Improving the screening of colorectal neoplasia by a mathematical model. Retrospective study (original) (raw)
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Introduction: Colonoscopy is a maximum accuracy diagnosis method for colorectal cancer and polyps, allowing multiple and guided biopsy of the observed lesions in the colonic lumen, but also the resection of the polyps. Aim: The purpose of this study was to perform an retrospective analyses of dates obtained from colonoscopy in order to create a mathematical model (risk calculator) which allows the selection of the patients with the highest probability to develop colorectal cancer. Matherial and methods: Dates from 2780 colonoscopies done in Timisoara County Clinical Emergency Hospital, during the period January 2005 to December 2008 were scrutinized. Results: 321 (11.5%) patients have had cancer. The classification of this group of 321 cancer cases by sex, has shown an increased incidence for men 193 (60.2 %) cases. The incidence of colorectal cancer increases with age. Based on our statistic results and other studies developed in the world, we developed a mathematical prediction scheme for the colorectal cancer risk. The mathematical prediction scheme considers: age, sex, weigh, height, personal history for polyps, and familial history of colorectal cancer, diet composition, physical activity and obesity. Conclusions: The development of this risk calculator for the colorectal cancer allows the selection of those particular subjects that have the highest probability for a colonic pathology, so that they would be the first to benefit from colonoscopy, without being necessary to use other screening methods.
Risk prediction of colorectal cancer
International journal of health sciences
Background: Colorectal cancer (CRC) is considered as a significant worldwide health problem1 and there is strong suggestion that screening can reduce CRC mortality rate. Thus, the effectiveness of awareness toward screening may be encouraged screening performance and screening intensity to those at highest risk. Aim: to find out community awareness regarding risk prediction of colorectal cancer at port-said city , Egypt. Subject and methods: A descriptive cross-sectional study was carried out using A purposive sampling on 220 female from age more than 20 year at outpatient clinics from governmental hospitals (Alsalam general , Port-fouad general hospital Algawhara hospital at Port-Said city, Egypt responded to the self-administered online survey. Statistical tools such as frequency, percentage, were used for the descriptive analysis. Results: there are a statistically significant relation was found in all items of participant's attitude regarding colorectal cancer with ps= (0....
Prediction of colorectal cancer risk among adults in a lower middle-income country
Journal of Gastrointestinal Oncology
Background: Globally, colorectal cancer (CRC) is ranked as the third most common cancer in men and the second in women. Use of a simple, validated risk prediction tool will offer a low-cost mechanism to identify the high-risk individuals for CRC. This will increase efficient use of limited resources and early identification of patients. The aim of our study was to develop and validate a risk prediction model for developing CRC for Sri Lankan adults. Methods: The risk predictors were based on the risk factors identified through a logistic regression model along with expert opinion. A case control design utilizing 65 CRC new cases and 65 hospital controls aged 30 years or more was used to assess the criterion validity and reliability of the model. The information was obtained using an interviewer administered questionnaire based on the risk prediction model. Results: The developed model consisted of eight predictors with an area under the curve (AUC) of 0.849 (95% CI: 0.8 to 0.9, P<0.001). It has a sensitivity of 76.9%, specificity of 83.1%, positive predictive value (PPV) of 82.0%, negative predictive value (NPV) of 79.3%. Positive and negative likelihood ratios are 4.6 and 0.3. Test re-test reliability revealed a Kappa coefficient of 0.88. Conclusions: The model developed to predict the risk of CRC among adults aged 30 years and above was proven to be valid and reliable and it is an effective tool to be used as the first step to identify the high-risk population who should be referred for colonoscopy examination.
2016
We aimed to develop a screening scoring scheme for colorectal cancer (CRC). The baseline and clinical information from the patients at the outpatient unit of Surgery Department, Faculty of Medicine Ramathibodi Hospital was used to develop a screening model. A binary logistic regression model was used to identify the independent risk variables and the beta-coefficients and a simple point scoring system was developed. The risk variables scoring system was based on 8 risk parameters: gender, family history of CRC in the first-degree relatives, exercise, bleeding per rectum, abdominal pain, weight loss, low density lipoprotein level and high density lipoprotein. The total score ranged from 0 to 11.5. The likelihood of colorectal cancer in people with low risk (scores 5.5) was 6.50. The receiver operating characteristic (ROC) curve of the study was 0.85 (95% CI 0.81-0.90). Our screening scoring system is simple and easy to use especially in primary care units. However, this study is the ...
