A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

Haranhalli, Neil; Javed, Kainaat; Boyke, Andre E.; Dardick, Joseph; Naidu, Ishan; Ryvlin, Jessica; Kadaba, Devikarani; Fluss, Rose; Derby, Carol A.; Altschul, David

doi:10.1016/j.jstrokecerebrovasdis.2021.106054

Cited by 7 publications

(8 citation statements)

References 24 publications

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“…These encompass patient demographics, clinical characteristics, and treatment modalities, as well as the exploration of machine learning models to predict prognosis. Age, gender, and pre-stroke health status, such as the pre-admission modified Rankin Score (mRS), were frequently noted as significant factors in the prognosis of patients post-thrombectomy ( 6 ). Similarly, lifestyle habits, like smoking, were found to influence outcomes, with non-smokers more likely to have a favorable recovery ( 7 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Abujaber,

Alkhawaldeh,

Imam

et al. 2023

Front. Neurol.

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BackgroundStroke is a significant global health burden and ranks as the second leading cause of death worldwide.ObjectiveThis study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score.MethodsThe study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study’s inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction.ResultsThe RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors.ConclusionThe RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Abujaber,

Alkhawaldeh,

Imam

et al. 2023

Front. Neurol.

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“…Several other prognostic models for patients treated with EVT also combined preprocedural and postprocedural characteristics . However, some models include rather homogenous patient populations limiting the generalizability of their findings; other models have methodologic shortcomings in model development, such as a small sample size for the amount of tested variables, dichotomization of variables, or no internal validation.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Postprocedural Model to Predict Outcome After Endovascular Treatment for Ischemic Stroke

et al. 2023

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ImportanceOutcome prediction after endovascular treatment (EVT) for ischemic stroke is important to patients, family members, and physicians.ObjectiveTo develop and validate a model based on preprocedural and postprocedural characteristics to predict functional outcome for individual patients after EVT.Design, Setting, and ParticipantsA prediction model was developed using individual patient data from 7 randomized clinical trials, performed between December 2010 and December 2014. The model was developed within the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration and external validation in data from the Dutch Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry of patients treated in clinical practice between March 2014 and November 2017. Participants included patients from multiple centers throughout different countries in Europe, North America, East Asia, and Oceania (derivation cohort), and multiple centers in the Netherlands (validation cohort). Included were adult patients with a history of ischemic stroke from an intracranial large vessel occlusion in the anterior circulation who underwent EVT within 12 hours of symptom onset or last seen well. Data were last analyzed in July 2022.Main Outcome(s) and Measure(s)A total of 19 variables were assessed by multivariable ordinal regression to predict functional outcome (modified Rankin Scale [mRS] score) 90 days after EVT. Variables were routinely available 1 day after EVT. Akaike information criterion (AIC) was used to optimize model fit vs model complexity. Probabilities for functional independence (mRS 0-2) and survival (mRS 0-5) were derived from the ordinal model. Model performance was expressed with discrimination (C statistic) and calibration.ResultsA total of 781 patients (median [IQR] age, 67 [57-76] years; 414 men [53%]) constituted the derivation cohort, and 3260 patients (median [IQR] age, 72 [61-80] years; 1684 men [52%]) composed the validation cohort. Nine variables were included in the model: age, baseline National Institutes of Health Stroke Scale (NIHSS) score, prestroke mRS score, history of diabetes, occlusion location, collateral score, reperfusion grade, NIHSS score at 24 hours, and symptomatic intracranial hemorrhage 24 hours after EVT. External validation in the MR CLEAN Registry showed excellent discriminative ability for functional independence (C statistic, 0.91; 95% CI, 0.90-0.92) and survival (0.89; 95% CI, 0.88-0.90). The proportion of functional independence in the MR CLEAN Registry was systematically higher than predicted by the model (41% vs 34%), whereas observed and predicted survival were similar (72% vs 75%). The model was updated and implemented for clinical use.Conclusion and relevanceThe prognostic tool MR PREDICTS@24H can be applied 1 day after EVT to accurately predict functional outcome for individual patients at 90 days and to provide reliable outcome expectations and personalize follow-up and rehabilitation plans. It will need further validation and updating for contemporary patients.

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“…These variables encompass patient demographics, clinical attributes, treatment approaches, and the application of machine learning models for prognostic purposes, including short and long-term mortality [ 21 ]. Importantly, elements like age, sex, and the health status before the stroke event, often assessed using the pre-admission modified Rankin Score (mRS), have been recognized as pivotal influencers on patients' prognosis following thrombectomy [ 22 ]. Furthermore, lifestyle behaviors, such as smoking, have been identified as having an impact on stroke outcomes, with non-smokers exhibiting a greater likelihood of achieving a favorable recovery [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Smaller infarct expansion and favorable initial perfusion have been associated with better patient results [ 24 ]. Similarly, post-thrombectomy, the National Institutes of Health Stroke Scale (NIHSS) scores and the necessity for decompressive hemicraniectomy have emerged as crucial predictors of mortality [ 22 , 25 ]. Furthermore, certain studies have identified the body mass index's (BMI) relevance in predicting mortality among stroke patients [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Hospital-acquired infections, such as urinary tract infections and pneumonia, have been noted as significant factors in forecasting stroke prognosis, and mortality [ 12 , 27 ]. In addition to clinical findings and stroke severity upon presentation, patients' comorbid conditions, such as diabetes mellitus (DM), hypertension (HTN), dyslipidemia, prior stroke, coronary artery disease (CAD), history of atrial fibrillation (AF), or congestive heart failure (CHF), have demonstrated substantial predictive roles in shaping stroke patients' prognosis, including disabilities and mortality [ 21 , 22 , 25 , 28 ]. Further factors, such as decrease in the duration from stroke onset to hospital arrival, prompt intervention, especially thrombolysis for IS, increases the chances of a favorable prognosis [ [29] , [30] , [31] ].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based prognostication of mortality in stroke patients

Abujaber,

Albalkhi,

Imam

et al. 2024

Heliyon

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A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

Cited by 7 publications

References 24 publications

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Development and Validation of a Postprocedural Model to Predict Outcome After Endovascular Treatment for Ischemic Stroke

Machine learning-based prognostication of mortality in stroke patients

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