Subarachnoid Hemorrhage, Aneurysmal Clinical Trial
Official title:
Using Machine Learning With Heart Rate Variability Signals to Detect Delayed Cerebral Ischemia After Subarachnoid Hemorrhage
The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected heart rate variability data in order to evaluate whether specific patterns could be found in patients developing delayed cerebral ischemia after subarachnoid hemorrhage.
Patients with aneurysmal subarachnoid haemorrhage (aSAH), develop delayed cerebral ischemia
(DCI) in about 30% of the cases. DCI is associated with increased mortality, persistent
neurological deficit as well as impaired quality of life. It would benefit both patients and
society to decrease these neurological injuries. One clinical problem is that the diagnosis
of cerebral ischemia in SAH patients often is delayed due to limitations in monitoring
abilities. When detected, the neurological damage often turns out to be irreversible.
Several studies have used univariate and multivariate logistic regression analysis to
identify risk factors for the development of delayed cerebral ischemia (DCI) in patients with
subarachnoid haemorrhage. However, these studies are based on data collected about the
patients (e.g. age, gender), and the precision of these statistical models has generally been
found to be low. Recently, machine learning algorithms for the prediction of DCI using a
combination of clinical and image data have also been evaluated .
However, prediction of DCI does not prevent DCI, to prevent DCI a monitoring system needs to
be developed that can warn physicians of imminent risk of cerebral ischemia, making it
possible to intervene and prevent cerebral ischemia.
Variations in the autonomous nervous system, such as changes in the balance between the
sympathetic and the parasympathetic nervous systems, can be detected by using heart rate
variability (HRV) monitoring. HRV has been reported as a predictor of poor outcome after
traumatic brain injury and stroke, including subarachnoid haemorrhage. However, HRV
monitoring for detection of incipient cerebral ischemia has not been thoroughly evaluated. In
a study of patients with aSAH, we collected HRV continuously in up to 10 days after
admission, but just a small part of the HRV data was analysed off-line. Fifteen of 55
patients developed DCI during the acute phase, and the off-line analyse of HRV showed that
the low/high-frequency ratio increased more in patients that developed DCI (Ref). This led us
to try to analyse all of the collected HRV with the help of machine learning processes, and a
collaboration with computer/data scientists was initiated.
The overall aim of this study is to, with the help of computer/data scientist and machine
learning processes, analyse collected HRV data in order to evaluate whether specific patterns
could be found in patients developing DCI during the acute phase after subarachnoid
hemorrhage.
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