A Novel 10-Point Score System to Predict Early Hematoma Growth in Patients With Spontaneous Intracerebral Hemorrhage.

Jingjing Fu,Xiuhua Song,Mar Irida Lloret-Villas,Atlantic D'Souza,Mi Yang,Zhaosheng Li,Ziwen Wang,Sheng Hu,Wenbo Xiao

doi:10.3389/fneur.2019.01417

Abstract

Background: A rapid and reliable method to predict significant early hematoma growth in the acute setting is of great important to better inform clinicians and researchers in their efforts to improve outcomes for patients.Methods: We established a 10-point score system to predict hematoma growth including four parameters: baseline intracerebral hemorrhage (ICH) volume > 30 mL, time to initial CT scan ≤ 3 h, island sign and black hole sign. Then, we reviewed our ICH database and assessed the predict value of the score system.Results: A total of 216 ICH patients were included. Patients with hematoma growth at 24 h had higher score than those without hematoma growth (7.6 ± 3.0 vs. 2.0 ± 2.4, p < 0.001). The optimal cut-off value of the score for predicting hematoma growth was 3 (area under curve, 0.937; 95% CI, 0.899–0.975, p < 0.001), with 95% CI of 0.896–0.965 in bootstrapping analysis. The sensitivity, specificity, positive predictive and negative predictive value of the score ≥ 3 for predicting hematoma growth were 97.8, 92.7, 90.9, and 98.3%.Conclusion: The 10-point score system could predict hematoma growth with high accuracy.

Highlights

Spontaneous intracerebral hemorrhage (ICH) is the most devastating form of stroke and accounts for ∼10–30% of strokes worldwide [1]
The optimal cut-off value of the score for predicting hematoma growth was 3, with 95% CI of 0.896–0.965 in bootstrapping analysis
Significant early hematoma growth occurs in about one third of ICH patients who present within few hours and was closely associated with poor outcome and higher mortality [2,3,4]

Summary

Introduction

Spontaneous intracerebral hemorrhage (ICH) is the most devastating form of stroke and accounts for ∼10–30% of strokes worldwide [1]. A number of prediction score models have been developed to predict early hematoma growth, mainly the 24-point clinical score (BRAIN) [5], 18-point clinical score [6], 9-point clinical score [7], and the value of combining predictors [8]. Non-contrast CT (NCCT) imaging signs including island sign, blend sign, and black hole sign showed high accuracy, especially high specificity, in predicting early hematoma growth [9,10,11]. Based on these NCCT imaging signs, we aimed to establish a simple, accurate, and easy to use score model to predict early hematoma growth. A rapid and reliable method to predict significant early hematoma growth in the acute setting is of great important to better inform clinicians and researchers in their efforts to improve outcomes for patients

Objectives

Methods

Results

Conclusion