Jia Xu Lim1,2, Julian Xinguang Han1,2, Angela An Qi See1,2, Voon Hao Lew1, Wan Ting Chock2, Vin Fei Ban2, Sohil Pothiawala3, Winston Eng Hoe Lim4, Louis Elliot McAdory4, Michael Lucas James5,6, Nicolas Kon Kam King7,8,9. 1. Department of Neurosurgery, National Neuroscience Institute, 11, Jalan Tan Tock Seng, Singapore, 308433, Singapore. 2. Department of Neurosurgery, Singapore General Hospital, Singapore, Singapore. 3. Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore. 4. Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore. 5. Departments of Anesthesiology, Brain Injury Translational Research Center, Duke University, Durham, NC, USA. 6. Departments of Neurology, Brain Injury Translational Research Center, Duke University, Durham, NC, USA. 7. Department of Neurosurgery, National Neuroscience Institute, 11, Jalan Tan Tock Seng, Singapore, 308433, Singapore. nicolas.kon.k.k@singhealth.com.sg. 8. Department of Neurosurgery, Singapore General Hospital, Singapore, Singapore. nicolas.kon.k.k@singhealth.com.sg. 9. Duke-NUS Medical School, Singapore, Singapore. nicolas.kon.k.k@singhealth.com.sg.
Abstract
BACKGROUND: Hematoma expansion (HE) occurs in approximately one-third of patients with intracerebral hemorrhage (ICH) and is known to be a strong predictor of neurological deterioration as well as poor functional outcome. This study aims to externally validate three risk prediction models of HE (PREDICT, 9-point, and BRAIN scores) in an Asian population. METHODS: A prospective cohort of 123 spontaneous ICH patients admitted to a tertiary hospital (certified stroke center) in Singapore was recruited. Logistic recalibrations were performed to obtain updated calibration slopes and intercepts for all models. The discrimination (c-statistic), calibration (Hosmer-Lemeshow test, le Cessie-van Houwelingen-Copas-Hosmer test, Akaike information criterion), overall performance (Brier score, R2), and clinical usefulness (decision curve analysis) of the risk prediction models were examined. RESULTS: Overall, the recalibrated PREDICT performed best among the three models in our study cohort based on the novel matrix comprising of Akaike information criterion and c-statistic. The PREDICT model had the highest R2 (0.26) and lowest Brier score (0.14). Decision curve analyses showed that recalibrated PREDICT was more clinically useful than 9-point and BRAIN models over the greatest range of threshold probabilities. The two scores (PREDICT and 9-point) which incorporated computed tomography (CT) angiography spot sign outperformed the one without (BRAIN). CONCLUSIONS: To our knowledge, this is the first study to validate HE scores, namely PREDICT, 9-Point and BRAIN, in a multi-ethnic Asian ICH patient population. The PREDICT score was the best performing model in our study cohort, based on the performance metrics employed in this study. Our findings also showed support for CT angiography spot sign as a predictor of outcome after ICH. Although the models assessed are sufficient for risk stratification, the discrimination and calibration are at best moderate and could be improved.
BACKGROUND:Hematoma expansion (HE) occurs in approximately one-third of patients with intracerebral hemorrhage (ICH) and is known to be a strong predictor of neurological deterioration as well as poor functional outcome. This study aims to externally validate three risk prediction models of HE (PREDICT, 9-point, and BRAIN scores) in an Asian population. METHODS: A prospective cohort of 123 spontaneous ICHpatients admitted to a tertiary hospital (certified stroke center) in Singapore was recruited. Logistic recalibrations were performed to obtain updated calibration slopes and intercepts for all models. The discrimination (c-statistic), calibration (Hosmer-Lemeshow test, le Cessie-van Houwelingen-Copas-Hosmer test, Akaike information criterion), overall performance (Brier score, R2), and clinical usefulness (decision curve analysis) of the risk prediction models were examined. RESULTS: Overall, the recalibrated PREDICT performed best among the three models in our study cohort based on the novel matrix comprising of Akaike information criterion and c-statistic. The PREDICT model had the highest R2 (0.26) and lowest Brier score (0.14). Decision curve analyses showed that recalibrated PREDICT was more clinically useful than 9-point and BRAIN models over the greatest range of threshold probabilities. The two scores (PREDICT and 9-point) which incorporated computed tomography (CT) angiography spot sign outperformed the one without (BRAIN). CONCLUSIONS: To our knowledge, this is the first study to validate HE scores, namely PREDICT, 9-Point and BRAIN, in a multi-ethnic Asian ICHpatient population. The PREDICT score was the best performing model in our study cohort, based on the performance metrics employed in this study. Our findings also showed support for CT angiography spot sign as a predictor of outcome after ICH. Although the models assessed are sufficient for risk stratification, the discrimination and calibration are at best moderate and could be improved.
Authors: J N Goldstein; L E Fazen; R Snider; K Schwab; S M Greenberg; E E Smith; M H Lev; J Rosand Journal: Neurology Date: 2007-03-20 Impact factor: 9.910
Authors: Andrea Morotti; Dar Dowlatshahi; Gregoire Boulouis; Fahad Al-Ajlan; Andrew M Demchuk; Richard I Aviv; Liyang Yu; Kristin Schwab; Javier M Romero; M Edip Gurol; Anand Viswanathan; Christopher D Anderson; Yuchiao Chang; Steven M Greenberg; Adnan I Qureshi; Jonathan Rosand; Joshua N Goldstein Journal: Stroke Date: 2018-04-18 Impact factor: 7.914
Authors: Candice Delcourt; Yining Huang; Hisatomi Arima; John Chalmers; Stephen M Davis; Emma L Heeley; Jiguang Wang; Mark W Parsons; Guorong Liu; Craig S Anderson Journal: Neurology Date: 2012-06-27 Impact factor: 9.910
Authors: Andrew L Thompson; Jayme C Kosior; David J Gladstone; Julia J Hopyan; Sean P Symons; Francisco Romero; Imanuel Dzialowski; Jayanta Roy; Andrew M Demchuk; Richard I Aviv Journal: Can J Neurol Sci Date: 2009-07 Impact factor: 2.104
Authors: Viesha A Ciura; H Bart Brouwers; Raffaella Pizzolato; Claudia J Ortiz; Jonathan Rosand; Joshua N Goldstein; Steven M Greenberg; Stuart R Pomerantz; R Gilberto Gonzalez; Javier M Romero Journal: Stroke Date: 2014-10-09 Impact factor: 7.914
Authors: Joseph Broderick; Sander Connolly; Edward Feldmann; Daniel Hanley; Carlos Kase; Derk Krieger; Marc Mayberg; Lewis Morgenstern; Christopher S Ogilvy; Paul Vespa; Mario Zuccarello Journal: Circulation Date: 2007-10-16 Impact factor: 29.690
Authors: Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins Journal: Ann Intern Med Date: 2015-01-06 Impact factor: 25.391
Authors: Xia Wang; Hisatomi Arima; Rustam Al-Shahi Salman; Mark Woodward; Emma Heeley; Christian Stapf; Pablo M Lavados; Thompson Robinson; Yining Huang; Jiguang Wang; Candice Delcourt; Craig S Anderson Journal: Stroke Date: 2014-12-11 Impact factor: 7.914