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INTRODUCTION: Intracerebral hemorrhage (ICH) is attributable to cerebral small vessel disease (cSVD), which includes cerebral amyloid angiopathy (CAA) and hypertensive-cSVD (HTN-cSVD). HTN-cSVD includes patients with strictly deep ICH/microbleeds and mixed location ICH/microbleeds, the latter representing a more severe form of HTN-cSVD. We test the hypothesis that more severe forms of HTN-cSVD are related to worse hypertension control in long-term follow-up after ICH. METHODS: From consecutive non-traumatic ICH patients admitted to a tertiary care center, we classified the ICH as CAA, strictly deep ICH/microbleeds, and mixed-location ICH/microbleeds. CSVD burden was quantified using a validated MRI-based score (range: 0-6 points). We created a multivariable (linear mixed effects) model adjusting for age, sex, race, year of inclusion, hypertension, and antihypertensive medication usage to investigate the association of average systolic blood pressure (SBP) during follow-up with cSVD etiology/severity. RESULTS: 796 ICH survivors were followed for a median of 48.8 months (IQR 41.5-60.4). CAA-related ICH survivors (n = 373) displayed a lower median SBP (138 mmHg, IQR 133-142 mmHg) compared to those of strictly deep ICH (n = 222, 141 mmHg, IQR 136-143 mmHg, p = 0.04), and mixed location ICH/microbleeds (n = 201, 142 mmHg, IQR 135-144 mmHg, p = 0.02). In the multivariable analysis, mixed location ICH/microbleeds (effect: + 3.8 mmHg, SE: 1.3 mmHg, p = 0.01) and increasing cSVD severity (+ 1.8 mmHg per score point, SE: 0.8 mmHg, p = 0.03) were associated with higher SBP in follow-up. CONCLUSION: CSVD severity and subtype predicts long-term hypertension control in ICH patients.
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BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS: We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS: Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION: We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.