Methods Inf Med 2018; 57(04): 185-193
DOI: 10.3414/ME18-01-0014
Original Articles
Georg Thieme Verlag KG Stuttgart · New York

Prediction of Sepsis and In-Hospital Mortality Using Electronic Health Records

Anahita Khojandi
1  Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA
,
Varisara Tansakul
1  Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA
,
Xueping Li
1  Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA
,
Rebecca S. Koszalinski
2  College of Nursing, University of Tennessee, Knoxville, TN, USA
,
William Paiva
3  Center For Health Systems Innovation, Oklahoma State University, Oklahoma, OK, USA
› Author Affiliations
Further Information

Publication History

received: 31 January 2018

accepted: 05 June 2018

Publication Date:
24 September 2018 (online)

Summary

Objectives: Our goal was to develop predictive models for sepsis and in-hospital mortality using electronic health records (EHRs). We showcased the efficiency of these algorithms in patients diagnosed with pneumonia, a group that is highly susceptible to sepsis.

Methods: We retrospectively analyzed the Health Facts® (HF) dataset to develop models to predict mortality and sepsis using the data from the first few hours after admission. In addition, we developed models to predict sepsis using the data collected in the last few hours leading to sepsis onset. We used the random forest classifier to develop the models.

Results: The data collected in the EHR system is generally sporadic, making feature extraction and selection difficult, affecting the accuracies of the models. Despite this fact, the developed models can predict sepsis and in-hospital mortality with accuracies of up to 65.26±0.33% and 68.64±0.48%, and sensitivities of up to 67.24±0.36% and 74.00±1.22%, respectively, using only the data from the first 12 hours after admission. The accuracies generally remain consistent for similar models developed using the data from the first 24 and 48 hours after admission. Lastly, the developed models can accurately predict sepsis patients (with up to 98.63±0.17% accuracy and 99.74%±0.13% sensitivity) using the data collected within the last 12 hours before sepsis onset. The results suggest that if such algorithms continuously monitor patients, they can identify sepsis patients in a manner comparable to current screening tools, such as the rulebased Systemic Inflammatory Response Syndrome (SIRS) criteria, while often allowing for early detection of sepsis shortly after admission.

Conclusions: The developed models showed promise in early prediction of sepsis, providing an opportunity for directing early intervention efforts to prevent/treat sepsis.