Calibration plots for multistate risk predictions models.

INTRODUCTION: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. METHODS: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. RESULTS: The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability. CONCLUSIONS: We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.

Association between psoriatic disease and lifestyle factors and comorbidities: cross-sectional analysis and Mendelian randomization.

OBJECTIVES: To examine associations between PsA and psoriasis vs lifestyle factors and comorbidities by triangulating observational and genetic evidence. METHODS: We analysed cross-sectional data from the UK Biobank (1836 PsA, 8995 psoriasis, 36 000 controls) to describe the association between psoriatic disease and lifestyle factors (including BMI and smoking) and 15 comorbidities [including diabetes and coronary artery disease (CAD)] using logistic models adjusted for age, sex and lifestyle factors. We applied bidirectional Mendelian randomization (MR) to genome-wide association data (3609 PsA and 7804 psoriasis cases, up to 1.2 million individuals for lifestyle factors and 757 601 for comorbidities) to examine causal direction, using the inverse-variance weighted method. RESULTS: BMI was cross-sectionally associated with risk of PsA (OR 1.31 per 5 kg/m2 increase; 95% CI 1.26, 1.37) and psoriasis (OR 1.23; 1.20, 1.26), with consistent MR estimates (PsA OR 1.38; 1.14, 1.67; psoriasis OR 1.36; 1.18, 1.58). In both designs, smoking was more strongly associated with psoriasis than PsA. PsA and psoriasis were cross-sectionally associated with diabetes (OR 1.35 and 1.39, respectively) and CAD (OR 1.56 and 1.38, respective). Genetically predicted glycated haemoglobin (surrogate for diabetes) increased PsA risk (OR 1.18 per 6.7 mmol/mol increase; 1.02, 1.36) but not psoriasis. Genetic liability to PsA (OR 1.05; 1.003, 1.09) and psoriasis (OR 1.03; 1.001, 1.06) were associated with increased risk of CAD. CONCLUSION: Observational and genetic evidence converge to suggest that BMI and glycaemic control are associated with increased psoriatic disease risk, while psoriatic disease is associated with increased CAD risk. Further research is needed to understand the mechanism of these associations.

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques.

INTRODUCTION: This study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis. METHODS: We considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring. RESULTS: Discrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors. DISCUSSION: We recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review.

BACKGROUND: Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. METHODS: We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. RESULTS: A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63-82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66-84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84). CONCLUSIONS: Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model.

Serum HE4 predicts progestin treatment response in endometrial cancer and atypical hyperplasia: A prognostic study.

OBJECTIVE: To investigate serum human epididymis-4 (HE4) as a predictive biomarker of intrauterine progestin response in endometrial cancer and atypical endometrial hyperplasia (AEH). DESIGN: Prospective prognostic factor study. SETTING: Consecutive sample of women attending a tertiary gynaecological oncology centre in northwest England. POPULATION: Women with AEH or early-stage, low-grade endometrial cancer who were unfit for or declined primary surgical management. METHODS: A total of 76 women, 32 with AEH and 44 with endometrial cancer, were treated with a levonorgestrel intrauterine system (LNG-IUS) for 12 months. Endometrial biopsies and imaging were performed to assess treatment response. Pretreatment serum HE4 was analysed by chemiluminescence immunoassay and diagnostic accuracy and logistic regression analyses were performed. MAIN OUTCOME MEASURES: Progestin response at 12 months defined by histology and imaging. RESULTS: The median age and body mass index (BMI) of the final cohort were 52 years (interquartile range [IQR] 33-62 years) and 46 kg/m2 (IQR 38-54 kg/m2 ), respectively. Baseline serum HE4 was significantly higher in non-responders than responders (119.2 pmol/L, IQR 94.0-208.4 pmol/L versus 71.8 pmol/L, IQR 56.1-84.2 pmol/L, p < 0.001). Older age (odds ratio [OR] 0.96, 95% CI 0.93-0.99, p = 0.02), baseline serum HE4 (OR 0.97, 95% CI 0.96-0.99, p = 0.001) and endometrial cancer histology (OR 0.22, 95% CI 0.72-0.68, p = 0.009) were associated with a lower likelihood of progestin treatment response. Serum HE4 remained independently associated with progestin treatment failure when adjusted for age and histology (adjusted hazard ratio 0.97, 95% CI 0.96-0.99, p = 0.008). CONCLUSION: Serum HE4 shows promise as a predictive biomarker of progestin treatment response in endometrial cancer and AEH.

