2021
DOI: 10.1161/svin.121.000167
|View full text |Cite
|
Sign up to set email alerts
|

Evaluating Outcome Prediction Models in Endovascular Stroke Treatment Using Baseline, Treatment, and Posttreatment Variables

Abstract: Background Statistical models to predict outcomes after endovascular therapy for acute ischemic stroke often incorporate baseline (pretreatment) variables only. We assessed the performance of stroke outcome prediction models for endovascular therapy in stroke in an iterative fashion using baseline, treatment‐related, and posttreatment variables. Methods Data from the ESCAPE‐NA1 (Safety and Efficacy of Nerinetide [NA‐1] in Subjects Undergoing Endovascula… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 81 publications
(113 reference statements)
0
2
0
Order By: Relevance
“…However, a recent study found that a large part of the variance in outcome after MT is explained by variables that are only known after the treatment decision has been made challenging the possibility to predict FRT in the emergency setting before knowing the outcome of the intervention -at least for MT patients. 48 Regarding the model performance, previous studies have shown that for most tabulated datasets, the differences in performance between different analytical machine learning methods and conventional logistic regression are minimal or non-existent. 21,29,37 Strenghts of this analysis include its large sample size with good quality data of predictors easily obtained in an emergency setting.…”
Section: Discussionmentioning
confidence: 99%
“…However, a recent study found that a large part of the variance in outcome after MT is explained by variables that are only known after the treatment decision has been made challenging the possibility to predict FRT in the emergency setting before knowing the outcome of the intervention -at least for MT patients. 48 Regarding the model performance, previous studies have shown that for most tabulated datasets, the differences in performance between different analytical machine learning methods and conventional logistic regression are minimal or non-existent. 21,29,37 Strenghts of this analysis include its large sample size with good quality data of predictors easily obtained in an emergency setting.…”
Section: Discussionmentioning
confidence: 99%
“…3 However, even when including the most comprehensive model (i.e., baseline + procedural + postprocedural variables), still one-fifth of patients were misclassified in post-EVT outcome. 4 Thus, exploring additional influencing factors, assessable at baseline, is crucial to refine prognostication and, at the same time, to inform clinical decision-making.…”
Section: Introductionmentioning
confidence: 99%