Kuhathasan Nirushi, Ballester Pedro L, Minuzzi Luciano, MacKillop James, Frey Benicio N
Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, 100 West 5th Street, Hamilton, ON L8N 3K7, Canada; Michael G. DeGroote Centre for Medicinal Cannabis Research, McMaster University, 100 West 5th Street, Hamilton, ON L8N 3K7, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, 100 West 5th Street, Hamilton, ON L8N 3K7, Canada.
Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, 100 West 5th Street, Hamilton, ON L8N 3K7, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, 100 West 5th Street, Hamilton, ON L8N 3K7, Canada.
Compr Psychiatry. 2023 Apr;122:152377. doi: 10.1016/j.comppsych.2023.152377. Epub 2023 Feb 10.
Despite limited clinical evidence of its efficacy, cannabis use has been commonly reported for the management of various mental health concerns in naturalistic field studies. The aim of the current study was to use machine learning methods to investigate predictors of perceived symptom change across various mental health symptoms with acute cannabis use in a large naturalistic sample.
Data from 68,819 unique observations of cannabis use from 1307 individuals using cannabis to manage mental health symptoms were analyzed. Data were extracted from Strainprint®, a mobile app that allows users to monitor their cannabis use for therapeutic purposes. Machine learning models were employed to predict self-perceived symptom change after cannabis use, and SHapley Additive exPlanations (SHAP) value plots were used to assess feature importance of individual predictors in the model. Interaction effects of symptom severity pre-scores of anxiety, depression, insomnia, and gender were also examined.
The factors that were most strongly associated with perceived symptom change following acute cannabis use were pre-symptom severity, age, gender, and the ratio of CBD to THC. Further examination on the impact of baseline severity for the most commonly reported symptoms revealed distinct responses, with cannabis being reported to more likely benefit individuals with lower pre-symptom severity for depression, and higher pre-symptom severity for insomnia. Responses to cannabis use also differed between genders.
Findings from this study highlight the importance of several factors in predicting perceived symptom change with acute cannabis use for mental health symptom management. Mental health profiles and baseline symptom severity may play a large role in perceived responses to cannabis. Distinct response patterns were also noted across commonly reported mental health symptoms, emphasizing the need for placebo-controlled cannabis trials for specific user profiles.
尽管大麻使用的疗效临床证据有限，但在自然主义的现场研究中，大麻使用已被普遍报道用于管理各种心理健康问题。本研究的目的是使用机器学习方法，在一个大型自然主义样本中，调查急性使用大麻后各种心理健康症状的感知症状变化的预测因素。
分析了1307名使用大麻管理心理健康症状的个体的68819次独特大麻使用观察数据。数据从Strainprint®中提取，这是一款允许用户出于治疗目的监测其大麻使用情况的移动应用程序。采用机器学习模型预测大麻使用后的自我感知症状变化，并使用SHapley加性解释（SHAP）值图评估模型中各个预测因素的特征重要性。还研究了焦虑、抑郁、失眠症状严重程度预评分与性别的交互作用。
与急性使用大麻后感知症状变化最密切相关的因素是症状前严重程度、年龄、性别以及CBD与THC的比例。对最常报告症状的基线严重程度影响的进一步检查显示出不同的反应，据报道，大麻对抑郁症状前严重程度较低的个体更可能有益，而对失眠症状前严重程度较高的个体更有益。大麻使用的反应在性别之间也存在差异。
本研究结果强调了几个因素在预测急性使用大麻治疗心理健康症状时感知症状变化方面的重要性。心理健康状况和基线症状严重程度可能在对大麻的感知反应中起很大作用。在常见的心理健康症状中也观察到了不同的反应模式，强调了针对特定用户群体进行安慰剂对照大麻试验的必要性。
