Your browser doesn't support javascript.
loading
Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.
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.
Affiliation
  • Gabbay FH; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland.
  • Wynn GH; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland.
  • Georg MW; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland.
  • Gildea SM; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland.
  • Kennedy CJ; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland.
  • King AJ; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
  • Sampson NA; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts.
  • Ursano RJ; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
  • Stein MB; Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
  • Wagner JR; Department of Psychiatry, Uniformed Services University, Bethesda, Maryland.
  • Kessler RC; Department of Psychiatry, University of California San Diego, La Jolla, California.
  • Capaldi VF; Psychiatric Service, VA San Diego Healthcare System, San Diego, California.
J Clin Sleep Med ; 20(6): 921-931, 2024 Jun 01.
Article in En | MEDLINE | ID: mdl-38300822
ABSTRACT
STUDY

OBJECTIVES:

The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response.

METHODS:

Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (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:

19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ21 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation.

CONCLUSIONS:

Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. 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 a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2024;20(6)921-931.
Subject(s)
Key words

Full text: 1 Database: MEDLINE Main subject: Cognitive Behavioral Therapy / Precision Medicine / Machine Learning / Sleep Initiation and Maintenance Disorders / Military Personnel Type of study: Guideline / Prognostic_studies / Risk_factors_studies Limits: Adult / Female / Humans / Male Country/Region as subject: America do norte Language: En Journal: J Clin Sleep Med Year: 2024 Type: Article

Full text: 1 Database: MEDLINE Main subject: Cognitive Behavioral Therapy / Precision Medicine / Machine Learning / Sleep Initiation and Maintenance Disorders / Military Personnel Type of study: Guideline / Prognostic_studies / Risk_factors_studies Limits: Adult / Female / Humans / Male Country/Region as subject: America do norte Language: En Journal: J Clin Sleep Med Year: 2024 Type: Article