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Background Predicting cause-specific mortality among people with HIV (PWH) could facilitate targeted care to improve survival. We assessed discrimination of the Veterans Aging Cohort Study (VACS) Index 2.0 in predicting cause-specific mortality among PWH on antiretroviral therapy (ART). Methods Using Antiretroviral Therapy Cohort Collaboration data for PWH who initiated ART between 2000 and 2018, VACS Index 2.0 scores (higher scores indicate worse prognosis) were calculated around a randomly selected visit date at least 1 year after ART initiation. Missingness in VACS Index 2.0 variables was addressed through multiple imputation. Cox models estimated associations between VACS Index 2.0 and causes of death, with discrimination evaluated using Harrell's C-statistic. Absolute mortality risk was modelled using flexible parametric survival models. Results Of 59 741 PWH (mean age: 43 years; 80% male), the mean VACS Index 2.0 at baseline was 41 (range: 0–129). For 2425 deaths over 168 162 person-years follow-up (median: 2.6 years/person), AIDS (n = 455) and non–AIDS-defining cancers (n = 452) were the most common causes. Predicted 5-year mortality for PWH with a mean VACS Index 2.0 score of 38 at baseline was 1% and approximately doubled for every 10-unit increase. The 5-year all-cause mortality C-statistic was.83. Discrimination with the VACS Index 2.0 was highest for deaths resulting from AIDS (0.91), liver-related (0.91), respiratory-related (0.89), non-AIDS infections (0.87), and non–AIDS-defining cancers (0.83), and lowest for suicides/accidental deaths (0.65). Conclusions For deaths among PWH, discrimination with the VACS Index 2.0 was highest for deaths with measurable physiological causes and was lowest for suicide/accidental deaths. [ABSTRACT FROM AUTHOR]

Background and Aim: People with new‐onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new‐onset diabetes. Methods: We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti‐diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer. Results: Among 99 687 women aged ≥ 50 years with new‐onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta‐blockers, acid disorder drugs, and lipid‐modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%. Conclusions: Our model doubled the PPV of pancreatic cancer in women with new‐onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge. [ABSTRACT FROM AUTHOR]

Background: Alcohol intake is associated with breast cancer (BC) risk, but estimates of greatest public health relevance have not been quantified in large studies with long duration. Materials and Methods: In this prospective cohort study of 39,811 women (median 25 years follow-up), we examined the association between alcohol consumption and BC incidence and mortality with adjusted hazard ratios (HRs), cubic splines, absolute risks, number needed to harm (NNH), and population-attributable fractions. Results: We documented 2,830 cases of BC, including 237 BC deaths. Each additional alcoholic drink/day was associated with a 10% higher rate (HR = 1.10, 95% confidence intervals [CIs]: 1.04–1.16) of total BC in a linear manner (p = 0.0004). The higher rate was apparent for estrogen receptor (ER)+ (HR = 1.12, 95% CI: 1.06–1.18) but not ER− tumors (HR = 0.95, 95% CI: 0.82–1.10), with a statistically significant difference between these associations (p = 0.03). We constructed models comparing BC incidence among 100,000 women followed for 10 years. Compared to a scenario where all women rarely or never consumed alcohol, we expect 63.79 (95% CI: 58.35–69.24) more cases (NNH = 1,567) had all women consumed alcohol at least monthly and 278.66 (95% CI: 268.70–288.62) more cases (NNH = 358) had all women consumed >1 drink/day. Approximately 4.1% of BC cases were attributable to consumption exceeding one drink/month. Conclusion: Alcohol consumption is associated with a linear dose–response increase in BC incidence even within recommended limits of up to one alcoholic drink/day, at least for ER+ tumors. Our estimates of risk differences, attributable fraction, and NNH quantify the burden that alcohol consumption imposes on women in the general population. ClinicalTrials.gov Identifier: NCT00000479. [ABSTRACT FROM AUTHOR]

Background: Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre‐clinical screen‐detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors. Methods: The authors used data from a population‐based case‐control study. Six categories of 10‐year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high‐risk groups. The authors constructed a continuous‐time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states. Results: There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen‐detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2–54.1) and 57.0 years (95% CI, 55.1–57.7) in the high‐risk and low‐risk women, respectively. Conclusion: In risk‐stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk‐stratified screening strategy that would improve the benefit‐to‐harm balance and the cost‐effectiveness of the screening programs needs to be studied. The age at which individuals with breast cancer enter a screen‐detectable state, as opposed to the length of time spent in that state, varied by absolute risk group. [ABSTRACT FROM AUTHOR]

We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors. [ABSTRACT FROM AUTHOR]

Background and Aims Knowledge of quantifiable cardiovascular disease (CVD) risk may improve health outcomes and trigger behavioural change in patients or clinicians. This review aimed to investigate the impact of CVD risk communication on patient-perceived CVD risk and changes in CVD risk factors. Methods PubMed, Embase, and PsycINFO databases were searched from inception to 6 June 2023, supplemented by citation analysis. Randomized trials that compared any CVD risk communication strategy versus usual care were included. Paired reviewers independently screened the identified records and extracted the data; disagreements were resolved by a third author. The primary outcome was the accuracy of risk perception. Secondary outcomes were clinician-reported changes in CVD risk, psychological responses, intention to modify lifestyle, and self-reported changes in risk factors and clinician prescribing of preventive medicines. Results Sixty-two trials were included. Accuracy of risk perception was higher among intervention participants (odds ratio = 2.31, 95% confidence interval = 1.63 to 3.27). A statistically significant improvement in overall CVD risk scores was found at 6–12 months (mean difference = −0.27, 95% confidence interval = −0.45 to −0.09). For primary prevention, risk communication significantly increased self-reported dietary modification (odds ratio = 1.50, 95% confidence interval = 1.21 to 1.86) with no increase in intention or actual changes in smoking cessation or physical activity. A significant impact on patients' intention to start preventive medication was found for primary and secondary prevention, with changes at follow-up for the primary prevention group. Conclusions In this systematic review and meta-analysis, communicating CVD risk information, regardless of the method, reduced the overall risk factors and enhanced patients' self-perceived risk. Communication of CVD risk to patients should be considered in routine consultations. [ABSTRACT FROM AUTHOR]

