NutriCare Intervention on Optimizing Nutritional Status, Reducing Treatment-Related Toxicities, and Improving Quality of Life Among Vulnerable Patients With Lung Cancer
Phase: NA
Conditions: Lung Cancer, Small Cell, Lung Cancer Metastatic, Lung Cancer Recurrent, Treatment Side Effects, Lung Cancer, Non-small Cell, Nutritional Imbalance
Sponsor: Tufts University

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STUDY TITLE: NutriCare Intervention on Optimizing Nutritional Status, Reducing 
Treatment-Related Toxicities, and Improving Quality of Life Among Vulnerable Patients 
with Lung Cancer 
 
VERSION DATE: 5/5/2023 
 
 
 

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Instructions: The written description of the clinical study, including objective(s), design, and methods. 
It may also include relevant scientific background and statistical considerations. The written 
description of the statistical considerations for analyzing the data collected in the study. Includes 
how data are analyzed, what specific statistical methods are used for each analysis, and how 
adjustments are made for testing multiple variables. If some analysis methods require critical 
assumptions, the written description should allow data users to understand how those assumptions 
were verified 
 
1. Background and Rationale 
Patients with lung cancer are among the most malnourished of all oncology patients, with 
depletion of body stores of fat and lean mass due to inadequate nutritional intake.(1) Up 
to 69% of the patients with lung cancer are malnourished,(2-4) and nearly 35% have 
clinically significant weight loss of more than 10%, indicating severe malnutrition.(5) 
Patients with lung cancer who are malnourished are significantly more likely to develop 
treatment-related toxicities, resulting in interruptions in treatment, and reduced treatment 
dose and/or completion.(6, 7) The prevalence of malnutrition could be even higher among 
cancer patients who are economically disadvantaged, uninsured, racial/ethnic minorities, 
the elderly, and rural residents, secondary to significant barriers to accessing healthcare 
and nutritious foods.(8, 9) Timely, effective, and tailored nutrition interventions are a 
critical component in countering lung cancer disparities among vulnerable populations in 
the US. Growing evidence also suggests that gut microbiota play a large role in how foods 
affect the body, including for cancer outcomes. Evaluating the response to a nutrition 
intervention benefits from taking into account differences in the microbiomes of 
participants. 
 
We aim to assess the efficacy of an innovative intervention strategy to integrate nutrition 
into standard oncology care using the 5As model (Ask, Advise, Assess, Assist, and 
Arrange), an evidence-based behavioral counseling framework with proven application in 
other areas of clinical practice.(10, 11)To further address health inequities and limited 
access to nutritious foods among the most vulnerable patients with lung cancer, we will 
incorporate the provision of home-delivered medically tailored meals (MTM), an emerging 
promising strategy for improving the health outcomes of patients with several chronic 
diseases through achieving optimal nutritional status.(12, 13) 
 
2. Objectives  
 
We aim to evaluate the impact of an innovative nutrition intervention on improving the 
outcomes of patients with lung cancer. Specifically, we aim to:  
 
Specific Aim 1. To assess the efficacy of the intervention for optimizing nutritional intake, 
reducing food insecurity, minimizing unintentional weight loss, improving treatment 
compliance, reducing treatment-related toxicities, reducing hospitalization, readmissions, 
and emergency department (ED) visits, and improving gut microbiome among vulnerable 
patients with lung cancer. We will examine: (a) change in diet quality and intake of key 
food groups and nutrients as assessed by the National Cancer Institute Diet History 
Questionnaire (DHQ III); (b) change in food insecurity measured by the U.S. Department 

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of Agriculture (USDA) Household Food Security Survey; (c) change in weight and percent 
weight loss as measured in the clinic; (d) treatment completion (dose reductions and 
treatment delays) assessed by the treating medical oncologist; (e) treatment-related 
toxicities assessed by the treating medical oncologist using the National Cancer Institute 
(NCI) Common Toxicity Criteria for Adverse Events (CTCAE); (f) hospitalizations and ED 
visits assessed by medical record review and linkage with discharge data; and (g) change 
in gut microbiome via metagenomic whole shotgun sequencing (mWGS). 
 
Specific Aim 2. To evaluate the efficacy of the intervention for improving patient-reported 
outcomes among vulnerable patients with lung cancer. We will assess: (a) change in 
patient-reported symptoms (fatigue, pain, nausea, vomiting, appetite loss, and sleep 
disturbance) as assessed by a Patient-Reported Outcomes version of the CTCAE (PRO-
CTCAE); (b) change in quality of life as assessed by EORTC-QLQ-30 and EORTC-QLQ-
LC13; (c) change in anxiety assessed by GAD-7; and (d) change in depressive symptoms 
assessed by PHQ-9. 
 
