Soetomo score: score model in early identification of acute haemorrhagic stroke
Moh Hasan Machfoed, Valentinus Besin, Riani Wisnujono
Department of Neurology, Faculty of Medicine Airlangga University/Dr. Soetomo Hospital, Surabaya, Indonesia
Correspondence: Valentinus Besin, Department of Neurology, Faculty of Medicine Airlangga University/Dr. Soetomo Hospital, Jl. Mayjend. Prof. Dr. Moestopo 6–8, Surabaya – 60286, Indonesia, tel.: 62-31-5501670, fax: 62-31-5501750, e-mail: email@example.com
Aim of the study: On financial or facility constraints of brain imaging, score model is used to predict the occurrence of acute haemorrhagic stroke. Accordingly, this study attempts to develop a new score model, called Soetomo score. Material and methods: The researchers performed a cross-sectional study of 176 acute stroke patients with onset of ≤24 hours who visited emergency unit of Dr. Soetomo Hospital from July 14th to December 14th, 2014. The diagnosis of haemorrhagic stroke was confirmed by head computed tomography scan. There were seven predictors of haemorrhagic stroke which were analysed by using bivariate and multivariate analyses. Furthermore, a multiple discriminant analysis resulted in an equation of Soetomo score model. The receiver operating characteristic procedure resulted in the values of area under curve and intersection point identifying haemorrhagic stroke. Afterward, the diagnostic test value was determined. Results: The equation of Soetomo score model was (3 × loss of consciousness) + (3.5 × headache) + (4 × vomiting) − 4.5. Area under curve value of this score was 88.5% (95% confidence interval = 83.3–93.7%). In the Soetomo score model value of ≥−0.75, the score reached the sensitivity of 82.9%, specificity of 83%, positive predictive value of 78.8%, negative predictive value of 86.5%, positive likelihood ratio of 4.88, negative likelihood ratio of 0.21, false negative of 17.1%, false positive of 17%, and accuracy of 83%. Conclusions: The Soetomo score model value of ≥−0.75 can identify acute haemorrhagic stroke properly on the financial or facility constrains of brain imaging.