AbstractGradient boosting methods become popular in recent years to analyze right-censored survival data where Cox’s proportional hazards model is the widely used statistical model. However, there are very limited studies on the differences between the two approaches for right-censored survival data. We compare two boosting methods with Cox’s proportional hazards model in this paper: one is the gradient boosting decision tree and the other is gradient boosting with component-wise linear models. The differences between the two boosting methods are presented. A simulation study is conducted to investigate the performance of the three methods in practice where only the main effects of covariates are included. The results show that the boosting methods outperform Cox’s proportional hazards model in both the relative and absolute risk estimation in the proportional hazards model except when Cox’s proportional hazards model is fully specified with nonlinear and interaction covariates effects. It indicates that the boosting methods, particularly the gradient boosting decision tree, is a very competitive method for right-censored survival data if complicated covariate effects may exist but are unknown to the investigator. We illustrate an application of the boosting methods with real data analysis. [ABSTRACT FROM AUTHOR]

Background Attitudes towards smoking, lung cancer screening, and perceived risk of lung cancer have not been widely studied in Malaysia. The primary objective of this study was to describe the factors affecting the willingness of high-risk current smokers and ex-smokers to undergo low-dose computed tomography (LDCT) screening for lung cancer. Methods A prospective, cross-sectional questionnaire study was conducted in current smokers or ex-smokers aged between 55 and 80 years at three hospitals in Kota Kinabalu, Sabah, Malaysia. The questionnaire recorded the following parameters: perceived lung cancer risk; Prostate Lung Colon Ovarian Cancer 2012 risk prediction model excluding race and ethnicity predictor (PLCOm2012norace); demographic characteristics; psychosocial characteristics; and attitudes towards lung cancer and lung cancer screening. Results A vast majority of the 95 respondents (94.7%) indicated their willingness to undergo screening. Stigma of lung cancer, low levels of knowledge about lung cancer symptoms, concerns about financial constraints, and a preference for traditional medication were still prevalent among the respondents, and they may represent potential barriers to lung cancer screening uptake. A desire to have an early diagnosis (odds ratio [OR], 11.33; 95% confidence interval [CI], 1.53 to 84.05; p=0.02), perceived time constraints (OR, 3.94; 95% CI, 1.32 to 11.73; p=0.01), and proximity of LDCT screening facilities (OR, 14.33; 95% CI, 1.84 to 111.4; p=0.01) had significantly higher odds of willingness to undergo screening. Conclusion Although high-risk current smokers and ex-smokers are likely to undergo screening for lung cancer, several psychosocial barriers persist. The results of this study may guide the policymakers and clinicians regarding the need to improve lung cancer awareness in our population.

To investigate associations of self-reported walking pace (SRWP) with relative and absolute risks of cause-specific mortality. In 391,652 UK Biobank participants recruited in 2006–2010, we estimated sex- and cause-specific (cardiovascular disease [CVD], cancer, other causes) mortality hazard ratios (HRs) and 10-year mortality risks across categories of SRWP (slow, average, brisk), accounting for confounders and competing risk. Censoring occurred in September 30, 2021 (England, Wales) and October 31, 2021 (Scotland). Over a median follow-up of 12.6 years, 22,413 deaths occurred. In women, the HRs comparing brisk to slow SRWP were 0.74 (95% CI: 0.67, 0.82), 0.40 (0.33, 0.49), and 0.29 (0.26, 0.32) for cancer, CVD, and other causes of death, respectively, and 0.71 (0.64, 0.78), 0.38 (0.33, 0.44), and 0.29 (0.26, 0.32) in men. Compared to CVD, HRs were greater for other causes (women: 39.6% [6.2, 72.9]; men: 31.6% [9.8, 53.5]) and smaller for cancer (−45.8% [−58.3, −33.2] and − 45.9% [−54.8, −36.9], respectively). For all causes in both sexes, the 10-year mortality risk was higher in slow walkers, but varied across sex, age, and cause, resulting in different risk reductions comparing brisk to slow: the largest were for other causes of death at age 75 years [women: −6.8% (−7.7, −5.8); men: −9.5% (−10.6, −8.4)]. Compared to slow walkers, brisk SRWP was associated with reduced cancer (smallest reduction), CVD, and other (largest) causes of death and may therefore be a useful clinical predictive marker. As absolute risk reductions varied across age, cause, and SRWP, certain groups may particularly benefit from interventions to increase SRWP. [ABSTRACT FROM AUTHOR]

Objective: COPD is the most common chronic respiratory disease with complex environmental and genetic etiologies. It was reported that EPAS1 might participate in the occurrence and development of respiratory diseases. However, the association between EPAS1 and COPD was unclear. Methods: First, a case-control study enrolling 1130 COPD patients and 1115 healthy controls in Guangzhou was conducted to clarify the association between EPAS1 polymorphisms and COPD susceptibility. Secondly, a prevalence study recruited 882 participants in Gansu to verify the effect of positive polymorphisms on lung function. Finally, the 10-year absolute risk considering environmental factors and genetic variations was calculated by the method of Gail and Bruzzi. Results: EPAS1 rs13419896 AA genotype reduced COPD risk in southern Chinese (AA vs. GG: adjusted OR = 0.689, 95% CI = 0.498–0.955; AA vs. GG/GA: adjusted OR = 0.701, 95% CI = 0.511–0.962). Further, the rs13419896 A allele was significantly associated with higher pre-FEV1/pre-FVC in both the Guangzhou and Gansu populations (P A were finally retained to develop a relative risk model for males. Smoking status, biomass as fuels, and rs13419896 G > A were retained in the female model. The population-attributable risk of the male or female model was 0.457 (0.283–0.632) and 0.421 (0.227–0.616), respectively. Conclusions: This study first revealed that EPAS1 rs13419896 G > A decreased COPD susceptibility and could be a genetic marker to predict the 10-year absolute risk for COPD.