The predictors of and reasons for non-adherence in an observational cohort of patients with rheumatoid arthritis commencing methotrexate.

OBJECTIVE: In order to develop interventions to optimize MTX use for the treatment of RA we evaluated the rate of, reasons for and predictors of MTX non-adherence during the first 6 months of therapy. METHODS: The Rheumatoid Arthritis Medication Study (RAMS) is a prospective multicentre cohort study of incident MTX users in the UK. Prior to MTX commencement demographic, clinical and psychological data were collected. A weekly patient-completed diary recorded MTX dose, possible side effects and adherence over 26 weeks. The number of non-adherent weeks was calculated. Potential baseline predictors of ever non-adherence (⩾1 week non-adherent) during the first 6 months of MTX therapy were identified using logistic regression analyses. RESULTS: 606 patients with RA were included; 69% female, mean (s.d.) age 60 (13) years and DAS28 score 4.2 (1.2). Over the first 6 months following MTX initiation, 158 (26%) patients were ever non-adherent (71% intentional, 19% non-intentional, 10% unexplained) and mean (s.d.) number of non-adherent weeks was 2.5 (2.1). Multivariable predictors of ever non-adherence included DAS28 [odds ratios (OR) 1.1, 95% CI 1.0, 1.4], fatigue (OR 1.1, 95% CI 1.0, 1.2 per cm), ⩾2 comorbidities vs no comorbidities (OR 1.9, 95% CI 1.1, 3.5) and high medication concerns despite perceived need (OR 1.1, 95% CI 1.0, 1.1 per unit decrease in need/concern differential). CONCLUSION: This is the largest study evaluating early intentional and non-intentional non-adherence to MTX, which has identified that patient beliefs and multi-morbidity strongly link with non-adherence. These findings can direct the design of and provide potential targets for interventions to improve patient adherence.

A review of the use of propensity score diagnostics in papers published in high-ranking medical journals.

BACKGROUND: Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity score diagnostics in the medical literature is unknown. The objectives of this study are to (1) assess the use of propensity score diagnostics in medical studies published in high-ranking journals, and (2) assess whether the use of propensity score diagnostics differs between studies (a) in different research areas and (b) using different propensity score methods. METHODS: A PubMed search identified studies published in high-impact journals between Jan 1st 2014 and Dec 31st 2016 using propensity scores to answer an applied medical question. From each study we extracted information regarding how propensity scores were assessed and which propensity score method was used. Research area was defined using the journal categories from the Journal Citations Report. RESULTS: A total of 894 papers were included in the review. Of these, 187 (20.9%) failed to report whether the propensity score had been assessed. Commonly reported diagnostics were p-values from hypothesis tests (36.6%) and the standardised mean difference (34.6%). Statistical tests provided marginally stronger evidence for a difference in diagnostic use between studies in different research areas (p = 0.033) than studies using different propensity score methods (p = 0.061). CONCLUSIONS: The use of diagnostics in the propensity score medical literature is far from optimal, with different diagnostics preferred in different areas of medicine. The propensity score literature may improve with focused efforts to change practice in areas where suboptimal practice is most common.

What is the inter-rater agreement of injury classification using the WHO minimum data set for emergency medical teams?

