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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.
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1 to 4 of 4 Research Studies DisplayedLindell RB, Nishisaki A, Weiss SL
Risk of mortality in immunocompromised children with severe sepsis and septic shock.
This study’s objective was to assess the risk of mortality for immunocompromised children admitted to the hospital with septic shock or sepsis. This retrospective multicenter cohort study used eighty-three centers in the Virtual Pediatric systems database. The cohort included children admitted to the pediatric intensive care unit (PICU) with severe sepsis or septic shock from 2012-2016. Across 83 centers, 10,768 PICU admissions with an International Classification of Diseases, 9th Revision, Clinical Modification code for severe sepsis or septic shock were identified; with 3,021 of these patients (28%) having an immunocompromised diagnosis. PICU mortality rates varied widely by center, and those centers with a higher mean number of sepsis patients per month in a center had a lower PICU mortality rate. Multiple prior malignancies, hemophagocytic lymphohistiocytosis, congenital immunodeficiency, and hematopoietic cell transplant are conditions independently associated with an increased odds of PICU mortality in children with severe sepsis or septic shock.
AHRQ-funded; HS024511; HS026939; HS021583; HS022464.
Citation: Lindell RB, Nishisaki A, Weiss SL .
Risk of mortality in immunocompromised children with severe sepsis and septic shock.
Crit Care Med 2020 Jul;48(7):1026-33. doi: 10.1097/ccm.0000000000004329..
Keywords: Children/Adolescents, Mortality, Sepsis, Risk, Intensive Care Unit (ICU), Hospitalization, Hospitals
Scott HF, Colborn KL, Sevick CJ
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
The purpose of this observational cohort study was to derive and validate a model of risk of septic shock among children with suspected sepsis, using data known in the electronic health record at hospital arrival. The investigators concluded that their model estimated the risk of septic shock in children at hospital arrival earlier than existing models. They indicate it leveraged the predictive value of routine electronic health record data through a modern predictive algorithm and suggest it has the potential to enhance clinical risk stratification in the critical moments before deterioration.
AHRQ-funded; HS025696.
Citation: Scott HF, Colborn KL, Sevick CJ .
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
J Pediatr 2020 Feb;217:145-51.e6. doi: 10.1016/j.jpeds.2019.09.079..
Keywords: Children/Adolescents, Sepsis, Emergency Department, Hospitals, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Delahanty RJ, Alvarez J, Flynn LM
Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis.
In this study, the investigators aimed to use machine learning to develop a new sepsis screening tool, the Risk of Sepsis (RoS) score, and compare it with a slate of benchmark sepsis-screening tools, including the Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment (SOFA), qSOFA, Modified Early Warning Score, and National Early Warning Score. The investigators concluded that in this retrospective study, RoS was more timely and discriminant than benchmark screening tools, including those recommend by the Sepsis-3 Task Force.
AHRQ-funded; HS024750.
Citation: Delahanty RJ, Alvarez J, Flynn LM .
Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis.
Ann Emerg Med 2019 Apr;73(4):334-44. doi: 10.1016/j.annemergmed.2018.11.036..
Keywords: Health Information Technology (HIT), Hospitals, Risk, Sepsis
Donnelly JP, Hohmann SF, Wang HE
Unplanned readmissions after hospitalization for severe sepsis at academic medical center-affiliated hospitals.
The researchers sought to characterize 7- and 30-day readmission rates following hospital admission for severe sepsis as well as institutional variations in readmission. They concluded that severe sepsis readmission places a substantial burden on the healthcare system, with one in 15 and one in five severe sepsis discharges readmitted within 7 and 30 days, respectively.
AHRQ-funded; HS013852.
Citation: Donnelly JP, Hohmann SF, Wang HE .
Unplanned readmissions after hospitalization for severe sepsis at academic medical center-affiliated hospitals.
Crit Care Med 2015 Sep;43(9):1916-27. doi: 10.1097/ccm.0000000000001147..
Keywords: Hospital Readmissions, Hospitals, Risk, Sepsis