Summary: Background: Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer, affecting more than 2 million people worldwide yearly and metastasising in 2–5% of patients. However, current clinical staging systems do not provide estimates of absolute metastatic risk, hence missing the opportunity for more personalised treatment advice. We aimed to develop a clinico-pathological model that predicts the probability of metastasis in patients with cSCC. Methods: Nationwide cohorts from (1) all patients with a first primary cSCC in The Netherlands in 2007–2008 and (2) all patients with a cSCC in 2013–2015 in England were used to derive nested case–control cohorts. Pathology records of primary cSCCs that originated a loco-regional or distant metastasis were identified, and these cSCCs were matched to primary cSCCs of controls without metastasis (1:1 ratio). The model was developed on the Dutch cohort (n = 390) using a weighted Cox regression model with backward selection and validated on the English cohort (n = 696). Model performance was assessed using weighted versions of the C-index, calibration metrics, and decision curve analysis; and compared to the Brigham and Women's Hospital (BWH) and the American Joint Committee on Cancer (AJCC) staging systems. Members of the multidisciplinary Skin Cancer Outcomes (SCOUT) consortium were surveyed to interpret metastatic risk cutoffs in a clinical context. Findings: Eight out of eleven clinico-pathological variables were selected. The model showed good discriminative ability, with an optimism-corrected C-index of 0.80 (95% Confidence interval (CI) 0.75–0.85) in the development cohort and a C-index of 0.84 (95% CI 0.81–0.87) in the validation cohort. Model predictions were well-calibrated: the calibration slope was 0.96 (95% CI 0.76–1.16) in the validation cohort. Decision curve analysis showed improved net benefit compared to current staging systems, particularly for thresholds relevant for decisions on follow-up and adjuvant treatment. The model is available as an online web-based calculator (https://emc-dermatology.shinyapps.io/cscc-abs-met-risk/). Interpretation: This validated model assigns personalised metastatic risk predictions to patients with cSCC, using routinely reported histological and patient-specific risk factors. The model can empower clinicians and healthcare systems in identifying patients with high-risk cSCC and offering personalised care/treatment and follow-up. Use of the model for clinical decision-making in different patient populations must be further investigated. Funding: PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships.

Summary: This article reviews the risk equations recommended for use in international cardiovascular disease (CVD) primary prevention guidelines and assesses their suitability for use in Australia against a set of a priori defined selection criteria.The review and assessment were commissioned by the National Heart Foundation of Australia on behalf of the Australian Chronic Disease Prevention Alliance to inform recommendations on CVD risk estimation as part of the 2023 update of the Australian CVD risk assessment and management guidelines.Selected international risk equations were assessed against eight selection criteria: development using contemporary data; inclusion of established cardiovascular risk factors; inclusion of ethnicity and deprivation measures; prediction of a broad selection of fatal and non‐fatal CVD outcomes; population representativeness; model performance; external validation in an Australian dataset; and the ability to be recalibrated or modified.Of the ten risk prediction equations reviewed, the New Zealand PREDICT equation met seven of the eight selection criteria, and met additional usability criteria aimed at assessing the ability to apply the risk equation in practice in Australia. [ABSTRACT FROM AUTHOR]

Head and neck cancer (HNC) risk prediction models based on risk factor profiles have not yet been developed. We took advantage of the large database of the International Head and Neck Cancer Epidemiology (INHANCE) Consortium, including 14 US studies from 1981-2010, to develop HNC risk prediction models. Seventy percent of the data were used to develop the risk prediction models; the remaining 30% were used to validate the models. We used competing-risk models to calculate absolute risks. The predictors included age, sex, education, race/ethnicity, alcohol drinking intensity, cigarette smoking duration and intensity, and/or family history of HNC. The 20-year absolute risk of HNC was 7.61% for a 60-year-old woman who smoked more than 20 cigarettes per day for over 20 years, consumed 3 or more alcoholic drinks per day, was a high school graduate, had a family history of HNC, and was non-Hispanic white. The 20-year risk for men with a similar profile was 6.85%. The absolute risks of oropharyngeal and hypopharyngeal cancers were generally lower than those of oral cavity and laryngeal cancers. Statistics for the area under the receiver operating characteristic curve (AUC) were 0.70 or higher, except for oropharyngeal cancer in men. This HNC risk prediction model may be useful in promoting healthier behaviors such as smoking cessation or in aiding persons with a family history of HNC to evaluate their risks.

The current study explores non-linear effects of absolute risk and effects of comparative risk information about skin cancer on individuals' safety ratings, affective responses and behavioral intentions. An experimental survey was conducted among college students (N = 563) to test the effects of absolute and comparative risk information across a range of risk levels from 3.7% to 60%. The non-linear model of absolute risk effect was confirmed by the data. Absolute risk information had detectable effects at low levels of risk, but the effects did not change appreciably at the highest absolute risk levels. Regarding comparative risk, the data did not support its effects on individuals' safety ratings, affective responses and behavioral intentions. The study found the non-linear model fits well to the skin cancer related feelings and behavioral intentions. It suggests future research apply non-linear models to a variety of health communication areas. [ABSTRACT FROM AUTHOR]

Simple Summary: Personalized breast cancer screening has the potential to improve the accuracy and effectiveness of breast cancer screening by tailoring the screening protocol to an individual's risk factors. Currently, most women undergo mammograms at regular intervals based on age and family history, regardless of their risk factors. We examined the applicability of the Gail model, a well-known breast cancer prediction tool including several risk factors, in an Asian population. While the tool predicts the risks of developing breast cancer in the next 2, 5, 10, and 15 years to some extent, the age at which the disease occurs cannot be estimated. In addition, the tool performs better for longer prediction horizons (10 or 15 years), limiting its utility for risk prediction for two-year screening intervals. Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580–0.628). Calibration was better for longer-term prediction horizons (E/Olong-term ranges: 0.86–1.71; E/Oshort-term ranges:1.24–3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval. [ABSTRACT FROM AUTHOR]

Recurrent events can occur more than once in the same individual; such events may be of different types, known as multitype recurrent events. They are very common in longitudinal studies. Often there is a terminating event, after which no further events can occur. The risk of any event, including terminating events such as death or cure, is typically affected by prior events. We propose a flexible joint multitype recurrent-events model that explicitly provides estimates of the change in risk for each event due to subject characteristics, including number and type of prior events and the absolute risk for every event type (terminating and nonterminating), and predicts event-free survival probability over a desired time period. The model is fully parametric, and therefore a standard likelihood function and robust standard errors can be constructed. We illustrate the model with applications to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (1994–2002) and provide discussion of the results and model features. [ABSTRACT FROM AUTHOR]

In the last four decades, median survival has increased in advanced cervical cancer from 7.1 with single-agent cisplatin to 24 months with doublet chemotherapy plus bevacizumab plus pembrolizumab. Coinciding with the irruption of targeted therapy, we observe higher pricing of novel cancer drugs and results presentation aimed to positively impact the audience. The first could result in poor drug affordability and financial toxicity, while the second can give patients a magnified sense of progress. In this review, we briefly comment on study designs that may favor obtaining positive results but, most notably, how results are presented pertaining the latest randomized clinical trials in advanced cervical cancer. We found that survival results are expressed in Hazard Risk (HR) reductions but communicated as a Relative Risk (RR) reduction for death. The HR of the control to the experimental regimen is given by exp[β]. An HR of 0.7 means an HR reduction of 30%. Risk reduction derived from HR may lead to a belief that the intervention can eliminate the chance of the event occurring, but decreased HR means a reduction in the speed of the event to happen, not the chances to occur. On the contrary, the 'risk reduction' based on RR means that patients have less chance of having the event because RR is a binary measure (alive or dead). Further, Absolute Risks and Number-To-Treat (NNT), in addition to Relative Risk, are omitted. The issue of how the results of cancer clinical trials are presented deserves open discussion. After all, it is the patient's right to make an informed decision for embarking on any cancer treatment.

