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Li Y, Du C, Ge S, Zhang R, Shao Y, Chen K, Li Z, Ma F. Hematoma expansion prediction based on SMOTE and XGBoost algorithm. BMC Med Inform Decis Mak 2024; 24:172. [PMID: 38898499 PMCID: PMC11186182 DOI: 10.1186/s12911-024-02561-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. Correct prediction of the occurrence of HE in advance is critical to help the doctors to determine the next step medical treatment. Most existing studies focus only on the occurrence of HE within 6 h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6 h but within 24 h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24 h, as well as the occurrence of HE every 6 h within 24 h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24 h. In this study, to solve the issue of highly imbalanced data set, which is a frequent case in medical data analysis, we used the SMOTE algorithm for data augmentation. To evaluate our method, we used a data set consisting of 582 patients records, and compared the results of proposed method as well as few machine learning methods. Our experiments show that XGBoost achieved the best prediction performance on the balanced dataset processed by the SMOTE algorithm with an accuracy of 0.82 and F1-score of 0.82. Moreover, our proposed method predicts the occurrence of HE within 6, 12, 18 and 24 h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24 h can be predicted accurately by the proposed method.
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Affiliation(s)
- Yan Li
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Chaonan Du
- Department of Neurosurgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Sikai Ge
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Ruonan Zhang
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Yiming Shao
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Keyu Chen
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Zhepeng Li
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Fei Ma
- Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
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Pezzini D, Nawabi J, Schlunk F, Li Q, Mazzacane F, Busto G, Scola E, Arba F, Brancaleoni L, Giacomozzi S, Simonetti L, Laudisi M, Cavallini A, Katsanos AH, Shoamanesh A, Zini A, Casetta I, Fainardi E, Morotti A, Padovani A. Predictors and Prognostic Impact of Hematoma Expansion in Infratentorial Cerebral Hemorrhage. Neurocrit Care 2024; 40:707-714. [PMID: 37667076 DOI: 10.1007/s12028-023-01819-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/24/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Hematoma expansion (HE) is common and predicts poor outcome in patients with supratentorial intracerebral hemorrhage (ICH). We investigated the predictors and prognostic impact of HE in infratentorial ICH. METHODS We conducted a retrospective analysis of patients with brainstem and cerebellar ICH admitted at seven sites. Noncontrast computed tomography images were analyzed for the presence of hypodensities according to validated criteria, defined as any hypodense region strictly encapsulated within the hemorrhage with any shape, size, and density. Occurrence of HE (defined as > 33% and/or > 6-mL growth) and mortality at 90 days were the outcomes of interest. Their predictors were investigated using logistic regression with backward elimination at p < 0.1. Logistic regression models for HE were adjusted for baseline ICH volume, antiplatelet and anticoagulant treatment, onset to computed tomography time, and presence of hypodensities. The logistic regression model for mortality accounted for the ICH score and HE. RESULTS A total of 175 patients were included (median age 75 years, 40.0% male), of whom 38 (21.7%) had HE and 43 (24.6%) died within 90 days. Study participants with HE had a higher frequency of hypodensities (44.7 vs. 24.1%, p = 0.013), presentation within 3 h from onset (39.5 vs. 24.8%, p = 0.029), and 90-day mortality (44.7 vs. 19.0%, p = 0.001). Hypodensities remained independently associated with HE after adjustment for confounders (odds ratio 2.44, 95% confidence interval 1.13-5.25, p = 0.023). The association between HE and mortality remained significant in logistic regression (odds ratio 3.68, 95% confidence interval 1.65-8.23, p = 0.001). CONCLUSION Early presentation and presence of noncontrast computed tomography hypodensities were independent predictors of HE in infratentorial ICH, and the occurrence of HE had an independent prognostic impact in this population.
