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Kerchberger, V. E.

Publications and source records attributed to Kerchberger, V. E..

2 recordsLinked to original sources

Critical illness expands a transcriptionally distinct hypometabolic CD8+ T effector program associated with respiratory failure and mortality

Immune dysfunction is a major driver of morbidity and mortality in critical illness syndromes including sepsis. Specifically, CD8+ T cell dysfunction has been linked to organ failure and death. To characterize the immune substructure of circulating CD8+ T cells in critical illness at high dimension, we used single-cell RNA sequencing of peripheral blood CD8+ T cells from 38 critically ill patients and 9 healthy controls. We annotated seven CD8+ T cell clusters, which included a CD8+ effector subset, termed T effector state 2 (TEff-2), that was only present in critically ill patients and associated with more severe respiratory failure and higher mortality. TEff-2 showed effector activation and inflammatory stress conditioning yet had markedly reduced metabolic transcripts without canonical features of exhaustion. Trajectory analyses positioned TEff-2 as a terminal CD8+ T effector cell fate driven in part by DDIT4 and DUSP1, which negatively regulate mTOR and MAPK signaling, respectively. Interestingly, this transcriptional program was indistinguishable by classical protein cytometry methods. These results, including the mortality association, were validated in a larger (n=91) independent external cohort of critically ill patients with sepsis. In summary, TEff-2 represents a latent transcriptional program that delineates a clinically high-risk CD8+ T cell state in critical illness.

immunology↗

A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records

RationaleChronic obstructive pulmonary disease (COPD) is a leading cause of mortality in the United States. Electronic health records provide large-scale healthcare data for clinical research, but have been underutilized in COPD research due to challenges identifying these individuals, especially in the absence of pulmonary function testing data.\n\nObjectivesTo develop an algorithm to electronically phenotype individuals with COPD at a large tertiary care center.\n\nMethodsWe identified individuals over 45 years of age at last clinic visit within Vanderbilt University Medical Center electronic health records. We tested phenotyping algorithms using combinations of both structured and unstructured text and examined the clinical characteristics of the resulting case sets.\n\nMeasurement and Main ResultsA simple algorithm consisting of 3 International Classification of Disease codes for COPD achieved a sensitivity of 97.6%, a specificity of 76.0%, a positive predictive value of 57.1%, and a negative predictive value of 99.0%. A more complex algorithm consisting of both billing codes and a mention of oxygen on the problem list that achieved a positive predictive value of 86.5%. However, the association of known risk factors with chronic obstructive pulmonary disease was consistent in both algorithm sets, suggesting a simple code-only algorithm may suffice for many research applications.\n\nConclusionsSimple code-only phenotyping algorithms for chronic obstructive pulmonary disease can identify case populations with epidemiologic and genetic profiles consistent with published literature. Implementation of this phenotyping algorithm will expand opportunities for clinical research and pragmatic trials for COPD.

bioinformatics↗