Background: In this study, we examined the relationship between polygenic liability for depression and number of stressful life events (SLEs) as risk factors for early-onset depression treated in inpatient, outpatient or emergency room settings at psychiatric hospitals in Denmark. Methods: Data were drawn from the iPSYCH2012 case-cohort sample, a population-based sample of individuals born in Denmark between 1981 and 2005. The sample included 18 532 individuals who were diagnosed with depression by a psychiatrist by age 31 years, and a comparison group of 20 184 individuals. Information on SLEs was obtained from nationwide registers and operationalized as a time-varying count variable. Hazard ratios and cumulative incidence rates were estimated using Cox regressions. Results: Risk for depression increased by 35% with each standard deviation increase in polygenic liability (p < 0.0001), and 36% (p < 0.0001) with each additional SLE. There was a small interaction between polygenic liability and SLEs (β = −0.04, p = 0.0009). The probability of being diagnosed with depression in a hospital-based setting between ages 15 and 31 years ranged from 1.5% among males in the lowest quartile of polygenic liability with 0 events by age 15, to 18.8% among females in the highest quartile of polygenic liability with 4+ events by age 15. Conclusions: These findings suggest that although there is minimal interaction between polygenic liability and SLEs as risk factors for hospital-treated depression, combining information on these two important risk factors could potentially be useful for identifying high-risk individuals. [ABSTRACT FROM AUTHOR]

Background longitudinal evidence concerning frailty phenotype and the risk of cardiovascular disease (CVD) remained insufficient, and whether CVD preventive strategies exert low CVD risk on frail adults is unclear. Objectives we aimed to prospectively evaluate the association of frailty phenotype, adherence to ideal cardiovascular health (CVH) and their joint associations with the risk of CVD. Methods a total of 314,093 participants from the UK Biobank were included. Frailty phenotype was assessed according to the five criteria of Fried et al.: weight loss, exhaustion, low physical activity, slow gait speed and low grip strength. CVH included four core health behaviours (smoking, physical activity and diet) and three health factors (weight, cholesterol, blood pressure and glycaemic control). The outcome of interest was incident CVD, including coronary heart disease, heart failure and stroke. Results compared with the non-frail people whose incident rate of overall CVD was 6.54 per 1,000 person-years, the absolute rate difference per 1,000 person-years was 1.67 (95% confidence interval, CI: 1.33, 2.02) for pre-frail and 5.00 (95% CI: 4.03, 5.97) for frail. The ideal CVH was significantly associated with a lower risk of all CVD outcomes. For the joint association of frailty and CVH level with incident CVD, the highest risk was observed among frailty accompanied by poor CVH with an HR of 2.92 (95% CI: 2.68, 3.18). Conclusions our findings indicate that physical frailty is associated with CVD incidence. Improving CVH was significantly associated with a considerable decrease in CVD risk, and such cardiovascular benefits remain for the frailty population. [ABSTRACT FROM AUTHOR]

Predicting risks of chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a cohort, there is a great interest to apply the model to other cohorts. Due to potential discrepancy in baseline disease incidences between different cohorts and shifts in patient composition, the risk predicted by the model built in the source cohort often under- or over-estimates the risk in a new cohort. In this article, we assume the relative risks of predictors are the same between the two cohorts, and propose a novel weighted estimating equation approach to recalibrating the projected risk for the targeted population through updating the baseline risk. The recalibration leverages the knowledge about survival probabilities for the disease of interest and competing events, and summary information of risk factors from the target population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation demonstrate that the proposed estimators are robust, even if the risk factor distributions differ between the source and target populations, and gain efficiency if they are the same, as long as the information from the target is precise. The method is illustrated with a recalibration of colorectal cancer prediction model. [ABSTRACT FROM AUTHOR]

Background: The occurrence and development of chronic obstructive pulmonary disease (COPD) are regulated by environmental and genetic factors. In hypoxia, Erythropoietin (EPO) satisfies the body’s need for oxygen by promoting the production of red blood cells. Hypoxia was proven to be a common physiological condition in COPD progression and associated with many complications. Some studies have found that EPO is involved in the development of COPD. But the mechanism has not been fully proven. Methods: We conducted a case-control study enrolled 1095 COPD patients and 1144 healthy controls in Guangdong Province to evaluate the association between EPO polymorphisms (rs1617640 A>C, rs507392 A>G, rs564449 G>T) and COPD susceptibility. 872 participants from southern Gansu Province were recruited to verify the effect of EPO polymorphisms on lung function. Results: EPO rs1617640 C allele reduced COPD susceptibility in southern Chinese significantly (AC vs. AA: adjusted Odds ratio (OR) = 0.805, 95% CI = 0.669–0.969; AC+CC vs. AA: adjusted OR = 0.822, 95% CI = 0.689–0.980). However, there was no association between rs507392 A>G and rs564449 G>T polymorphisms and COPD susceptibility (p > 0.05). We further observed that the rs1617640 C allele was associated with higher FEV1 and FVC in Guangdong and Gansu populations significantly (both p < 0.05). In brief, the level of FEV1 and FVC increased with the C allele number. We modeled the relative risk for men and women, in which the population-attributable risks chances were 0.449 (0.258–0.641) and 0.262 (0.128–0.396) respectively. In this model, smoking status, coal as fuels, education level, and rs1617640 A>C were finally retained for males, while smoking status, biomass as fuels, and1617640 A>C were retained for females. In the end, using the method developed by Gail and Bruzzi, we fitted a 10-year absolute risk model for southern Chinese with different individual relative risks, which was presented as a table. Conclusions: In conclusion, this study found that EPO rs1617640 A>C polymorphism is associated with COPD susceptibility in southern Chinese, and the C allele was associated with better lung function. In addition, it could also be considered a genetic marker associated with environmental factors to predict the absolute 10-year risk of COPD in southern Chinese.

