Background: 90-day home-time, the number of days a patient is back at their premorbid living situation without an increase in level of care in the first 90 days post stroke, is routinely collected in administrative data. We evaluated the prediction of 90-day home-time using machine learning modelling in patients undergoing endovascular treatment (EVT). Methods: We used the QuICR provincial stroke registry and administrative data from Southern Alberta from Jan 2015-Dec 2019 to identify patients who underwent EVT. Imaging data were scored by 2-physician consensus. The primary outcome was 90 day home-time, which has a highly non-normal distribution with excess zero’s. We modelled using generalized boosting machine model with Gaussian distribution. Contribution of different covariates to hometime was determined using partial dependence plots. Results: We identified 659 EVT patients from Jan 2015 to Dec 2019 treated in Calgary, Alberta. Overall,median predicted 90d home-time was 41days (IQR 5.5 to 77.8) with good model accuracy (Root mean square error 6.96). Holding other covariates constant, factors predicting lower 90d-hometime were diabetes mellitus(-14.1d), hypertension (-7d), low baseline ASPECTS (≤5) (-5.6d) and symptomatic intracerebral hemorrhage (sICH) on follow up scan (-13.8d). There was a consistent improvement in the predicted home-time over the last 5 years. There was no meaningful difference in predicted 90d-home-time by age, baseline NIHSS, sex, atrial fibrillation, occlusion site, tandem occlusion, thrombolysis, or successful reperfusion (Figure 1) Conclusions: Predicting 90-d hometime using boosting machine learning modelling is useful to assess complex relationships between predictors and home-time. Hypertension, diabetes, low ASPECTS and sICH were predictors of lower 90-d home-time in this registry.