Sepsis can be defined as an extreme response by the body to an infection that is life-threatening if not detected and treated early enough. It mostly occurs when an infection in the skin, urinary tract, lungs, or any other part of the body elicits a series of reactions all over the body, resulting in tissue injury, organ failure, and death. In the US, around 1.7 million adults contract Sepsis, while approximately 270,000 die annually due to Sepsis. This paper discusses one main problem associated with early recognition of Sepsis and analyzing a qualitative improvement initiative that will help improve Sepsis outcome.
Sepsis high mortality rates are mainly attributed to delayed detection of the disease due to its similarity with SIRS regarding their pathophysiological symptoms and patterns. According to Mission Health, a hospital-based in Asheville North Carolina, this derailment in early detection was majorly contributed to the use of Clinical decision rules (CDRs) that were mainly simple heuristics and scoring systems, mainly based on evidenced-based sepsis care bundles. The use of CDRs had some limitations in the diagnosis of Sepsis. Firstly, CDRs lack the mechanism to be updated when new statistics become available, and it takes more years for it to be developed. Secondly, CDRs had the problem of having very generalized questions, which did not aid in proper diagnosis (Topiwara et al., 2019). These limitations led to fragmented and varied processes, which were often slow in diagnosis, thereby having a negative impact on the Sepsis diagnosis and patient outcomes.
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This derailment in early Sepsis diagnosis caused by the use of traditional analytic methods and CDRs that results in high mortality rates can be overcome by the use of a comprehensive data-driven approach which has the ability to integrate large volumes of variables that are located in the electronic health records(EHRs) which may help in the deployment of efficient clinical decision support systems which may better the detection of Sepsis (Taylor et al., 2016). A randomized control trial was done in a German tertiary hospital where a random forest approach was used to identify the best set of predictors out of the total 44 variables measured at the beginning of the disease. According to research by (Lamping et al., 2018), 870 patients were enrolled for the research. The research had positive results where the test dataset indicated an AUC of 0.78, which was more superior to hitherto recommended biomarkers such as CRP, which had a maximum AUC of 0.63. Moreover, in the complete identification of Sepsis cases to those of SIRS, the data-driven approach had a high percentage compared to CRP with 95% accuracy compared to CRPs accuracy of 28% (Lamping et al., 2018). From this research, it is evident that the comprehensive data approach is more efficient in the early recognition of Sepsis due to its high accuracy compared to other recommended biomarkers.
To allow for the use of a comprehensive data-approach, Mission health had to modify their SIRS screening tool by refining sign inclusion standards used in determining the likely source of infection. When this is done, a sepsis alert is triggered, which activates RN to obtain labs such as blood cultures. If a patient scores two or more from the lab, a sepsis code is activated, bringing a response team to expedite care to the patient. According to Mission Health, this has greatly improved the outcomes where there has been a 14.1% reduction in mortality rates of patients with Sepsis (Mission Health, 2019). There has also been a 6.4 % reduction in ED LOS and a 4% reduction in ICU intake for severe sepsis patients.
References
Lamping, F., Jack, T., Rübsamen, N., Sasse, M., Beerbaum, P., Mikolajczyk, R. T., Boehne, M., & Karch, A. (2018). Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms. BMC Pediatrics , 18 (1). https://doi.org/10.1186/s12887-018-1082-2
Mission Health. (2019, October 23). Inpatient sepsis care improved through early recognition . Health Catalyst. https://www.healthcatalyst.com/success_stories/inpatient-sepsis-care-mission-health
Taylor, R. A., Pare, J. R., Venkatesh, A. K., Mowafi, H., Melnick, E. R., Fleischman, W., & Hall, M. K. (2016). Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach. Academic Emergency Medicine , 23 (3), 269-278. https://doi.org/10.1111/acem.12876
Topiwala, R., Patel, K., Twigg, J., Rhule, J., & Meisenberg, B. (2019). Retrospective observational study of the clinical performance characteristics of a machine learning approach to early sepsis identification. Critical Care Explorations , 1 (9), e0046. https://doi.org/10.1097/cce.0000000000000046
