2019
DOI: 10.1001/jama.2019.5791
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Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

Abstract: IMPORTANCE Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. OBJECTIVE To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). DESIGN, SETTINGS, AND PARTICIPANTS Retrospective analysis of data sets using statistical, machine learning, and sim… Show more

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Cited by 874 publications
(932 citation statements)
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References 34 publications
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“…Artificial intelligence was used to define differing phenotypes of sepsis, demonstrating the heterogeneity of the condition. 29 Use of artificial intelligence embedded in electronic medical records looking for changes in vital parameters by collecting and analyzing longitudinal data on routine assessments may play an important role in the early identification of risk for sepsis as well as a number of other conditions in the LTCF population.…”
Section: Next Steps and Recommendationsmentioning
confidence: 99%
“…Artificial intelligence was used to define differing phenotypes of sepsis, demonstrating the heterogeneity of the condition. 29 Use of artificial intelligence embedded in electronic medical records looking for changes in vital parameters by collecting and analyzing longitudinal data on routine assessments may play an important role in the early identification of risk for sepsis as well as a number of other conditions in the LTCF population.…”
Section: Next Steps and Recommendationsmentioning
confidence: 99%
“…Once triggered, this systemic process was believed to progress independent of the inciting infection and thus patients with different types of infection have been grouped together in clinical trials [7]. Unfortunately, innumerable trials of promising immune-modulating interventions have failed to reduce early mortality and the recent consensus is that heterogeneity played a major role in these dismal failures [8][9][10]. It has been shown that site of infection is an independent predictor of early mortality [11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…Gårdlund et al used latent class analysis to identify six distinct subphenotypes of septic shock using clinical data from a previous large clinical trial cohort [47]. Seymour et al used a different approach (machine learning applied to electronic health record data) and identified four subphenotypes with different genetic and inflammatory markers and markedly different mortality rates [48].…”
Section: Beyond Ards: Subphenotypes In Other Heterogeneous Syndromesmentioning
confidence: 99%