Opracowanie i walidacja wytłumaczalnego modelu predykcyjnego dla padaczki po udarze mózgu u pacjentów z niedokrwiennym udarze po mechanicznej trombektomii: wieloośrodkowe retrospektywne badanie kohortowe
Development and validation of an explainable prediction model for post-stroke epilepsy in patients with ischaemic stroke following mechanical thrombectomy: a multicentre retrospective cohort study
W skrócie
Naukowcy opracowali model sztucznej inteligencji, który potrafi przewidzieć ryzyko padaczki pojawiającej się po udarze mózgu u pacjentów poddanych zabiegowi usunięcia skrzepliny z naczynia mózgowego. Model został przetestowany na danych z pięciu szpitali i wykazał dużą dokładność w przewidywaniu ryzyka padaczki w okresie 1, 3 i 5 lat. Stworzono również bezpłatny kalkulator online, który lekarze mogą wykorzystać do identyfikacji pacjentów zagrożonych padaczką i dostosowania dla nich odpowiedniego leczenia.
Oryginalny abstract (angielski)
OBJECTIVE: In this retrospective multicentre cohort study, we aimed to develop and validate an interpretable machine learning (ML) model for early post-stroke epilepsy (PSE) prediction in patients who underwent mechanical thrombectomy (MT). We hypothesised that an ML model developed using electronic medical record data can accurately predict the risk of PSE in patients with acute ischaemic stroke treated with MT. METHODS: We collected data from 1185 adult patients who underwent MT between 2017 and 2022 at two hospitals for model development and used data from three external centres for validation. The key variables were selected using Cox regression, least absolute shrinkage and selection operator regression and Boruta. Eight ML algorithms were used for model development. The model performance was evaluated using the concordance index (C-index) and integrated Brier scores. Additionally, the 1-year, 3-year and 5-year PSE risk prediction accuracies were assessed using the areas under the receiver operating characteristic curves (AUCs) and calibration curves. Decision curve analysis was used to evaluate the clinical utility, and the feature importance was quantified. RESULTS: An optimal combination comprising nine features was used for model development. Of the eight ML models, the random survival forest (RSF) model demonstrated optimal performance and maintained robust discriminative accuracy across the three external validation cohorts (C-index: 0.812; AUC: 0.748-0.837; C-index: 0.822; AUC: 0.795-0.839 and C-index: 0.763; AUC: 0.763-0.833). Finally, we developed an online calculator (https://pseshiny.shinyapps.io/shinydashboard_sa_1model/) based on the RSF model. SIGNIFICANCE: This prediction model could help identify patients at high risk of developing PSE following MT and support individualised treatment strategies.