Determinants and mediating mechanisms of quality of life and disease-specific symptoms among thyroid cancer patients: the design of the WaTCh study

BACKGROUND: Thyroid cancer (TC) patients are understudied but appear to be at risk for poor physical and psychosocial outcomes. Knowledge of the course and determinants of these deteriorated outcomes is lacking. Furthermore, little is known about mediating biological mechanisms. OBJECTIVES: The WaTCh-study aims to; 1. Examine the course of physical and psychosocial outcomes. 2. Examine the association of demographic, environmental, clinical, physiological, and personality characteristics to those outcomes. In other words, who is at risk? 3. Reveal the association of mediating biological mechanisms (inflammation, kynurenine pathway) with poor physical and psychological outcomes. In other words, why is a person at risk? DESIGN AND METHODS: Newly diagnosed TC patients from 13 Dutch hospitals will be invited. Data collection will take place before treatment, and at 6, 12 and 24 months after diagnosis. Sociodemographic and clinical information is available from the Netherlands Cancer Registry. Patients fill-out validated questionnaires at each time-point to assess quality of life, TC-specific symptoms, physical activity, anxiety, depression, health care use, and employment. Patients are asked to donate blood three times to assess inflammation and kynurenine pathway. Optionally, at each occasion, patients can use a weighing scale with bioelectrical impedance analysis (BIA) system to assess body composition; can register food intake using an online food diary; and can wear an activity tracker to assess physical activity and sleep duration/quality. Representative Dutch normative data on the studied physical and psychosocial outcomes is already available. IMPACT: WaTCh will reveal the course of physical and psychosocial outcomes among TC patients over time and answers the question who is at risk for poor outcomes, and why. This knowledge can be used to provide personalized information, to improve screening, to develop and provide tailored treatment strategies and supportive care, to optimize outcomes, and ultimately increase the number of TC survivors that live in good health

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains

Background: Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains. Methods: In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: Global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping. Results: Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83-0.93). Conclusions: The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains

Background: Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains. Methods: In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: Global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping. Results: Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83-0.93). Conclusions: The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice.</p

Mental Wellbeing in Prostate Cancer Treatment and Survivorship:Outcome Definition, Prognostic Factors, and Prognostic Model Development

A prostate cancer diagnosis and its subsequent management can produce numerous challenges to patients. With already significant and further improving survival rates there is a growing realisation that living longer does not always equate to living well. This means that issues pertaining to quality of life and wellbeing are of particular importance to this group of patients. The focus around this has long been on the physical sequelae of disease and treatment, but there is now an increasing amount of evidence to demonstrate the significant impact that exists on the mental wellbeing of individuals. However, whilst this is being increasingly acknowledged, less is understood about what exact mental wellbeing outcomes are of importance in this group of patients. Additionally, little is known about which specific individuals subsequently appear to have poorer mental wellbeing outcomes after their diagnosis.The current work therefore aimed to evaluate the following for patients with prostate cancer: 1) Define important mental wellbeing outcomes of interest, 2) Summarise existing quantitative evaluation methods for defined mental wellbeing outcomes, 3) Explore important prognostic factors for poorer mental wellbeing post diagnosis, and 4) Develop and internally validate a prognostic model for the development of significant mental wellbeing issues. Part 1 of this thesis sets out to define important mental wellbeing outcomes of interest and their evaluation methods through four chapters. This includes multiple independent systematic reviews of the literature and a qualitative study conducting patient interviews to explore their lived experiences post diagnosis. Through these chapters five important constructs were selected as key mental wellbeing outcomes of interest including depression, anxiety, body image perception, fear of cancer recurrence/progression, and masculinity. Additionally, for each of these outcomes the most utilised and validated quantitative psychometric tools were identified and summarised. These selected outcomes were subsequently taken forward for Part 2 of this thesis to evaluate important patient, oncological, and treatment prognostic factors associated with poorer mental wellbeing outcomes in this cohort. This included a systematic review and meta- analysis utilising prognosis research methodology, a cross-sectional survey of healthcare professionals, and a prospective multi-institutional cohort study of newly diagnosed patients entitled MIND-P. These methodologically differing studies were utilised in a triangulation approach together to identify potentially important prognostic factors for the previously selected outcomes. These highlight several potential factors of interest including age, a previous psychiatric diagnosis, mental health symptoms at baseline, co-morbidities, marital status, functional symptoms, stage at diagnosis, and undergoing hormone therapy. Lastly, Part 3 of this thesis culminates in the development and internal validation of a novel multivariable prognostic model for individual patient prediction. This focussed on a composite mental wellbeing outcome as well as risk prediction for each individual mental wellbeing outcome previously defined. Utilising candidate predictors established within Part 2 of this thesis and a sample from the MIND-P study, a final model was developed which utilised age, a previous psychiatric diagnosis, stage of disease, baseline anxiety symptoms, and baseline urinary and sexual function as predictors. The developed model demonstrated acceptable overall performance, calibration, and discrimination during its internal validation. Additionally, instability was seen to be minimal in most measures evaluated. This developed prognostic model offers a first of its kind model within prostate cancer care, and the first to evaluate multiple mental wellbeing outcomes within cancer care in general. Overall, the findings of this thesis highlight the importance of mental wellbeing for patients with prostate cancer and hence the key need to monitor these outcomes in routine follow up care for all patients. This should include the identified outcomes of interest and their respective measurement tools. Additionally, the highlighted prognostic factors and the prognostic model offer potential methods to better target screening and prevention strategies to improve mental wellbeing for these patients. However, the formal evaluation of these was beyond the scope of this thesis and hence should be considered within future research, along with the external validation and clinical utility of the developed model to better define its performance across different populations and understand its impact on outcomes when utilised prior to its widespread clinical utilisation