عنوان انگلیسی مقاله:
Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
ترجمه فارسی عنوان مقاله:
توسعه الگوریتم های یادگیری ماشین برای پیش بینی مرگ و میر در آبسه اپیدورال ستون فقرات
Sciencedirect - Elsevier - The Spine Journal, Corrected proof: doi:10:1016/j:spinee:2019:06:024
Aditya V. Karhade, BEa, Akash A. Shah, MDb, Christopher M. Bono, MDa, Marco L. Ferrone, MDc, Sandra B. Nelson, MDd, Andrew J. Schoenfeld, MD, MScc, Mitchel B. Harris, MDa, Joseph H. Schwab, MD, MS
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 inindependent populations.
Keywords: Artificial intelligence | Healthcare | Machine learning | Mortality | Spinal epidural abscess | Spine surgery