Massachusetts General Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.
Psychol Med. 2023 Jun;53(8):3366-3376. doi: 10.1017/S0033291721005390. Epub 2022 Jan 10.
Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes.
The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response.
SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive.
The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.
选择性 5-羟色胺再摄取抑制剂（SSRIs）是治疗躯体变形障碍（BDD）的一线药物治疗方法，BDD 是一种常见且严重的疾病。然而，先前的研究并未关注或确定 SSRIs 治疗结果的明确预测因素。利用机器学习等精准医学技术可以促进治疗结果的预测。
该研究使用 10 折交叉验证支持向量机（SVM）学习模型，对 97 名接受 SSRIs 依地普仑（escitalopram）为期最长 14 周的开放标签治疗的 BDD 患者的三种治疗结果（即反应、部分缓解和完全缓解）进行预测。SVM 模型使用基线临床和人口统计学变量作为预测因子。特征重要性分析补充了传统的 SVM 建模，以确定哪些变量最成功地预测了治疗反应。
SVM 模型对预测治疗反应的表现可接受，曲线下面积（AUC）为 0.77（敏感性=0.77，特异性=0.63），部分缓解的 AUC 为 0.75（敏感性=0.67，特异性=0.73），完全缓解的 AUC 为 0.79（敏感性=0.70，特异性=0.79）。特征重要性分析支持以下假设，如更好的生活质量和更轻的抑郁、一般精神病理学症状和无望感是更好的治疗结果的更具预测性因素；而人口统计学变量的预测性最低。
本研究首次证明机器学习算法可以成功预测 BDD 药物治疗的治疗结果。与精神病学精准医学倡议一致，本研究为 BDD 患者的个性化药物治疗策略提供了基础。
