Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

Autor: Marrero-Polanco J; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA., Joyce JB; Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA., Grant CW; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA., Croarkin PE; Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA., Athreya AP; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.; Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.; Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA., Bobo WV; Department of Behavioral Science and Social Medicine, Florida State University College of Medicine, Tallahassee, Florida, USA.
Jazyk: angličtina
Zdroj: Bipolar disorders [Bipolar Disord] 2024 Oct 03. Date of Electronic Publication: 2024 Oct 03.
DOI: 10.1111/bdi.13506
Abstrakt: Objectives: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]).
Methods: Supervised machine learning models were trained on data from BP-D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission.
Results: An AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine-treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week-4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week-4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45-5.76; p < 9.3E-11).
Conclusion: Machine learning strategies achieved cross-trial and cross-drug replication in predicting remission after 8 weeks of pharmacotherapy for BP-D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.
(© 2024 The Author(s). Bipolar Disorders published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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