Development of machine learning algorithms for prediction of mortality in spinal epidural abscess.
Artificial intelligence
Healthcare
Machine learning
Mortality
Spinal epidural abscess
Spine surgery
Journal
The spine journal : official journal of the North American Spine Society
ISSN: 1878-1632
Titre abrégé: Spine J
Pays: United States
ID NLM: 101130732
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
30
03
2019
revised:
23
06
2019
accepted:
26
06
2019
pubmed:
1
7
2019
medline:
22
5
2020
entrez:
1
7
2019
Statut:
ppublish
Résumé
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. The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA. Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016. Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA. In-hospital and 90-day postdischarge mortality. 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. 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/. 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.
Sections du résumé
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.
Identifiants
pubmed: 31255788
pii: S1529-9430(19)30841-1
doi: 10.1016/j.spinee.2019.06.024
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1950-1959Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.