Sepsis, a life-threatening syndrome, is a factor in more than 1.3 million hospitalizations each year and is the single most expensive condition to treat in the U.S. Mortality increases 8% for every hour sepsis goes untreated. Thus, it is crucial for clinicians to detect sepsis early, a significant challenge because no gold standard exists. The purpose of this study was to examine the potential to use nursing assessment findings routinely documented in the electronic health record (EHR) to aid in the early identification of infection-related organ dysfunction. The aims were to identify (a) which specific nursing assessment findings were reliable surrogates to individual organ system dysfunction, (b) which findings may be feasible for characterization of intestinal dysfunction, (c) which specific SOFA subscore the surrogate is most associated with, and (d) the relationship between expanded criteria and time to recognition and sepsis diagnosis. With a modified version of the Quality Health Outcomes Model as the foundation, this retrospective exploratory cohort studied adults hospitalized from 2016 to 2020 where infection was suspected or confirmed. Electronic health record data from 492,766 cases in 16 tertiary care hospitals operating as part of a healthcare system located in the south-central United States were examined. Surrogate candidates were identified a priori and statically examined
using univariate logistic regression, diagnostic testing, CHAID, area under the receiving operator curve (AUROC), and t-test analyses. Results showed that nursing assessment findings are reliable surrogates for respiratory, liver, cardiovascular, central nervous system, and renal systems and did not degrade or improve the power of SOFA to predict sepsis diagnosis or mortality. Organ dysfunction could be identified as much as 5.7 hours before the original SOFA criteria. Nursing assessment documentation can be used as reliable surrogates for organ dysfunction when original SOFA criteria are missing or out of date.
Date of publication
Dr. Barbara Haas, Dr. Cheryl Parker, and Dr. Susan McBride
Doctor of Philosophy in Nursing
Nelson, Tanna L., "Predicting Infection-Related Organ Failure: Expansion of SOFA Algorithm to Include Nursing Documentation" (2021). Nursing Theses and Dissertations. Paper 129.