Guillaume Labilloy, Sébastien Tanaka, Lauren Page Black, Beulah Augustin, Charlotte Hopson, Joanne Bethencourt, Dongyuan Wu, Dawoud Sulaiman, Andrew Bertrand, Reinaldo Salomão, Kiley Graim, Susmita Datta, Srinivasa Reddy, Faheem W Guirgis, Daniel A Hofmaenner
{"title":"IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.","authors":"Guillaume Labilloy, Sébastien Tanaka, Lauren Page Black, Beulah Augustin, Charlotte Hopson, Joanne Bethencourt, Dongyuan Wu, Dawoud Sulaiman, Andrew Bertrand, Reinaldo Salomão, Kiley Graim, Susmita Datta, Srinivasa Reddy, Faheem W Guirgis, Daniel A Hofmaenner","doi":"10.1097/SHK.0000000000002605","DOIUrl":null,"url":null,"abstract":"<p><strong>Abstract: </strong>Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar ( P = 0.99), but HDL-C ( P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C ( P = 0.99) and HDL-C ( P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.</p>","PeriodicalId":21667,"journal":{"name":"SHOCK","volume":" ","pages":"218-225"},"PeriodicalIF":2.9000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278747/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SHOCK","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/SHK.0000000000002605","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/23 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract: Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar ( P = 0.99), but HDL-C ( P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C ( P = 0.99) and HDL-C ( P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.
期刊介绍:
SHOCK®: Injury, Inflammation, and Sepsis: Laboratory and Clinical Approaches includes studies of novel therapeutic approaches, such as immunomodulation, gene therapy, nutrition, and others. The mission of the Journal is to foster and promote multidisciplinary studies, both experimental and clinical in nature, that critically examine the etiology, mechanisms and novel therapeutics of shock-related pathophysiological conditions. Its purpose is to excel as a vehicle for timely publication in the areas of basic and clinical studies of shock, trauma, sepsis, inflammation, ischemia, and related pathobiological states, with particular emphasis on the biologic mechanisms that determine the response to such injury. Making such information available will ultimately facilitate improved care of the traumatized or septic individual.