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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 5 of 5 Research Studies DisplayedScott HF, Brilli RJ, Paul R
Evaluating pediatric sepsis definitions designed for electronic health record extraction and multicenter quality improvement.
The purpose of this study was to describe the Children's Hospital Association's Improving Pediatric Sepsis Outcomes sepsis definitions and to evaluate the definition using a published framework. The investigators concluded that the Improving Pediatric Sepsis Outcomes Sepsis definitions demonstrated feasibility for large-scale data abstraction. When operationalized, these definitions enabled multicenter identification and data aggregation, indicating practical utility for quality improvement.
AHRQ-funded; HS025696.
Citation: Scott HF, Brilli RJ, Paul R .
Evaluating pediatric sepsis definitions designed for electronic health record extraction and multicenter quality improvement.
Crit Care Med 2020 Oct;48(10):e916-e26. doi: 10.1097/ccm.0000000000004505..
Keywords: Children/Adolescents, Electronic Health Records (EHRs), Health Information Technology (HIT), Sepsis, Quality Improvement, Quality of Care
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)
Rhee C, Jentzsch MS, Kadri SS
Variation in identifying sepsis and organ dysfunction using administrative versus electronic clinical data and impact on hospital outcome comparisons.
Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. In this study, the investigators evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons.
AHRQ-funded; HS025008.
Citation: Rhee C, Jentzsch MS, Kadri SS .
Variation in identifying sepsis and organ dysfunction using administrative versus electronic clinical data and impact on hospital outcome comparisons.
Crit Care Med 2019 Apr;47(4):493-500. doi: 10.1097/ccm.0000000000003554..
Keywords: Sepsis, 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
Rhee C, Zhang Z, Kadri SS
Sepsis surveillance using adult sepsis events simplified eSOFA criteria versus sepsis-3 sequential organ failure assessment criteria.
This retrospective cohort study compared simplified organ dysfunction criteria optimized for electronic health records (eSOFA) with Sequential Organ Failure Assessment with regard to sepsis prevalence, overlap, and outcomes using 111 U.S. hospitals in the Cerner HealthFacts dataset. Clinical indicators of presumed infection, such as blood cultures and antibiotics, were identified concurrent with either: an increase in Sequential Organ Failure Assessment score by 2 or more points (Sepsis-3) or one or more eSOFA criteria. The authors conclude that the Adult Sepsis Event's eSOFA organ dysfunction criteria identify a smaller, more severely ill sepsis cohort when compared with the Sequential Organ Failure Assessment score, although there is overlap between the two and both show similar clinical characteristics.
AHRQ-funded; HS025008.
Citation: Rhee C, Zhang Z, Kadri SS .
Sepsis surveillance using adult sepsis events simplified eSOFA criteria versus sepsis-3 sequential organ failure assessment criteria.
Crit Care Med 2019 Mar;47(3):307-14. doi: 10.1097/ccm.0000000000003521..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Sepsis