Artificial Intelligence in Subarachnoid Hemorrhage
Condition: Subarachnoid Hemorrhage, Aneurysmal
MeSH terms: Subarachnoid Hemorrhage; Hemorrhage
Study type: Observational

Summary: 
      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.
    

Detailed description: 
      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.
    

Intervention name(s): No intervention, observational study
Intervention type(s): Other
Observational model: Cohort
Time perspective: Prospective
Sampling method: Probability Sample
Study population: 
        Adult patients with aneurysmal subarachnoid hemorrhage, admitted to the Intensive Care Unit
        at Sahlgrenska University Hospital.