Li Yange, Zhang Lei, Zhang Yan, Wen Hui, Huang Jingjing, Shen Yifeng, Li Huafang
Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Shanghai Key Laboratory of Psychotic Disorders, Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Neuropsychiatr Dis Treat. 2021 Mar 19;17:847-857. doi: 10.2147/NDT.S280757. eCollection 2021.
Impaired social functions contribute to the burden of schizophrenia patients and their families, but predictive tools of social functioning prognosis and specific factors are undefined in Chinese clinical practice. This article explores a machine learning tool to identify whether patients will achieve significant social functional improvement after 3 months of atypical antipsychotic monopharmacy and finds the defined risk factors using a multicenter clinical study.
A multicenter study on atypical antipsychotic (AAP) treatment in Chinese patients with schizophrenia (SALT-C) was conducted from July 2011 to August 2018. Data from 550 patients with AAP monopharmacy from their baseline to their 3-month follow-up were used to establish machine learning tools after screening. The positive outcome was an increase in the Personal and Social Performance (PSP) scale score by ≥10 points. The predictors were a range of investigator-rated assessments on symptoms, functioning, the safety of AAPs and illness history. The Least Absolute Shrinkage and Selection Operator (LASSO) was used for the feature screening and ranking of the predicted variables. The random forest algorithm and five-fold cross-validation for optimizing the model were selected to ensure the generalizability and precision.
There were 137 patients (mean [SD] age, 41.1 [16.8] years; 77 [58.8%] female) who had a good social functional prognosis. A lower PSP score, taking a mood stabilizer, a high total Positive and Negative Symptom Scale (PANSS) and PANSS general subscale score, unemployment, a hepatic injury with medication, comorbid cardiovascular disease and being male predicted poor PSP outcomes. The generalizability of the PSP predictive tool was estimated with the precision-recall curve (accuracy of 79.5%, negative predictive value of 92.6% and positive predictive value of 57.1%) and receiver operating characteristic curve (ROC) (specificity of 81.8% and sensitivity of 78.7%).
The machine learning tool established using our current real-world data could assist in predicting PSP outcome by several clinical factors.
社会功能受损加重了精神分裂症患者及其家庭的负担，但在中国临床实践中，社会功能预后的预测工具及具体因素尚不明确。本文探索一种机器学习工具，以识别非典型抗精神病药物单药治疗3个月后患者的社会功能是否会实现显著改善，并通过一项多中心临床研究找出明确的危险因素。
2011年7月至2018年8月开展了一项针对中国精神分裂症患者非典型抗精神病药物治疗（SALT-C）的多中心研究。筛选后，使用550例接受非典型抗精神病药物单药治疗患者从基线到3个月随访的数据来建立机器学习工具。阳性结果为个人和社会表现（PSP）量表评分增加≥10分。预测因素包括一系列由研究者评定的关于症状、功能、非典型抗精神病药物安全性及病史的评估。使用最小绝对收缩和选择算子（LASSO）对预测变量进行特征筛选和排序。选择随机森林算法和五折交叉验证来优化模型，以确保模型的可推广性和精确性。
137例患者（平均[标准差]年龄，41.1[16.8]岁；77例[58.8%]为女性）社会功能预后良好。PSP评分较低、服用心境稳定剂、阳性和阴性症状量表（PANSS）总分及PANSS一般因子分较高、失业、药物性肝损伤、合并心血管疾病以及男性预示着PSP结局较差。通过精确召回率曲线（准确率79.5%，阴性预测值92.6%，阳性预测值57.1%）和受试者工作特征曲线（ROC）（特异性81.8%，敏感性78.7%）评估了PSP预测工具的可推广性。
利用我们当前的真实世界数据建立的机器学习工具可通过多种临床因素辅助预测PSP结局。
