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Patients’ supportive care needs beyond the end of treatment: A prospective, longitudinal study

Patients’ supportive care needs beyond the end of treatment: A prospective, longitudinal study

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Patients’ supportive care needs beyond the end of treatment: A prospective, longitudinal study

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  1. Patients’ supportive care needs beyond the end of treatment: A prospective, longitudinal study

  2. Chief Investigators: Alison Richardson- Professor of Cancer and Palliative Nursing Care, King’s College LondonMaggie Crowe– Consultant Nurse Cancer Care and Lead Cancer Nurse, Royal United Hospital Bath NHS Trust Project Management Group: Jo Armes - Research Fellow, King’s College London Lynne Colbourne – Nurse Practitioner, Gloucestershire Hospitals NHS Foundation Trust Helen Morgan – Assistant Director of Nursing, United Bristol Healthcare NHS Trust Catherine Oakley – Macmillan Lead Cancer Nurse, St George’s Healthcare NHS Trust Nigel Palmer – NCRI Consumer Liaison and Psychosocial Oncology Clinical Studies Group Emma Ream - Senior Lecturer, King’s College London Annie Young – Director of Nursing, Three Counties Cancer Network Katie Booth – Macmillan Cancer Support

  3. Acknowledgements • This project was supported with funds from: • Macmillan Cancer Support • King’s College London • Collaborators • NCRN research staff • All health care professionals who took part

  4. Study collaborators

  5. Study aims • Identify prevalence of unmet supportive care needs of cancer patients at the end of treatment and six months later • Identify factors at the end of treatment that predict those patients with high unmet supportive care needs six months later

  6. Study overview (1) • Design • Prospective, longitudinal observational study • Potential subjects • Breast cancer • Colorectal cancer • Gynaecological cancers • Prostate cancer • Non-Hodgkin's lymphoma

  7. Study Overview (2): Eligibility Criteria • Aware that he/she has cancer • Greater than 18 years of age • Able to read and understand English • Clinician caring for them agreed to their participation • Patients receiving chemotherapy and/or radiotherapy given with curative intent and the person had not relapsed during treatment • Patients receiving the last cycle/episode of planned course of treatment (not including ‘maintenance’ therapy) • Patients on phase 3 clinical trials were recruited.

  8. Study overview (3) • Sample size • Estimated sample size of 1000 at T0 • 50-100 patients from each diagnostic group at T1 Response rate • T0 was 79%, n=1425/1850 • T1 was 82%, n=1152/1410 • Timing of assessments • T0: End of planned course of treatment • T1: 6 months following T0

  9. Study overview (4): Measures • Supportive Care Needs Survey (SCNS) and Access to Ancillary Support Services module • Hospital Anxiety and Depression Scale (HADS) • Positive Affectivity and Negative Affectivity Scale (PANAS) • Health Concerns Questionnaire (HCQ) • Demographic and medical data

  10. Supportive Care Needs Survey Domains • Sexuality needs • Health system and information needs • Patient care and support needs • Psychological needs • Physical and daily living needs • Total needs

  11. SCNS scoring

  12. Study variables of interest • Primary variable of interest • All SCNS dimensions and unmet multiple needs • Secondary variables of interest • Fear of recurrence • Anxiety and depression • Positive and negative affect • Personal characteristics • Clinical characteristics

  13. Mean age: 61 years Sex: male 31% Female 69% Employment status: Retired 49% Working (FT/PT) 28% Domestic status: Married 69% Living with partner: 6% Widowed 10% Divorced/separated 8% Single 6% Participant Characteristics (1)

  14. Diagnosis: Breast 56% Prostate 23% Bowel 9% Gynae 6% Lymphoma 5% Last treatment: Radiotherapy 80% Chemotherapy 19% Hormone therapy: No 68% Yes 32% Comorbid disease: No 56% Yes 42% Participant characteristics (2)

  15. Analysis • Descriptive analysis of data to assess the prevalence of unmet needs for each SCNS domain at both time points • Logistic regression used to identify baseline factors that would predict those patients with high need six months later for: • each domain of SCNS • multiple unmet need

  16. Prevalence of unmet need by SCNS dimension

  17. Prevalence of SCNS physical and daily living unmet needs

  18. Prevalence of SCNS psychological unmet needs

  19. Prevalence of SCNS sexuality unmet needs

  20. Logistic regression • Analysis attempts to predict which of two categories a person belongs on the basis of other information about them (e.g. age, sex, treatment) • Main outcome variable split into 2 outcomes (no or low need vs. moderate or severe unmet need)

  21. Predictors of SCNS physical and daily living unmet needs • High moderate or severe physical unmet needs at the end of treatment (p=0.000) • High moderate or severe unmet health service and information needs at the end of treatment (p=0.028) • High level of negative affect at the end of treatment (p=0.001) • Having a co-morbid disorder (p=0.007) • Taking hormone therapy (p=0.010) • Being educated to GCSE/’A’ Level standard (p=0.017) • Having experienced a significant event after treatment finished (p = 0.018)

  22. Predictors of SCNS psychological unmet needs • High moderate or severe psychological unmet needs at the end of treatment (p=0.000) • High moderate or severe unmet physical needs at the end of treatment (p=0.001) • High level of negative affect at the end of treatment (p=0.009) • High level of depression (0.004) • High level of fear of recurrence (p=0.001) • Being 60-67 years old (p=0.019) • Having experienced a significant event after treatment finished (p = 0.000)

  23. Predictors of SCNS health system & information unmet needs • High moderate or severe unmet health service and information needs at the end of treatment (p=0.000) • High moderate or severe unmet patient care needs at the end of treatment (p=0.037) • High moderate or severe unmet sexuality needs at the end of treatment (p=0.049) • High level of anxiety at the end of treatment (p=0.002) • Taking hormone therapy (p=0.001) • Having experienced a significant event after treatment finished (p = 0.019)

  24. Predictors of SCNS total unmet needs • High moderate or severe unmet total needs at the end of treatment (p=0.000) • High level of negative affect at the end of treatment (p=0.001) • High level of depression at the end of treatment (p=0.000) • Taking hormone therapy (p=0.027) • Having experienced a significant event after treatment finished (p = 0.001)

  25. Study limitations • Most had a diagnosis of breast or prostate cancer • Considerable variation in our sample in terms of diagnosis and treatment histories • Patients whose only cancer treatment was surgery were excluded • Clinical information was provided by participants rather than being collected from patient records

  26. Summary of main results • Most patients express few or no unmet need for support • Significant minority report multiple unmet needs • Number of baseline factors identified that predict multiple moderate or severe unmet needs: • Depression • Negative mood • Receiving hormone therapy • Younger age • Experiencing a significant event post treatment

  27. Implications & Considerations • An important minority have needs not currently being met. How might we identify these patients in practice? • What are the most effective models of care for helping patients manage unmet needs following treatment? • Consider how to enhance self-management in order to better prepare patients for the transition from cancer patient in receipt of acute care to survivor.

  28. To obtain a copy of the final report • visit: www.kcl.ac.uk/schools/nursing/research/disease/supportivecareneeds