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Article summary:
1. The authors conducted a retrospective cohort study to determine radiographic predictors of aneurysmal etiology based on admission noncontrast head CT scans in patients with suspected aneurysmal subarachnoid hemorrhage.
2. The mean blood thickness was greater in the sylvian fissure and interhemispheric cisterns, and there was a greater median degree of extension of blood in the sylvian fissures in aneurysmal SAH than in non-perimesencephalic SAH patients.
3. The authors’ proposed risk stratification model showed acceptable accuracy in predicting aneurysmal etiology.
Article analysis:
The article is generally trustworthy and reliable, as it is based on a retrospective cohort study that was conducted by experienced researchers at an academic center from 2016 to 2021. The authors used appropriate methods to compare blood thickness in the basal cisterns and sylvian fissures and modified Graeb scores on admission head CT scans between two groups, and subsequently developed a predictive model to identify aneurysmal etiology. The results showed that there were differences in radiographic features on admission head CT between an-NPSAH and aSAH patients, and the authors’ proposed risk stratification model had acceptable accuracy in predicting aneurysmal etiology.
However, there are some potential biases that should be noted when considering this article's trustworthiness and reliability. For example, the sample size of 259 included patients may not be large enough to draw definitive conclusions about the predictive value of the proposed risk stratification model for identifying aneurysmal etiology. Additionally, since this is a retrospective study, it is possible that some important data points may have been missed or overlooked due to recall bias or other factors. Furthermore, since this study was conducted at one academic center over a five-year period, its results may not be generalizable to other populations or settings. Finally, it should also be noted that this article does not explore any potential risks associated with using the proposed risk stratification model for identifying aneurysmal etiology.