Delirium is a heterogeneous syndrome characterized by an acute change in level of consciousness that is associated with inattention and disorganized thinking. Delirium affects most critically ill patients and is associated with poor patient-oriented outcomes such as increased mortality, longer ICU and hospital length of stay, and worse long-term cognitive outcomes. The concept of delirium and its subtypes has existed since nearly the beginning of recorded medical literature, yet robust therapies have yet to be identified. Analogous to other critical illness syndromes, we suspect the lack of identified therapies stems from patient heterogeneity and prior subtyping efforts that do not capture the underlying etiology of delirium. The time has come to leverage machine learning approaches, such as supervised and unsupervised clustering, to identify clinical and pathophysiological distinct clusters of delirium that will likely respond differently to various interventions. We use sedation in the ICU as an example of how precision therapies can be applied to critically ill patients, highlighting the fact that while for some patients a sedative drug may cause delirium, in another cohort sedation is the specific treatment. Finally, we conclude with a proposition to move away from the term delirium, and rather focus on the treatable traits that may allow precision therapies to be tested.

中文翻译：

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从传统打字到智能洞察：谵妄症靶向治疗方向的叙述性回顾。


谵妄是一种异质性综合征，其特征是意识水平急剧变化，与注意力不集中和思维混乱有关。谵妄影响大多数危重患者，并与以患者为中心的不良结果相关，例如死亡率增加、ICU和住院时间延长以及长期认知结果较差。谵妄及其亚型的概念几乎自有医学文献记录以来就已存在，但尚未确定有效的治疗方法。与其他危重疾病综合征类似，我们怀疑缺乏已确定的治疗方法是由于患者的异质性和之前的亚型分型工作未能捕获谵妄的根本病因。现在是时候利用机器学习方法（例如监督和无监督聚类）来识别临床和病理生理学上不同的谵妄簇，这些簇可能对各种干预措施有不同的反应。我们以 ICU 中的镇静为例，说明如何将精准治疗应用于危重患者，强调这样一个事实：虽然对于某些患者来说，镇静药物可能会导致谵妄，但在另一组患者中，镇静才是具体的治疗方法。最后，我们提出一个建议，即放弃“谵妄”一词，而将重点放在可以测试精确疗法的可治疗特征上。