The SAFEHEART tool has shown good discrimination in predicting cardiovascular events in a bespoke genotyped cohort with familial hypercholesterolaemia (FH). We assessed whether the tool could aid clinical decision making in an English routine care cohort with FH. A historical (2000–2017) open cohort of 3643 participants aged 18–79 years and ≥6-months since FH diagnosis was derived from the Clinical Practice Research Datalink. Individual 10-year cardiovascular risks were predicted using the SAFEHEART model, with multiple imputation used to manage missing data. Outcomes were the first occurrence of myocardial infarction, coronary revascularisation, ischaemic stroke, carotid revascularisation, peripheral arterial revascularisation, non-traumatic lower limb amputation, or cardiovascular death. Model performance was assessed using standard measures of calibration and discrimination, and decision curve analysis. 147 outcome events were observed over a median 3.73 (IQR 1.59–6.48) years follow-up. While the model had some discriminatory value (Harrell's c-statistic 0.67 (95% CI 0.61–0.72)), observed outcome risks departed substantially from predicted risks. Calibration slopes for men and women by age decile were 10.09 (95% CI 7.40–12.77) and 2.85 (1.25–4.45), respectively. Recalibration-in-the-large led to closer alignment of observed and predicted risks (recalibration slopes 3.48 (2.55–4.41) and 1.14 (0.50–1.79), respectively). Decision curve analysis suggested the recalibrated model had net benefit at predicted risks of 10–30%. The original SAFEHEART model has limited generalisability to the routinely identifiable English primary care FH population. With recalibration it appears to have moderate utility at 10–30% predicted risk. It may have greater validity in more bespoke genetically defined FH populations. [Display omitted] • There are no well validated cardiovascular risk prediction tools for people with familial hypercholesterolaemia. • The SAFEHEART model was recently developed in a bespoke genetically defined cohort. • Outcomes departed substantially from predicted risks in a routine English cohort. • A recalibrated model had moderate utility across the 10–30% predicted risk range. • The model would likely be of limited utility in current practice in England. [ABSTRACT FROM AUTHOR]

People with physical diseases are reported to be at elevated risk of subsequent mental disorders. However, previous studies have considered only a few pairs of conditions, or have reported only relative risks. This study aimed to systematically explore the associations between physical diseases and subsequent mental disorders. It examined a population-based cohort of 7,673,978 people living in Denmark between 2000 and 2021, and followed them for a total of 119.3 million person-years. The study assessed nine broad categories of physical diseases (cardiovascular, endocrine, respiratory, gastrointestinal, urogenital, musculoskeletal, hematological and neurological diseases, and cancers), encompassing 31 specific diseases, and the subsequent risk of mental disorder diagnoses, encompassing the ten ICD-10 groupings (organic, including symptomatic, mental disorders; mental disorders due to psychoactive substance use; schizophrenia and related disorders; mood disorders; neurotic, stress-related and somatoform disorders; eating disorders; personality disorders; intellectual disabilities; pervasive developmental disorders; and behavioral and emotional disorders with onset usually occurring in childhood and adolescence). Using Poisson regression, the overall and time-dependent incidence rate ratios (IRRs) for pairs of physical diseases and mental disorders were calculated, adjusting for age, sex and calendar time. Absolute risks were estimated with the Aalen-Johansen estimator. In total, 646,171 people (8.4%) were identified as having any mental disorder during follow-up. All physical diseases except cancers were associated with an elevated risk of any mental disorder. For the nine broad pairs of physical diseases and mental disorders, the median point estimate of IRR was 1.51 (range: 0.99-1.84; interquartile range: 1.29-1.59). The IRRs ranged from 0.99 (95% CI: 0.98-1.01) after cancers to 1.84 (95% CI: 1.83-1.85) after musculoskeletal diseases. Risks varied over time after the diagnosis of physical diseases. The cumulative mental disorder incidence within 15 years after diagnosis of a physical disease varied from 3.73% (95% CI: 3.67-3.80) for cancers to 10.19% (95% CI: 10.13-10.25) for respiratory diseases. These data document that most physical diseases are associated with an elevated risk of subsequent mental disorders. Clinicians treating physical diseases should constantly be alert to the possible development of secondary mental disorders., (© 2024 World Psychiatric Association.)

Objective Information about prognostic outcomes can be of great help for people with prediabetes and for physicians in the face of scientific controversy about the cutoff point for defining prediabetes. We aimed to estimate different prognostic outcomes in people with prediabetes. Design Prospective cohort of subjects with prediabetes according to American Diabetes Association guidelines. Main Outcome measures The probabilities of diabetes onset versus non-onset, the odds against diabetes onset, and the probability of reverting to normoglycemia according to different prediabetes categories were calculated. Results The odds against diabetes onset ranged from 29:1 in individuals with isolated FPG of 100–109 mg/dL to 1:1 in individuals with FPG 110–125 mg/dL plus HbA1c 6.0–6.4%. The probability of reversion to normoglycemia was 31.2% (95% CI 24.0–39.6) in those with isolated FPG 100–109 mg/dL and 6.2% (95% CI 1.4–10.0) in those with FPG 110–125 mg/dL plus HbA1c 6.0–6.4%. Of every 100 participants in the first group, 97 did not develop diabetes and 31 reverted to normoglycemia, while in the second group those figures were 52 and 6. Conclusions Using odds of probabilities and absolute numbers might be useful for people with prediabetes and physicians to share decisions on potential interventions.Key points Communicating knowledge on the course of the disease to make clinical decisions is not always done appropriately. Prediabetes is an example where risk communication is important because the prognosis of subjects with prediabetes is very heterogeneous. Depending on fasting plasma glucose and HbA1c levels, the odds of probabilities against diabetes onset ranged from 29: 1 to 1: 1. Depending on fasting plasma glucose and HbA1c levels, the number of subjects in 100 who revert to normoglycemia ranged from 31 to 6. Using probabilities and number absolutes on the prognosis of prediabetes may be useful for people with prediabetes and physicians to share decisions on potential interventions.

Abstract Backgrounds In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. Methods A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women’s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. Results During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94–1.09) and 0.94 (95% CI, 0.89–0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608–0.661) and 0.585 (95% CI, 0.564–0.605) in the CKB and the SWHS, respectively. Conclusions Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and aid risk-stratified screening and prevention strategies.

Abstract Background The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS. Methods Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23–75 years from the Generations Study. Results The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 − 1.86) for women younger than 50 years and 1.09 (0.66 − 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 − 2.92) for younger and 1.73 (1.03 − 2.90) for older women. Conclusion The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry.

Abstract Background In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years. Methods For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50–75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell’s optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts. Results The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model’s prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity. Conclusions A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.

