Key points are not available for this paper at this time.
Substantial efforts focus on monitoring and reducing delays in antibiotic treatment for sepsis, but little has been done to characterize the balancing measure of sepsis overtreatment. We aimed to establish preliminary validity and usefulness of electronic health record (EHR) data-derived criteria for sepsis overtreatment surveillance (SEP-OS). We evaluated adults with potential sepsis (≥2 Systemic Inflammatory Response Syndrome criteria within 6 hours of arrival) presenting to the emergency department of 12 hospitals, excluding patients with shock. We defined SEP-OS as the proportion of patients receiving rapid IV antibiotics (≤3 hours) who did not ultimately meet the Centers for Disease Control Adult Sepsis Event "true sepsis" definition. We evaluated the frequency and characteristics of patients meeting overtreatment criteria and outcomes associated with sepsis overtreatment. Of 113 764 eligible patients, the prevalence of sepsis overtreatment was 22.5%. The measure met prespecified criteria for reliability, content, construct, and criterion validity. Patients classified by the SEP-OS overtreatment criteria had higher median antibiotic days (4 days IQR, 2-5 vs 1 day 1-2; P < .01), longer median length of stay (4 days 2-6 vs 3 days 2-5; P < .01), higher hospital mortality (2.4% vs 2.1%; P = .01), and higher frequency of Clostridioides difficile infection within 6 months of hospital discharge (P < .01) compared with "true negative" cases. We developed a novel, valid EHR metric for clinical surveillance of sepsis overtreatment. Applying this metric to a large cohort of potential sepsis patients revealed a high rate of overtreatment and provides a useful tool to inform sepsis quality-improvement targets.
Building similarity graph...
Analyzing shared references across papers
Loading...
Stephanie Parks Taylor
University of Michigan
Jessica A. Palakshappa
Wake Forest University
Shih‐Hsiung Chou
Advocate Health Care
Clinical Infectious Diseases
University of Michigan
Wake Forest University
Atrium Medical Cente
Building similarity graph...
Analyzing shared references across papers
Loading...
Taylor et al. (Thu,) studied this question.
synapsesocial.com/papers/68e55ee1e2b3180350efba4a — DOI: https://doi.org/10.1093/cid/ciae445