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Affiliation(s)
- Debora Pezzini
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy.
| | - Jawed Nawabi
- Department of Radiology (CCM), Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin Institute of Health, Humboldt-Universitätzu Berlin, FreieUniversität Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
| | - Frieder Schlunk
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
- Department of Neuroradiology, Charité-Universitätsmedizin Berlin, FreieUniversität Berlin, Humboldt-Universitätz Berlin, Berlin, Germany
| | - Qi Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Federico Mazzacane
- U.C. Malattie Cerebrovascolari e Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italy
| | - Giorgio Busto
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Elisa Scola
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Francesco Arba
- Stroke Unit, Careggi University Hospital, Florence, Italy
| | - Laura Brancaleoni
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Neurologia e Rete Stroke Metropolitana, Ospedale Maggiore, Bologna, Italy
| | - Sebastiano Giacomozzi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Neurologia e Rete Stroke Metropolitana, Ospedale Maggiore, Bologna, Italy
| | - Luigi Simonetti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UO (SSI) di Neuroradiologia, Ospedale Maggiore, Bologna, Italy
| | - Michele Laudisi
- Clinica Neurologica, Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Università degli Studi di Ferrara, Ospedale Universitario S. Anna, Ferrara, Italy
| | - Anna Cavallini
- U.C. Malattie Cerebrovascolari e Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italy
| | - Aristeidis H Katsanos
- Division of Neurology, McMaster University/Population Health Research Institute, Hamilton, ON, Canada
- Second Department of Neurology, Attikon Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Ashkan Shoamanesh
- Division of Neurology, McMaster University/Population Health Research Institute, Hamilton, ON, Canada
| | - Andrea Zini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Neurologia e Rete Stroke Metropolitana, Ospedale Maggiore, Bologna, Italy
| | - Ilaria Casetta
- Clinica Neurologica, Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Università degli Studi di Ferrara, Ospedale Universitario S. Anna, Ferrara, Italy
| | - Enrico Fainardi
- Neuroradiology Unit, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Andrea Morotti
- Neurology Unit, Department of Neurological Sciences and Vision, ASST Spedali Civili, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
- Neurology Unit, Department of Neurological Sciences and Vision, ASST Spedali Civili, Brescia, Italy
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3
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Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A, Petersen NH, de Havenon A, Falcone GJ, Sheth KN, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digit Med 2024; 7:26. [PMID: 38321131 PMCID: PMC10847454 DOI: 10.1038/s41746-024-01007-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Abstract
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.
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Grants
- K23 NS110980 NINDS NIH HHS
- U24 NS107136 NINDS NIH HHS
- UL1 TR001863 NCATS NIH HHS
- K76 AG059992 NIA NIH HHS
- P30 AG021342 NIA NIH HHS
- R03 NS112859 NINDS NIH HHS
- U24 NS107215 NINDS NIH HHS
- U01 NS106513 NINDS NIH HHS
- 2020097 Doris Duke Charitable Foundation
- K23 NS118056 NINDS NIH HHS
- R01 NR018335 NINR NIH HHS
- Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- Doris Duke Charitable Foundation (DDCF)
- Doris Duke Charitable Foundation (2020097), American Society of Neuroradiology, and National Institutes of Health (K23NS118056).
- National Institutes of Health (K76AG059992, R03NS112859, and P30AG021342), the American Heart Association (18IDDG34280056), the Yale Pepper Scholar Award, and the Neurocritical Care Society Research Fellowship
- National Institutes of Health (U24NS107136, U24NS107215, R01NR018335, and U01NS106513) and the American Heart Association (18TPA34170180 and 17CSA33550004) and a Hyperfine Research Inc research grant.
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Affiliation(s)
- Anh T Tran
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Gaby Abou Karam
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elisa R Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Adnan I Qureshi
- Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Pina C Sanelli
- Department of Radiology, Northwell Health, Manhasset, NY, USA
| | - David J Werring
- Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, UK
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nils H Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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Nawabi J, Schlunk F, Dell'Orco A, Elsayed S, Mazzacane F, Desser D, Vu L, Vogt E, Cao H, Böhmer MFH, Akkurt BH, Sporns PB, Pasi M, Jensen-Kondering U, Broocks G, Penzkofer T, Fiehler J, Padovani A, Hanning U, Morotti A. Non-contrast computed tomography features predict intraventricular hemorrhage growth. Eur Radiol 2023; 33:7807-7817. [PMID: 37212845 PMCID: PMC10598100 DOI: 10.1007/s00330-023-09707-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/09/2023] [Accepted: 03/18/2023] [Indexed: 05/23/2023]
Abstract
OBJECTIVES Non-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth. METHODS Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models. RESULTS A total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16-2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48-2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91-3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion. CONCLUSIONS NCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies. CLINICAL RELEVANCE STATEMENT Non-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies. KEY POINTS • NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences. • The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion. • Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.
