Transcript King’s College London

Patients’ supportive care needs
beyond the end of treatment: A
prospective, longitudinal study
Chief Investigators:
Alison Richardson - Professor of Cancer and Palliative Nursing Care, King’s College
London
Maggie 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
Acknowledgements
•This project was supported with funds from:
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Macmillan Cancer Support
King’s College London
•Collaborators
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NCRN research staff
All health care professionals who took part
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Study collaborators
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Study aims
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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
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Study overview (1)
Design
• Prospective, longitudinal observational
study
Potential subjects
• Breast cancer
• Colorectal cancer
• Gynaecological cancers
• Prostate cancer
• Non-Hodgkin's lymphoma
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Study Overview (2): Eligibility Criteria
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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.
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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
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Study overview (4): Measures
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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
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Supportive Care Needs Survey Domains
1.
2.
3.
4.
5.
Sexuality needs
Health system and information needs
Patient care and support needs
Psychological needs
Physical and daily living needs
Total needs
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SCNS scoring
NO
NEED
HIGH
NEED
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Not applicable – This was not a problem for me
as a result of having cancer.
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Satisfied - I did need help with this, but my need
for help was satisfied at the time.
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Low need - This item caused me only a little
concern or discomfort. I had only a little need for
additional help.
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Moderate need – This item caused me some
concern or discomfort. I had some need for
additional help.
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High need - This item caused me a lot of
concern or discomfort. I had a strong need for
additional help.
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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
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Participant Characteristics (1)
Mean age:
61 years
Sex:
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male 31%
Female 69%
Employment status:
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Domestic status:
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Married 69%
Living with partner: 6%
Widowed 10%
Divorced/separated 8%
Single 6%
Retired 49%
Working (FT/PT) 28%
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Participant characteristics (2)
Diagnosis:
• Breast 56%
• Prostate 23%
• Bowel 9%
• Gynae 6%
• Lymphoma 5%
Hormone therapy:
• No 68%
• Yes 32%
Comorbid disease:
• No 56%
• Yes 42%
Last treatment:
• Radiotherapy 80%
• Chemotherapy 19%
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Analysis
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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
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Prevalence of unmet need by SCNS
dimension
T0 (n=1425)
Sexuality needs
T1 (n=1152)
Patient care needs
Psychological needs
Physical needs
Information needs
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5
10
15
20
25
30
35
40
45
Percentage
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Prevalence of SCNS physical and daily
living unmet needs
T0 (n=1425)
Pain
T1(n=1152)
Feeling unwell a lot
Work around home
Unable to do things
used to
Tiredness
0
5
10
15
20
25
Percentage
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Prevalence of SCNS psychological
unmet needs
Feelings re death & dying
T0 (n=1425)
T1(n=1152)
Learning to feel in co ntro l o f situatio n
Feeling sad
Depressio n
A nxiety
Wo rry that treatment results beyo nd yo ur co ntro l
Uncertainty abo ut future
Co ncerns re family wo rries
Fear o f cancer spreading
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5
10
15
20
25
30
35
P ercentage
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Prevalence of SCNS sexuality unmet
needs
T0 (n=1425)
Information on sexual
relationships
T1 (n=1152)
Changes in sexual
relationships
Changes in sexual
feelings
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5
10
15
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Percentage
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Logistic regression
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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)
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Predictors of SCNS physical and
daily living unmet needs
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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)
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Predictors of SCNS psychological
unmet needs
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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)
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Predictors of SCNS health system &
information unmet needs
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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)
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Predictors of SCNS total unmet
needs
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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)
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Study limitations
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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
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Summary of main results
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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
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Implications & Considerations
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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.
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To obtain a copy of the final report
visit:
www.kcl.ac.uk/schools/nursing/research/disease/supportivecareneeds
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