Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression A Multimodal, Multisite Machine Learning Analysis
Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression A Multimodal, Multisite Machine Learning Analysis
复制标题
DOI：
10.1001/jamapsychiatry.2018.2165
复制
发表时间：
2018-11-01
期刊：
影响因子：
25.8
通讯作者：
Borgwardt, Stefan
中科院分区：
文献类型：
--
作者：
Koutsouleris, Nikolaos;Kambeitz-Ilankovic, Lana;Borgwardt, Stefan
重要性社会和职业损伤有助于精神病和抑郁症的负担。有必要为风险分层工具，以告知个性化的功能残疾预防策略，为个人在风险和早期阶段的这些diseases. ObjectiveTo确定是否与社会和角色功能相关的预测因素可以确定在患者中的临床高危（ESTA）状态的精神病或与最近发作的抑郁症（ROD）使用临床，基于成像，并结合机器学习;评估机器学习的地理、跨诊断和预后的可推广性，并将其与人类的可推广性进行比较;设计、设置和参与者这项多地点的自然主义研究随访了患有ROD和近期发作精神病的精神分裂症患者，和健康对照参与者在5个欧洲国家的7个学术早期识别服务中进行了18个月的研究。参与者于2014年2月至2016年5月期间招募，数据于2017年4月至2018年1月进行分析。AIN结局和指标预后模型的性能和普适性。对120例ROD患者（平均[SD]年龄，24.0 [5.1]岁; 58例[50.0%]女性）和120例ROD患者（平均[SD]年龄，26.1 [6.1]岁; 65例[54.2%]女性）进行了平均（SD）329（142）天的随访。使用临床基线数据，机器学习预测了1年的社会功能结局，平衡准确率为76.9%的抑郁症患者和66.2%的ROD患者。结构神经影像学模型的平衡准确率分别为：正常状态76.2%，ROD 65.0%，联合模型的平衡准确率分别为：正常状态82.7%，ROD 70.3%。在进入研究之前，低功能是一个跨诊断预测因子。内侧前额叶和颞顶枕灰质体积（GMV）的减少和小脑和背外侧前额叶GMV增量具有预测价值，在ROD组中，减少的中颞叶和增加的前额叶-侧裂周GMV具有预测价值。在随访中，抑郁状态患者的精神病性、抑郁性和焦虑性障碍的风险增加，但与ROD患者无关。机器学习优于专家推理。将神经影像学机器学习添加到临床机器学习中，在不确定的情况下，患者的预后确定性增加了1.9倍，ROD患者的预后确定性增加了10.5倍。结论和相关性精密医疗工具可以增加有效的治疗策略，旨在预防患者的社会功能障碍。
IMPORTANCE Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses.OBJECTIVE To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning.DESIGN, SETTING, AND PARTICIPANTS This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018.AIN OUTCOMES AND MEASURES Performance and generalizability of prognostic models.RESULTS A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD.CONCLUSIONS AND RELEVANCE Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.