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Affiliation(s)
- Jawed Nawabi
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany.
| | - Frieder Schlunk
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
- Department of Neuroradiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Andrea Dell'Orco
- Department of Neuroradiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Sarah Elsayed
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Federico Mazzacane
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- U.C. Malattie Cerebrovascolari E Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italy
| | - Dmitriy Desser
- Department of Neuroradiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Ly Vu
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Estelle Vogt
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Haoyin Cao
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Maik F H Böhmer
- Department of Radiology, University Hospital Muenster, Muenster, Germany
| | - Burak Han Akkurt
- Department of Radiology, University Hospital Muenster, Muenster, Germany
| | - Peter B Sporns
- Department of Neuroradiology, Clinic for Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Marco Pasi
- Department of Neurology, University Hospital of Tours, Tours, France
| | - Ulf Jensen-Kondering
- Department of Neuroradiology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Tobias Penzkofer
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, Neurology Clinic, University of Brescia, Brescia, Italy
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Andrea Morotti
- Neurology Unit, Department of Neurological Sciences and Vision, ASST-Spedali Civili, Brescia, Italy
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Rusche T, Wasserthal J, Breit HC, Fischer U, Guzman R, Fiehler J, Psychogios MN, Sporns PB. Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage. J Clin Med 2023; 12:jcm12072631. [PMID: 37048712 PMCID: PMC10094957 DOI: 10.3390/jcm12072631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.
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Affiliation(s)
- Thilo Rusche
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Correspondence:
| | - Jakob Wasserthal
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Hanns-Christian Breit
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Urs Fischer
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland
| | - Raphael Guzman
- Department of Neurosurgery, University Hospital Basel, 4031 Basel, Switzerland
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Peter B. Sporns
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
- Department of Radiology and Neuroradiology, Stadtspital Zürich, 8063 Zürich, Switzerland
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6
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Morotti A, Boulouis G, Nawabi J, Li Q, Charidimou A, Pasi M, Schlunk F, Shoamanesh A, Katsanos AH, Mazzacane F, Busto G, Arba F, Brancaleoni L, Giacomozzi S, Simonetti L, Warren AD, Laudisi M, Cavallini A, Gurol EM, Viswanathan A, Zini A, Casetta I, Fainardi E, Greenberg SM, Padovani A, Rosand J, Goldstein JN. Using Noncontrast Computed Tomography to Improve Prediction of Intracerebral Hemorrhage Expansion. Stroke 2023; 54:567-574. [PMID: 36621819 PMCID: PMC10037534 DOI: 10.1161/strokeaha.122.041302] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/12/2022] [Indexed: 01/10/2023]
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.
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Affiliation(s)
- Andrea Morotti
- Neurology Unit, Department of Neurological Sciences and Vision, ASST-Spedali Civili, Brescia, Italy
| | - Gregoire Boulouis
- Neuroradiology Department, University Hospital of Tours, CEDEX 09, 37044 Tours, France
| | - Jawed Nawabi
- Department of Radiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
| | - Qi Li
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Anhui, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Andreas Charidimou
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco Pasi
- Neurology department, University Hospital of Tours, CEDEX 09, 37044 Tours, France
| | - Frieder Schlunk
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
- Department of Neuroradiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ashkan Shoamanesh
- Division of Neurology, McMaster University and Population Health Research Institute, Hamilton, ON, Canada
| | - Aristeidis H. Katsanos
- Division of Neurology, McMaster University and Population Health Research Institute, Hamilton, ON, Canada
| | - Federico Mazzacane
- U.C. Malattie Cerebrovascolari e Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italia
| | - Giorgio Busto
- Department of Biomedical Experimental and Clinical, Neuroradiology, University of Firenze, AOU Careggi, Firenze, Italy
| | | | - Laura Brancaleoni
- IRCCS Istituto delle Scienze Neurologiche di Bologna,UOC Neurologia e Rete Stroke Metropolitana,Ospedale Maggiore, Bologna, Italia
| | - Sebastiano Giacomozzi
- IRCCS Istituto delle Scienze Neurologiche di Bologna,UOC Neurologia e Rete Stroke Metropolitana,Ospedale Maggiore, Bologna, Italia
| | - Luigi Simonetti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unità di Neuroradiologia, Ospedale Maggiore, Bologna, Italia