Background: This study aims to identify predictors of self-perceived risk of myocardial infarction (MI). Methods: Among 564 men and women (50–65 years; randomly selected from the Swedish population), we assessed risk perception as relative self-perceived risk compared to others (lower, same, higher) and percentage ten-year absolute risk. Predictors (added blockwise) were identified using multinomial or linear regression, providing odds ratios (ORs) or β coefficients with their 95% confidence intervals (CI). Results: The mean of self-perceived 10-year MI risk was 12%. Lower BMI (AOR 0.57, 95% CI: 0.44–0.75), low stress (AOR 2.51, 95% CI: 1.39–4.52), high level of physical activity (AOR 1.66, 95% CI:1.01–2.74), hypertension (AOR 0.42, 95% CI: 0.23–0.76), family history (AOR 0.38, 95% CI: 0.21–0.69), and poor general health (AOR 0.41, 95% CI: 0.19–0.89) predicted if respondents perceived their MI risk as lower. Poor general health (AOR 1.94, 95% CI: 1.01–3.73), family history (AOR 2.72, 95% CI: 1.57–4.72), and high cholesterol (AOR 2.45, 95% CI: 1.18–5.09) predicted if respondents perceived their MI risk as higher. Low level of self-perceived CVD knowledge and low numeracy predicted if respondents perceived their MI risk as the same as others. High cholesterol (B 6.85, 95% CI: 2.47–11.32) and poor general health (B 8.75, 95% CI: 4.58–13.00) predicted a higher percentage of perceived ten-year risk. Conclusion: General health was a common predictor of self-perceived MI risk. Lifestyle factors (BMI, physical activity) and stress dominated the predictors for perceiving MI risk as lower than others, while high cholesterol predicted perception of high risk.

Due to the imperfect nature of risk assessment and the long subclinical disease period, RCTs assessing the clinical impact of CVD risk assessment would need to be large-scale and long term to detect changes in CVD outcomes. Keywords: Risk management; Absolute risk; Surveillance EN Risk management Absolute risk Surveillance 103 105 3 02/21/23 20230215 NES 230215 Data on the expected effectiveness of a formal cardiovascular risk screening program are needed Population-based screening programs for early disease detection are important for preventing morbidity, disability, and premature death. A formal CVD risk screening program is likely to reduce the burden of CVD across the population, but we currently lack data to support this. Rather than diagnosing CVD, absolute CVD risk assessment quantifies the likelihood that an individual will experience a primary CVD event in given period of time. [Extracted from the article]

Background: High plasma lipoprotein(a) and high body mass index are both causal risk factors for calcific aortic valve disease.Objectives: This study sought to test the hypothesis that risk of calcific aortic valve disease is the highest when both plasma lipoprotein(a) and body mass index are extremely high.Methods: From the Copenhagen General Population Study, we used information on 69,988 randomly selected individuals recruited from 2003 to 2015 (median follow-up 7.4 years) to evaluate the association between high lipoprotein(a) and high body mass index with risk of calcific aortic valve disease.Results: Compared with individuals in the 1st to 49th percentiles for both lipoprotein(a) and body mass index, the multivariable adjusted HRs for calcific aortic valve disease were 1.6 (95% CI: 1.3-1.9) for the 50th to 89th percentiles of both (16% of all individuals) and 3.5 (95% CI: 2.5-5.1) for the 90th to 100th percentiles of both (1.1%) (P for interaction = 0.92). The 10-year absolute risk of calcific aortic valve disease increased with higher lipoprotein(a), body mass index, and age, and was higher in men than in women. For women and men 70-79 years of age with body mass index ≥30.0 kg/m2, 10-year absolute risks were 5% and 8% for lipoprotein(a) ≤42 mg/dL (88 nmol/L), 7% and 11% for 42-79 mg/dL (89-169 nmol/L), and 9% and 14% for lipoprotein(a) ≥80 mg/dL (170 nmol/L), respectively.Conclusions: Extremely high lipoprotein(a) levels and extremely high body mass index together conferred a 3.5-fold risk of calcific aortic valve disease. Ten-year absolute risk of calcific aortic valve disease by categories of lipoprotein(a) levels, body mass index, age, and sex ranged from 0.4% to 14%. [ABSTRACT FROM AUTHOR]

Abstract Backgrounds The CONSORT for Abstracts checklist published in 2008 recommends that authors report effect size for their studies. Meanwhile, the FDA strongly recommends reporting both ratio and difference measures. However, the measures of effect used in recent clinical trial reports remain unknown. This study is aimed to reveal trends regarding the measures of effect of interventions described in abstracts of recent randomized controlled trials (RCTs) in leading journals. Methods A bibliometric analysis of data was obtained by electronic searches. Human RCTs published in 2016 in the following five journals were searched using PubMed: Annals of Internal Medicine, British Medical Journal, Journal of American Medical Association, The Lancet, and New England Journal of Medicine. Main outcome is numbers of studies reporting each measure in their abstracts. Results Among abstracts of 334 articles, measures most frequently used were relative risk alone (n = 169), followed by absolute risk alone (n = 92), and raw data alone (n = 58). Reporting of the following measures was relatively limited: both ratio and difference measures (n = 8), raw data with ratio measures (n = 5), and raw data with difference measures (n = 2). None of the studies reported raw data with both ratio and difference measures. Only 15 articles described multiple measures of effect in their abstracts. Conclusions More than half of the RCT abstracts published in the five leading journals in 2016 reported risk ratio alone to indicate effect size. Even abstracts in the five leading journals did not adhere fully to the CONSORT for Abstracts or FDA recommendations.

Introduction: The optimal length for clinical follow-up of renal cell carcinoma (RCC) patients is unclear. We evaluated the impact of ISUP/WHO tumor grade and histological subtype on short- and long-term survival and risk of recurrence/metastasis in a large cohort of RCC patients. Patients and Methods: We studied 1679 RCC patients from a single referral center in Italy. Adjusted hazard ratios for overall survival were estimated using Cox regression models. Adjusted absolute risk of developing recurrence or metastasis was computed considering competing risks of mortality. Results: During up to 13 years of follow-up, 175 (10.4%) RCC patients died, of whom 92% beyond 5 years. Hazard ratio of grade IV clear cell carcinomas (ccRCC) was 3.82 compared to grade II. Notably, 33% of recurrences and 56% of distant metastases occurred beyond 5 years of follow-up. The estimated probabilities of recurrence/metastasis were 15% and 45% within and beyond 5 years of follow-up, respectively. After 5 years, the absolute risk of recurrences increased also for papillary renal cell carcinoma type I (35.2%) and grade I ccRCC (17%). Conclusion: After 5 years of follow-up, both risk of mortality and recurrences or metastases were high and were modified by histological types and tumor grade. These data strongly support histology- and grade-tailored surveillance strategies and long-term follow-up for RCC patients. [ABSTRACT FROM AUTHOR]

