Munroe Elizabeth S, Spicer Alexandra, Castellvi-Font Andrea, Zalucky Ann, Dianti Jose, Graham Linck Emma, Talisa Victor, Urner Martin, Angus Derek C, Baedorf-Kassis Elias, Blette Bryan, Bos Lieuwe D, Buell Kevin G, Casey Jonathan D, Calfee Carolyn S, Del Sorbo Lorenzo, Estenssoro Elisa, Ferguson Niall D, Giblon Rachel, Granholm Anders, Harhay Michael O, Heath Anna, Hodgson Carol, Houle Timothy, Jiang Cong, Kramer Lina, Lawler Patrick R, Leligdowicz Aleksandra, Li Fan, Liu Kuan, Maiga Amelia, Maslove David, McArthur Colin, McAuley Daniel F, Serpa Neto Ary, Oosthuysen Charissa, Perner Anders, Prescott Hallie C, Rochwerg Bram, Sahetya Sarina, Samoilenko Mariia, Schnitzer Mireille E, Seitz Kevin P, Shah Faraaz, Shankar-Hari Manu, Sinha Pratik, Slutsky Arthur S, Qian Edward T, Webb Steve A, Young Paul J, Zampieri Fernando G, Zarychanski Ryan, Fan Eddy, Semler Matthew W, Churpek Matthew, Goligher Ewan C
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Division of Pulmonary and Critical Care, Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA.
Lancet Respir Med. 2025 Jun;13(6):556-568. doi: 10.1016/S2213-2600(25)00054-2. Epub 2025 Apr 15.
Clinicians aim to provide treatments that will result in the best outcome for each patient. Ideally, treatment decisions are based on evidence from randomised clinical trials. Randomised trials conventionally report an aggregated difference in outcomes between patients in each group, known as an average treatment effect. However, the actual effect of treatment on outcomes (treatment response) can vary considerably between individuals, and can differ substantially from the average treatment effect. This variation in response to treatment between patients-heterogeneity of treatment effect-is particularly important in critical care because common critical care syndromes (eg, sepsis and acute respiratory distress syndrome) are clinically and biologically heterogeneous. Statistical approaches have been developed to analyse heterogeneity of treatment effect and predict individualised treatment effects for each patient. In this Review, we outline a framework for deriving and validating individualised treatment effects and identify challenges to applying individualised treatment effect estimates to inform treatment decisions in clinical care.
临床医生旨在提供能为每位患者带来最佳治疗效果的治疗方案。理想情况下，治疗决策基于随机临床试验的证据。随机试验通常报告每组患者结局的汇总差异，即平均治疗效果。然而，治疗对结局的实际影响（治疗反应）在个体之间可能有很大差异，且可能与平均治疗效果有显著不同。患者对治疗反应的这种差异——治疗效果的异质性——在重症监护中尤为重要，因为常见的重症监护综合征（如脓毒症和急性呼吸窘迫综合征）在临床和生物学上都是异质的。已经开发出统计方法来分析治疗效果的异质性，并预测每位患者的个体化治疗效果。在本综述中，我们概述了推导和验证个体化治疗效果的框架，并确定了在临床护理中应用个体化治疗效果估计值以指导治疗决策所面临的挑战。
