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

Author:

Ospel Johanna M.123,Ganesh Aravind3,Kappelhof Manon4,McDonough Rosalie3,Menon Bijoy K.3,Almekhlafi Mohammed3,Demchuk Andrew M.3,McTaggart Ryan A.5,Field Thalia S.6,Dowlatshahi Dar7,Nogueira Raul G.8,Tarpley Jason W.9,Puetz Volker10,Nagel Simon11,Tymianski Michael12,Hill Michael D.1,Goyal Mayank12ORCID,

Affiliation:

1. Department of Clinical Neurosciences University of Calgary Calgary Alberta Canada

2. Department of Radiology University of Calgary Calgary Alberta Canada

3. Department of Radiology Amsterdam University Medical Center Amsterdam The Netherlands

4. Department of Neuroradiology University Hospital Basel Basel Switzerland

5. Department of Interventional Radiology Warren Alpert Medical School of Brown University Providence RI

6. Department of Neurology University of British Columbia Vancouver Canada

7. Ottawa Hospital University of Ottawa Ottawa Canada

8. Department of Neurology Emory University School of Medicine Atlanta GA

9. Providence Saint John's Health Center and The Pacific Neuroscience Institute Providence Little Company of Mary Medical Center Torrance CA

10. University Hospital Carl Gustav Carus at the Technische Universität Dresden Dresden Neurovascular Center Dresden Germany

11. University Hospital Heidelberg Heidelberg Germany

12. NoNO Inc. Toronto Canada

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 Endovascular Thrombectomy for Stroke) trial were used to build 4 outcome prediction models using multivariable logistic regression: model 1 included baseline variables available before treatment decision making, model 2 included additional treatment‐related variables, model 3 additional posttreatment variables that become available early (within 24–48 hours), and model 4 later (beyond 48 hours) after endovascular therapy. The primary outcome was functional independence (90‐day Modified Rankin Scale score 0–2). Model performance was compared using the area under the receiver operating characteristic curve (AUC). Shapley values were used to determine marginal contributions of variables to outcome variance in the regression models. Results Among 1105 patients, functional independence was achieved by 666 (60.3%). When using baseline variables only (model 1), the AUC was 0.74 (95% CI, 0.71–0.77); this iteratively improved when treatment and posttreatment variables were added to the models (model 2: AUC, 0.77; 95% CI, 0.74–0.80; model 3: AUC, 0.80; 95% CI, 0.77–0.83; model 4: AUC, 0.82; 95% CI, 0.79–0.85). With baseline variables alone, 26% of patients who achieved functional independence were erroneously classified as not achieving functional independence. Even with the most comprehensive model, 19.8% of patients were misclassified as such. Patient age contributed most to outcome variance (Shapley value, 0.28), followed by severe adverse events including pneumonia (0.16) and intracranial hemorrhage at 24‐hours imaging (0.13). Conclusions A substantial contribution to outcomes after endovascular therapy comes from factors unrelated to currently collected baseline patient variables. One‐fifth of patients achieving functional independence were misclassified as not achieving independence, even with the most comprehensive model. Our findings suggest that the achievable accuracy of current outcome prediction models is limited, and caution should be used when applying them in clinical practice.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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