Optimizing Outcome Prediction Scores in Patients Undergoing Endovascular Thrombectomy for Large Vessel Occlusions Using Collateral Grade on Computed Tomography Angiography

Ryu, Chang‐Woo; Kim, Byung Moon; Kim, Hyug Gi; Heo, Ji Hoe; Nam, Hyo Suk; Kim, Dong Joon

doi:10.1093/neuros/nyy316

Cited by 15 publications

(17 citation statements)

References 25 publications

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“…Our focus was routine stroke practice (139) and our comments on feasibility may not apply to specialist stroke centers. It takes time for scales to become established and our review did not include recently published scales, for example those designed to inform thrombectomy decisions (140). However, the literature describing these scales is increasing rapidly and soon there may be sufficient validation studies.…”

Section: Discussionmentioning

confidence: 99%

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

2019

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Background: There are many prognostic scales that aim to predict functional outcome following acute stroke. Despite considerable research interest, these scales have had limited impact in routine clinical practice. This may be due to perceived problems with internal validity (quality of research), as well as external validity (generalizability of results). We set out to collate information on exemplar stroke prognosis scales, giving particular attention to the scale content, derivation, and validation. Methods: We performed a focused literature search, designed to return high profile scales that use baseline clinical data to predict mortality or disability. We described prognostic utility and collated information on the content, development and validation of the tools. We critically appraised chosen scales based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS). Results: We chose 10 primary scales that met our inclusion criteria, six of which had revised/modified versions. Most primary scales used 5 input variables (range: 4–13), with substantial overlap in the variables included. All scales included age, eight included a measure of stroke severity, while five scales incorporated pre-stroke level of function (often using modified Rankin Scale), comorbidities and classification of stroke type. Through our critical appraisal, we found issues relating to excluding patients with missing data from derivation studies, and basing the selection of model variable on significance in univariable analysis (in both cases noted for six studies). We identified separate external validation studies for all primary scales but one, with a total of 60 validation studies. Conclusions: Most acute stroke prognosis scales use similar variables to predict long-term outcomes and most have reasonable prognostic accuracy. While not all published scales followed best practice in development, most have been subsequently validated. Lack of clinical uptake may relate more to practical application of scales rather than validity. Impact studies are now necessary to investigate clinical usefulness of existing scales.

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Section: Discussionmentioning

confidence: 99%

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

2019

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“…The mean or median ages of the study participants ranged from 64.0 to 86.0 years, and the proportion of male participants ranged from 35.0 to 65.9%. Only one US study ( 24 ) specifically described the self-reported ethnicity of the patients (63.0–69.0% European ancestry); the other studies reported the place of patient recruitment [USA: 1 ( 32 ); Europe: 10 ( 22 , 23 , 26 – 29 , 31 , 33 – 35 ); Asia: 4 ( 25 , 30 , 36 , 37 )]. The training sample sizes ranged widely, from 109 to 1,401.…”

Section: Resultsmentioning

confidence: 99%

“…The training sample sizes ranged widely, from 109 to 1,401. Regarding the testing sample, two studies used hold-out test sets, respectively containing 208 patients ( 30 ) and 100 patients ( 35 ). The remaining studies performed cross-validation ( 23 – 26 , 28 , 29 , 31 – 34 , 36 , 37 ) or bootstrap approach ( 22 , 27 ).…”

Section: Resultsmentioning

confidence: 99%

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

et al. 2022

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IntroductionMachine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.MethodsLiterature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.ResultsSixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68).ConclusionsConventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524

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“…While missing statistical significance, there was an 18% difference in independent function for patients presenting initially to non-thrombectomy-capable centers for whom the ATA was visualized versus not visualized. Furthermore, it is possible that as with collateral grading, combining ATA visualization with outcome prediction scores will result in a better prediction of poor outcomes after transfer for EVT compared to the prediction scores alone [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

Visualization of the Anterior Temporal Artery as a Predictor of Outcome in Middle Cerebral Artery Occlusion Patients Achieving Successful Recanalization After Transfer

Parish¹,

Rhoten²,

Strong³

et al. 2022

Cureus

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Optimizing Outcome Prediction Scores in Patients Undergoing Endovascular Thrombectomy for Large Vessel Occlusions Using Collateral Grade on Computed Tomography Angiography

Abstract: The addition of the collateral grade to outcome prediction scores resulted in better prediction of poor outcome after EVT for AIS compared to the prediction scores alone.

Cited by 15 publications

References 25 publications

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

Visualization of the Anterior Temporal Artery as a Predictor of Outcome in Middle Cerebral Artery Occlusion Patients Achieving Successful Recanalization After Transfer

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