Background
Aneurysmal subarachnoid hemorrhage can lead to multi-organ disturbances as a result of central and autonomic nervous system injuries. Alterations in the brain-body interface associated with this cerebrovascular disorder have significant impact on patient morbidity and mortality. Knowledge of the most pertinent brain-body associations, as well as demographic, systemic and neurological prognostic factors on hospital admission, along with their progression during hospitalization can assist the clinician and patient family in the process of treatment decision making.
Objectives
The goals of this dissertation are to:
(1) synthesize and critically appraise the methodologic quality of existing studies that derive clinical predictor tools and clinical predictors used to determine outcome prognosis in patients with aneurysmal subarachnoid hemorrhage (SAH),
(2) synthesize and critically appraise the methodologic quality of existing studies that derive pathophysiologic mechanisms of brain-body associations in aneurysmal SAH,
(3) provide new insights into the significance of brain-body associations that are essential in influencing outcome in aneurysmal SAH, and
(4) create a decision making algorithm for aneurysmal SAH patients that is useful in bedside prognostication and clinical treatment decision making.
Methods
Existing prospective and retrospective cohort studies and randomized controlled trials were included in the systematic review investigating prognostic factors and clinical prediction tools associated with determining the neurologic outcome in adults patients with aneurysmal SAH. Existing prospective and retrospective cohorts were included in the systematic review investigating the pathophysiologic mechanisms of brain-body associations in patients with ruptured brain aneurysms. The multicenter Tirilazad database (3551 patients) was used to create the aneurysmal SAH prognostic model, in order to elucidate significant brain-body associations. Traditional binary logistic regression models were used. The classification and regression tree analysis technique is applied to the multicenter Tirilazad database in order to create a decision making algorithm.
Results
Systematic review of the literature confirmed the most frequently retained clinical outcome predictors, namely, age, neurological grade, aneurysm size and blood clot thickness. Systematic review of the literature clarified currently known pathophysiologic mechanisms of brain-body associations in aneurysmal SAH, specifically, sympathetic activation of the cardiopulmonary system with subsequent delayed activation of neuro-cardio-endocrinological responses as part of the secondary injury cascade in response to the primary ictus of aneurysmal SAH. Logistic regression models found the significance of hepatic disease and hypertension in development of brain edema, and the negative consequences of seizures in those with history of myocardial infarction and post admission fever worsening neurological outcome. A clinically useful classification and regression tree revealed prognostic subgroups with important explanatory nodes including neurological grade, age, post admission fever and post-admission stroke.
Discussion
This dissertation clarified existing information on clinical predictors and pathophysiologic mechanisms of brain-body associations in aneurysmal SAH. It also provides novel information on brain-body associations that are essential in influencing outcome in aneurysmal SAH patients despite scarce existing literature on such important relationships. A clinically useful classification and regression tree was generated to guide both bedside prognostication and clinical treatment decision making in aneurysmal SAH patients. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20698 |
| Date | January 2016 |
| Creators | Lo, Benjamin W. Y. |
| Contributors | Levine, Mitchell A. H., Thabane, Lehana, Farrohkyar, Forough, Clinical Epidemiology/Clinical Epidemiology & Biostatistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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