Early Integrated Supportive Care Study for Gastrointestinal Cancer Patients
Phase: NA
Conditions: Cancer
Sponsor: Pippa Hawley

 
 
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Research Proposal:  
Early Integrated Supportive Care Study 
 
Primary Investigator: Dr. Philippa Hawley, Palliative Care Specialist, BC Cancer Agency, Vancouver Centre 
 
Study location: BC Cancer Agency, Vancouver Cancer Centre, 600 West 10th Ave, Vancouver, British Columbia, 
V5Z 4E6 
 
 
1. Summary 
 
Currently at the BC Cancer Agency oncologists refer a patient to the Pain and Symptom Management/Palliative 
Care team (PSMPC) based on their practice. Their decisions are made subjectively and without standard 
guidelines/symptom assessment tools. Patients are sometimes referred quite late in their illness. The PSMPC 
team sees patients in their own clinic, separate from the oncologists, and do not often collaborate in a patient’s 
care. Early integration of palliative care into oncological care has been shown to improve quality of life and to 
prolong survival, to reduce inappropriately aggressive oncological care at end of life, and to reduce costs of care. 
We are testing an earlier integrated oncology-palliative care model at the time of cancer diagnosis, with the 
aims of determining whether:  
 
1) the introduction of PSMPC support at the time of diagnosis will lead to better symptom management and 
quality of life, 
 
2) early integration of palliative care into medical oncology care will reduce aggressiveness of cancer treatment 
near end of life  
 
3) a fully integrated service delivery model is sustainable.  
 
Our model’s intervention will provide for the early involvement of the PSMPC team in collaboration with the 
gastrointestinal (GI) oncology team in order to provide patients with support from both subspecialties at a single 
visit. Patients in need of pain and symptom support will be identified using a validated symptom assessment 
tool. The GI cancer team was approached for testing this model as their patients are referred more frequently to 
the PSMPC clinic in the Vancouver Centre than any other tumour site - 23.4% of total referrals. This intervention 
would be aimed particularly at helping the most unwell patients who find attendance at multiple appointments 
very difficult.  
 
This study builds on work published in 2010 by Temel et al1, which showed that early palliative care intervention 
improved the quality of life and survival time of metastatic lung cancer patients at the Massachusetts General 
Hospital, Boston.  A 2014 study conducted by Bakitas et al in mixed cancers has shown similar benefits2. Both 
these studies were in patients with advanced cancer and a short life expectancy. We propose to move the 
integration even earlier, to time of referral to oncology, to maximize the potential benefits. If the intervention is 
shown to benefit patients, we will have evidence to support the implementation of a new standard of cancer 
patient care.   
2. Objectives 
A) To test whether the introduction of PSMPC support at a stage of patient care earlier than current 
practice (referral by the oncologist  when deemed necessary) allows for better management of patients’ 
pain and symptoms from cancer or its treatment, and improves quality of life of patients  
 

 
 
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B) To test whether early integration of palliative care into medical oncology care reduces aggressiveness of 
cancer treatment near end of life 
 
C) To test whether a service delivery model in which PSMPC support is introduced at an early stage in 
patient care is sustainable  
 
3. Intervention 
The intervention is earlier PSMPC team involvement in patient care as informed by patients’ self-reported 
symptom assessment.  
 
Tool:  
 
 
symptom assessment questionnaire 
Decision:  
patients with symptom scores ≥4 are identified as needing symptom 
management 
Specialist involvement:  
patients in need will meet with a PSMPC team member either at the GI 
appointment (collaboration with GI team) or at a later date.  
 
4. Assessment tool 
 
Our patient assessment will be conducted using a modified BC Cancer Agency Patient Pain and Symptom 
Questionnaire, which includes the Edmonton Symptom Assessment Scale (ESAS) and the Canadian Problem 
Checklist  (CPC). Our primary outcome of symptom assessment will be collected using the ESAS, which is a 
validated, reliable tool for patient symptom intensity34.  Several past palliative care studies have used the ESAS 
as an assessment tool, and have shown decreases in symptom scores following similar specialist team 
interventions5678. 
5. Eligible patients 
 
diagnosed with a GI cancer (small intestine, colorectal, stomach*, esophageal*, pancreatic*, liver, 
gallbladder) 
 
have appointments in GI clinic during study days 
 
not yet receiving care from the PSMPC team 
 
can complete symptom assessment questionnaire on their own or with the help of a family member 
or interpreter 
 
6. Study Design/Randomization 
 
This is a cluster randomized study with the randomization being performed at the level of the oncologists. Controlled 
randomization allows us to make the intervention and control groups comparable with respect to all factors that 
could influence the outcome of the intervention. 
 
