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BACKGROUND AND PURPOSE: National quality registries for stroke care operate under the assumption that the included patients are correctly diagnosed. We aimed to validate the clinical diagnosis of spontaneous intracerebral hemorrhage (ICH) in Riksstroke (RS) by evaluating radiological data from a large, unselected ICH population. METHODS: We conducted a retrospective, multicenter study including all ICH patients registered in RS between 2016 and 2020 residing in Skåne County in Sweden (1.41 million inhabitants). Radiological data from first imaging were evaluated for the presence of spontaneous ICH. Other types of bleeds were registered if a spontaneous ICH was not identified on imaging. The radiological evaluation was independently performed by one radiology fellow and one senior neuroradiologist. RESULTS: Between 2016 and 2020, 1784 ICH cases were registered in RS, of which 1655 (92.8%) had a radiological diagnosis consistent with spontaneous ICH. In the 129 (7.2%) remaining cases, the radiological diagnosis was instead traumatic bleed (n = 80), subarachnoid hemorrhage (n = 15), brain tumor bleed (n = 14), ischemic lesion with hemorrhagic transformation (n = 14), ischemic lesion (n = 3), or no bleed at all (n = 3). There was a higher degree of incorrect coding in the older age groups. CONCLUSION: At radiological evaluation, 92.8% of ICH diagnoses in RS were consistent with spontaneous ICH, yielding a high rate of agreement that strengthens the validity of the diagnostic accuracy in the register, justifying the use of high coverage quality register data for epidemiological purposes. The most common coding error was traumatic bleeds that were classified as spontaneous ICH.
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PURPOSE: Hematoma volume is the strongest predictor of patient outcome after intracerebral hemorrhage (ICH). The aim of this study was to validate novel fully automated software for quantification of ICH volume on non-contrast computed tomography (CT). METHODS: The population was defined from the Swedish Stroke Register (RS) and included all patients with an ICH diagnosis during 2016-2019 in Region Skåne. Hemorrhage volume on their initial head CT was measured using ABC/2 and manual segmentation (Sectra IDS7 volume measurement tool) and the automated volume quantification tool (qER-NCCT) by Qure.ai. The first 500 were examined by two independent readers. RESULTS: A total of 1649 ICH patients were included. The qER-NCCT had 97% sensitivity in identifying ICH. In total, there was excellent agreement between volumetric measurements of ICH volumes by qER-NCCT and manual segmentation by interclass correlation (ICC = 0.96), and good agreement (ICC = 0.86) between qER-NCCT and ABC/2 method. The qER-NCCT showed volume underestimation, mainly in large (> 30 ml) heterogenous hemorrhages. Interrater agreement by (ICC) was 0.996 (95% CI: 0.99-1.00) for manual segmentation. CONCLUSION: Our study showed excellent agreement in volume quantification between the fully automated software qER-NCCT and manual segmentation of ICH on NCCT. The qER-NCCT would be an important additive tool by aiding in early diagnostics and prognostication for patients with ICH and in provide volumetry on a population-wide level. Further refinement of the software should address the underestimation of ICH volume seen in a portion of large, heterogenous, irregularly shaped ICHs.