Information about prognostic outcomes can be of great help for people with prediabetes and for physicians in the face of scientific controversy about the cutoff point for defining prediabetes. We aimed to estimate different prognostic outcomes in people with prediabetes. Prospective cohort of subjects with prediabetes according to American Diabetes Association guidelines. The probabilities of diabetes onset versus non-onset, the odds against diabetes onset, and the probability of reverting to normoglycemia according to different prediabetes categories were calculated. The odds against diabetes onset ranged from 29:1 in individuals with isolated FPG of 100–109 mg/dL to 1:1 in individuals with FPG 110–125 mg/dL plus HbA1c 6.0–6.4%. The probability of reversion to normoglycemia was 31.2% (95% CI 24.0–39.6) in those with isolated FPG 100–109 mg/dL and 6.2% (95% CI 1.4–10.0) in those with FPG 110–125 mg/dL plus HbA1c 6.0–6.4%. Of every 100 participants in the first group, 97 did not develop diabetes and 31 reverted to normoglycemia, while in the second group those figures were 52 and 6. Using odds of probabilities and absolute numbers might be useful for people with prediabetes and physicians to share decisions on potential interventions. Communicating knowledge on the course of the disease to make clinical decisions is not always done appropriately. Prediabetes is an example where risk communication is important because the prognosis of subjects with prediabetes is very heterogeneous. Depending on fasting plasma glucose and HbA1c levels, the odds of probabilities against diabetes onset ranged from 29: 1 to 1: 1. Depending on fasting plasma glucose and HbA1c levels, the number of subjects in 100 who revert to normoglycemia ranged from 31 to 6. Using probabilities and number absolutes on the prognosis of prediabetes may be useful for people with prediabetes and physicians to share decisions on potential interventions. [ABSTRACT FROM AUTHOR]

Abstract Background A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making. Results U.S. incidence and mortality data and published relative risk estimates from observational studies and clinical trials were used to calculate absolute risk of EAC over 10 years adjusting for competing risks. These input parameters varied depending on presence of the key precursor, Barrett’s esophagus. The open source application works across common devices to gather risk factor data and graphically illustrate estimated risk on a single page. Changes to input data are immediately reflected in the colored graphs. We used the calculator to compare the risk distribution between EAC cases and controls from six population-based studies to gain insight into the discrimination metrics of current practice guidelines for screening, observing that current guidelines sacrifice a significant amount of specificity to identify 78–86% of eventual cases in the US population. Conclusions This educational tool provides a simple and rapid means to graphically communicate risk of EAC in the context of other health risks, facilitates “what-if” scenarios regarding potential preventative actions, and can inform discussions regarding screening, surveillance and treatment options. Its generic architecture lends itself to being easily extended to other cancers with distinct pathways and/or intermediate stages, such as hepatocellular cancer. IC-RISC™ extends current qualitative clinical practice guidelines into a quantitative assessment, which brings the possibility of preventative actions being offered to persons not currently targeted for screening and, conversely, reducing unnecessary procedures in those at low risk. Prospective validation and application to existing well-characterized cohort studies are needed.

Objectives: To assess the efficacy of a pro‐active, absolute cardiovascular risk‐guided approach to opportunistically modifying cardiovascular risk factors in patients without coronary ischaemia attending a chest pain clinic. Design: Prospective, randomised, open label, blinded endpoint study. Setting: The rapid access chest pain clinic of Royal Hobart Hospital, a tertiary hospital. Participants: Patients who presented to the chest pain clinic between 1 July 2014 and 31 December 2017 who had intermediate to high absolute cardiovascular risk scores (5‐year risk ≥ 8%). Patients with known cardiac disease or from groups with clinically determined high risk of cardiovascular disease were excluded. Main outcome measures: The primary endpoint was change in 5‐year absolute risk score (Australian absolute risk calculator) at follow‐up (at least 12 months after baseline assessment). Secondary endpoints were changes in lipid profile, blood pressure, smoking status, and body mass index, and major adverse cardiovascular events. Results: The mean change in risk at follow‐up was +0.4 percentage points (95% CI, –0.8 to 1.5 percentage points) for the 98 control group patients and –2.4 percentage points (95% CI, –1.5 to –3.4 percentage points) for the 91 intervention group patients; the between‐group difference in change was 2.7 percentage points (95% CI, 1.2–4.1 percentage points). Mean changes in lipid profile, systolic blood pressure, and smoking status were larger for the intervention group, but not statistically different from those for the control group. Conclusions: An absolute cardiovascular risk‐guided, pro‐active risk factor management strategy employed opportunistically in a chest pain clinic significantly improved 5‐year absolute cardiovascular risk scores. Trial registration: Australia New Zealand Clinical Trial Registry, ACTRN12617000615381 (retrospective). [ABSTRACT FROM AUTHOR]

Background Carotenoids represent 1 of few modifiable factors to reduce breast cancer risk. Elucidation of interactions between circulating carotenoids and genetic predispositions or mammographic density (MD) may help inform more effective primary preventive strategies in high-risk populations. Objectives We tested whether women at high risk for breast cancer due to genetic predispositions or high MD would experience meaningful and greater risk reduction from higher circulating levels of carotenoids in a nested case-control study in the Nurses' Health Studies (NHS and NHSII). Methods This study included 1919 cases and 1695 controls in a nested case-control study in the NHS and NHSII. We assessed both multiplicative and additive interactions. RR reductions and 95% CIs were calculated using unconditional logistic regressions, adjusting for matching factors and breast cancer risk factors. Absolute risk reductions (ARR) were calculated based on Surveillance, Epidemiology, and End Results incidence rates. Results We showed that compared with women at low genetic risk or low MD, those with higher genetic risk scores or high MD had greater ARRs for breast cancer as circulating carotenoid levels increase (additive P -interaction = 0.05). Among women with a high polygenic risk score, those in the highest quartile of circulating carotenoids had a significant ARR (28.6%; 95% CI, 14.8–42.1%) compared to those in the lowest quartile of carotenoids. For women with a high percentage MD (≥50%), circulating carotenoids were associated with a 37.1% ARR (95% CI, 21.7–52.1%) when comparing the highest to the lowest quartiles of circulating carotenoids. Conclusions The inverse associations between circulating carotenoids and breast cancer risk appeared to be more pronounced in high-risk women, as defined by germline genetic makeup or MD. [ABSTRACT FROM AUTHOR]