Turkish Journal of Gastroenterology
Background: Colorectal cancer is one of the most commonly diagnosed types of cancer worldwide. An early diagnosis and detection of colon cancer and polyp can reduce mortality and morbidity from colorectal cancer. Even though there are a variety of options in screening tests, the question remains on which test is the most effective for the early detection of colorectal cancer. In this prospective study, we aimed to develop a simple, useful, effective, and reliable scoring system to detect colon polyp and colorectal cancer. Methods: We enrolled 6508 subjects over the age of 18 from 16 centers, with colonoscopy screening. The age, smoking status, alcohol consumption, body mass index, polyp incidence, polyp size, number and localization, and pathologic findings were recorded. Results: The age, male gender, obesity, smoking, and family history were found as independent risk factors for adenomatous polyp. We have developed a new scoring system which can be used for these factors. With a score of 4 or above, we found the following: sensitivity 81%, specificity 40%, positive predictive value 25.68%, and negative predictive value 89.84%, for adenomatous polyp detection; and sensitivity 96%, specificity 39%, positive predictive value 3.35%, negative predictive value 99.29%, for colorectal cancer detection. Conclusion: Even though the first colorectal cancer screening worldwide is generally performed for individuals over 50 years of age, we recommend that screening for colorectal cancer might begin for those under 50 years of age as well. Individuals with a score ≥ 4 must be included in the screening tests for colorectal cancer.
Medicine
A good clinical prediction score can help in the risk stratification of patients with colorectal cancer (CRC) undergoing colonoscopy screening. The aim of our study was to compare model performance of binary logistic regression (BLR), polytomous logistic regression (PLR), and classification and regression tree (CART) between the clinical prediction scores of advanced colorectal neoplasia (ACN) in asymptomatic Thai patients. We conducted a cross-sectional study of 1311 asymptomatic Thai patients to develop a clinical prediction model. The possible predictive variables included sex, age, body mass index, family history of CRC in first-degree relatives, smoking, diabetes mellitus, and the fecal immunochemical test in the univariate analysis. Variables with a P value of .1 were included in the multivariable analysis, using the BLR, CART, and PLR models. Model performance, including the area under the receiver operator characteristic curve (AUROC), was compared between the model types. ACN was diagnosed in 53 patients (4.04%). The AUROCs were not significantly different between the BLR and CART models for ACN prediction with an AUROC of 0.774 (95% confidence interval [95% CI]: 0.706-0.842) and 0.765 (95% CI: 0.698-0.832), respectively (P = .712). A significant difference was observed between the PLR and CART models in predicting average to moderate ACN risk with an AUROC of 0.767 (95% CI: 0.695-0.839 vs AUROC 0.675 [95% CI: 0.599-0.751], respectively; P = .009). The BLR and CART models yielded similar accuracies for the prediction of ACN in Thai patients. The PLR model provided higher accuracy for ACN prediction than the CART model. Abbreviations: ACN = advanced colorectal neoplasia, AUROC = area under the receiver operator characteristic curve, BLR = binary logistic regression, BMI = body mass index, CART = classification and regression tree, CRC = colorectal cancer, FIT = fecal immunochemical test, PLR = polytomous logistic regression.
