Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage.

Deterioration of neurovascular conditions can be rapid in patients with spontaneous subarachnoid haemorrhage (SAH) and often lead to poor clinical outcomes. Therefore, it is crucial to promptly assess and continually track the progression of the disease. This study incorporated baseline clinical conditions, repeatedly measured neurological grades and haematological biomarkers for dynamic outcome prediction in patients with spontaneous SAH. Neurological intervention, mainly aneurysm clipping and endovascular embolisation, was also incorporated as an intermediate event in developing a neurological intervention transition (NIT) joint model. A retrospective cohort study was performed on 701 patients in spontaneous SAH with a study period of 14 days from the MIMIC-IV dataset. A dynamic prognostic model predicting outcome of patients was developed based on combination of Cox model and piecewise linear mixed-effect models to incorporate different types of prognostic information. Clinical baseline covariates, including cerebral oedema, cerebral infarction, respiratory failure, hydrocephalus and vasospasm, as well as repeated measured Glasgow Coma Scale (GCS), glucose and white blood cell (WBC) levels were covariates contributing to the optimal model. Incorporation of neurological intervention as an intermediate event increases the prediction performance compared with baseline joint modelling approach. The average AUC of the optimal model proposed in this study is 0.7783 across different starting points of prediction and prediction intervals. The model proposed in this study can provide dynamic prognosis for spontaneous SAH patients and significant potential benefits in critical care management.

Oxygen delivery from the cerebral microvasculature to tissue is governed by a single time constant of approximately 6 seconds.

OBJECTIVE: The cerebral microvasculature plays a key role in the coupling between cerebral blood flow and metabolism. Although experimental imaging techniques now allow for finely detailed measurements of flow and oxygenation, within humans measurements remain confined to a voxel-level scale, of order 1 mm. Mathematical models are thus key in interpreting such data. However, these can be highly complicated, due to the large number of vessels and the nonlinearities in the governing equations. METHODS: We thus propose here a new model of the cerebral microvasculature and show how its behavior can be simplified based on the order of magnitude arguments. RESULTS: The resulting model shows a dependence upon just two time constants, termed "slow" and "metabolic" time constants; the tissue oxygenation response can be characterized by convolution of the difference between the fractional flow and metabolic responses with a single exponential, with time constant equal to half the ratio of tissue volume to blood flow multiplied by the ratio of effective oxygen solubility in tissue and blood. CONCLUSIONS: The overall response time for the whole network is approximately 6 seconds; this value indicates that the flow response to increases in metabolic activity cannot be driven solely by changes in tissue oxygenation.

The effects of non-linearities on shear stress in periodic flow in axi-symmetric vessels.

In this paper, a power series and a Fourier series approach is used to solve the governing equations of motion in an elastic axi-symmetric vessel, assuming that blood is an incompressible Newtonian fluid. The time averaged flow has shown to be greater than the steady state flow leading to a larger wall shear stress. Oscillations can also be observed, which is not present in the steady state solution. This is due to the nonlinear momentum terms causing interaction between the harmonics.