Annually, approximately 30,000 people suffer from aneurysmal subarachnoid hemorrhage (SAH) in the United States. In an estimated 5% of these patients, the hemorrhage is difficult to diagnose using conventional methods. Clinicians must rely upon a combination of clinical history, Computerized Tomography (CT) scan evidence and lumbar puncture results to diagnose and differentiate SAH from a traumatic spinal tap (blood in the spinal fluid due to the procedure). Here we describe an algorithm based development of an analytic methodology using visible spectroscopy to reliably quantify bilirubin in hemorrhagic spinal fluid. The analysis, which may be useful for diagnoses concerning hemorrhagic stroke, is based on the detection of bilirubin, and concomitant blood products produced within the Cerebral Spinal Fluid (CSF) following SAH. The algorithm quantifies bilirubin (0.3 to 10 mg/dL) from the resultant absorption spectrum. A model is developed from standard visible spectroscopic absorption curves of bilirubin and hemoglobin by applying traditional Beers Law principles. The model is coupled to a modified partial least square analysis and control theory concept where the bilirubin is the “signal” and is masked by hemoglobin “noise.” This paper describes the computational methods, sensitivity and utility of a system to quantify bilirubin in CSF like solutions containing hemoglobin and bilirubin over 0.5 g/dL-10g/dL of hemoglobin concentrations.
Keywords: Subarachnoid hemorrhage, bilirubin, hemoglobin, visible spectroscopy, algorithm, stroke, quantification, signal to noise, metabolomics
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