Symptom based referral criteria for colorectal cancer (CRC) detection are the cornerstone of the strategy to improve prognosis in CRC. In 2017, the National Institute for Health and Care Excellence ...(NICE) updated their referral criteria (2017 NG12). Recently, several studies have evaluated the faecal haemoglobin (f-Hb) concentration in this setting. The aim of this study is to evaluate the diagnostic accuracy of the 2017 NG12 referral criteria and to compare them with the CG27 referral criteria, the f-Hb concentration and two f-Hb based prediction model: COLONPREDICT and FAST Score.
This is a post-hoc diagnostic test study performed within the COLONPREDICT study database (1572 patients, CRC prevalence 13.6%). We assessed symptoms, the 2017 NG12 and CG27 referral criteria and determined the f-Hb before performing a colonoscopy. We compared the discriminatory ability using the area under the curve (AUC) and the sensitivity and specificity at pre-stablished thresholds with the McNemar's test.
The 2017 NG12 referral criteria discriminatory ability (AUC 0.53; 95% confidence interval- CI 0.49-0.57) was inferior to the CG27 version (AUC 0.59; 95% CI 0.55-0.63; p = 0.01), the f-Hb concentration (AUC 0.86; 95% CI 0.84-0-89; p < 0.001), the COLONPREDICT Score (AUC 0.92; 95% CI 0.91-0.94; p < 0.001) or the FAST Score (AUC 0.87; 95% CI 0.85-0.89; p < 0.001). The number of patients meeting each criteria were as follows: 2017 NG12 and CG27 = 94.1% and 52.2%; f-Hb ≥20 and ≥ 10 μg/g faeces = 38.6 and 44.3%; COLONPREDICT Score ≥ 5.6 and ≥ 3.2 = 29.4 and 63.2% and FAST Score ≥ 4.50 and ≥ 2.12 = 37.1 and 87.0%. The 2017 NG12 criteria were more sensitive (100%) than the CG27 criteria (68.2%), the f-Hb (≥20 μg/g) (91.2%), the f-Hb (≥10 μg/g) (93.5%), the COLONPREDICT Score (≥5.6) (90.1%) and the FAST Score (≥4.50) (89.8%) (p ≤ 0.001) and equivalent to the COLONPREDICT Score (≥3.5) (99.5%) or the FAST Score (≥2.12) (100.0%) (p = 1). However, their specificity (6.8%) was significantly lower than any of the evaluated criteria (50.3%, 69.6%, 63.4%, 78.7%, 45.8%, 71.3%, 13.9%; p < 0.001).
Referral criteria based on f-Hb measurement, either as a single test or within prediction models, are more accurate than symptom-based referral criteria for CRC detection in symptomatic patients.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, ...hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.
To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC.
A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots.
From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models.
Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Prediction models for colorectal cancer (CRC) detection in symptomatic patients, based on easily obtainable variables such as fecal haemoglobin concentration (f‐Hb), age and sex, may simplify CRC ...diagnosis. We developed, and then externally validated, a multivariable prediction model, the FAST Score, with data from five diagnostic test accuracy studies that evaluated quantitative fecal immunochemical tests in symptomatic patients referred for colonoscopy. The diagnostic accuracy of the Score in derivation and validation cohorts was compared statistically with the area under the curve (AUC) and the Chi‐square test. 1,572 and 3,976 patients were examined in these cohorts, respectively. For CRC, the odds ratio (OR) of the variables included in the Score were: age (years): 1.03 (95% confidence intervals (CI): 1.02–1.05), male sex: 1.6 (95% CI: 1.1–2.3) and f‐Hb (0–<20 µg Hb/g feces): 2.0 (95% CI: 0.7–5.5), (20‐<200 µg Hb/g): 16.8 (95% CI: 6.6–42.0), ≥200 µg Hb/g: 65.7 (95% CI: 26.3–164.1). The AUC for CRC detection was 0.88 (95% CI: 0.85–0.90) in the derivation and 0.91 (95% CI: 0.90–093; p = 0.005) in the validation cohort. At the two Score thresholds with 90% (4.50) and 99% (2.12) sensitivity for CRC, the Score had equivalent sensitivity, although the specificity was higher in the validation cohort (p < 0.001). Accordingly, the validation cohort was divided into three groups: high (21.4% of the cohort, positive predictive value—PPV: 21.7%), intermediate (59.8%, PPV: 0.9%) and low (18.8%, PPV: 0.0%) risk for CRC. The FAST Score is an easy to calculate prediction tool, highly accurate for CRC detection in symptomatic patients.