BACKGROUND: In 2017, the WHO produced its first minimum data set (MDS) for emergency medical team (EMT) daily reporting during the sudden-onset disasters (SODs), following expert consensus. The MDS was deliberately designed to be simple in order to improve the rate of data capture; however, it is new and untested. This study assesses the inter-rater agreement between practitioners when performing the injury aspect of coding within the WHO EMT MDS. METHODS: 25 clinical case vignettes were developed, reflecting potential injuries encountered in an SOD. These were presented online from April to July 2018 to practitioners who have experience of/training in managing patients in SODs The practitioners were from UK-Med's members, Australian Medical Assistance Team's Northern Territory members and New Zealand Medical Assistance Team members. Practitioners were asked to code injuries according to WHO EMT MDS case classifications. Randolph's kappa statistic for free-marginal multirater data was calculated for the whole dataset as well as subgroups to ascertain inter-rater agreement. RESULTS: 86 practitioners responded (20.6% response rate), giving >2000 individual case responses. Overall agreement was moderate at 67.9% with a kappa of 0.59 (95% CI 0.49 to 0.69). Despite subgroups of paramedics (kappa 0.63, 95% CI 0.53 to 0.72), doctors (kappa 0.61, 95% CI 0.52 to 0.69) and those with disaster experience (kappa 0.62, 95% CI 0.52 to 0.71) suggesting slightly higher agreement, their CIs (and those of other subgroups) suggest overall similar and moderate levels of practitioner agreement in classifying injuries according to the MDS categories. CONCLUSIONS: An inter-rater agreement of 0.59 is moderate, at best, however, it gives ministries of health some sense of how tightly they may interpret injury data derived from daily reports using WHO EMT MDS. Furthermore, this kappa is similar to established but more complex (thus more contextually impractical) injury scores. Similar studies, with weighting for injury likelihood using sample data from SODs would further refine the level of expected inter-rater agreement.

The temporal relationship between cancer and adult onset anti-transcriptional intermediary factor 1 antibody-positive dermatomyositis.

OBJECTIVES: To characterize the 10 year relationship between anti-transcriptional intermediary factor 1 antibody (anti-TIF1-Ab) positivity and cancer onset in a large UK-based adult DM cohort. METHODS: Data from anti-TIF1-Ab-positive/-negative adults with verified diagnoses of DM from the UK Myositis Network register were analysed. Each patient was followed up until they developed cancer. Kaplan-Meier methods and Cox proportional hazard modelling were employed to estimate the cumulative cancer incidence. RESULTS: Data from 263 DM cases were analysed, with a total of 3252 person-years and a median 11 years of follow-up; 55 (21%) DM cases were anti-TIF1-Ab positive. After 10 years of follow-up, a higher proportion of anti-TIF1-Ab-positive cases developed cancer compared with anti-TIF1-Ab-negative cases: 38% vs 15% [hazard ratio 3.4 (95% CI 2.2, 5.4)]. All the detected malignancy cases in the anti-TIF1-Ab-positive cohort occurred between 3 years prior to and 2.5 years after DM onset. No cancer cases were detected within the following 7.5 years in this group, whereas cancers were detected during this period in the anti-TIF1-Ab-negative cases. Ovarian cancer was more common in the anti-TIF1-Ab-positive vs -negative cohort: 19% vs 2%, respectively (P < 0.05). No anti-TIF1-Ab-positive case <39 years of age developed cancer, compared with 21 (53%) of those ≥39 years of age. CONCLUSION: Anti-TIF1-Ab-positive-associated malignancy occurs exclusively within the 3 year period on either side of DM onset, the risk being highest in those ≥39 years of age. Cancer types differ according to anti-TIF1-Ab status, and this may warrant specific cancer screening approaches.

Avoiding pitfalls when combining multiple imputation and propensity scores.

Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data.

A comparison of five methods of measuring mammographic density: a case-control study.

BACKGROUND: High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. METHODS: Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. RESULTS: The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05). CONCLUSIONS: Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

The prevalence of co-morbidities and their impact on physical activity in people with inflammatory rheumatic diseases compared with the general population: results from the UK Biobank.

