{"title":"Clinical phenotypes of sepsis: a narrative review.","authors":"Beibei Liu, Qingtao Zhou","doi":"10.21037/jtd-24-114","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objective: </strong>Sepsis, characterized by an aberrant immune response to infection leading to acute organ dysfunction, impacts millions of individuals each year and carries a substantial risk of mortality, even with prompt care. Despite notable medical advancements, managing sepsis remains a formidable challenge for clinicians and researchers, with treatment options limited to antibiotics, fluid therapy, and organ-supportive measures. Given the heterogeneous nature of sepsis, the identification of distinct clinical phenotypes holds the promise of more precise therapy and enhanced patient care. In this review, we explore various phenotyping schemes applied to sepsis.</p><p><strong>Methods: </strong>We searched PubMed with the terms \"Clinical phenotypes AND sepsis\" for any type of article published in English up to September 2023. Only reports in English were included, editorials or articles lacking full text were excluded. A review of clinical phenotypes of sepsis is provided.</p><p><strong>Key content and findings: </strong>While discerning clinical phenotypes may seem daunting, the application of artificial intelligence and machine learning techniques provides a viable approach to quantifying similarities among individuals within a sepsis population. These methods enable the differentiation of individuals into distinct phenotypes based on not only factors such as infectious diseases, infection sites, pathogens, body temperature changes and hemodynamics, but also conventional clinical data and molecular omics.</p><p><strong>Conclusions: </strong>The classification of sepsis holds immense significance in improving clinical cure rates, reducing mortality, and alleviating the economic burden associated with this condition.</p>","PeriodicalId":17542,"journal":{"name":"Journal of thoracic disease","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11320222/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of thoracic disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/jtd-24-114","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/26 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0
Abstract
Background and objective: Sepsis, characterized by an aberrant immune response to infection leading to acute organ dysfunction, impacts millions of individuals each year and carries a substantial risk of mortality, even with prompt care. Despite notable medical advancements, managing sepsis remains a formidable challenge for clinicians and researchers, with treatment options limited to antibiotics, fluid therapy, and organ-supportive measures. Given the heterogeneous nature of sepsis, the identification of distinct clinical phenotypes holds the promise of more precise therapy and enhanced patient care. In this review, we explore various phenotyping schemes applied to sepsis.
Methods: We searched PubMed with the terms "Clinical phenotypes AND sepsis" for any type of article published in English up to September 2023. Only reports in English were included, editorials or articles lacking full text were excluded. A review of clinical phenotypes of sepsis is provided.
Key content and findings: While discerning clinical phenotypes may seem daunting, the application of artificial intelligence and machine learning techniques provides a viable approach to quantifying similarities among individuals within a sepsis population. These methods enable the differentiation of individuals into distinct phenotypes based on not only factors such as infectious diseases, infection sites, pathogens, body temperature changes and hemodynamics, but also conventional clinical data and molecular omics.
Conclusions: The classification of sepsis holds immense significance in improving clinical cure rates, reducing mortality, and alleviating the economic burden associated with this condition.
期刊介绍:
The Journal of Thoracic Disease (JTD, J Thorac Dis, pISSN: 2072-1439; eISSN: 2077-6624) was founded in Dec 2009, and indexed in PubMed in Dec 2011 and Science Citation Index SCI in Feb 2013. It is published quarterly (Dec 2009- Dec 2011), bimonthly (Jan 2012 - Dec 2013), monthly (Jan. 2014-) and openly distributed worldwide. JTD received its impact factor of 2.365 for the year 2016. JTD publishes manuscripts that describe new findings and provide current, practical information on the diagnosis and treatment of conditions related to thoracic disease. All the submission and reviewing are conducted electronically so that rapid review is assured.