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. 2021 Jul 8;9(7):e29986.
doi: 10.2196/29986.

Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study

Jewel Y Lee  1 , Sevda Molani  1 , Chen Fang  1 , Kathleen Jade  1 , D Shane O'Mahony  2 , Sergey A Kornilov  1 , Lindsay T Mico  3 , Jennifer J Hadlock  1
Affiliations

Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study

Jewel Y Lee et al. JMIR Med Inform. .

Abstract

Background: Sepsis is a life-threatening condition that can rapidly lead to organ damage and death. Existing risk scores predict outcomes for patients who have already become acutely ill.

Objective: We aimed to develop a model for identifying patients at risk of getting sepsis within 2 years in order to support the reduction of sepsis morbidity and mortality.

Methods: Machine learning was applied to 2,683,049 electronic health records (EHRs) with over 64 million encounters across five states to develop models for predicting a patient's risk of getting sepsis within 2 years. Features were selected to be easily obtainable from a patient's chart in real time during ambulatory encounters.

Results: The models showed consistent prediction scores, with the highest area under the receiver operating characteristic curve of 0.82 and a positive likelihood ratio of 2.9 achieved with gradient boosting on all features combined. Predictive features included age, sex, ethnicity, average ambulatory heart rate, standard deviation of BMI, and the number of prior medical conditions and procedures. The findings identified both known and potential new risk factors for long-term sepsis. Model variations also illustrated trade-offs between incrementally higher accuracy, implementability, and interpretability.

Conclusions: Accurate implementable models were developed to predict the 2-year risk of sepsis, using EHR data that is easy to obtain from ambulatory encounters. These results help advance the understanding of sepsis and provide a foundation for future trials of risk-informed preventive care.

Keywords: clinical decision making; electronic health records; machine learning; prevention; risk factors; risk prediction; sepsis.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Training, validation, and test split for modeling of the long-term risk of sepsis.
Figure 2
Figure 2
The Shapley Additive exPlanations (SHAP) algorithm results for long-term sepsis risk in model 1. (A) The influence of higher and lower values of the feature on the patient's outcome. The left side of this graph represents reduced risk of developing sepsis, and the right side of the graph represents increased risk of developing sepsis. Red dots represent higher values of the feature, and blue dots represent lower values of the feature. Nominal classes are binary (0,1). (B) The ranking of feature importance indicated by SHAP.
Figure 3
Figure 3
The Shapley Additive exPlanations (SHAP) algorithm results for long-term sepsis risk in model 2. (A) The influence of higher and lower values of the feature on the patient's outcome. The left side of this graph represents reduced risk of developing sepsis, and the right side of the graph represents increased risk of developing sepsis. Red dots represent higher values of the feature, and blue dots represent lower values of the feature. Nominal classes are binary (0,1). (B) The ranking of feature importance indicated by SHAP. BP: blood pressure; BT: body temperature; HR: heart rate; RR: respiratory rate.
Figure 4
Figure 4
The Shapley Additive exPlanations (SHAP) algorithm results for long-term sepsis risk in model 3. (A) The influence of higher and lower values of the feature on the patient's outcome. The left side of this graph represents reduced risk of developing sepsis, and the right side of the graph represents increased risk of developing sepsis. Red dots represent higher values of the feature, and blue dots represent lower values of the feature. Nominal classes are binary (0,1). (B) The ranking of feature importance indicated by SHAP. BP: blood pressure; BT: body temperature; COPD: chronic obstructive pulmonary disease; HR: heart rate; RR: respiratory rate.
Figure 5
Figure 5
The Shapley Additive exPlanations (SHAP) algorithm results for long-term sepsis risk in model 4. (A) The influence of higher and lower values of the feature on the patient's outcome. The left side of this graph represents reduced risk of developing sepsis, and the right side of the graph represents increased risk of developing sepsis. Red dots represent higher values of the feature, and blue dots represent lower values of the feature. Nominal classes are binary (0,1). (B) The ranking of feature importance indicated by SHAP. BP: blood pressure; BT: body temperature; COPD: chronic obstructive pulmonary disease; HR: heart rate; LOS: length of hospital stay; RR: respiratory rate.
Figure 6
Figure 6
The L1-regularized logistic regression (L1-LR) algorithm results (A) and permutation testing results for long-term sepsis risk in model 4 (B). BP: blood pressure; BT: body temperature; COPD: chronic obstructive pulmonary disease; HR: heart rate; LOS: length of hospital stay; RR: respiratory rate.
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