| | - Andrew D. Warren
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michele Laudisi
- Clinica Neurologica, Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Università degli studi di Ferrara, Ospedale Universitario S. Anna,Ferrara, Italia
| | - Anna Cavallini
- U.C. Malattie Cerebrovascolari e Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italia
| | - Edip M Gurol
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anand Viswanathan
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrea Zini
- IRCCS Istituto delle Scienze Neurologiche di Bologna,UOC Neurologia e Rete Stroke Metropolitana,Ospedale Maggiore, Bologna, Italia
| | - Ilaria Casetta
- Clinica Neurologica, Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Università degli studi di Ferrara, Ospedale Universitario S. Anna,Ferrara, Italia
| | - Enrico Fainardi
- Department of Biomedical Experimental and Clinical, Neuroradiology, University of Firenze, AOU Careggi, Firenze, Italy
| | - Steven M. Greenberg
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, Neurology Unit, University of Brescia, Italy
| | - Jonathan Rosand
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Joshua N. Goldstein
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
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7
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Diagnostic Accuracy and Reliability of Noncontrast Computed Tomography Markers for Acute Hematoma Expansion among Radiologists. Tomography 2022; 8:2893-2901. [PMID: 36548534 PMCID: PMC9785236 DOI: 10.3390/tomography8060242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience. METHODS Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen's kappa (κ) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater κ statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters. RESULTS Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen's kappa from 0.70 (95% CI 0.65-0.81) to 0.96 (95% CI 0.94-0.98). The interrater Cohen's kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52-0.65) to 94 (95% CI 92-0.97). The intrarater gold-standard Cohen's kappa was almost perfect and ranged from 0.79 (95% CI 0.78-0.90) to 0.98 (95% CI 0.78-0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57-0.66) to 0.93 (95%CI 0.89-0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p-values < 0.05. CONCLUSION The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction.
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8
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Nehme A, Ducroux C, Panzini MA, Bard C, Bereznyakova O, Boisseau W, Deschaintre Y, Diestro JDB, Guilbert F, Jacquin G, Maallah MT, Nelson K, Padilha IG, Poppe AY, Rioux B, Roy D, Touma L, Weill A, Gioia LC, Létourneau-Guillon L. Non-contrast CT markers of intracerebral hematoma expansion: a reliability study. Eur Radiol 2022; 32:6126-6135. [PMID: 35348859 DOI: 10.1007/s00330-022-08710-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 02/21/2022] [Accepted: 03/01/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES We evaluated whether clinicians agree in the detection of non-contrast CT markers of intracerebral hemorrhage (ICH) expansion. METHODS From our local dataset, we randomly sampled 60 patients diagnosed with spontaneous ICH. Fifteen physicians and trainees (Stroke Neurology, Interventional and Diagnostic Neuroradiology) were trained to identify six density (Barras density, black hole, blend, hypodensity, fluid level, swirl) and three shape (Barras shape, island, satellite) expansion markers, using standardized definitions. Thirteen raters performed a second assessment. Inter- and intra-rater agreement were measured using Gwet's AC1, with a coefficient > 0.60 indicating substantial to almost perfect agreement. RESULTS Almost perfect inter-rater agreement was observed for the swirl (0.85, 95% CI: 0.78-0.90) and fluid level (0.84, 95% CI: 0.76-0.90) markers, while the hypodensity (0.67, 95% CI: 0.56-0.76) and blend (0.62, 95% CI: 0.51-0.71) markers showed substantial agreement. Inter-rater agreement was otherwise moderate, and comparable between density and shape markers. Inter-rater agreement was lower for the three markers that require the rater to identify one specific axial slice (Barras density, Barras shape, island: 0.46, 95% CI: 0.40-0.52 versus others: 0.60, 95% CI: 0.56-0.63). Inter-observer agreement did not differ when stratified for raters' experience, hematoma location, volume, or anticoagulation status. Intra-rater agreement was substantial to almost perfect for all but the black hole marker. CONCLUSION In a large sample of raters with different backgrounds and expertise levels, only four of nine non-contrast CT markers of ICH expansion showed substantial to almost perfect inter-rater agreement. KEY POINTS • In a sample of 15 raters and 60 patients, only four of nine non-contrast CT markers of ICH expansion showed substantial to almost perfect inter-rater agreement (Gwet's AC1> 0.60). • Intra-rater agreement was substantial to almost perfect for eight of nine hematoma expansion markers. • Only the blend, fluid level, and swirl markers achieved substantial to almost perfect agreement across all three measures of reliability (inter-rater agreement, intra-rater agreement, agreement with the results of a reference reading).