Cluster randomization - oncologists will be randomized into either the intervention or the control group, and 
patients will be automatically sorted according to their oncologist’s assigned group. By randomizing at the level 
of the oncologist and including multiple oncologists in each study arm, we can adjust for differences in schedules 
and referral patterns to ensure the groups are comparable. 
 
Update 9-Feb-18: 
All oncologists will be allocated to the intervention arm for the remainder of the study now that we have the 
desired sample size for the control group. By recording which oncologist saw which patient this will not be an 
issue for obtaining study outcomes. 
 
7. Groups  

 
 
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Intervention  
 
Assessment questionnaire + early PSMPC involvement  
 
GI cancer patients complete a symptom assessment questionnaire prior to their oncology appointment.  
 
The questionnaire is reviewed and discussed with the oncologist. 
 
 If a patient self-reports at least one symptom score ≥4, the PSMPC team will be from the PSMPC clinic 
area to meet with the patient in the GI clinic, during or immediately following the oncology 
appointment.  
 
Control  
 
Assessment questionnaire for data collection only, not to be assessed 
 
GI cancer patients will complete a symptom assessment questionnaire prior to their oncology 
appointment.  
 
The questionnaire data will not be shared with the oncologist. Control patients will be made aware that 
their questionnaire will not be discussed at this appointment, but that they are free to raise pain and 
symptom/palliative care concerns with the oncology team.  
 
Oncologists may refer patients to the PSMPC team if/when they deems necessary.  
 
 
8. Sample Size 
 
Our total estimated sample size is 176 and assumes a 5%* drop-out rate: 
Integrated arm = 88 
Control arm = 88 
 
The sample size calculations were derived using Hemming et al9. 
 
9. Patient recruitment 
We will be recruiting new patients for a period of approximately 4-6 months. Based on the current new 
patient appointments, we estimate that for each study arm we can approach 17-20 patients a month on 
study days. 
 
 

 
 
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10. Patient following  
Each patient will have data collected for four months. 
 
 
 
Survival data 

 
 
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11. 
Summary of Procedures   
 

 
 
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12. Outcome measures 
Outcome category 
Measure 
Symptom assessment 
(aim: reduce symptoms) 
Pain and symptom assessment questionnaire (ESAS) 
 
change in individual symptom scores between visits 
 
change in composite score of symptoms 
 
proportion with change in individual symptom scores of ≥2 
points between first and last visits  
Use of health services  
(aim: reduce incidence of non-
treatment visits to hospital) 
  
  
# of hospital admissions for non-treatment reasons 
# of ER visits  
# of referrals to PSMPC 
# of PSMPC follow up visits per patient 
Aggressiveness of cancer treatment 
(aim: provide alternative to cancer 
treatment when no longer beneficial) 
# of patients being treated with chemotherapy in last 2 - 4 weeks 
of life 
# of patients admitted to home hospice in last 3 days - 2 weeks of 
life 
 Details of death 
(aims: improve end of life care,  
improve survival time) 
survival time (from first appointment at BC Cancer)  
location of death  
  
Our primary outcome is the total distress score over time for each patient. 
 
13. Estimated time frame 
Activity 
Length of time 
Point in timeline 
Patient recruitment and primary 
data collection 
8-10 months 
8-10 months 
Survival follow up 
12 months 
20-22 months 
Primary data analysis 
2 months ? 
 
22-24 months 
 
study write up 
 4 months 
26-28 months (2 years, 2-4 
mon.) 
 
14. Statistical Analysis 
Our primary outcome is the total distress score over time for each patient. We will compare the individual 
distress scores within groups and between groups.   
 
The intervention effect is expressed as the difference in mean changes in symptom scores from baseline to last 
follow-up between the two treatment groups.  Our hypothesized effect size is 2 points. Based on previous 
studies710111213, this difference is both realistic and clinically meaningful.     
 
