A novel toolkit for the prediction of clinical outcomes following mechanical thrombectomy. Issue 10 (October 2020)

• Record Type: Journal Article
• Authors: Raseta, M.; Bazarova, A.; Wright, H.; Parrott, A.; Nayak, S.
• Is Part Of: Clinical radiology. Volume 75:Issue 10(2020)
• Page Start: 795.e15
• Page End: 795.e21
• Publication Date: 2020-10
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2020.06.026
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : Aim: To develop a robust toolkit to aid decision-making for mechanical thrombectomy (MT) based on readily available patient variables that could accurately predict functional outcome following MT. Materials and methods: Data from patients with anterior circulation stroke who underwent MT between October 2009 and January 2018 ( n =239) were identified from our MT database. Patient explanatory variables were age, sex, National Institutes of Health Stroke Scale (NIHSS), Alberta Stroke Program Early CT Score (ASPECTS), collateral score, and Glasgow Coma Scale. Five models were developed from the data to predict five outcomes of interest: model 1: prediction of survival: modified Rankin Scale (mRS) of 0–5 (alive) or 6 (dead); model 2: prediction of good/poor outcome: mRS of 0–3 (good), or 4–6 (poor); model 3: prediction of good/poor outcome: mRS of 0–2 (good), or 3–6 (poor); model 4: prediction of mRS category: mRS of 0–2 (no disability), 3 (minor disability), 4–5 (severe disability) or 6 (dead); model 5: prediction of the exact mRs score (mRs as a continuous variable). The accuracy and discriminative power of each predictive model were tested. Results: Prediction of survival was 87% accurate (area under the curve [AUC] 0.89). Prediction of good/poor outcome was 91% accurate (AUC 0.94) for Model 2 and 95% accurate (AUC 0.98) for Model 3. Prediction of mRS category was 76% accurate, and increased to 98% using the "one-score-out rule". Prediction of the exact mRS value was accurate to an error of 0.89. Conclusions: This novel toolkit provided accurate estimations of outcome for MT. Highlights: Developed an easy-to use tool to predict outcome following mechanical thrombectomy. Easily-obtained, pre-procedural variables accurately predicted functional outcomes. Predicted survival with 87% accuracy, a good/poor outcome with 91% or 95% accuracy. Predicted mRS category with 76% accuracy which increased to 98% using the 'one-score-out rule'. Based on our model we now plan to develop an online predictor tool.