 
3. Design and Methods  
 
3.1 Study Design  
Using a randomized controlled trial design, we aim to recruit 270 vulnerable patients with 
a newly diagnosed stage I-IV lung cancer from four sites (Fox Chase Cancer Center, 
Tufts Medical Center, M.D. Anderson Cancer Center, and Ohio State University) and 
randomize them equally into an intervention group (NutriCare) and an enhanced control 
group (NutriTool). There will be two cohorts for NutriCare with cohort 1 recruiting 150 
patients completing an 8-month intervention and cohort 2 recruiting 120 patients 
completing a 6-month intervention. 
The NutriTool (enhanced control) Group. Participants in the NutriTool group will 
receive a printed copy of a Nutrition Toolkit from providers and monthly emails with 
general nutrition information and healthy recipes.  
The NutriCare (intervention) Group. For participants randomized to the NutriCare 
Group, participants will additionally receive the home delivery of Medically Tailored Meals 
and remote nutrition counseling provided by registered dietitian nutritionists.  
• Medically Tailored Meals (MTMs): Medically tailored meals will be provided to 
participants in the intervention group for a total of 24 weeks for both cohorts. During 
the first 8 weeks of the intervention, 3 meals/day will be provided each week for a total 
of 168 meals per participant. It will be followed by less frequent meal provision during 
the subsequent 16 weeks following this schedule: 3 meals/day will be provided every 
other week for the next 8 weeks (a total of 84 meals per participant); and 3 meals/day 
will be provided every four weeks during the last 8 weeks (a total of 42 meals per 
participant). The number of meals provided to each participant may be adjusted 
according to participant’s preference and needs.  
• Nutrition Counseling:  Medical oncology providers will refer participants to oncology 
RDs for remotely delivered medical nutrition therapy counseling. For cohort 1, 

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participants will receive nutrition counseling for 8 months. The counseling will be 
provided on a weekly basis during the first 6 months and every other week during the 
last 2 months (for cohort 1 only). For cohort 2, participants will receive nutrition 
counseling for 6 months. The ultimate frequency of nutrition counseling being provided 
to each participant will also be adjusted according to the participant’s preference and 
needs.   
3.2. Methods 
 
Screening and Recruitment:  The clinical team at each site will assess patient’s eligibility 
based on inclusion and exclusion criteria. For patients who are determined eligible, the 
clinical team will consent the patient if there is interest. Consented patients will be 
randomized to either the NutriCare or the NutriTool group and asked to complete two pre-
intervention forms:  Contact Information Sheet that asks for their mailing address, phone 
number, and email address; Demographic and Health Survey that asks participants’ 
socio-demographics (such as education and marital status) and health behaviors (such 
as cigarette smoking, alcohol intake, and physical activity).  
 
Research Data Collection: Research data will be collected for all enrolled patients. 
Outcomes will be assessed at three time points (baseline, 3 months, and 6 months [for 
cohort 2] or 8 months [for cohort 1]) for most outcomes except weight, treatment 
completion, treatment-related toxicities, and hospitalization and ED visits will be assessed 
throughout the intervention.  
 
At baseline, 3 months, and 6 months [for cohort 2] or 8 months [for cohort 1]), outcome 
data will be collected for: (a) dietary intake patterns and quality using the National Cancer 
Institute (NCI)’s Dietary History Questionnaire III (DHQ III); (b) malnutrition risk using the 
Patient-Generated Subjective Global Assessment (PG-SGA) short form; (c) food security 
using the Household Food Security Survey Module developed by the U.S. Department of 
Agriculture (USDA); (d) participant-reported outcomes, including the participant-reported 
outcomes version of the CTCAE (PRO-CTCAE), the EORTC QLQ-C30 and EORTC 
QLQ-LC13, Generalized Anxiety Disorder (GAD-7); the Patient Health Questionnaire 
(PHQ-9); and (e) Gut microbiome via metagenomic whole shotgun sequencing (mWGS) 
based on stool samples.  
At each clinical visit, outcome data will be collected for (f) weight using calibrated 
electronic scales, (g) treatment-related toxicities using the NCI Common Toxicity Criteria 
for Adverse Events (CTCAE) version 5.0; and (h) hospitalization and emergency 
department (ED) visits through medical record review.  
Other research data including cancer diagnosis, treatment plan, medical conditions, 
medication use, and lifestyle factors will be collected at baseline. New medical conditions 
and medication use will be collected during the course the study. Treatment history will 
be updated at 3 months and 6 months [for cohort 2] or 8 months [for cohort 1]) through 
chart review. 
 
Laboratory tests: Gut microbiome sequencing:  upon receipt samples will be aliquoted 
into two cryovials and stored at -80C in a locked freezer until batch processing. Cryovials 

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will be labeled with coded study ids.  Upon study completion DNA will be purified 
(PowerFecal Pro, QIAGEN), sequencing libraries generated (QIAmp FX, QIAGEN) and 
sequencing performed (Illumina HiSeq 4000) on all samples ensuring data quality any 
remaining aliquots of stool samples will be destroyed. 
 
4. Statistical Analysis  
 
Descriptive statistics will first be compiled. Mean and standard deviation (and median and 
25th and 75th quartiles, if appropriate) will be reported for continuous variables. Counts 
and percentiles will be reported for binary and categorial variables. 
 
