Department of Psychiatry, University College London, University of London, London, United Kingdom.
Camden and Islington NHS foundation Trust, London, United Kingdom.
PeerJ. 2024 Oct 14;12:e17841. doi: 10.7717/peerj.17841. eCollection 2024.
Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited.
We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders.
We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models.
Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium . olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium . olanzapine responders.
优化双相情感障碍患者的维持药物治疗选择具有挑战性。有一些证据表明，临床和人口统计学特征可能预测锂的反应。然而，个性化治疗选择的尝试受到了限制。
我们旨在确定应用于电子健康记录的机器学习方法是否可以预测锂或奥氮平的差异反应。我们从英国的电子初级保健记录中提取了一组接受锂（19106 人）或奥氮平（12412 人）单药治疗的个体。使用 113 个临床和人口统计学变量，对 8017 名（41.96%）锂反应者和 3831 名（30.87%）奥氮平反应者进行了预测成功的单药维持治疗的机器学习模型。
我们发现了一种定量结构差异，即锂维持反应者在我们的保留样本中具有较弱的可预测性，该样本由最近暴露的患者的 5%组成。首次诊断时的年龄、首次治疗时的年龄以及两者之间的时间是所有模型中最重要的变量。
即使我们未能预测成功的单药奥氮平治疗，因此无法明确区分锂、奥氮平反应者，这两组的特征也可以用于代理分类。这反过来又可以用于建立维持治疗。未来，电子病历数据中药物治疗选择的机器学习方法的进一步探索可能会在临床决策支持中发挥作用。数据中的信号鼓励使用更大的数据集进行进一步的实验，以明确区分锂、奥氮平反应者。
