To specify when delays of specific 3-hour bundle Surviving Sepsis Campaign guideline recommendations applied to severe sepsis or septic shock become harmful and impact mortality.
Retrospective cohort study.
One health system composed of six hospitals and 45 clinics in a Midwest state from January 01, 2011, to July 31, 2015.
All adult patients hospitalized with billing diagnosis of severe sepsis or septic shock.
Four 3-hour Surviving Sepsis Campaign guideline recommendations: 1) obtain blood culture before antibiotics, 2) obtain lactate level, 3) administer broad-spectrum antibiotics, and 4) administer 30 mL/kg of crystalloid fluid for hypotension (defined as “mean arterial pressure” < 65) or lactate (> 4).
To determine the effect of t minutes of delay in carrying out each intervention, propensity score matching of “baseline” characteristics compensated for differences in health status. The average treatment effect in the treated computed as the average difference in outcomes between those treated after shorter versus longer delay. To estimate the uncertainty associated with the average treatment effect in the treated metric and to construct 95% CIs, bootstrap estimation with 1,000 replications was performed. From 5,072 patients with severe sepsis or septic shock, 1,412 (27.8%) had in-hospital mortality. The majority of patients had the four 3-hour bundle recommendations initiated within 3 hours. The statistically significant time in minutes after which a delay increased the risk of death for each recommendation was as follows: lactate, 20.0 minutes; blood culture, 50.0 minutes; crystalloids, 100.0 minutes; and antibiotic therapy, 125.0 minutes.
The guideline recommendations showed that shorter delays indicates better outcomes. There was no evidence that 3 hours is safe; even very short delays adversely impact outcomes. Findings demonstrated a new approach to incorporate time t when analyzing the impact on outcomes and provide new evidence for clinical practice and research.
1Population Health and Systems, University of Minnesota, School of Nursing, Minneapolis, MN.
2University of Minnesota, Institute for Health Informatics, Minneapolis, MN.
3Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN.
4Department of Medicine, University of Minnesota, School of Medicine, Minneapolis, MN.
*See also p. 642.
The contents of this document are the sole responsibility of the authors and do not necessarily represent official views of the National Science Foundation/National Institutes of Health.
Supported, in part, by National Science Foundation Grant number 1602394 and by the National Institutes of Health grant number GM120079.
Drs. Westra, Kumar, and Simon received support for article research from the National Institutes of Health (NIH). Dr. Westra disclosed that this study is supported by National Science Foundation (NSF) grant IIS-1344135 and partially supported by Grant Number 1UL1RR033183 from the National Center for Research Resources of the NIH to the University of Minnesota Clinical and Translational Science Institute. Dr. Steinbach disclosed that he is supported, in part, by NSF and NIH grants and by the University of Minnesota, although no grants were received for this specific work. For this article, he received NSF-related funding. The remaining authors have disclosed that they do not have any potential conflicts of interest.
For information regarding this article, E-mail: pruin001@umn.edu
