Uncertainty quantification in breast cancer risk prediction models using self-reported family health history.
Ontology highlight
ABSTRACT:
SUBMITTER: Pflieger LT
PROVIDER: S-EPMC5483939 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:AimsGenetic risk scores (GRSs) have been associated with coronary heart disease (CHD) in large studies. We asked whether expanding an established 27-variant GRS (GRS27) to a 50-variant GRS (GRS50) improved CHD prediction and whether GRSs are independent of self-reported family history of CHD.Methods and resultsThe association between GRSs and incident CHD was assessed in Cox models adjusting for established risk factors in 23 595 participants of the Malmö Diet and Cancer study--a prospective, population-based study. During a median follow-up of 14.4 years, 2213 participants experienced a first CHD event. After adjustment for established risk factors, both GRS27 and GRS50 were associated with incident CHD [hazard ratio (HR) = 1.70 for high (top quintile) vs. low (bottom quintile) of GRS27; 95% confidence interval (CI): 1.48-1.94; Ptrend = 1.6 × 10(-15) and HR = 1.92 for GRS50; 95% CI: 1.67-2.20; Ptrend = 6.2 × 10(-22)]. Adding 23 single nucleotide polymorphisms (SNPs) to GRS27 improved risk prediction (P = 3 × 10(-6)). Further adjustment for self-reported family history did not appreciably change the risk estimates of either GRS27 (HR = 1.65; 95% CI: 1.45-1.89) or GRS50 (HR = 1.87; 95% CI: 1.63-2.14). The addition of GRS50 to established risk factors, including self-reported family history, improved discrimination (P < 0.0001) and reclassification (continuous net reclassification improvement index = 0.17, P < 0.0001). In young participants (below median age), those with high GRS50 had 2.4-fold greater risk (95% CI: 1.85-3.12) than those with low GRS50.ConclusionThe addition of 23 SNPs to an existing GRS27 improved CHD risk prediction and was independent of self-reported family history. Coronary heart disease risk assessment by GRS could be particularly useful in young individuals.
| S-EPMC4744619 | biostudies-literature
Project description:Geothermal energy plays an important role in the energy transition by providing a renewable energy source with a low CO2 footprint. For this reason, this paper uses state-of-the-art simulations for geothermal applications, enabling predictions for a responsible usage of this earth's resource. Especially in complex simulations, it is still common practice to provide a single deterministic outcome although it is widely recognized that the characterization of the subsurface is associated with partly high uncertainties. Therefore, often a probabilistic approach would be preferable, as a way to quantify and communicate uncertainties, but is infeasible due to long simulation times. We present here a method to generate full state predictions based on a reduced basis method that significantly reduces simulation time, thus enabling studies that require a large number of simulations, such as probabilistic simulations and inverse approaches. We implemented this approach in an existing simulation framework and showcase the application in a geothermal study, where we generate 2D and 3D predictive uncertainty maps. These maps allow a detailed model insight, identifying regions with both high temperatures and low uncertainties. Due to the flexible implementation, the methods are transferable to other geophysical simulations, where both the state and the uncertainty are important.
| S-EPMC8913627 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death; approximately 5-10% of PDAC is hereditary. Self-administered health history questionnaires (HHQs) may provide a low-cost method to detail family history (FH) of malignancy. Pancreas Center patients were asked to enroll in a registry; 149 with PDAC completed a HHQ which included FH data. Patients with FH of PDAC, or concern for inherited PDAC syndrome, were separately evaluated in a Prevention Program and additionally met with a genetic counselor (GC) to assess PDAC risk (n?=?61). FH obtained through GC and HHQ were compared using Wilcoxon signed-rank sum and generalized linear mixed models with Poisson distribution. Agreement between GC and HHQ risk-assessment was assessed using kappa (?) statistic. In the Prevention Program, HHQ was as precise in detecting FH of cancer as the GC (all p?>?0.05). GC and HHQ demonstrated substantial agreement in risk-stratification of the Prevention Program cohort (??=?0.73, 95% CI 0.59-0.87.) The sensitivity of the HHQ to detect a patient at elevated risk (i.e., moderate- or high-risk) of PDAC, compared to GC, was 82.9% (95% CI 67.3-92.3%) with a specificity of 95% (95% CI 73.1-99.7%). However, seven patients who were classified as average-risk by the HHQ were found to be at an elevated-risk of PDAC by the GC. In the PDAC cohort, 30/149 (20.1%) reported at least one first-degree relative (FDR) with PDAC. The limited sensitivity of the HHQ to detect patients at elevated risk of PDAC in the Prevention Program cohort suggests that a GC adds value in risk-assessment in this population. The HHQ may offer an opportunity to identify high-risk patients in a PDAC population.
| S-EPMC5498249 | biostudies-literature
Project description:Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., model geometry, material properties, and boundary conditions) are often associated with significant uncertainty and variability. As a result, uncertainty quantification (UQ), which involves quantification of the effect of this uncertainty and variability on the simulated response, becomes critical to ensure reliability of model predictions. Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods on these systems. In this study, a two-stage, data-driven manifold learning-based framework is proposed for UQ of computational head models. This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i.e., output), given variability in the material properties of different brain substructures (i.e., input). In the first stage, a data-driven method based on multi-dimensional Gaussian kernel-density estimation and diffusion maps is used to generate realizations of the input random vector directly from the available data. Computational simulations of a small number of realizations provide input-output pairs for training data-driven surrogate models in the second stage. The surrogate models employ nonlinear dimensionality reduction using Grassmannian diffusion maps, Gaussian process regression to create a low-cost mapping between the input random vector and the reduced solution space, and geometric harmonics models for mapping between the reduced space and the Grassmann manifold. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost. Monte Carlo simulations of the surrogate models are used for uncertainty propagation. UQ of the strain fields highlights significant spatial variation in model uncertainty, and reveals key differences in uncertainty among commonly used strain-based brain injury predictor variables.
