Detecting sepsis: Are two opinions better than one?
The diagnosis of sepsis requires that objective criteria be met with a corresponding subjective suspicion of infection. We conducted a study to characterize the agreement between different providers’ suspicion of infection and the correlation with patient outcomes using prospective data from a general medicine ward. Registered nurse (RN) suspicion of infection was collected every 12 hours and compared with medical doctor or advanced practice professional (MD/APP) suspicion, defined as an existing order for antibiotics or a new order for blood or urine cultures within the 12 hours before nursing screen time. During the study period, 1386 patients yielded 11,489 screens, 3744 (32.6%) of which met at least 2 systemic inflammatory response syndrome (SIRS) criteria. Infection was suspected by RN and MD/APP in 5.8% of cases, by RN only in 22.2%, by MD/APP only in 7.2%, and by neither provider in 64.7%. Overall agreement rate was 80.7% for suspicion of infection (κ = 0.11, P < 0.001). Progression to severe sepsis or shock was highest when both providers suspected infection in a SIRS-positive patient (17.7%), was substantially reduced with single-provider suspicion (6.0%), and was lowest when neither provider suspected infection (1.5%) (P < 0.001). Provider disagreement regarding suspected infection is common, with RNs suspecting infection more often, suggesting that a collaborative model for sepsis detection may improve timing and accuracy. Journal of Hospital Medicine 2017;12:256-258. © 2017 Society of Hospital Medicine
© 2017 Society of Hospital Medicine
Acknowledgments
The authors thank the members of the Surviving Sepsis Campaign (SSC) Quality Improvement Learning Collaborative at the University of Chicago for their help in data collection and review, especially Meredith Borak, Rita Lanier, Mary Ann Francisco, and Bill Marsack. The authors also thank Thomas Best and Mary-Kate Springman for their assistance in data entry and Nicole Twu for administrative support. Data from this study were provided by the Clinical Research Data Warehouse (CRDW) maintained by the Center for Research Informatics (CRI) at the University of Chicago. CRI is funded by the Biological Sciences Division of the Institute for Translational Medicine/Clinical and Translational Science Award (CTSA) (National Institutes of Health UL1 TR000430) at the University of Chicago.
Disclosures
Dr. Bhattacharjee is supported by postdoctoral training grant 4T32HS000078 from the Agency for Healthcare Research and Quality. Drs. Churpek and Edelson have a patent pending (ARCD.P0535US.P2) for risk stratification algorithms for hospitalized patients. Dr. Churpek is supported by career development award K08 HL121080 from the National Heart, Lung, and Blood Institute. Dr. Edelson has received research support from Philips Healthcare (Andover, Massachusetts), American Heart Association (Dallas, Texas), and Laerdal Medical (Stavanger, Norway) and has ownership interest in Quant HC (Chicago, Illinois), which is developing products for risk stratification of hospitalized patients. The other authors report no conflicts of interest.