A score to estimate the likelihood of detecting advanced colorectal neoplasia at colonoscopy
Gut, 2014
Objective This study aimed to develop and validate a model to estimate the likelihood of detecting advanced colorectal neoplasia in Caucasian patients. Design We performed a cross-sectional analysis of database records for 40-year-old to 66-year-old patients who entered a national primary colonoscopy-based screening programme for colorectal cancer in 73 centres in Poland in the year 2007. We used multivariate logistic regression to investigate the associations between clinical variables and the presence of advanced neoplasia in a randomly selected test set, and confirmed the associations in a validation set. We used model coefficients to develop a risk score for detection of advanced colorectal neoplasia. Results Advanced colorectal neoplasia was detected in 2544 of the 35 918 included participants (7.1%). In the test set, a logistic-regression model showed that independent risk factors for advanced colorectal neoplasia were: age, sex, family history of colorectal cancer, cigarette smoking (p<0.001 for these four factors), and Body Mass Index (p=0.033). In the validation set, the model was well calibrated (ratio of expected to observed risk of advanced neoplasia: 1.00 (95% CI 0.95 to 1.06)) and had moderate discriminatory power (c-statistic 0.62). We developed a score that estimated the likelihood of detecting advanced neoplasia in the validation set, from 1.32% for patients scoring 0, to 19.12% for patients scoring 7-8. Conclusions Developed and internally validated score consisting of simple clinical factors successfully estimates the likelihood of detecting advanced colorectal neoplasia in asymptomatic Caucasian patients. Once externally validated, it may be useful for counselling or designing primary prevention studies.
BMC Gastroenterology, 2011
Background: Colonoscopy is an invasive and costly procedure with a risk of serious complications. It would therefore be useful to prioritise colonoscopies by identifying people at higher risk of either cancer or premalignant adenomas. The aim of this study is to assess a model that identifies people with colorectal cancer, advanced, large and small adenomas. Methods: Patients seen by gastroenterologists and colorectal surgeons between April 2004 and December 2006 completed a validated, structured self-administered questionnaire prior to colonoscopy. Information was collected on symptoms, demographics and medical history. Multinomial logistic regression was used to simultaneously assess factors associated with findings on colonoscopy of cancer, advanced adenomas and adenomas sized 6 -9 mm, and ≤ 5 mm. The area under the curve of ROC curve was used to assess the incremental gain of adding demographic variables, medical history and symptoms (in that order) to a base model that included only age. Results: Sociodemographic variables, medical history and symptoms (from 8,204 patients) jointly provide good discrimination between colorectal cancer and no abnormality (AUC 0.83), but discriminate less well between adenomas and no abnormality (AUC advanced adenoma 0.70; other adenomas 0.67). Age is the dominant risk factor for cancer and adenomas of all sizes. Having a colonoscopy within the last 10 years confers protection for cancers and advanced adenomas.
Validation of a Colorectal Cancer Risk Prediction Model Among White Patients Age 50 Years and Older
Journal of Clinical Oncology, 2009
Purpose Validation of an absolute risk prediction model for colorectal cancer (CRC) by using a large, population-based cohort. Patients and Methods The National Institutes of Health (NIH) –American Association of Retired Persons (AARP) diet and health study, a prospective cohort study, was used to validate the model. Men and women age 50 to 71 years at baseline answered self-administered questionnaires that asked about demographic characteristics, diet, lifestyle, and medical histories. We compared expected numbers of CRC patient cases predicted by the model to the observed numbers of CRC patient cases identified in the NIH-AARP study overall and in subgroups defined by risk factor combinations. The discriminatory power was measured by the area under the receiver-operating characteristic curve (AUC). Results During an average of 6.9 years of follow-up, we identified 2,092 and 832 incident CRC patient cases in men and women, respectively. The overall expected/observed ratio was 0.99 ...
Risk Prediction Models for Colorectal Cancer: A Review
Cancer Epidemiology, Biomarkers & Prevention, 2011
Risk prediction models are important to identify individuals at high risk of developing the disease who can then be offered individually tailored clinical management, targeted screening and interventions to reduce the burden of disease. They are also useful for research purposes when attempting to identify new risk factors for the disease. In this article, we review the risk prediction models that have been developed for colorectal cancer and appraise their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for colorectal cancer risk prediction. We conclude that there is no model that sufficiently covers the known risk factors for colorectal cancer that is suitable for assessment of people from across the full range of risk and that a new comprehensive model is needed. Cancer Epidemiol Biomarkers Prev; 21(3); 398–410. ©2011 AACR.