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts., King AJ; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts., Sampson NA; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts., Ursano RJ; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland., Stein MB; Department of Psychiatry, University of California San Diego, La Jolla, California.; Psychiatric Service, VA San Diego Healthcare System, San Diego, California., Wagner JR; Institute for Social Research, University of Michigan, Ann Arbor, Michigan., Kessler RC; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts., Capaldi VF; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland. |
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| Jazyk: | angličtina |
| Zdroj: | Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine [J Clin Sleep Med] 2023 Aug 01; Vol. 19 (8), pp. 1399-1410. |
| DOI: | 10.5664/jcsm.10574 |
| Abstrakt: | 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 (χ 2 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. Citation: 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. J Clin Sleep Med . 2023;19(8):1399-1410. (© 2023 American Academy of Sleep Medicine.) |
| Databáze: | MEDLINE |
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