A clinical prediction rule for identifying South African colorectal cancer patients who will fail to engage in oncology care

用于识别无法参与肿瘤护理的南非结直肠癌患者的临床预测规则

• 批准号: 10689780
• 负责人: Yoshan Moodley
• 金额: $ 6.08万
• 依托单位: UNIVERSITY OF KWAZULU-NATAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-02 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689780
• 关键词: Address; Adherence; Advisory Committees; Algorithmic Analysis; Applications Grants; Appointment; Artificial Intelligence; Award; Biopsy; Books; Caring; Characteristics; Clinic; Clinic Visits; Clinical; Colorectal Cancer; Communities; Competence; Country; Coupled; Data; Data Collection; Data Sources; Decision Making; Diagnosis; Disease; Disease Management; Electronics; Enrollment; Epidemiologist; Failure; Funding; Future; Government; Grant; Health Care Sector; Healthcare Systems; Intention; Intervention; Laboratories; Literature; Logistic Regressions; Machine Learning; Malignant Neoplasms; Malignant neoplasm of gastrointestinal tract; Mentors; Meta-Analysis; Newly Diagnosed; Oncologist; Oncology; Operative Surgical Procedures; Outcome; Outcome Study; Patient-Focused Outcomes; Patients; Population; Prevention strategy; Program Development; Public Health; Public Sector; Publishing; Questionnaires; Research; Research Assistant; Research Personnel; Resource-limited setting; Resources; Review Literature; South Africa; South African; Specialist; Statistical Data Interpretation; Surgical Oncologist; Surveys; Time; Training; Universities; Validation; Visit; Vocational Guidance; advanced disease; burden of illness; care systems; career development; clinical practice; clinical predictors; cohort; colon cancer patients; colorectal cancer screening; comorbidity; data registry; design; electronic data; electronic registry; feature selection; flexibility; gradient boosting; high risk; improved; machine learning algorithm; machine learning method; model development; mortality; neural network; novel; patient registry; predictive modeling; prospective; random forest; scale up; screening; screening program; sociodemographics; statistics; support vector machine; systematic review; tool

PROJECT SUMMARY/ABSTRACT Colorectal cancer (CRC) is an emerging public health problem in South Africa (SA). Preliminary research from KwaZulu-Natal (KZN), SA shows that 23% of newly diagnosed CRC patients in the public healthcare sector will fail to engage in oncology care (FEOC). This complicates oncologists’ and oncologic surgeons’ efforts to effectively manage CRC disease in this setting. There are a variety of resource-intensive interventions for addressing FEOC. However, implementing these in all newly diagnosed CRC patients would be unfeasible, given the high patient volumes and existing resource constraints in the SA public healthcare sector. A clinical prediction rule (CPR) is proposed for identifying newly diagnosed SA CRC patients who are at high risk of FEOC, thereby enabling more efficient targeting of these specific, high risk patients for interventions which seek to address FEOC. The CPR will be developed by: 1) Identifying potential predictors of FEOC in CRC populations through a systematic review of the literature and a survey of 109 patients; 2) Constructing an appropriate electronic data collection tool that will streamline prospective data collection for these potential predictors and the study outcome, FEOC, from a cohort of 398 newly diagnosed CRC patients; and 3) Applying machine learning methods to the data to derive and internally validate the CPR. The CPR will be an important first step in tackling the high FEOC rates amongst newly diagnosed CRC patients in KZN, SA and will ultimately contribute toward improving both CRC disease management and patient outcomes in this setting. The proposed K43 research has the potential to be scaled up to the national level, and this will be the basis of a future R01 grant application. Dr. Moodley (K43 applicant) wants to become an independent gastrointestinal cancer epidemiologist and a global leader in CPRs, particularly CPRs that seek to address breakdowns in linkage to care and reduce the burden of disease attributed to gastrointestinal cancers, such as CRC. This award will provide him with protected time to conduct the proposed research and engage in an intensive career development program while based at one of the top universities in SA, the University of KwaZulu-Natal. He will be mentored by esteemed senior researchers from SA (Prof. Thandinkosi Madiba, University of KwaZulu-Natal) and the US (Prof. Ravi Pokala Kiran, Columbia University) who have a strong track record of mentoring young researchers. A research advisory committee, comprised of other senior researchers, will provide additional research and career guidance to Dr. Moodley. This will be coupled with didactic and “hands on” training in the core competencies required to develop CPRs. It is envisioned that by the end of his K43 training, Dr. Moodley will have developed the proposed CPR and generated new preliminary data. He will use this new data to successfully apply for an R01 grant, thus establishing his independence as gastrointestinal cancer epidemiologist and a global leader in CPRs.

项目总结/文摘

项目成果

Self-adherence to post-colonoscopy consults in patients undergoing diagnostic colonoscopy: Findings from a cross-sectional, quantitative survey at a South African quaternary hospital.

• DOI: 10.1371/journal.pone.0288752
• 发表时间: 2023
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Moodley, Yoshan;van Wyk, Jacqueline;Ning, Yuming;Wexner, Steven;Gounden, Cathrine;Naidoo, Vasudevan;Kader, Shakeel;Neugut, Alfred I.;Kiran, Ravi P.
• 通讯作者: Kiran, Ravi P.