BACKGROUND CONTEXT

In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning.

PURPOSE

The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA.

STUDY DESIGN/SETTING: Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016.

PATIENTS SAMPLE

Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA.

OUTCOME MEASURES

In-hospital and 90-day postdischarge mortality.

METHODS

Five machine learning algorithms (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed and assessed by discrimination, calibration, overall performance, and decision curve analysis.

RESULTS

Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count, neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/.

CONCLUSIONS

Machine learning algorithms show promise on internal validation for prediction of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms in independent populations.