The type-1 error level and power of the study are pre-specified as 5% and 80%, respectively14.     
 
The standard deviation is assumed to be the same in each treatment group (4 points), yielding a “medium” 
standardized effect size of 2 points/4 points = 0.515.    

 
 
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The Intra-Cluster Correlation (ICC) coefficient, which represents how strongly patients within the same study 
group (cluster) are related to each other, is assumed to be 𝜌 =  0.00516.   
 
 
                                                          
1 Temel JS, Greer JA, Muzikansky A, Gallagher ER, Admane S, Jackson VA, Dahlin CM, Blinderman CD, Jacobsen J, Pirl WF, 
Billings JA, Lynch TJ (2010) Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med Aug 
19;363(8):733-42. 
 
2 Bakitas M, Doyle Lyons K, Hegel MT, Balan S, Brokaw FC, Seville J, Hull JG, Li Z, Tosteson TD, Byock IR, Ahles TA (2009) 
Effects of a palliative care intervention on clinical outcomes in patients with advanced cancer (the Project ENABLE II  
randomized controlled trial). JAMA 302(7):741-49 
 
3 Chang VT, Hwant SS, Meuerman M (2000) Validation of the Edmonton Symptom Assessment Scale. Cancer 88(9):2164-71. 
 
4 Philip J, Smith WB, Craft P, Lickiss N (1998) Concurrent validity of the modified Edmonton Symptom Assessment System 
with the Rotterdam Symptom Checklist and the Brief Pain Inventory. Support Care Cancer 6:539-41 
 
5 Bakitas M, Doyle Lyons K, Hegel MT, Balan S, Brokaw FC, Seville J, Hull JG, Li Z, Tosteson TD, Byock IR, Ahles TA (2009) 
Effects of a palliative care intervention on clinical outcomes in patients with advanced cancer (the Project ENABLE II  
randomized controlled trial). JAMA 302(7):741-49 
 
6 Bischoff K, Weinberg V, Rabow MW (2013) Palliative and oncologic co-management: symptom management for 
outpatients with cancer. Support Care Cancer 21:3031-37 
 
7 Yennurajalingam S, Urbauer DL, Casper KLB, Reyes-Gibby CC, Chacko R, Poulter V, Bruera E (2011). Impact of a palliative 
care consultation team on cancer-related symptoms in advanced cancer patients referred to an outpatient supportive care 
clinic. J Pain Sym Man 41(1): 49-56 
 
8 Zimmermann C, Swami N, Hannon B, Leighl N, Oza A, Moore M, Rydall A, Rodin G, Tannock I, Donner A, Lo C (2014) Early 
palliative care for patients with advanced cancer: a cluster-randomized controlled trial.  The Lancet 383(9930):1721-1730. 
 
9 Hemming K, Girling AJ, Sitch AJ, Marsh J, Lilford RJ (2011) Sample size calculations for cluster randomized controlled trials 
with a fixed number of clusters. BMC Medical Research Methodology 11:102 
(http://www.biomedcentral.com/content/pdf/1471-2288-11-102.pdf)  
 
10 Osoba D, Rodrigues G, Myles J, Zee B, Pater J (1998) Interpreting the significance of changes in health-related quality-of-
life scores. J Clin Oncol 16:139-44 
 
11 Osoba D (2002) A taxonomy of the uses of health-related quality-of-life instruments in cancer care and the clinical 
meaningfulness of the results. Med Care 40:11131-38 
 
12 Barrett B, Brown D, Mundt M, Brown R (2005) Sufficiently important difference: expanding the framework of clinical 
significance. Med Decis Making 25:250-61 
 
13 Ringash J, O’Sullivan B, Bezjak A, Redelmeier DA (2007) Interpreting clinically significant changes in patient reported 
outcomes. Cancer 110:196-202 
 
14 Suresh KP and Chandrashekara S (2012) Sample size estimation and power analysis for clinical research studies. J Hum 
Reprod Sci  5(1): 7–13. 
 
15 Book:  “Statistical Power Analysis for the Behavioral Sciences”, Second Edition, by Jacob Cohen, 1988 (Publisher:  
Lawrence Erlbaum Associates)  

 
 
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16 Bland JM (2004) Cluster randomised trials in the medical literature: two bibliometric surveys. BMC Medical Research 
Methodology 4, 21.