Circulating cells associated with cardiovascular outcomes in peripheral arterial occlusive disease

Martin, David Terence (2018) Circulating cells associated with cardiovascular outcomes in peripheral arterial occlusive disease. PhD thesis, James Cook University.

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View at Publisher Website: https://doi.org/10.25903/5be9097a81718
 
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Abstract

Background: Peripheral arterial occlusive disease is a manifestation of the inflammatory disease atherosclerosis, characterised by reduced blood flow to the limbs by narrowing and blocking of arteries. This disease affects more than 200 million adults worldwide and is a powerful indicator of widespread arterial disease. It is associated with increased risk of death, heart attack and stroke. Models to predict which patients with peripheral arterial occlusive disease will suffer these outcomes are lacking. Traditional null hypothesis testing methods to develop outcome models for this population are limited by the complexity of variable interaction and some variables exhibiting small although clinically important effects. The Information-Theoretical approach and particularly multi-model analysis and inference has been applied in other scientific fields but has not been applied to the population of patients with peripheral arterial occlusive disease.

Objective: The specific aims of this study were (1) to determine the association of the circulating cells of inflammation with clinical disease severity and traditional risk factors for the composite end-point of major adverse event consisting of death, heart attack or stroke and death alone using traditional null hypothesis testing statistics (Kaplan-Meir survival analysis and Cox proportional hazards analysis) compared to the novel approach of multi-model analysis; (2) to generate a predictive model for these cardiovascular outcomes in patients with peripheral arterial occlusive disease.

Design: Longitudinal cohort design with 632 patients considered and 398 patients included.

Methods: Patients were prospectively recruited from 2002 to 2014 from The Townsville Hospital and Townsville Mater Hospital and followed up until death, discharge from clinic or the conclusion of data collection on 1/12/2014. The blood sample for this study was obtained at recruitment if the patient was well, or otherwise at greater than one month post interventional procedure, major adverse event or resolution of infection. Kaplan-Meir analysis was performed for each endpoint and Cox proportional hazards analysis was used to test the association of each of the circulating cell types (total white cell count and its subsets neutrophils, lymphocytes, monocytes, the calculated neutrophil lymphocyte ratio and haemoglobin) with the endpoint of major adverse event and death a priori and with adjustment for risk factors. Further analysis was undertaken using a novel approach of multi-model analysis to generate an average best fit model for major adverse event and death.

Results: Disease severity was significantly associated with major adverse event using Kaplan- Meier and log rank analysis. Cox proportional hazards analysis for each cell type with major adverse event demonstrated high total white cell count, high neutrophil count and high monocyte count to be significantly associated in all analyses with both mid and high lymphocyte counts significantly associated. For the outcome of death high total white cell count, high neutrophil count, and high neutrophil/lymphocyte ratio were significantly associated in all analyses with high lymphocyte count protective. The importance of adjusting for traditional risk factors, disease severity and medication use in Cox proportional hazards analysis was demonstrated. Both the average best fit models from multi-model averaging for major adverse event and death feature clinical disease severity and circulating cell counts as stronger predictors than the risk factors traditionally associated with the establishment of the disease. High monocyte category was the cell type with the strongest positive influence in the model for major adverse event, with high lymphocyte count having the strongest negative (i.e. protective) influence. The model for the outcome of death showed the disease severity categories of tissue loss and rest pain as having the strongest influence with high neutrophil count the strongest influencing cell type, and again high lymphocyte count having the strongest protective influence. Methods to enable the clinician to apply the model at the bedside or clinic are discussed using information from clinical history, disease severity and full blood count alone.

Conclusion: The Information-Theoretic approach of multi-model averaging is more appropriate and meaningful than traditional null hypothesis testing approaches for patients with peripheral arterial occlusive disease. Disease severity and circulating cell counts better discriminate patients at high risk of major adverse events and death than traditional risk factors. Circulating cell counts may be modelled with disease severity and traditional risk factors to guide treatment selection, aid patient compliance with lifestyle change and medical therapy, drive future pathophysiological research and generate potential treatments for patients with peripheral arterial occlusive disease.

Item ID: 56110
Item Type: Thesis (PhD)
Keywords: peripheral arterial occlusive disease, peripheral arterial disease, leucocytes, total white cell count, death, mortality, major adverse event, multi-model averaging, inflammation
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Copyright Information: Copyright © 2018 David Terence Martin
Additional Information:

Publications arising from this thesis are available from the Related URLs field. The publications are:

Chapter 3: Martin, D., Wallace, D., Crowe, M., Rush, C., Tosenovsky, P., and Golledge, J. (2014) Association of total white cell count with mortality and major adverse events in patients with peripheral arterial disease: a systematic review. European Journal of Vascular and Endovascular Surgery, 47 (4). pp. 422-432.

Date Deposited: 12 Nov 2018 05:16
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1102 Cardiovascular Medicine and Haematology > 110201 Cardiology (incl Cardiovascular Diseases) @ 100%
SEO Codes: 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920103 Cardiovascular System and Diseases @ 100%
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