Abstract
ObjectiveApproximately one-third of sepsis patients experience poor outcomes including chronic critical illness (CCI, intensive care unit (ICU) stay > 14 days) or early death (in-hospital death within 14 days). We sought to characterize lipoprotein predictive ability for poor outcomes and contribution to sepsis heterogeneity.DesignProspective cohort study with independent replication cohort.SettingEmergency department and surgical ICU at two hospitals.PatientsSepsis patients presenting within 24 h.MethodsMeasures included cholesterol levels (total cholesterol, high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C]), triglycerides, paraoxonase-1 (PON-1), and apolipoprotein A-I (Apo A-I) in the first 24 h. Inflammatory and endothelial markers, and sequential organ failure assessment (SOFA) scores were also measured. LASSO selection assessed predictive ability for outcomes. Unsupervised clustering was used to investigate the contribution of lipid variation to sepsis heterogeneity.Measurements and main results172 patients were enrolled. Most (~ 67%, 114/172) rapidly recovered, while ~ 23% (41/172) developed CCI, and ~ 10% (17/172) had early death. ApoA-I, LDL-C, mechanical ventilation, vasopressor use, and Charlson Comorbidity Score were significant predictors of CCI/early death in LASSO models. Unsupervised clustering yielded two discernible phenotypes. The Hypolipoprotein phenotype was characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1), higher SOFA scores, and worse clinical outcomes (45% rapid recovery, 40% CCI, 16% early death; 28-day mortality, 21%). The Normolipoprotein cluster patients had higher cholesterol levels, less endothelial dysfunction, lower SOFA scores and better outcomes (79% rapid recovery, 15% CCI, 6% early death; 28-day mortality, 15%). Phenotypes were validated in an independent replication cohort (N = 86) with greater sepsis severity, which similarly demonstrated lower HDL-C, ApoA-I, and higher ICAM-1 in the Hypolipoprotein cluster and worse outcomes (46% rapid recovery, 23% CCI, 31% early death; 28-day mortality, 42%). Normolipoprotein patients in the replication cohort had better outcomes (55% rapid recovery, 32% CCI, 13% early death; 28-day mortality, 28%) Top features for cluster discrimination were HDL-C, ApoA-I, total SOFA score, total cholesterol level, and ICAM-1.ConclusionsLipoproteins predicted poor sepsis outcomes. A Hypolipoprotein sepsis phenotype was identified and characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1) and organ failure, and worse clinical outcomes.
Highlights
Sepsis is a prevalent, morbid and costly condition
One-third of sepsis patients develop a state of chronic critical illness (CCI), defined as intensive care unit (ICU) stay ≥ 14 days with continued organ dysfunction
We were able to phenotype sepsis patients into Hypolipoprotein and Normolipoprotein phenotypes, with the Hypolipoprotein phenotype being characterized by lower lipoprotein levels, and increased endothelial dysfunction (ICAM-1) as well as higher sequential organ failure assessment (SOFA) scores
Summary
Clinical outcomes and adjudication The primary outcome was one of three categories: (1) early death (within 2 weeks of sepsis onset), (2) CCI (total ICU stay > 14 days with organ dysfunction or total ICU ≤ 14 days but discharged to long-term acute care, another hospital, or hospice), or (3) rapid recovery (all others) [46]. The following variables were included in the full logistic regression model and entered the LASSO selection: age, gender, race, PON1, ApoA-I, HDL-C, LDL-C, total cholesterol, triglycerides, G-CSF, GM-CSF, IFN-γ, IL-10, IL-12p70, IL-6, IL-8, IP-10, MCP-1, MIP1a, TNFα, SOFA score, initial lactate, mechanical ventilation use, statin use, vasopressor use, vasopressor duration, volume of intravenous fluids in the first 24 h, APACHE II Score and Charlson Comorbidity Score. After extracting clusters, we compared differences in mortality and rates of CCI, early death, and rapid recovery among the derived clusters. We built the hierarchical clustering using the Spearman correlation and Ward method and extracted the first two clusters for analysis as above
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