Objectives: To compare the prevalence and incidence of chronic co-morbidities in people with inflammatory rheumatic and musculoskeletal diseases (iRMDs), and to determine whether the prevalent co-morbidities are associated with physical activity levels in people with iRMDs and in those without iRMDs. Methods: Participants were recruited to the UK Biobank; a population-based cohort. Data were collected about demographics, physical activity, iRMDs (RA, PsA, AS, SLE) and other chronic conditions, including angina, myocardial infarction, stroke, hypertension, pulmonary disease, diabetes and depression. The standardized prevalence of co-morbidities in people with iRMDs was calculated. Cox regression was used to determine the relationship between the presence of an iRMD and an incident co-morbidity. The relationship between the presence (versus absence) of a (co-)morbidity and physical activity level (low, moderate, high) in people with iRMDs and in those without was assessed using multinomial logistic regression. Results: A total of 488 991 participants were included. The estimated prevalence of each co-morbidity was increased in participants with an iRMD, compared with in those without, particularly for stroke in participants with SLE (standardized morbidity ratio (95% CI), 4.9 (3.6, 6.6). Compared with people with no iRMD and no morbidity, the odds ratios (95% CI) for moderate physical activity were decreased for: no iRMD and morbidity, 0.87 (0.85, 0.89); iRMD and no co-morbidity, 0.71 (0.64, 0.80); and iRMD and co-morbidity, 0.58 (0.54, 0.63). Conclusion: Having a (co-)morbidity is associated with reduced physical activity in the general population, and to a greater extent in participants with an iRMD. Optimal management of both iRMDs and co-morbidities may help to reduce their impact on physical activity.

Using serum troponins to screen for cardiac involvement and assess disease activity in the idiopathic inflammatory myopathies.

Objectives: Limitations in the methods available for identifying cardiac involvement and accurately quantifying disease activity in the idiopathic inflammatory myopathies (IIMs) may contribute to poor outcomes. We investigated the utility of different serum muscle damage markers [total creatine kinase (CK), cardiac troponin T (cTnT) and cardiac troponin I (cTnI)] to address these issues. Methods: We assessed disease activity and cardiac involvement using the International Myositis Assessment and Clinical Studies Group core set measures in 123 participants with confirmed adult-onset IIM from the UK and Denmark. Total CK, cTnT and cTnI were measured. Associations were assessed using logistic regression and Spearman's ranked correlation. Results: Cardiac involvement (n = 18) was associated with higher cTnI levels, independent of overall disease activity [adjusted odds ratio 1.03 (95% CI 1.01, 1.05); P = 0.002]. An abnormal cTnI had the highest specificity and positive predictive value for cardiac involvement (95% and 62%, respectively). In those with a normal CK but elevated cTnT or cTnI, an association with increased disease activity scores was observed. Serum cTnT correlated with the physician (ρ = 0.39) and patient-assessed (ρ = 0.28) global visual analogue scales and HAQ (ρ = 0.41) more strongly than CK or cTnI levels. cTnT was the only marker to correlate with manual muscle testing scores (ρ = -0.24). Conclusion: Serum cTnI testing may have a role in screening for cardiac involvement in IIMs. Abnormal levels of serum cTnT and cTnI are associated with increased disease activity, including in those with a normal CK.

Long-term persistence with rituximab in patients with rheumatoid arthritis.