BackgroundEsophageal squamous cell carcinoma (ESCC) has a high incidence rate and poor prognosis. In this study, we aimed to develop a predictive model to estimate the individualized 5-year absolute risk for ESCC in Chinese populations living in the high-risk areas of China.MethodsWe developed a risk-predicting model based on the epidemiologic data from a population-based case-control study including 244 newly diagnosed ESCC patients and 1,220 healthy controls. Initially, we included easy-to-obtain risk factors to construct the model using the multivariable logistic regression analysis. The area under the ROC curves (AUC) with cross-validation methods was used to evaluate the performance of the model. Combined with local age- and sex-specific ESCC incidence and mortality rates, the model was then used to estimate the absolute risk of developing ESCC within 5 years.ResultsA relative risk model was established that included eight factors: age, sex, tobacco smoking, alcohol drinking, education, and dietary habits (intake of hot food, intake of pickled/salted food, and intake of fresh fruit). The relative risk model had good discrimination [AUC, 0.785; 95% confidence interval (CI), 0.749–0.821]. The estimated 5-year absolute risk of ESCC for individuals varied widely, from 0.0003% to 19.72% in the studied population, depending on the exposure to risk factors.ConclusionsOur model based on readily identifiable risk factors showed good discriminative accuracy and strong robustness. And it could be applied to identify individuals with a higher risk of developing ESCC in the Chinese population, who might benefit from further targeted screening to prevent esophageal cancer.

Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS.Methods: Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23-75 years from the Generations Study.Results: The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 - 1.86) for women younger than 50 years and 1.09 (0.66 - 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 - 2.92) for younger and 1.73 (1.03 - 2.90) for older women.Conclusion: The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) has a high incidence rate and poor prognosis. In this study, we aimed to develop a predictive model to estimate the individualized 5-year absolute risk for ESCC in Chinese populations living in the high-risk areas of China. Methods: We developed a risk-predicting model based on the epidemiologic data from a population-based case-control study including 244 newly diagnosed ESCC patients and 1,220 healthy controls. Initially, we included easy-to-obtain risk factors to construct the model using the multivariable logistic regression analysis. The area under the ROC curves (AUC) with cross-validation methods was used to evaluate the performance of the model. Combined with local age- and sex-specific ESCC incidence and mortality rates, the model was then used to estimate the absolute risk of developing ESCC within 5 years. Results: A relative risk model was established that included eight factors: age, sex, tobacco smoking, alcohol drinking, education, and dietary habits (intake of hot food, intake of pickled/salted food, and intake of fresh fruit). The relative risk model had good discrimination [AUC, 0.785; 95% confidence interval (CI), 0.749–0.821]. The estimated 5-year absolute risk of ESCC for individuals varied widely, from 0.0003% to 19.72% in the studied population, depending on the exposure to risk factors. Conclusions: Our model based on readily identifiable risk factors showed good discriminative accuracy and strong robustness. And it could be applied to identify individuals with a higher risk of developing ESCC in the Chinese population, who might benefit from further targeted screening to prevent esophageal cancer. [ABSTRACT FROM AUTHOR]

Background: In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years.Methods: For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50-75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell's optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts.Results: The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model's prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity.Conclusions: A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions. [ABSTRACT FROM AUTHOR]

Dermatologic care of inflammatory skin conditions has been transformed over recent decades through the use of small molecules disease‐modifying anti‐rheumatic drugs and targeted biologic therapies. Alongside the tremendous benefit of these agents, concerns remain regarding possible side effects, particularly cancer risk. To improve guidance and counseling of patients with skin diseases who are considering treatment with such agents, this article reviews available information on the risk of malignancies in patients treated with these agents. When possible, this article adds clinical context to risk through a number needed to harm that estimates the number of patients a provider would need to treat with a given agent in 1 year to cause a single adverse outcome over time. [ABSTRACT FROM AUTHOR]

Abstract Research that is poorly communicated or presented is as potentially damaging as research that is poorly conducted or fraudulent. Recent examples illustrate how the problem often lies with researchers, not press officers or journalists. The quest for publication and ‘impact’ must not outweigh the importance of accurate representation of science; herein, we suggest steps that researchers, journalists and press officers can take to help ensure this.

Abstract Background Non-optimal blood pressure (BP) levels are a major cause of disease burden globally. We describe current BP and treatment patterns in rural India and compare different approaches to BP lowering in this setting. Methods All individuals aged ≥40 years from 54 villages in a South Indian district were invited and 62,194 individuals (84%) participated in a cross-sectional study. Individual 10-year absolute cardiovascular disease (CVD) risk was estimated using WHO/ISH charts. Using known effects of treatment, proportions of events that would be averted under different paradigms of BP lowering therapy were estimated. Results After imputation of pre-treatment BP levels for participants on existing treatment, 76·9% (95% confidence interval, 75.7–78.0%), 5·3% (4.9–5.6%), and 17·8% (16.9–18.8%) of individuals had a 10-year CVD risk defined as low ( 160/100 mmHg), respectively. Compared to the 19.6% (18.4–20.9%) of adults treated with current practice, a slightly higher or similar proportion would be treated using an intermediate (23·2% (22.0–24.3%)) or high (17·9% (16.9–18.8%) risk threshold for instituting BP lowering therapy and this would avert 87·2% (85.8–88.5%) and 62·7% (60.7–64.6%) more CVD events over ten years, respectively. These strategies were highly cost-effective relative to the current practice. Conclusion In a rural Indian community, a substantial proportion of the population has elevated CVD risk. The more efficient and cost-effective clinical approach to BP lowering is to base treatment decisions on an estimate of an individual’s short-term absolute CVD risk rather than with BP based strategy. Clinical trial registration Clinical Trials Registry of India CTRI/2013/06/003753, 14 June 2013.

Several vaccines are being developed as part of the COVID‐19 pandemic. The results of clinical trials for these vaccines were published with efficacy values of more than 90%, using mainly relative risk (RR). In this paper, we decided to reanalyse the data using the different validated methods of risk expression. Using main publications, absolute risks (AR), AR reduction (ARR), number needed to treat (NNT) were calculated for five COVID‐19 vaccines (tozinameran Comirnaty®, Moderna, Vaxzevria®, Janssen, and Sputnik V vaccines). AR, ARR, NNT, and RR values varied according to COVID‐19 vaccines. The order of the different vaccines was not the same according to the chosen efficacy parameters. This is a further example of the need to express results of clinical trials, using not only RR, but also AR, ARR, and NNT in order to clearly present the clinical interest of drugs. [ABSTRACT FROM AUTHOR]