J. Kenneth Baillie, Derek Angus, Katie Burnham, Thierry Calandra, Carolyn Calfee, Alex Gutteridge, Nir Hacohen, Purvesh Khatri, Raymond Langley, Avi Ma’ayan, John Marshall, David Maslove, Hallie C. Prescott, Kathy Rowan, Brendon P. Scicluna, Christopher Seymour, Manu Shankar-Hari, Nathan Shapiro, W. Joost Wiersinga, Mervyn Singer, Adrienne G. Randolph
{"title":"因果推论可引导我们找到败血症的可调节机制和信息原型","authors":"J. Kenneth Baillie, Derek Angus, Katie Burnham, Thierry Calandra, Carolyn Calfee, Alex Gutteridge, Nir Hacohen, Purvesh Khatri, Raymond Langley, Avi Ma’ayan, John Marshall, David Maslove, Hallie C. Prescott, Kathy Rowan, Brendon P. Scicluna, Christopher Seymour, Manu Shankar-Hari, Nathan Shapiro, W. Joost Wiersinga, Mervyn Singer, Adrienne G. Randolph","doi":"10.1007/s00134-024-07665-4","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":13665,"journal":{"name":"Intensive Care Medicine","volume":null,"pages":null},"PeriodicalIF":27.1000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis\",\"authors\":\"J. Kenneth Baillie, Derek Angus, Katie Burnham, Thierry Calandra, Carolyn Calfee, Alex Gutteridge, Nir Hacohen, Purvesh Khatri, Raymond Langley, Avi Ma’ayan, John Marshall, David Maslove, Hallie C. Prescott, Kathy Rowan, Brendon P. Scicluna, Christopher Seymour, Manu Shankar-Hari, Nathan Shapiro, W. Joost Wiersinga, Mervyn Singer, Adrienne G. Randolph\",\"doi\":\"10.1007/s00134-024-07665-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":13665,\"journal\":{\"name\":\"Intensive Care Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":27.1000,\"publicationDate\":\"2024-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intensive Care Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00134-024-07665-4\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intensive Care Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00134-024-07665-4","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis
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.
期刊介绍:
Intensive Care Medicine is the premier publication platform fostering the communication and exchange of cutting-edge research and ideas within the field of intensive care medicine on a comprehensive scale. Catering to professionals involved in intensive medical care, including intensivists, medical specialists, nurses, and other healthcare professionals, ICM stands as the official journal of The European Society of Intensive Care Medicine. ICM is dedicated to advancing the understanding and practice of intensive care medicine among professionals in Europe and beyond. The journal provides a robust platform for disseminating current research findings and innovative ideas in intensive care medicine. Content published in Intensive Care Medicine encompasses a wide range, including review articles, original research papers, letters, reviews, debates, and more.