Articles: sepsis.
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Journal of critical care · Dec 2024
ReviewMachine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review.
Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis. ⋯ ML models demonstrate a modest improvement in predicting sepsis-associated mortality. The certainty of these findings remains low due to the high risk of bias and significant heterogeneity. Studies should include comprehensive methodological details on calibration and hyperparameter selection, adopt a standardized definition of sepsis, and conduct multicenter prospective designs along with external validations.
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Pediatr Crit Care Me · Dec 2024
Observational StudyPharmacokinetic Factors Associated With Early Meropenem Target Attainment in Pediatric Severe Sepsis.
To determine the frequency of early meropenem concentration target attainment (TA) in critically ill children with severe sepsis; to explore clinical, therapeutic, and pharmacokinetic factors associated with TA; and to assess how fluid resuscitation and volume status relate to early TA. ⋯ Eight of 29 pediatric patients with early severe sepsis did not meet the selected TA threshold within the first 24 hours of meropenem therapy. Higher clearance was associated with failure to meet targets. Identifying patients likely to have higher meropenem clearance could help with dosing regimens.
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Observational Study
Social Determinants of Health and Risk-Adjusted Sepsis Mortality in the Nationwide Veterans Affairs Healthcare System.
Traditional risk prediction and risk adjustment models have focused on clinical characteristics, but accounting for social determinants of health (SDOH) and complex health conditions could improve understanding of sepsis outcomes and our ability to predict outcomes, treat patients, and assess quality of care. ⋯ In patients with community-acquired sepsis, adjusting for community SDOH variables such as ADI did not improve 90-day sepsis mortality predictions in mortality models and did not substantively alter hospital performance within the VA Healthcare System. Understanding the role of SDOH in risk prediction and risk adjustment models is vital because it could prevent hospitals from being negatively evaluated for treating less advantaged patients. However, we found that in VA hospitals, the potential impact of SDOH on 90-day sepsis mortality was minimal.
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Critical care medicine · Dec 2024
Adjudication of Codes for Identifying Sepsis in Hospital Administrative Data by Expert Consensus.
Refine the administrative data definition of sepsis in hospitalized patients, including less severe cases. ⋯ Compared with other code-based algorithms, AlgorithmL includes more infection and organ dysfunction codes. AlgorithmL incidence rates are higher; hospital mortality rates are lower. AlgorithmL may more fully encompass the full range of sepsis severity.