Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine—the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g., trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.

医学进步反映在从广泛的临床综合征到机械上一致的诊断的进步。从这个指标来看，脓毒症的研究远远落后于其他医学领域——这个词本身将多种不同的疾病机制混为一谈，同时排除了具有相似发病机制的非感染性综合征（例如创伤、胰腺炎）。用于深度表型分析和数据分析的新技术提供了以极高深度定义生物状态的能力。进展受到一个基本问题的限制：观察到的缺乏共同因果机制的患者分组对治疗反应的预测能力非常差。在这里，我们讨论了识别反映疾病原型的患者组的具体步骤，这些患者群体具有共同的潜在发病机制。最近的证据表明，来自宿主遗传学和随机临床试验的因果推断在为分层分析提供信息方面的作用。遗传研究可以直接阐明药物靶点，但除此之外，它们还创造了一个统计能力库，可以在潜在的患者亚组之间多次划分，以测试机制的一致性，从而加速发现试验中可改变的测试机制。新方法（例如临床试验中正在进行的亚组鉴定）将提高效率。在未来十年内，我们预计正在进行的大规模合作项目将发现和测试与治疗相关的脓毒症原型。