IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM

• Published in: Shock (Augusta, Ga.) Vol. 64; no. 2; p. 218
• Main Authors: Labilloy, Guillaume, Tanaka, Sébastien, Black, Lauren Page, Augustin, Beulah, Hopson, Charlotte, Bethencourt, Joanne, Wu, Dongyuan, Sulaiman, Dawoud, Bertrand, Andrew, Salomão, Reinaldo, Graim, Kiley, Datta, Susmita, Reddy, Srinivasa, Guirgis, Faheem W, Hofmaenner, Daniel A
• Format: Journal Article
• Language: English
• Published: United States 01.08.2025
• Subjects: Aged; Algorithms; Cholesterol, HDL - blood; Female; Humans; Lipoproteins - metabolism; Male; Middle Aged; Phenotype; Prospective Studies; Sepsis - blood; Sepsis - metabolism; LDL-C—low-density lipoprotein cholesterol; CI—confidence interval; HDL-C—high-density lipoprotein cholesterol; lipids; NORMO—previously subphenotyped normolipoprotein phenotype; subphenotyping; IQR—interquartile range; LOS—length of stay; SOFA—sequential organ failure assessment; AUC—area under the receiver operating characteristic curve; HYPO—previously subphenotyped hypolipoprotein phenotype; Sepsis; cholesterol; REDCap—Research Electronic Data Capture Database; lipid dysregulation; ICU—intensive care unit; lipoproteins
• ISSN: 1540-0514; 1073-2322; 1540-0514
• DOI: 10.1097/SHK.0000000000002605

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.