Artificial Intelligence in Subarachnoid Hemorrhage
Artificial Intelligence in 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.
Inclusion Criteria:

 -  Adult patients

 -  aneurysmal subarachnoid hemorrhage

 -  admitted to Neurointensive care unit at Sahlgrenska University Hospital, Gothenburg,
 Sweden

 Exclusion Criteria:

 -  cardiac arrythmias

 -  use of pacemaker