What's new?
Lower gastrointestinal symptoms potentially indicative of colorectal cancer (CRC) are a common reason for physician visits. While the probability that any one of those symptoms is associated with CRC is low, identifying patients for further screening remains a challenge. Here, the possibility of improving CRC diagnostic accuracy and risk stratification was explored using a three‐variable FAST Score based on fecal hemoglobin concentration, age, and sex. Among symptomatic patients referred to colonoscopy, the FAST Score prediction model identified three risk groups, the lowest of which ruled out CRC. Threshold scores were sensitive across variables, including country, age, and sex.
To forecast the annual burden of type 2 diabetes and related socio-demographic disparities in Belgium until 2030.
This study utilized a discrete-event transition microsimulation model. A synthetic ...population was created using 2018 national register data of the Belgian population aged 0-80 years, along with the national representative prevalence of diabetes risk factors obtained from the latest (2018) Belgian Health Interview and Examination Surveys using Multiple Imputation by Chained Equations (MICE) as inputs to the Simulation of Synthetic Complex Data (simPop) model. Mortality information was obtained from the Belgian vital statistics and used to calculate annual death probabilities. From 2018 to 2030, synthetic individuals transitioned annually from health to death, with or without developing type 2 diabetes, as predicted by the Finnish Diabetes Risk Score, and risk factors were updated via strata-specific transition probabilities.
A total of 6722 95% UI 3421, 11,583 new cases of type 2 diabetes per 100,000 inhabitants are expected between 2018 and 2030 in Belgium, representing a 32.8% and 19.3% increase in T2D prevalence rate and DALYs rate, respectively. While T2D burden remained highest for lower-education subgroups across all three Belgian regions, the highest increases in incidence and prevalence rates by 2030 are observed for women in general, and particularly among Flemish women reporting higher-education levels with a 114.5% and 44.6% increase in prevalence and DALYs rates, respectively. Existing age- and education-related inequalities will remain apparent in 2030 across all three regions.
The projected increase in the burden of T2D in Belgium highlights the urgent need for primary and secondary preventive strategies. While emphasis should be placed on the lower-education groups, it is also crucial to reinforce strategies for people of higher socioeconomic status as the burden of T2D is expected to increase significantly in this population segment.
Risk prediction models for colorectal cancer (CRC) detection in symptomatic patients based on available biomarkers may improve CRC diagnosis. Our aim was to develop, compare with the NICE referral ...criteria and externally validate a CRC prediction model, COLONPREDICT, based on clinical and laboratory variables.
This prospective cross-sectional study included consecutive patients with gastrointestinal symptoms referred for colonoscopy between March 2012 and September 2013 in a derivation cohort and between March 2014 and March 2015 in a validation cohort. In the derivation cohort, we assessed symptoms and the NICE referral criteria, and determined levels of faecal haemoglobin and calprotectin, blood haemoglobin, and serum carcinoembryonic antigen before performing an anorectal examination and a colonoscopy. A multivariate logistic regression analysis was used to develop the model with diagnostic accuracy with CRC detection as the main outcome.
