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Predictive Scoring System Supports Brain Hemorrhage Risk Reduction

New research shows that care for arteriovenous malformations can benefit from a predictive scoring system, limiting unexpected ruptures and brain hemorrhage risk.

Risk management.

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By Mark Melchionna

- A recent study published in JAMA Network Open found that patients with unruptured arteriovenous malformations (AVMs) benefited from a predictive scoring system developed based on several risk factors, helping clinicians reduce the risk of intracranial hemorrhage.

According to the National Institutes of Health (NIH), AVMs are abnormal tangles of blood vessels that lead to disruptions in connections between the arteries and veins. These malformations most often occur in the spinal cord and brain.

According to the study, prominent concerns regarding this condition are the risks surrounding natural rupture and the outcomes of unnecessary intervention. This led researchers to create a scoring system intended to predict the long-term rupture risk of AVMs.

Researchers developed the prediction model based on data from a single-center cohort (deviation cohort), which they then validated in a multicenter external cohort (multicenter external validation cohort) and patients engaged in conservative treatment management (conservative treatment validation cohort). There were 3,962 total patients included in the study, who were all enrolled in a nationwide multicenter prospective collaboration registry in China between Aug. 1, 2011, and Sept. 1, 2021.

With this patient data, researchers developed a scoring system based on risk factors identified from a literature review and selection process. They then assigned patients to various risk groups depending on hemorrhage-free probability scores. They also determined risk stratification using Kaplan-Meier curves.

Of the total patient population, 3,585 were in the deviation cohort, and 377 were in the multicenter external validation cohort. Researchers also noted that 1,028 patients in the deviation cohort with time-to-event data and pre-rupture imaging results were included in the conservative treatment validation cohort. Regarding demographics, 58.3 percent were men, and the median age was 26.1 years.

Among the conservative treatment validation cohort, 36 hemorrhages occurred throughout a median follow-up period of 4.2 years.

The researchers developed the scoring system known as VALE based on four risk factors: ventricular system involvement, venous aneurysm, deep location, and exclusively deep drainage.

Researchers tested the scoring system on all three cohorts. It performed well across all the cohorts. In the low-risk, moderate-risk, and high-risk groups, the 10-year hemorrhage-free rates were 95.5 percent, 92.8 percent, and 75.8 percent, respectively.

"These findings suggest that the scoring system is a reliable and applicable tool that can be used to facilitate patient and physician decision-making and reduce unnecessary interventions or unexpected AVM ruptures," the researchers concluded. 

But they also noted the limitations of the study, which included the use of only four variables of imaging data, a low number of hemorrhagic events in the conservative treatment validation cohort, which may have affected the annual rupture rate, and the creation and application of the VALE scoring system based on data from a solely Chinese population.

Similarly, in January, a group of researchers created a prediction model to estimate the number of oocytes needed to fertilize during assisted reproductive technology treatments.

The study noted that although the traditional in vitro fertilization process consists of four steps involving egg extraction and embryo implantation, researchers need to limit surplus embryo creation, particularly following the overturning of Roe v Wade. Thus, the researchers aimed to develop and apply a prediction model.

After leveraging data from member clinics of the Society for Assisted Reproductive Technology (SART) Clinical Outcomes Reporting System, researchers created the model. They tested it, concluding that the prediction model helped reduce the number of unused embryos created.