The surgical epidemiology of frailty in the colorectal cancer population
File(s)
Author(s)
Rafique, Henna
Type
Thesis
Abstract
The purpose of this thesis was to examine the surgical epidemiology of frailty in the colorectal cancer (CRC) population.
In order to achieve this, firstly I have explored the current literature for the use of frailty definitions and associated outcomes as a background to this research work. A lack of a universal, operational definition for frailty was found and this presents an issue in the management planning of these patients. The magnitude of CRC-frailty as a problem has been demonstrated using incidence and prevalence. The impact of CRC-frailty has been scrutinised by a methodical investigation of adverse outcomes, who may or may not undergo resectional surgery.
Thus, the logical next step and indeed, the key pillar to this thesis, has been to create and validate a frailty assessment risk prediction tool that has superior discriminatory power compared to current frailty measurements. This is an entirely novel endeavour in that: Big Data from a healthcare administrative dataset has been used, it is the largest cohort of this type of patients in a study that has ever been documented in the scientific literature and, it has been developed by the use of robust and complex, numerous iterations of multiple logistic regression. This is a unique and sizeable contribution to the scientific body of evidence and has the potential to hugely benefit clinical practice.
Further, I have analysed the variation in healthcare utilisation by the frail in order to obtain an understanding of the pressure on healthcare services and inform public spending. The literature about prehabilitation, as a possible means to improve outcomes in this vulnerable group, has been investigated.
“Ageing is not lost youth but a new state of opportunity and strength” (Betty Friedan 1921-2006). To improve outcomes nationally, it is vital that we strengthen our understanding of CRC-frailty.
In order to achieve this, firstly I have explored the current literature for the use of frailty definitions and associated outcomes as a background to this research work. A lack of a universal, operational definition for frailty was found and this presents an issue in the management planning of these patients. The magnitude of CRC-frailty as a problem has been demonstrated using incidence and prevalence. The impact of CRC-frailty has been scrutinised by a methodical investigation of adverse outcomes, who may or may not undergo resectional surgery.
Thus, the logical next step and indeed, the key pillar to this thesis, has been to create and validate a frailty assessment risk prediction tool that has superior discriminatory power compared to current frailty measurements. This is an entirely novel endeavour in that: Big Data from a healthcare administrative dataset has been used, it is the largest cohort of this type of patients in a study that has ever been documented in the scientific literature and, it has been developed by the use of robust and complex, numerous iterations of multiple logistic regression. This is a unique and sizeable contribution to the scientific body of evidence and has the potential to hugely benefit clinical practice.
Further, I have analysed the variation in healthcare utilisation by the frail in order to obtain an understanding of the pressure on healthcare services and inform public spending. The literature about prehabilitation, as a possible means to improve outcomes in this vulnerable group, has been investigated.
“Ageing is not lost youth but a new state of opportunity and strength” (Betty Friedan 1921-2006). To improve outcomes nationally, it is vital that we strengthen our understanding of CRC-frailty.
Version
Open Access
Date Issued
2024-05-20
Date Awarded
01/08/2025
License URL
Advisor
Faiz, Omar
Athanasiou, Thanos
Bottle, Alex
Publisher Department
Department of Surgery & Cancer
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)