We included 1572 patients in the derivation cohort and 1481 in the validation cohorts, with a 13.6 % and 9.1 % CRC prevalence respectively. The final prediction model included 11 variables: age (years) (odds ratio OR 1.04, 95 % confidence interval CI 1.02-1.06), male gender (OR 2.2, 95 % CI 1.5-3.4), faecal haemoglobin ≥20 μg/g (OR 17.0, 95 % CI 10.0-28.6), blood haemoglobin <10 g/dL (OR 4.8, 95 % CI 2.2-10.3), blood haemoglobin 10-12 g/dL (OR 1.8, 95 % CI 1.1-3.0), carcinoembryonic antigen ≥3 ng/mL (OR 4.5, 95 % CI 3.0-6.8), acetylsalicylic acid treatment (OR 0.4, 95 % CI 0.2-0.7), previous colonoscopy (OR 0.1, 95 % CI 0.06-0.2), rectal mass (OR 14.8, 95 % CI 5.3-41.0), benign anorectal lesion (OR 0.3, 95 % CI 0.2-0.4), rectal bleeding (OR 2.2, 95 % CI 1.4-3.4) and change in bowel habit (OR 1.7, 95 % CI 1.1-2.5). The area under the curve (AUC) was 0.92 (95 % CI 0.91-0.94), higher than the NICE referral criteria (AUC 0.59, 95 % CI 0.55-0.63; p < 0.001). On the basis of the thresholds with 90 % (5.6) and 99 % (3.5) sensitivity, we divided the derivation cohort into three risk groups for CRC detection: high (30.9 % of the cohort, positive predictive value PPV 40.7 %, 95 % CI 36.7-45.9 %), intermediate (29.5 %, PPV 4.4 %, 95 % CI 2.8-6.8 %) and low (39.5 %, PPV 0.2 %, 95 % CI 0.0-1.1 %). The discriminatory ability was equivalent in the validation cohort (AUC 0.92, 95 % CI 0.90-0.94; p = 0.7).
COLONPREDICT is a highly accurate prediction model for CRC detection.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BackgroundColorectal cancer (CRC) is the second most prevalent cancer in Europe, with one-fifth of cases attributable to unhealthy lifestyles. Risk prediction models for quantifying CRC risk and ...identifying high-risk groups have been developed or validated across European populations, some considering lifestyle as a predictor.PurposeTo identify lifestyle predictors considered in existing risk prediction models applicable for European populations and characterise their corresponding parameter values for an improved understanding of their relative contribution to prediction across different models.MethodsA systematic review was conducted in PubMed and Web of Science from January 2000 to August 2021. Risk prediction models were included if (1) developed and/or validated in an adult asymptomatic European population, (2) based on non-invasively measured predictors and (3) reported mean estimates and uncertainty for predictors included. To facilitate comparison, model-specific lifestyle predictors were visualised using forest plots.ResultsA total of 21 risk prediction models for CRC (reported in 16 studies) were eligible, of which 11 were validated in a European adult population but developed elsewhere, mostly USA. All models but two reported at least one lifestyle factor as predictor. Of the lifestyle factors, the most common predictors were body mass index (BMI) and smoking (each present in 13 models), followed by alcohol (11), and physical activity (7), while diet-related factors were less considered with the most commonly present meat (9), vegetables (5) or dairy (2). The independent predictive contribution was generally greater when they were collected with greater detail, although a noticeable variation in effect size estimates for BMI, smoking and alcohol.ConclusionsEarly identification of high-risk groups based on lifestyle data offers the potential to encourage participation in lifestyle change and screening programmes, hence reduce CRC burden. We propose the commonly shared lifestyle predictors to be further used in public health prediction modelling for improved uptake of the model.
Administrative and health surveys are used in monitoring key health indicators in a population. This study investigated the agreement between self-reported disease status from the Belgian Health ...Interview Survey (BHIS) and pharmaceutical insurance claims extracted from the Belgian Compulsory Health Insurance (BCHI) in ascertaining the prevalence of diabetes, hypertension, and hypercholesterolemia.
Linkage was made between the BHIS 2018 and the BCHI 2018, from which chronic condition was ascertained using the Anatomical Therapeutic Chemical (ATC) classification and defined daily dose. The data sources were compared using estimates of disease prevalence and various measures of agreement and validity. Multivariable logistic regression was performed for each chronic condition to identify the factors associated to the agreement between the two data sources.
