Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Risk prediction rule for advanced neoplasia on screening colonoscopy for average-risk individuals
Ala I Sharara, Ali El Mokahal, Ali H Harb, Natalia Khalaf, Fayez S Sarkis, Mustapha M El-Halabi, Nabil M Mansour, Ahmad Malli, Robert Habib
Ala I Sharara, Ali El Mokahal, Division of Gastroenterology, Department of Internal Medicine, American University of Beirut Medical Center, Beirut 1107 2020, Lebanon
Ali H Harb, Digestive and Liver Diseases Division, University of Texas-Southwestern, Dallas, TX 75390, United States
Natalia Khalaf, Division of Gastroenterology, Department of Internal Medicine, Baylor College of Medicine, Houston, TX 77030, United States
Fayez S Sarkis, Division of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, MO 66160, United States
Mustapha M El-Halabi, Division of Gastroenterology, St Elizabeth Healthcare, Crestview Hills, KY 41017, United States
Nabil M Mansour, Department of Internal Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX 77030, United States
Ahmad Malli, Gastroenterology, Hennepin Healthcare, Minneapolis, MN 55404, United States
Robert Habib, Department of Internal Medicine, American University of Beirut, Beirut 1107 2020, Lebanon
Author contributions: Sharara AI contributed to study conception, design and supervision; oversight of data collection and interpretation; review of literature; drafting of the manuscript; guarantor of the study; El Mokahal A and Harb AH contributed to review of the literature, regulatory administration, data entry and analysis, drafting and critical review of the manuscript; Khalaf N contributed to review of the literature, data analysis, critical review and editing of the manuscript; Sarkis F, El-Halabi MM, Mansour NM and Malli A contributed to regulatory administration, data entry and analysis, critical review and editing of the manuscript; Habib R contributed to interpretation of data, statistical expertise, critical review and editing of the manuscript; all authors have read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the institutional review board of the American University of Beirut Medical Center (AUBMC).
Informed consent statement: All study participants or their legal guardian provided written consent prior to study enrollment.
Conflict-of-interest statement: The authors of this manuscript have no conflict of interest to disclose.
Data sharing statement: No additional data available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Ala I Sharara, AGAF, FACG, FRCP, MD, Attending Doctor, Professor, Division of Gastroenterology, Department of Internal Medicine, American University of Beirut Medical Center, Cairo Street, PO Box 11-0236/16-B, Beirut 1107 2020, Lebanon. as08@aub.edu.lb
Received: April 17, 2020
Peer-review started: April 17, 2020
First decision: May 15, 2020
Revised: May 20, 2020
Accepted: September 12, 2020
Article in press: September 12, 2020
Published online: October 7, 2020
ARTICLE HIGHLIGHTS
Research background
Colorectal cancer is the third leading cause of cancer globally. Screening for colorectal cancer has been shown to decrease colon cancer mortality. While colonoscopy is the best modality to screen for colon cancer, it is also the most expensive. In resource-limited countries, risk stratification may be useful to optimize colorectal cancer screening.
Research motivation
Few prospective risk prediction models exist for advanced neoplasia (AN) in true average-risk individuals.
Research objectives
To create a validated risk prediction model to predict advanced neoplasia in average risk patients.
Research methods
980 consecutive, average-risk, asymptomatic patients undergoing their first screening colonoscopy were prospectively enrolled. We completed a detailed assessment of risk factors, and collected results of endoscopy findings from the endoscopy and pathology reports. Group comparisons of categorical factors were done using χ2, and for quantitative variables independent t-test or Mann Whitney tests were used based on normality of data. Multivariate logistic regression analysis was performed to identify independent predictors of AN in our cohort. Discriminatory ability of the model was assessed through the area under the curve (AUC) of the receiver-operator-characteristic curve. Model calibration was examined through observed vs expected rates of advanced neoplasia as the derived probability of AN decile groups. Internal validation of the model was done by bootstrapping. The multivariate model coefficients were used to present the percent risk of AN in nomogram format as a function of age and separately for different categories of BMI. The model coefficients were then used to develop a risk calculator.
Research results
Adenoma detection and advanced neoplasia detection rates were 36.6% (F 29%: M 45%; P < 0.001) and 5.1% (F 3.8%; M 6.5%) respectively. On multivariate analysis, the predictors of AN were age [1.036 (1.00-1.07); P = 0.048], BMI [overweight 2.21 (0.98-5.00); obese 3.54 (1.48-8.50); P = 0.018], smoking [< 40 pack-years 2.01 (1.01-4.01); ≥ 40 pack-years 3.96 (1.86-8.42); P = 0.002], and daily red meat consumption [2.02 (0.92-4.42) P = 0.079]. The model had an AUC = 0.73 (CI = 0.66-0.79, P < 0.001) and R2 = 0.8509.
Research conclusions
The prevalence of adenoma and AN in the average-risk Lebanese population is 5.1%, similar to those in the West. Age, smoking and BMI are important predictors of AN in our study cohort, and our model had good calibration and discrimination.
Research perspectives
In this project, we developed a risk prediction tool for advanced neoplasia at first screening colonoscopy for average risk individuals. We provide an important platform for improved risk-stratification for screening programs in resource limiting settings, although external validation of our model is needed.
