Abstract WP94: Prediction Of Stroke Thrombectomy Outcomes From The Neurovascular Quality Initiative-quality Outcomes Database (NVQI-QOD) Registry Using Probabilistic Learning Models
| Autor: | Chaochao Zhou, Syed Hasib Akhter Faruqui, Abhinav Patel, Ramez N Abdalla, Ali Shaibani, Matthew B Potts, Babak S Jahromi, Sameer A Ansari, Donald R Cantrell |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Stroke. 54 |
| ISSN: | 1524-4628 0039-2499 |
| DOI: | 10.1161/str.54.suppl_1.wp94 |
| Popis: | Introduction: Mechanical Thrombectomy (MT) is standard-of-care interventional management of Acute Ischemic Stroke (AIS) due to large vessel occlusions. However, patient outcomes remain variable after intervention, with unclear optimization strategies for patient selection. The NVQI-QOD registry documents detailed patient characteristics, pre-operative imaging, procedure metrics, and post-operative outcomes of neurointerventional procedures. Data are highly informative, but there is inherent uncertainty in all medical interventions. We introduce a probabilistic learning model that predicts the expected distribution of MT outcomes. Methods: We identified two groups of variables from the NVQI-QOD AIS Thrombectomy registry: 1) data available at the time of MT (Group-preop), and 2) data available 24 h after MT, which can be valuable for prognostication (Group-24h). After filtering missing values, there were 1174 and 1405 examples in the two groups, respectively. In each group, training and test datasets were split using a ratio of 8:2. A probabilistic Neural Network (NN) ( Fig. 1a ) was developed to predict the distribution of changes in pre- and post-MT NIH Stroke Scale (NIHSS) ( y ), and it was trained using both groups of variables as inputs. Results: After training, the probabilistic network accurately described the distributions of changes in the NIHSS (represented by predicted means and SDs) based on input variables ( Fig. 1b - upper ). Notably, in Group-preop, even patients with the worst predicted outcomes had an approximately 50% chance of improvement. Fig. 1b - lower demonstrates the relative importance of variables to the NN. Conclusions: This NN model demonstrates the utility of probabilistic learning in clinical decision-making and prognosis. Our results reinforce the substantial benefits of MT, that can still improve outcomes in nearly half of patients with the worst predicted change in NIHSS on pre-operative analysis. |
| Databáze: | OpenAIRE |
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