Researchers from the First Affiliated Hospital of Wenzhou Medical University have developed a machine learning model that successfully predicts the likelihood of hematoma expansion in patients suffering from spontaneous intracerebral hemorrhage (ICH). This study, which leverages the support vector machine (SVM) method, could fundamentally improve the way healthcare providers manage and treat ICH, potentially saving lives and reducing long-term disabilities associated with this severe stroke subtype.
DOI: 10.1016/j.ebiom.2019.04.040
Intracerebral hemorrhage occurs when a blood vessel within the brain bursts, allowing blood to spill into the surrounding brain tissue, causing damage, and in many cases, significant morbidity and mortality. Quick and accurate prediction of hematoma expansion in ICH patients is crucial for treatment and prognosis.
The study, titled “Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine,” was published in EBioMedicine and is available at: S2352-3964(19)30279-8.
Researchers conducted a retrospective review of 1,157 patients with spontaneous ICH who underwent initial computed tomography (CT) scans within six hours and follow-up CT scans within 72 hours from symptom onset at their hospital between September 2013 and August 2018.
Summary of Key Findings
1. Hematoma expansion occurred in 246 of the 1,157 patients (21.3%).
2. Six independent factors were associated with hematoma expansion: male gender, time to initial CT scan, lower Glasgow Coma Scale score, lower fibrinogen level, presence of a black hole sign, and presence of a blend sign on initial CT.
3. The SVM model displayed a sensitivity of 81.3%, specificity of 84.8%, an overall accuracy of 83.3%, and an area under the receiver operating characteristic curve (AUC) of 0.89.
The ability to predict whether a hematoma will expand is a significant advancement in the management of ICH. By identifying those at risk for expansion, healthcare providers can make informed decisions about aggressive interventions that could limit the size of the hemorrhage and its devastating effects.
Keywords
1. Intracerebral Hemorrhage
2. Hematoma Expansion Prediction
3. Support Vector Machine in Healthcare
4. Stroke Prognostic Tool
5. Brain Hemorrhage Imaging Biomarkers
References
1. Liu, J., Xu, H., Chen, Q., Zhang, T., Sheng, W., Huang, Q.,… & Yang, Y. (2019). Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine. EBioMedicine, 43, 454-459. DOI:10.1016/j.ebiom.2019.04.040
2. Qureshi, A.I., et al. (2001). Spontaneous intracerebral hemorrhage. N Engl J Med, 344(19), 1450–1460. DOI:10.1056/NEJM200105103441907
3. Broderick, J.P., et al. (1993). Volume of intracerebral hemorrhage. A powerful and easy-to-use predictor of 30-day mortality. Stroke, 24(7), 987–993. DOI:10.1161/01.STR.24.7.987
4. Dowlatshahi, D., et al. (2011). Defining hematoma expansion in intracerebral hemorrhage: relationship with patient outcomes. Neurology, 76(14), 1238. DOI:10.1212/WNL.0b013e3182143317
5. Li, Q., et al. (2015). Black hole sign: novel imaging marker that predicts hematoma growth in patients with intracerebral hemorrhage. Stroke, 47(7), 1777. DOI:10.1161/STROKEAHA.115.009643
In-Depth Analysis of the Study
The research team, led by Dr. Yang Yunjun and Dr. Chen Weijian, conducted a thorough evaluation of the roles that various clinical variables play in hematoma expansion. This included evaluating common signs found in CT scans like the “black hole” and “blend” signs known to be associated with poor outcomes.
Using these insights, the team was able to establish an SVM model. This form of machine learning algorithm is traditionally used for classification and regression challenges. It works by finding the hyperplane that best separates the categories of data – in this case, patients who would experience hematoma expansion and those who would not.
With an AUC of 0.89, the SVM model shows high levels of diagnostic accuracy, edging closer to the realms of accuracy usually associated with human radiologists. Importantly, the sensitivity and specificity levels indicate that the model is reliable for predicting hematoma expansion with limited false positives or negatives.
Implications for Medical Practice
This advancement could revolutionize the initial assessment and ongoing management of patients with ICH. For instance, the rapid identification of those at risk could shift a treatment team’s approach to more aggressively manage blood pressure, or prep the patient for surgical intervention, all aimed at curbing the spread of the hematoma.
Moreover, the integration of such a model into emergency radiology workflows could ease the burden on radiologists and expedite care for high-risk patients, potentially improving survival rates and functional outcomes.
Challenges and Considerations
While the study’s results are promising, they hinge on the availability of high-quality CT imaging and reliable manual segmentation of hematoma regions. In practical terms, achieving this level of prediction accuracy will require standardized imaging protocols and possibly further automation in image analysis.
Another point to consider is the generalization of the SVM model to other populations, as the study was conducted in a single hospital in China. Multicentric studies would be necessary to validate the model across diverse demographics and healthcare settings.
Next Steps
Building on this research foundation, the team aims to refine their SVM approach further and incorporate more advanced imaging biomarkers into the model. Training the algorithm with more extensive data will also address the concern of overfitting – ensuring the model can maintain its predictive power in the broader patient populations.
Looking ahead, as machine learning continues to integrate deeply into clinical settings, the fusion of artificial intelligence with medical expertise may well become standard of care for conditions like ICH. Teams around the globe could adopt similar algorithms, potentially leading to substantial improvements in stroke care and patient outcomes.
Conclusion
This study provides hope for the future of stroke management in the context of ICH. With high predictive accuracy, the SVM model represents a significant stride in improving patient care and outcomes through the efficient use of advanced analytics in healthcare. More research and development could see this innovation transforming the practices of emergency and neuro-radiology domains globally.