Griffiths Siân Lowri, Birchwood Max
Institute for Mental Health, University of Birmingham, Birmingham B15 2TT, UK.
Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.
Medicina (Kaunas). 2020 Nov 24;56(12):638. doi: 10.3390/medicina56120638.
Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a 'one-size fits all' approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards 'big data' and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one's illness may also be key to understanding the factors that drive illness course.
治疗抵抗在精神病早期干预服务中普遍存在，给个体带来了沉重负担。大量变量被证明与首发精神病（FEP）的治疗抵抗有关。病程的异质性以及康复概念的复杂多维度性质，需要有一个证据基础来更好地指导个体层面的实践。当前的金标准治疗采用“一刀切”的方法，可能无法满足许多个体的需求。以下综述将对当前理解、识别和管理早期精神病早期治疗抵抗的临床实践和方法进行更新和批判性评估。还将讨论潜在的新治疗方法以及新的研究途径。最后，我们将讨论并批评机器学习方法在该领域的应用和转化。向“大数据”和机器学习的转变为对基于干预的个体亚组进行分层带来了一些希望。展望未来，需要更好地识别早期治疗抵抗，同时在预测结果方面更加精细和精确，以便能够为个体量身定制有效的循证治疗。了解疾病的前驱因素和早期发展轨迹也可能是理解驱动病程的因素的关键。
