Using Noncontrast Computed Tomography to Improve Prediction of Intracerebral Hemorrhage Expansion. Issue 2 (9th January 2023)

• Record Type: Journal Article
• Authors: Morotti, Andrea; Boulouis, Gregoire; Nawabi, Jawed; Li, Qi; Charidimou, Andreas; Pasi, Marco; Schlunk, Frieder; Shoamanesh, Ashkan; Katsanos, Aristeidis H.; Mazzacane, Federico; Busto, Giorgio; Arba, Francesco; Brancaleoni, Laura; Giacomozzi, Sebastiano; Simonetti, Luigi; Warren, Andrew D.; Laudisi, Michele; Cavallini, Anna; Gurol, Edip M.; Viswanathan, Anand; Zini, Andrea; Casetta, Ilaria; Fainardi, Enrico; Greenberg, Steven M.; Padovani, Alessandro; Rosand, Jonathan; Goldstein, Joshua N.
• Is Part Of: Stroke. Volume 54:Issue 2(2023)
• Page Start: 567
• Page End: 574
• Publication Date: 2023-01-09
• Subjects: computed tomography -- hematoma expansion -- intracerebral hemorrhage -- ROC curve -- stroke; Cerebrovascular disease -- Periodicals; Cerebral circulation -- Periodicals; 616.81
• DOI: 10.1161/STROKEAHA.122.041302
• Languages: English
• ISSNs: 0039-2499

Abstract

Abstract : Background: Noncontrast computed tomography hypodensities are a validated predictor of hematoma expansion (HE) in intracerebral hemorrhage and a possible alternative to the computed tomography angiography (CTA) spot sign but their added value to available prediction models remains unclear. We investigated whether the inclusion of hypodensities improves prediction of HE and compared their added value over the spot sign. Methods: Retrospective analysis of patients admitted for primary spontaneous intracerebral hemorrhage at the following 8 university hospitals in Boston, US (1994–2015, prospective), Hamilton, Canada (2010–2016, retrospective), Berlin, Germany (2014–2019, retrospective), Chongqing, China (2011–2015, retrospective), Pavia, Italy (2017–2019, prospective), Ferrara, Italy (2010–2019, retrospective), Brescia, Italy (2020–2021, retrospective), and Bologna, Italy (2015–2019, retrospective). Predictors of HE (hematoma growth >6 mL and/or >33% from baseline to follow-up imaging) were explored with logistic regression. We compared the discrimination of a simple prediction model for HE based on 4 predictors (antitplatelet and anticoagulant treatment, baseline intracerebral hemorrhage volume, and onset-to-imaging time) before and after the inclusion of noncontrast computed tomography hypodensities, using receiver operating characteristic curve and De Long test for area under the curve comparison. Results: A total of 2465 subjects were included, of whom 664 (26.9%) had HE and 1085 (44.0%) had hypodensities. Hypodensities were independently associated with HE after adjustment for confounders in logistic regression (odds ratio, 3.11 [95% CI, 2.55–3.80]; P <0.001). The inclusion of noncontrast computed tomography hypodensities improved the discrimination of the 4 predictors model (area under the curve, 0.67 [95% CI, 0.64–0.69] versus 0.71 [95% CI, 0.69–0.74]; P =0.025). In the subgroup of patients with a CTA available (n=895, 36.3%), the added value of hypodensities remained statistically significant (area under the curve, 0.68 [95% CI, 0.64–0.73] versus 0.74 [95% CI, 0.70–0.78]; P =0.041) whereas the addition of the CTA spot sign did not provide significant discrimination improvement (area under the curve, 0.74 [95% CI, 0.70–0.78]). Conclusions: Noncontrast computed tomography hypodensities provided a significant added value in the prediction of HE and appear a valuable alternative to the CTA spot sign. Our findings might inform future studies and suggest the possibility to stratify the risk of HE with good discrimination without CTA.