Intention-to-treat analysis will first be conducted. A series of unadjusted and adjusted 
models will be estimated. For the unadjusted models, the dependent variable will be each 
of the outcomes (see below). The main independent variables will be group assignment 
(binary), time (3-level categorical at baseline, 3-month, and 6 months [for cohort 2] or 8 
months [for cohort 1]), and their interaction terms.   
 
For inferential statistics, pairwise examinations between each time point will be conducted 
using paired sample t-test, Wilcoxon signed rank test, and chi-square test. Afterwards, 
we will apply generalized linear mixed-effects model since it can flexibly accommodate 
continuous, binary, as well as count dependent variables. Clustering at participant level 
within each site will be incorporated into the model as two-level nested random intercepts. 
If the site-level clustering deems negligible (indicated by intra-class correlation), we would 
simplify the model by only adjusting for participant-level clustering. Below we detail the 
analysis and modeling approach, followed by sample size justification. 
 
For Aim 1, we will assess changes in outcomes from baseline (T0) to 3 months (T3) and 
from baseline (T0) to 6-months (T6) [for cohort 2] or 8 months (T8) [for cohort 1] within 
the two groups in most outcomes including diet, malnutrition risk, food security, weight, 
and participant-reported outcomes (3 time points: T0, T3, and T6/T8) using paired-sample 
t-test (continuous outcome) and McNemar’s test (binary). Changes in weight and 
microbiome diversity (Simpson’s index) will be assessed throughout the intervention 
using repeated measures mixed-effects linear regression. Treatment completion and the 
number of treatment-related toxicities, rehospitalization, and ER visits will be compared 
at 6-months (T6) [for cohort 2] or 8 months (T8) [for cohort 1] between the two groups. To 
evaluate the efficacy of the NutriCare intervention, we will apply the “difference-in-
difference” approach in mixed-effects linear regression models. As an exploratory 
analysis, we will evaluate changes in weight and treatment-related toxicities are mediated 
by changes in gut microbiome. A subset of our outcomes such as knowledge will be 
collected in ordinal Likert’s scale format. We will apply the overall analysis scheme but 
modify the regression into a mixed-effects ordered logistic regression to better capture 
the change in ranks. 
 
For Aim 2, we will assess change in patient-reported outcomes in function and symptom 
domains from T0 to T3 and from T0 to T6/T8 within the two groups using paired sample 
t-test. Similar mixed-effects linear regression models will be used to evaluate the 

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effectiveness of the NutriCare intervention at the 3rd and 6th month [for cohort 2] or 8th 
month [for cohort 1].  
 
We propose the follow per-protocol analyses. First, a complete-case analysis will be 
conducted using cases with data collected at all three time points. Second, a dose-
response analysis will be conducted using the cases from the intervention group, 
adjusting for programmatic exposure such as total meals delivered and proportion of 
mean consumed. Third, a data missing pattern analysis will be performed to assess if we 
should employ imputation method to validate the findings from the intention-to-treat 
analysis. 
 
Power analysis 
Power calculations were performed for two of the outcomes: Healthy Eating Index and 
body weight. We chose these two based on the availability of reference literature with 
similar study setting as well as their immediate relevance to our study’s aims. With 
budgetary and time constraints optimized, the study will be able to recruit 270 participants 
at most. 
 
We estimated the power using the following procedures. First, we identified point and 
dispersion estimates from published literature and determine the monthly change in the 
outcomes. Second, we simulated the sample population (135 intervention, 135 control) 
at baseline, 3-month, and 6 months [for cohort 2] or 8 months [for cohort 1] with different 
degrees of monotonous loss-to-follow-up; the correlation between time points was set at 
0.6. Third, for each loss-to-follow-up scenario, we simulated 1,000 data sets, examined 
the p-value of the difference-in-difference (time x group interaction), and tallied if the null 
hypothesis was rejected. Finally, the power was computed by estimating the prevalence 
of null hypothesis rejections. The results are tabulated as follows: 
 
HEI: Anderson et. al. found 6.80 points increase in HEI for the intervention group, and 
3.05 points increase in HEI for the control group over 6 months.(14) The cross-sectional 
standard deviation was found to be about 9.27. 
 
Monotonous missing pattern 
(baseline  3m  6m/8m) 
Simulated power to detect a difference as 
extreme as that in the cited literature: 
0%  15%  20% 
95.5% 
0%  20%  35% 
92.1% 
0%  25%  40% 
89.6% 
 
Body weight: Kiss et. al. found a 1.0 kg increase in body for the intervention group, and 
0.10 points increase in body weight for the control group over 5 months.(15) The cross-
sectional standard deviation was found to be about 3.3 kg. 
 
Monotonous missing pattern 
(baseline  3m  6m/8m) 
Simulated power to detect a difference as 
extreme as that in the cited literature: 
0%  15%  20% 
90.3% 
0%  20%  35% 
82.9% 
0%  25%  40% 
80.2% 
 

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