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Grant: Op. Grant: NWHRI: Innovation Fund - Translational Research

McMaster University

$600,000.00

Development of a novel risk prediction model for chronic postsurgical pain after breast cancer surgery

FY 2023-24

Summary
Breast cancer is the most common cancer in women worldwide. Survival after breast cancer surgery is high, approaching 90% at 5 years; however, 1 in 4 survivors develop moderate to severe chronic post-surgical pain (CPSP). Identification of patients at high risk of pain could inform shared decision-making and direct prevention strategies. Current pain risk prediction models have been limited by small sample sizes, inadequate capture of risk factors, and poor validity and reliability. The optimal approach for developing risk prediction models requires individual data from patients. We aim to develop a novel risk prediction model and related knowledge translation (KT) strategies for the early identification of breast cancer patients at high risk of developing CPSP. We will conduct systematic reviews of observational studies and trials that explored predictors of CPSP after breast cancer surgery, and establish an international consortium and data repository by inviting principal investigators of relevant studies to share individual patient data (IPD). We will then perform an advanced IPD meta-analysis to identify predictors of CPSP, and develop a novel risk prediction model and KT strategies to early identify high-risk patients of developing CPSP after breast cancer surgery for targeted prevention. Our team is well-positioned to complete this work, with key members who have led eligible studies, and who collaborate closely with relevant national and international research centers, societies, and patient networks. We have already secured commitments from investigators to provide data from ~56,000 individual patients. The anticipated outcomes will be the establishment of an international consortium and repository of individual patient data, the development of a novel prediction model and related KT strategies to support early identification of breast cancer patients at high risk of developing CPSP, who may then be candidates for interventions to improve their prognosis.
Program
Op. Grant: NWHRI: Innovation Fund - Translational Research
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
CIHR Funding Decisions Database

As published

Id
505341
Name
Wang Li
Region
Ontario
Country
Canada
Orgtype
Institution Paid; Research Institution
Instname2
Cancer Research
Theme2
Clinical
Prcname2
National Women's Health Research Initiative: Innovation Fund
Projecttitle
Development of a novel risk prediction model for chronic postsurgical pain after breast cancer surgery
Programtype2
Operating Grants
Orgnameinp2
McMaster University
Cihramount2
$600,000
Cihrequipment2
$0
Programname2
Op. Grant: NWHRI: Innovation Fund - Translational Research
Keyworddelim
Breast Cancer; Chronic Pain; Chronic Post-Surgical Pain; Data Repository; International Consortium; Knowledge Translation; Prediction Model; Predictors; Risk Assessment Tool; Risk Factors
Abstract
Breast cancer is the most common cancer in women worldwide. Survival after breast cancer surgery is high, approaching 90% at 5 years; however, 1 in 4 survivors develop moderate to severe chronic post-surgical pain (CPSP). Identification of patients at high risk of pain could inform shared decision-making and direct prevention strategies. Current pain risk prediction models have been limited by small sample sizes, inadequate capture of risk factors, and poor validity and reliability. The optimal approach for developing risk prediction models requires individual data from patients. We aim to develop a novel risk prediction model and related knowledge translation (KT) strategies for the early identification of breast cancer patients at high risk of developing CPSP. We will conduct systematic reviews of observational studies and trials that explored predictors of CPSP after breast cancer surgery, and establish an international consortium and data repository by inviting principal investigators of relevant studies to share individual patient data (IPD). We will then perform an advanced IPD meta-analysis to identify predictors of CPSP, and develop a novel risk prediction model and KT strategies to early identify high-risk patients of developing CPSP after breast cancer surgery for targeted prevention. Our team is well-positioned to complete this work, with key members who have led eligible studies, and who collaborate closely with relevant national and international research centers, societies, and patient networks. We have already secured commitments from investigators to provide data from ~56,000 individual patients. The anticipated outcomes will be the establishment of an international consortium and repository of individual patient data, the development of a novel prediction model and related KT strategies to support early identification of breast cancer patients at high risk of developing CPSP, who may then be candidates for interventions to improve their prognosis.
Competitiondate
202403
Approvedterm2
2 yrs 10 mths
Pinamesdelim
Wang, Li; Coburn, Natalie; Crijns, Anne P; Khan, James S; Busse, Jason W; Sadeghirad, Behnam; Shu, Xiao-Ou
Approvedterm
2 ans 10 mois
Cihrcontribution2
IGH~ $193,333~ $0~SPOR~ $406,667~ $0~
McMaster University - $600,000 Grant (2023-24) | Canada Spends