The prevalence estimates computed from the BCHI and the self-reported disease definition in BHIS, respectively, are 5.8% and 5.9% diabetes cases, 24.6% and 17.6% hypertension cases, and 16.2% and 18.1% of hypercholesterolemia cases. The overall agreement and kappa coefficient between the BCHI and the self-reported disease status is highest for diabetes and is equivalent to 97.6% and 0.80, respectively. The disagreement between the two data sources in ascertaining diabetes is associated with multimorbidity and older age categories.
This study demonstrated the capability of pharmacy billing data in ascertaining and monitoring diabetes in the Belgian population. More studies are needed to assess the applicability of pharmacy claims in ascertaining other chronic conditions and to evaluate the performance of other administrative data such as hospital records containing diagnostic codes.
the Galician Health Service established indications and priority levels (I = fast track, II = preferential, III = normal) for colonoscopy, according to the risk of colorectal cancer and significant ...colonic lesions detection with access from primary health care. Our aim is to show the results of the implementation.
we included colonoscopies requested in symptomatic patients from June to October 2012 in a prospective observational cross sectional study. We collected health care level (primary, secondary), priority, appropriateness to the established criteria, wait times (from colonoscopy application and initial consultation) and diagnostic yield for colorectal cancer and/or significant colonic lesion. We compared health care levels in priorities I and II.
425 colonoscopies were included (I = 221, II = 141, III = 63). The appropriateness rate to the protocol was 67.5 %. Priority levels were significantly associated to wait times (days) from application (I = 8.7 ± 8.9, II = 50 + or - 20.3, III = 80.2 + or - 32.2; p < 0.001) and initial consultation (I = 32.2 + or - 38, II = 74.5 + or - 44.2, III = 128.5 + or - 47.4; p < 0.001), and with colorectal cancer (I = 20.1 %, II = 19.1 %, III = 4.8 %, p < 0.001) and significant colonic lesion (I = 35.3 %, II = 34 %, III = 19 %, p = 0.002) detection rates. In priority I and II, 21.8 % of colonoscopies were requested from primary health care. Referral form primary health care reduced wait times from initial consultation to colonoscopy (primary = 29.3 + or - 26, secondary = 55.2 + or - 48.6, p < 0.001). Instead, colorectal cancer (OR 2.41, 95 % CI 1.31-4.42) and significant colonic lesion (OR 1.88, 95 % CI 1.13- 3.15) detection rate was increased.
Galician Health Service priority levels are significantly associated with colorectal cancer and significant colonic lesion detection. Referrals to colonoscopy from primary health care reduce waiting times and increase diagnostic yield.
Background
Aspirin (ASA) is a drug that can cause gastrointestinal lesions and symptoms. Colorectal cancer (CRC) is the most prevalent type of cancer in Western countries. We assessed the effect of ...aspirin on the diagnostic accuracy of the faecal immunochemical test (FIT) for CRC and/or advanced neoplasia (AN) in patients undergoing colonoscopy for gastrointestinal symptoms.
Methods
We conducted a prospective multicentre observational study of diagnostic tests that included patients with gastrointestinal symptoms undergoing colonoscopy between March 2012 and 2014 (the COLONPREDICT study). Symptoms were assessed and a FIT and blood tests assessing haemoglobin and carcinoembryonic antigen (CEA) levels were performed.
Results
The study included 3052 patients: A total of 2567 did not take aspirin (non-user group) and 485 (16%) took aspirin (user group). Continuous treatment with ASA did not change the AUC (0.88, 0.82; p = 0.06), sensitivity (92%, 88%; p = 0.5) or specificity (71%, 67%; p = 0.2) of the FIT for CRC detection. Similarly, we found no differences in the AUC (0.81, 0.79; p = 0.6), sensitivity (74%, 75.5%; p = 0.3) or specificity (76%, 73.6%; p = 0.3) for AN detection. Patients with an aspirin use of ≥ 300 mg/day had a lower prevalence of AN and the sensitivity, specificity and AUC for AN for these patients were 54%, 68% and 0.66, significantly lower than for the non-user group (p = 0.03).
Conclusions
Aspirin does not modify the diagnostic accuracy of FIT for CRC and/or AN in patients with gastrointestinal symptoms. Aspirin use of ≥ 300 mg/day decreases the accuracy of the test.
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