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Affiliation(s)
- Ahmad Nehme
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada.
| | - Célina Ducroux
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Marie-Andrée Panzini
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Céline Bard
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Olena Bereznyakova
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Axe Neurosciences, Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Département de Neurosciences, Université de Montréal, Montréal, Québec, Canada
| | - William Boisseau
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Yan Deschaintre
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Axe Neurosciences, Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Département de Neurosciences, Université de Montréal, Montréal, Québec, Canada
| | | | - François Guilbert
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Grégory Jacquin
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Axe Neurosciences, Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Département de Neurosciences, Université de Montréal, Montréal, Québec, Canada
| | - Mohamed Taoubane Maallah
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Kristoff Nelson
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Igor Gomes Padilha
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Alexandre Y Poppe
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Axe Neurosciences, Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Département de Neurosciences, Université de Montréal, Montréal, Québec, Canada
| | - Bastien Rioux
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Daniel Roy
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Lahoud Touma
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Alain Weill
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Laura C Gioia
- Neurovascular Health Program, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Axe Neurosciences, Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Département de Neurosciences, Université de Montréal, Montréal, Québec, Canada
| | - Laurent Létourneau-Guillon
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Imaging and Engineering Axis, Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
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9
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Li Q, Dong F, Wang Q, Xu F, Zhang M. A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features. Eur Radiol 2021; 31:9131-9138. [PMID: 34109487 DOI: 10.1007/s00330-021-08061-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/17/2021] [Accepted: 05/07/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To predict early intracerebral haemorrhage expansion (HE) by comprehensive evaluation of commonly used noncontrast computed tomography (NCCT) features. METHODS Two hundred eighty-eight patients who had a spontaneous intracerebral haemorrhage (ICH) were included. All of the patients had undergone baseline NCCT within 6 h after ICH symptom onset. Ten NCCT features were extracted. Univariate analysis and multivariable logistic regression analysis were used to select the features. Using the finally selected features, a logistic regression model was built with a training cohort (n = 202) and subsequently validated in an independent test cohort (n = 86). Additionally, stratification analysis was performed in cases with and without anticoagulant therapy. RESULTS HE was found in 78 patients (27.1%). The blend sign and black hole sign were finally selected. The logistic regression model built with the two features exhibited accuracies of 76.7% and 75.6%, specificities of 98.6% and 98.4%, and positive predictive values (PPVs) of 83.3% and 75.0% for the training and test cohorts, respectively. The model also showed specificities of 100% and 98.5% and PPVs of 100% and 76.9% for the anticoagulant and non-anticoagulant drug use groups, respectively. These performances were better than those of each of the separate features. CONCLUSIONS By comprehensive evaluation, the model comprising the blend sign and black hole sign showed good performance for predicting early intracerebral haemorrhage expansion, particularly for high specificity and PPV, regardless of the anticoagulant status. KEY POINTS • Early identification of patients who are more likely to have haematoma expansion is important for therapeutic intervention. • Many radiological features have been reported to correlate with intracerebral haemorrhage expansion. • By integrating only the blend sign and black hole sign, the logistic regression model showed good performance for predicting early intracerebral haemorrhage expansion.
