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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedDonnelly JP, Locke JE, MacLennan PA
Inpatient mortality among solid organ transplant recipients hospitalized for sepsis and severe sepsis.
The researchers investigated the impact of solid organ transplant (SOT) on outcomes following sepsis. They found that among patients hospitalized for severe sepsis or sepsis, those with SOT had lower inpatient mortality than those without SOT.
AHRQ-funded; HS013852.
Citation: Donnelly JP, Locke JE, MacLennan PA .
Inpatient mortality among solid organ transplant recipients hospitalized for sepsis and severe sepsis.
Clin Infect Dis 2016 Jul 15;63(2):186-94. doi: 10.1093/cid/ciw295.
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Keywords: Transplantation, Mortality, Sepsis, Hospitalization, Patient-Centered Outcomes Research
Moore JX, Donnelly JP, Griffin R
Defining sepsis mortality clusters in the United States.
The researchers aimed to identify US counties with high sepsis mortality and to assess the community characteristics associated with increased sepsis mortality. They found that sepsis mortality is higher in the southern United States and that regions of high sepsis mortality are characterized by lower education, income, employment, and insurance coverage.
AHRQ-funded; HS013852.
Citation: Moore JX, Donnelly JP, Griffin R .
Defining sepsis mortality clusters in the United States.
Crit Care Med 2016 Jul;44(7):1380-7. doi: 10.1097/ccm.0000000000001665.
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Keywords: Mortality, Sepsis, Social Determinants of Health
Taylor RA, Pare JR, Venkatesh AK
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing clinical decision rules (CDRs) and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. It concluded that this approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis.
AHRQ-funded; HS021271.
Citation: Taylor RA, Pare JR, Venkatesh AK .
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
Acad Emerg Med 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876.
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Keywords: Emergency Medical Services (EMS), Mortality, Clinical Decision Support (CDS), Sepsis, Health Information Technology (HIT)