Clinical factors associated with rapid treatment of sepsis

Abstract

Purpose: To understand what clinical presenting features of sepsis patients are historically associated with rapid treatment involving antibiotics and fluids, as appropriate.

Design: This was a retrospective, observational cohort study using a machine-learning model with an embedded feature selection mechanism (gradient boosting machine).

Methods: For adult patients (age ≥ 18 years) who were admitted through Emergency Department (ED) meeting clinical criteria of severe sepsis from 11/2007 to 05/2018 at an urban tertiary academic medical center, we developed gradient boosting models (GBMs) using a total of 760 original and derived variables, including demographic variables, laboratory values, vital signs, infection diagnosis present on admission, and historical comorbidities. We identified the most impactful factors having strong association with rapid treatment, and further applied the Shapley Additive exPlanation (SHAP) values to examine the marginal effects for each factor.

Results: For the subgroups with or without fluid bolus treatment component, the models achieved high accuracy of area-under-receiver-operating-curve of 0.91 [95% CI, 0.86-0.95] and 0.84 [95% CI, 0.81-0.86], and sensitivity of 0.81[95% CI, 0.72-0.87] and 0.91 [95% CI, 0.81-0.97], respectively. We identified the 20 most impactful factors associated with rapid treatment for each subgroup. In the non-hypotensive subgroup, initial physiological values were the most impactful to the model, while in the fluid bolus subgroup, value minima and maxima tended to be the most impactful.

Conclusion: These machine learning methods identified factors associated with rapid treatment of severe sepsis patients from a large volume of high-dimensional clinical data. The results provide insight into differences in the rapid provision of treatment among patients with sepsis.

Fig 1. Consort diagram for cohort inclusion and exclusion.

Fig 2. Prediction performance metrics.

Fig 3. Model performance comparisons over calendar years.

Fig 4. Variable importance plot for Model 1 and Model 2.

The importance score of each variable has been scaled to a maximum value of 100. The colors indicate marginal associations of variables with rapid treatment, which are abstractions calculated by comparing SHAP values at 25th, 50th and 75th percentiles of the variable values.

Fig 5

Marginal effects of variables ranked top 12 for Model 1 (Panel A) and Model 2 (Panel B) based on SHAP values, i.e. exponential of the SHAP value. Each dot represents an average change of odds ratio for a variable, taking certain values within a bootstrapped sample. Each colored vertical line depicts a 95% bootstrap confidence interval based on 100 bootstrapped samples. A brown line suggests an odds ratio change significantly higher than 1.0; a blue line suggests an odds ratio change significantly lower than 1.0; a yellow line suggests an odds ratio not significantly different from 1.0. Orange dots represent the odds ratio effect of not having the particular data point recorded for the model. The dashed horizontal line shows an odds ratio of 1.

Fig 6. The correlation heatmap among different abstractions of the same clinical variable with repeated measurement.

Note that the “Initial” values are not always very different from the other types of summaries.