Objectives: To investigate the long term persistence of rituximab (RTX) in a large observational RA cohort, investigate persistence of RTX when used as a first or second line biologic DMARD (bDMARD), to characterize subsequent bDMARD treatment following RTX. Methods: Patients with RA starting treatment with RTX (MabThera) between 2008 and 2011 were recruited into the British Society for Rheumatology Biologics Register for RA. Duration of RTX treatment over the first 4 years after initiation was estimated via Kaplan-Meier estimates and the reason for discontinuation was ascertained. Subsequent bDMARD use following RTX discontinuation was characterised. Treatment survival in bDMARD-naïve (first line RTX use) and experienced (second line RTX use) cohorts was described. Results: One thousand six hundred and twenty-nine patients were recruited (1371 bDMARD-experienced and 258 bDMARD-naïve). Sixty percent of the whole cohort remained on RTX after 4 years. Ineffectiveness (46%) and death (24%) were the most common reason for RTX discontinuation. RTX discontinuation was associated with RF negativity for the bDMARD-experienced cohort. Of those that discontinued RTX, 46% initiated treatment with another bDMARD, with tocilizumab being the most common. Conclusion: This large study of patients initiating RTX treatment for severe RA found that 60% persisted with treatment after 4 years. This study also identified that RTX is tolerated well when used as a first or second line bDMARD.

Management of symptomatic knee osteoarthritis in obesity: a survey of orthopaedic surgeons' opinions and practice.

INTRODUCTION: The aim of this survey was to explore knee surgeons' opinions and practices regarding the management of symptomatic knee osteoarthritis in obesity. METHODS: Questionnaires were sent electronically to all consultant members of the British Association for Surgery of the Knee. RESULTS: The response rate was 52%. The survey showed considerable practice variation and divided opinion on the evidence base. The majority stated that weight loss should be the first-line treatment. 53% (91/172) stated that such cases should ideally be assessed by a specialist multidisciplinary service; however, only 24% (41/169) would be interested in being the orthopaedic surgeon in such a service. DISCUSSION: The optimal pathway of care for the obese patient with symptomatic knee osteoarthritis remains unclear. Given recent debate around the rationing of knee arthroplasty surgery in obesity, we felt it was timely to survey knee surgeons' current practice. CONCLUSION: Our survey has shown considerable variation in the opinions and practice of surgeons on the management of symptomatic knee osteoarthritis in obesity, together with divided views on current literature.

Correction to: Management of symptomatic knee osteoarthritis in obesity: a survey of orthopaedic surgeons' opinions and practice.

The corresponding author would like to state a different e-mail address: drdanielhill@icloud.com.

Breast cancer risk feedback to women in the UK NHS breast screening population.

INTRODUCTION: There are widespread moves to develop risk-stratified approaches to population-based breast screening. The public needs to favour receiving breast cancer risk information, which ideally should produce no detrimental effects. This study investigates risk perception, the proportion wishing to know their 10-year risk and whether subsequent screening attendance is affected. METHODS: Fifty thousand women attending the NHS Breast Screening Programme completed a risk assessment questionnaire. Ten-year breast cancer risks were estimated using a validated algorithm (Tyrer-Cuzick) adjusted for visually assessed mammographic density. Women at high risk (⩾8%) and low risk (<1%) were invited for face-to-face or telephone risk feedback and counselling. RESULTS: Of those invited to receive risk feedback, more high-risk women, 500 out of 673 (74.3%), opted to receive a consultation than low-risk women, 106 out of 193 (54.9%) (P<0.001). Women at high risk were significantly more likely to perceive their risk as high (P<0.001) and to attend their subsequent mammogram (94.4%) compared with low-risk women (84.2%; P=0.04) and all attendees (84.3%; ⩽0.0001). CONCLUSIONS: Population-based assessment of breast cancer risk is feasible. The majority of women wished to receive risk information. Perception of general population breast cancer risk is poor. There were no apparent adverse effects on screening attendance for high-risk women whose subsequent screening attendance was increased.

Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort.

INTRODUCTION: The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model). METHODS: Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression. RESULTS: The analysis included 50,628 women aged 47-73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34-1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25-1.48), O/E 60 % (95 % CI 44-74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12-1.33), O/E 46 % (95 % CI 26-65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33-1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32-1.60), combined AUC 0.59]. CONCLUSIONS: Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model.