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Affiliation(s)
- Qian Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Fei Dong
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
| | - Qiyuan Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Fangfang Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
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10
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Sporns PB, Psychogios MN, Boulouis G, Charidimou A, Li Q, Fainardi E, Dowlatshahi D, Goldstein JN, Morotti A. Neuroimaging of Acute Intracerebral Hemorrhage. J Clin Med 2021; 10:1086. [PMID: 33807843 PMCID: PMC7962049 DOI: 10.3390/jcm10051086] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/16/2021] [Accepted: 03/02/2021] [Indexed: 01/25/2023] Open
Abstract
Intracerebral hemorrhage (ICH) accounts for 10% to 20% of all strokes worldwide and is associated with high morbidity and mortality. Neuroimaging is clinically important for the rapid diagnosis of ICH and underlying etiologies, but also for identification of ICH expansion, often as-sociated with an increased risk for poor outcome. In this context, rapid assessment of early hema-toma expansion risk is both an opportunity for therapeutic intervention and a potential hazard for hematoma evacuation surgery. In this review, we provide an overview of the current literature surrounding the use of multimodal neuroimaging of ICH for etiological diagnosis, prediction of early hematoma expansion, and prognostication of neurological outcome. Specifically, we discuss standard imaging using computed tomography, the value of different vascular imaging modalities to identify underlying causes and present recent advances in magnetic resonance imaging and computed tomography perfusion.
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Affiliation(s)
- Peter B. Sporns
- Department of Neuroradiology, Clinic for Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland;
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic for Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland;
| | - Grégoire Boulouis
- Neuroradiology Department, University Hospital of Tours, CEDEX 09, 37044 Tours, France;
| | - Andreas Charidimou
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
- Department of Neurology, Boston University School of Medicine, Boston Medical Centre, Boston, MA 02118, USA
| | - Qi Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 40016, China;
| | - Enrico Fainardi
- Section of Neuroradiology, Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50134 Florence, Italy;
| | - Dar Dowlatshahi
- Department of Medicine (Neurology), Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada;
| | - Joshua N. Goldstein
- Department of Emergency Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Andrea Morotti
- ASST Valcamonica, UOSD Neurology, Esine (BS), 25040 Brescia, Italy;
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11
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Wang J, Wang W, Liu Y, Zhao X. Associations Between Levels of High-Sensitivity C-Reactive Protein and Outcome After Intracerebral Hemorrhage. Front Neurol 2020; 11:535068. [PMID: 33123072 PMCID: PMC7573166 DOI: 10.3389/fneur.2020.535068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 08/27/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Patients with spontaneous intracerebral hemorrhage (ICH) have high mortality and morbidity rates; approximately one-third of patients with ICH experience hematoma expansion (HE). The spot sign is an established and validated imaging marker for HE. High-sensitivity C-reactive protein (hs-CRP) is an established laboratory marker for inflammation and secondary brain injury following ICH. Objective: To determine the association between the spot sign and hs-CRP, hematoma expansion, and clinical outcomes. Methods: Between December 2014 and September 2016, we prospectively recruited 1,964 patients with acute symptomatic ICH at 13 hospitals in Beijing, China. Next, we selected 92 patients within 24 h of the onset of symptoms from this cohort for the present study. ICH was diagnosed in the emergency room by non-contrast computed tomography (NCCT) scans. Follow-up scans were carried out within 48 h to evaluate patients for HE. Multidetector computed tomography angiography (MDCTA) was also used to identify spot signs. Blood samples were collected from each patient at admission in EDTA tubes (for plasma) or vacutainer tubes (for serum). hs-CRP values were determined by a particle-enhanced immunoturbidimetric assay in the laboratory at Beijing Tiantan Hospital, Capital Medical University. Patients were categorized into two groups according to their hs-CRP levels (hs-CRP <3 mg/L, hs-CRP ≥3 mg/L). Results: The incidences of spot sign and HE in our study cohort were 31.5 and 29.3%, respectively. Following the removal of potential confounding variables, stepwise-forward logistic regression analysis identified that an hs-CRP level ≥3 mg/L was not a significant indicator for either spot sign (p = 0.68) or HE (p = 0.07). However, an hs-CRP level ≥3 mg/L (odds ratio: 16.64, 95% confidence interval: 2.11-131.45, p = 0.008) was identified as an independent predictor of an unfavorable outcome 1 year after acute ICH. Conclusions: Our analyses identified that an hs-CRP level ≥3 mg/L was a significant indicator for an unfavorable outcome 1 year after acute ICH.
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Affiliation(s)
- Jing Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.,Beijing Key Laboratory of Central Nervous System Injury, Beijing, China
| | - Wenjuan Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.,Beijing Key Laboratory of Central Nervous System Injury, Beijing, China
| | - Yanfang Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.,Beijing Key Laboratory of Central Nervous System Injury, Beijing, China
| | - Xingquan Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.,Beijing Key Laboratory of Central Nervous System Injury, Beijing, China
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