Cancer risk models and preselection for screening

Assessing Individual Risk for High-Risk Early Colorectal Neoplasm for Pre-Selection of Screening in Shanghai, China: A Population-Based Nested Case-Control Study

Objective

To identify people with high-risk early colorectal neoplasm is highly desirable for pre-selection in colorectal cancer (CRC) screening in low-resource countries. We aim to build and validate a risk-based model so as to improve compliance and increase the benefits of screening.

Patients and Methods

Using data from the Shanghai CRC screening cohort, we conducted a population-based nested case–control study to build a risk-based model. Cases of early colorectal neoplasm were extracted as colorectal adenomas and stage 0-I CRC. Each case was matched with five individuals without neoplasm (controls) by the screening site and year of enrollment. Cases and controls were then randomly divided into two groups, with two thirds for building the risk prediction model and the other one third for model validation. Known risk factors were included for risk prediction models using logistic regressions. The area under the receiver operating characteristic curve (AUC) and Hosmer–Lemeshow chi-square statistics were used to evaluate model discrimination and calibration. The predicted individual risk probability was calculated under the risk regression equation.

Results

The model incorporating age, sex, family history and lifestyle factors including body mass index (BMI), smoking status, alcohol, regular moderate-to-intensity physical activity showed good calibration and discrimination. When the risk cutoff threshold was defined as 17%, the sensitivity and specificity of the model were 63.99% and 53.82%, respectively. The validation data analysis also showed well discrimination.

Conclusion

A risk prediction model combining personal and lifestyle factors was developed and validated for high-risk early colorectal neoplasm among the Chinese population. This risk-based model could improve the pre-selection for screening and contribute a lot to efficient population-based screening in low-resource countries.

Prediction models for clinical outcome after cochlear implantation: a systematic review

OBJECTIVES

Cochlear implants (CIs) are implantable hearing devices with a wide variation in clinical outcome between patients. We aim to provide an overview of the literature on prediction models and their performance for clinical outcome after cochlear implantation in bilateral hearing loss or deafness.

STUDY DESIGN AND SETTING

In this systematic review, studies describing the development or external validation of a multivariable model for predicting clinical CI outcome were eligible for selection.

RESULTS

A total of 4,042 references were screened. We included nine development studies and one external validation study. The outcome measure of all development studies was speech perception performance after cochlear implantation. The most commonly used model predictors were duration of hearing loss or deafness (n = 7), different types of preoperative measurements (n = 5), and etiology (n = 3). In three studies, crucial information to enable the model to be used for individual risk prediction was missing. One study performed internal validation,two models were externally validated. One study reported specific discrimination or calibration performance measures.

CONCLUSION

Although many articles describe development studies of prediction models for speech perception performance after cochlear implantation, the value of most of these models for their application in clinical practice remains unclear. Therefore, research should focus on increasing the clinical relevance of existing CI outcome prediction models.

Prognostic models for locally advanced cervical cancer: external validation of the published models

OBJECTIVE

To externally validate the prognostic models for predicting the time-dependent outcome in patients with locally advanced cervical cancer (LACC) who were treated with concurrent chemoradiotherapy in an independent cohort.

METHODS

A historical cohort of 297 women with LACC who were treated with radical concurrent chemoradiotherapy from 1999 to 2014 at the 12 de Octubre University Hospital (H12O), Madrid, Spain. The external validity of prognostic models was quantified regarding discrimination, calibration, measures of overall performance, and decision curve analyses.

RESULTS

The review identified 8 studies containing 13 prognostic models. Different (International Federation of Gynecology and Obstetrics [FIGO] stages, parametrium involvement, hydronephrosis, location of positive nodes, and race) but related cohorts with validation cohort (5-year overall survival [OS]=70%; 5-year disease-free survival [DFS]=64%; average age of 50; and over 79% squamous cell) were evaluated. The following models exhibited good external validity in terms of discrimination and calibration but limited clinical utility: the OS model at 3 year from Kidd et al.'s study (area under the receiver operating characteristic curve [AUROC]=0.69; threshold of clinical utility [TCU] between 36% and 50%), the models of DFS at 1 year from Kidd et al.'s study (AUROC=0.64; TCU between 24% and 32%) and 2 years from Rose et al.'s study (AUROC=0.70; TCU between 19% and 58%) and the distant recurrence model at 5 years from Kang et al.'s study (AUROC=0.67; TCU between 12% and 36%).

CONCLUSION

The external validation revealed the statistical and clinical usefulness of 4 prognostic models published in the literature.

The impact of stratifying by family history in colorectal cancer screening programs

In the province-wide colorectal cancer (CRC) screening program in Ontario, Canada, individuals with a family history of CRC are offered colonoscopy screening and those without are offered guaiac fecal occult blood testing (gFOBT, Hemoccult II). We used microsimulation modeling to estimate the cumulative number of CRC deaths prevented and colonoscopies performed between 2008 and 2038 with this family history-based screening program, compared to a regular gFOBT program. In both programs, we assumed screening uptake increased from 30% (participation level in 2008 before the program was launched) to 60%. We assumed that 11% of the population had a family history, defined as having at least one first-degree relative diagnosed with CRC. The programs offered screening between age 50 and 74 years, every two years for gFOBT, and every ten years for colonoscopy. Compared to opportunistic screening (2008 participation level kept constant at 30%), the gFOBT program cumulatively prevented 6,700 more CRC deaths and required 570,000 additional colonoscopies by 2038. The family history-based screening program increased these numbers to 9,300 and 1,100,000, a 40% and 93% increase, respectively. If biennial gFOBT was replaced with biennial fecal immunochemical test (FIT), annual Hemoccult Sensa or five-yearly sigmoidoscopy screening, both the added benefits and colonoscopies required would decrease. A biennial gFOBT screening program that identifies individuals with a family history of CRC and recommends them to undergo colonoscopy screening would prevent 40% (range in sensitivity analyses: 20-51%) additional deaths while requiring 93% (range: 43-116%) additional colonoscopies, compared to a regular gFOBT screening program.

Development and validation of a risk score predicting risk of colorectal cancer

BACKGROUND

Quantifying the risk of colorectal cancer for individuals is likely to be useful for health service provision. Our aim was to develop and externally validate a prediction model to predict 5-year colorectal cancer risk.

METHODS

We used proportional hazards regression to develop the model based on established personal and lifestyle colorectal cancer risk factors using data from 197,874 individuals from the 45 and Up Study, Australia. We subsequently validated the model using 24,233 participants from the Melbourne Collaborative Cohort Study (MCCS).

RESULTS

A total of 1,103 and 224 cases of colorectal cancer were diagnosed in the development and validation sample, respectively. Our model, which includes age, sex, BMI, prevalent diabetes, ever having undergone colorectal cancer screening, smoking, and alcohol intake, exhibited a discriminatory accuracy of 0.73 [95% confidence interval (CI), 0.72-0.75] and 0.70 (95% CI, 0.66-0.73) using the development and validation sample, respectively. Calibration was good for both study samples. Stratified models according to colorectal cancer screening history, that additionally included family history, showed discriminatory accuracies of 0.75 (0.73-0.76) and 0.70 (0.67-0.72) for unscreened and screened individuals of the development sample, respectively. In the validation sample, discrimination was 0.68 (0.64-0.73) and 0.72 (0.67-0.76), respectively.

CONCLUSION

Our model exhibited adequate predictive performance that was maintained in the external population.

IMPACT

The model may be useful to design more powerful cancer prevention trials. In the group of unscreened individuals, the model may be useful as a preselection tool for population-based screening programs.