| S-EPMC10664838 | biostudies-literature
Project description:Family history of cancer remains underused in general clinical practice. We assess age at diagnosis and the role of family history in risk of breast cancer. Prospective follow-up of nurses' health study participants from 1980 to 2006. Updated assessment of family history in mother and sister including age at diagnosis. We used youngest age at diagnosis for family member when classifying risk. We confirmed 4327 incident invasive breast cancers confirmed. Breast cancer incidence models fitted to women with and without family history to assess variation in the risk for established risk factors. Compared to women with no family history those whose mother was diagnosed before age 50 had an adjusted relative risk of 1.69 (95% CI 1.39-2.05) and those with mother diagnosed at 50 or older had a relative risk of 1.37 (1.22-1.53). Sister history was associated with increased relative risk; 1.66 (1.38-1.99) for those with sister history before age 50 and 1.52 (1.29-1.77) for those with sister diagnosed at age 50 or older. Women with either mother or sister diagnosed before age 50 had a relative risk of 1.70 (1.48-1.95) significantly higher than those with diagnosis at age 50 or older (RR = 1.30; (1.27-1.54), P = 0.016). The magnitude of risk associated with established reproductive and lifestyle risk factors did not differ significantly between women with and those without family history with the exception of risk after bilateral oophorectomy in which setting women with family history had greater reduction in risk of subsequent breast cancer. Women with a family member diagnosed with breast cancer before age 50 had increased risk of breast cancer compared to women with family members diagnosed at older ages. Consistent findings for risk factors regardless of family history adds to robust evidence for risk identification and risk stratification in clinical settings where prevention strategies will apply equally to women with and without family history.
| S-EPMC3387322 | biostudies-literature
Project description:BackgroundAssessment of family history of cancer (FHC) mostly relies on self-report. Our goal was to find out whether there is a systematic gender difference in self-reported FHC.MethodsWe identified nine population-based studies which provided statistics of FHC in men and women (N1 = 404 541). Furthermore, we analyzed data (N2 = 167 154) from several iterations of the US-based Health Information National Trends Survey (HINTS) and the National Health Interview Survey (NHIS). We calculated the proportion of positive FHC, odds ratios (OR M/F), 95% confidence intervals, and aggregated statistics. We additionally analyzed in-depth questions about FHC from HINTS 5 Cycle 2.ResultsIn the reviewed studies the odds of men reporting a FHC were lower compared with the odds of women with an average OR of 0.84 [0.71; 1.00] across all studies and an OR of 0.75 [0.70; 0.80] for the six studies from the US and Europe. The gender gap was replicated in our own analyses of HINTS and NHIS with an average OR of 0.75 [0.71; 0.79]. In HINTS 5 Cycle 2 men described themselves as less familiar with their FHC and less confident answering questions regarding FHC. They were also less likely to discuss FHC with family members.ConclusionsMen- at least in the US and Europe-were consistently less likely to report FHC compared with women. Future research should investigate how the assessment of FHC can be improved to reduce these differences. Health care professionals should also consider the potential for biased reporting by gender when assessing FHC.
| S-EPMC7571831 | biostudies-literature
Project description:Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.
| S-EPMC5676245 | biostudies-literature
Project description:The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate-demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose (Chen caeruslescens atlantica), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.
| S-EPMC5204174 | biostudies-literature
Project description:BackgroundConcerns about the safety of silicone breast implants (SBIs) have existed for years, but a causal relation between systemic complaints and SBIs has not been proven. Nevertheless, some women are worried and even request explantation.ObjectivesThis study aimed to review the explantation procedures performed, focusing on patient-reported symptoms preoperatively, the effect of explantation, and the effect of breast reconstruction on these symptoms.MethodsA retrospective chart review was performed for patients who had undergone explantation between 2010 and 2020 at Maastricht University Medical Center. Patients who had undergone tissue expander removal, tissue expander-implant exchange, or direct implant exchange were excluded.ResultsMore than half of the patients undergoing explantation reported complaints, mostly pain. Some 15% reported systemic complaints they believed were implant related. Breast implant illness (BII) was found to be the fifth most common indication for explantation (11.2%). A history of either allergies or implant rupture resulted in higher odds ratios of having BII (odd ratios, 2.1 and 2.1, respectively). Subjective improvement of BII after explantation was reported by about 60% of patients.ConclusionsA relatively low prevalence of suggested BII exists among women undergoing explantation; 1 in 9 procedures were performed for this reason. Allergy and implant rupture may increase the likelihood of having BII. About 60% of BII patients experienced an improvement in their complaints after implant removal. Autologous breast reconstruction appears a good alternative. Prospective studies into health complaints and quality of life should be performed to confirm the effectiveness of explantation as a therapy for BII.Level of evidence: 4
| S-EPMC8756082 | biostudies-literature
Project description:Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs-typically resulting from natural variability or measurement error-and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single "hyper-parameter" to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including "model failure". This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty - to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.
| S-EPMC6607060 | biostudies-literature