Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors.

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.

A multistate survival model of the natural history of cancer using data from screened and unscreened population.

One of the main aims of models using cancer screening data is to determine the time between the onset of preclinical screen-detectable cancer and the onset of the clinical state of the cancer. This time is called the sojourn time. One problem in using screening data is that an individual can be observed in preclinical phase or clinically diagnosed but not both. Multistate survival models provide a method of modeling the natural history of cancer. The natural history model allows for the calculation of the sojourn time. We developed a continuous-time Markov model and the corresponding likelihood function. The model allows for the use of interval-censored, left-truncated and right-censored data. The model uses data of clinically diagnosed cancers from both screened and nonscreened individuals. Parameters of age-varying hazards and age-varying misclassification are estimated simultaneously. The mean sojourn time is calculated from a micro-simulation using model parameters. The model is applied to data from a prostate screening trial. The simulation study showed that the model parameters could be estimated accurately.

Health utilities and costs for neuromyelitis optica spectrum disorder.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) is a rare, neurological disease that places a significant burden on patients, their carers, and healthcare systems. OBJECTIVES: To estimate patient and carer health utilities and costs of NMOSD within the UK setting. METHODS: Patients with NMOSD and their carers, recruited via a regional specialist treatment centre, completed a postal questionnaire that included a resource use measure, the EuroQoL (EQ)-5D-5L, EQ-5D-VAS, Vision and Quality of Life Index (VisQoL), Carer Experience Survey (CES) and the Expanded Disability Status Scale (EDSS). The questionnaire asked about respondents' use of health and community care services, non-medical costs, informal care and work capacity. Data were analysed descriptively. Uncertainties in costs and utilities were assessed using bootstrap analysis. RESULTS: 117 patients and 74 informal carers responded to the survey. Patients' mean EQ-5D-5L and VisQoL health utilities (95% central range) were 0.54 (- 0.29, 1.00) and 0.79 (0.11, 0.99), respectively. EQ-5D-5L utility decreased with increasing EDSS score bandings, from 0.80 (0.75, 0.85) for EDSS ≤ 4.0, to 0.20 (- 0.29, 0.56) for EDSS 8.0 to 9.5. Mean, 3-month total costs were £5623 (£2096, £12,156), but ranged from £562 (£381, £812) to £32,717 (£2888, £98,568) for these EDSS bandings. Carer-reported EQ-5D-5L utility and CES index scores were 0.85 (0.82, 0.89) and 57.67 (52.69, 62.66). Mean, 3-month costs of informal care were £13,150 to £24,560. CONCLUSIONS: NMOSD has significant impacts on health utilities and NHS and carer costs. These data can be used as inputs to cost-effectiveness analyses of new medicines for NMOSD.

Untargeted Metabolomics Identifies a Novel Panel of Markers for Autologous Blood Transfusion.

Untargeted metabolomics was used to analyze serum and urine samples for biomarkers of autologous blood transfusion (ABT). Red blood cell concentrates from donated blood were stored for 35−36 days prior to reinfusion into the donors. Participants were sampled at different time points post-donation and up to 7 days post-transfusion. Metabolomic profiling was performed using ACQUITY ultra performance liquid chromatography (UPLC), Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The markers of ABT were determined by principal component analysis and metabolites that had p < 0.05 and met ≥ 2-fold change from baseline were selected. A total of 11 serum and eight urinary metabolites, including two urinary plasticizer metabolites, were altered during the study. By the seventh day post-transfusion, the plasticizers had returned to baseline, while changes in nine other metabolites (seven serum and two urinary) remained. Five of these metabolites (serum inosine, guanosine and sphinganine and urinary isocitrate and erythronate) were upregulated, while serum glycourdeoxycholate, S-allylcysteine, 17-alphahydroxypregnenalone 3 and Glutamine conjugate of C6H10O2 (2)* were downregulated. This is the first study to identify a panel of metabolites, from serum and urine, as markers of ABT. Once independently validated, it could be universally adopted to detect ABT.