Interview With Mary Barrett (Nee Dettmann)

OBJECTIVE: Randomized trials showed that changes in healthcare organization improved diabetes care. This study aimed to identify which organizational determinants were associated with patient outcomes in routine diabetes care. DESIGN: Observational study, in which multilevel regression analyses were applied to examine the impact of 12 organizational determinants on diabetes care as separate measures and as a composite score. SETTING: Primary care practices in the Netherlands. SUBJECTS: 11,751 patients with diabetes in 354 practices. MAIN OUTCOME MEASURES: Patients' recorded glycated hemoglobin (HbA1c), systolic blood pressure, and serum cholesterol levels. RESULTS: A higher score on the composite measure of organizational determinants was associated with better control of systolic blood pressure (p = 0.017). No effects on HbA1C or cholesterol levels were found. Exploration of specific organizational factors found significant impact of use of an electronic patient registry on HbA1c (OR = 1.80, 95% CI 1.12-2.88), availability of patient leaflets on systolic blood pressure control (OR = 2.59, 95% CI 1.06-6.35), and number of hours' nurse education on cholesterol control (OR = 2.51, 95% CI 1.02-6.15). CONCLUSION: In routine primary care, it was found that favorable healthcare organization was associated with a number of intermediate outcomes in diabetes care. This finding lends support to the findings of trials on organizational changes in diabetes care. Notably, the composite measure of organizational determinants had most impact

Labour intensity of guidelines may have a greater effect on adherence than GPs' workload

Background: Physicians' heavy workload is often thought to jeopardise the quality of care and to be a barrier to improving quality. The relationship between these has, however, rarely been investigated. In this study quality of care is defined as care 'in accordance with professional guidelines'. In this study we investigated whether GPs with a higher workload adhere less to guidelines than those with a lower workload and whether guideline recommendations that require a greater time investment are less adhered to than those that can save time. Methods: Data were used from the Second Dutch National survey of General Practice (DNSGP- 2). This nationwide study was carried out between April 2000 and January 2002. A multilevel logistic-regression analysis was conducted of 170,677 decisions made by GPs, referring to 41 Guideline Adherence Indicators (GAIs), which were derived from 32 different guidelines. Data were used from 130 GPs, working in 83 practices with 98,577 patients. GP-characteristics as well as guideline characteristics were used as independent variables. Measures include workload (number of contacts), hours spent on continuing medical education, satisfaction with available time, practice characteristics and patient characteristics. Outcome measure is an indicator score, which is 1 when a decision is in accordance with professional guidelines or 0 when the decision deviates from guidelines. Results: On average, 66% of the decisions GPs made were in accordance with guidelines. No relationship was found between the objective workload of GPs and their adherence to guidelines. Subjective workload (measured on a five point scale) was negatively related to guideline adherence (OR = 0.95). After controlling for all other variables, the variation between GPs in adherence to guideline recommendations showed a range of less than 10%. 84% of the variation in guideline adherence was located at the GAI-level. Which means that the differences in adherence levels between guidelines are much larger than differences between GPs. Guideline recommendations that require an extra time investment during the same consultation are significantly less adhered to: (OR = 0.46), while those that can save time have much higher adherence levels: OR = 1.55). Recommendations that reduce the likelihood of a follow-up consultation for the same problem are also more often adhered to compared to those that have no influence on this (OR = 3.13). Conclusion: No significant relationship was found between the objective workload of GPs and adherence to guidelines. However, guideline recommendations that require an extra time investment are significantly less well adhered to while those that can save time are significantly more often adhered to.

Strategies for validation and testing of DNA methylation biomarkers

DNA methylation is a stable covalent epigenetic modification of primarily CpG dinucleotides that has recently gained considerable attention for its use as a biomarker in different clinical settings, including disease diagnosis, prognosis and therapeutic response prediction. Although the advent of genome-wide DNA methylation profiling in primary disease tissue has provided a manifold resource for biomarker development, only a tiny fraction of DNA methylation-based assays have reached clinical testing. Here, we provide a critical overview of different analytical methods that are suitable for biomarker validation, including general study design considerations, which might help to streamline epigenetic marker development. Furthermore, we highlight some of the recent marker validation studies and established markers that are currently commercially available for assisting in clinical management of different cancers

The most efficient and effective BRCA1/2 testing strategy in epithelial ovarian cancer:Tumor-First or Germline-First?

Objective: Genetic testing in epithelial ovarian cancer (OC) is essential to identify a hereditary cause like a germline BRCA1/2 pathogenic variant (PV). An efficient strategy for genetic testing in OC is highly desired. We evaluated costs and effects of two strategies; (i) Tumor-First strategy, using a tumor DNA test as prescreen to germline testing, and (ii) Germline-First strategy, referring all patients to the clinical geneticist for germline testing.Methods: Tumor-First and Germline-First were compared in two scenarios; using real-world uptake of testing and setting implementation to 100%. Decision analytic models were built to analyze genetic testing costs (including counseling) per OC patient and per family as well as BRCA1/2 detection probabilities. With a Markov model, the life years gained among female relatives with a germline BRCA1/2 PV was investigated.Results: Focusing on real-world uptake, with the Tumor-First strategy more OC patients and relatives with a germline BRCA1/2 PV are detected (70% versus 49%), at lower genetic testing costs (€1898 versus €2502 per patient, and €2511 versus €2930 per family). Thereby, female relatives with a germline BRCA1/2 PV can live on average 0.54 life years longer with Tumor-First compared to Germline-First. Focusing on 100% uptake, the genetic testing costs per OC patient are substantially lower in the Tumor-First strategy (€2257 versus €4986).Conclusions: The Tumor-First strategy in OC patients is more effective in identifying germline BRCA1/2 PV at lower genetic testing costs per patient and per family. Optimal implementation of Tumor-First can further improve detection of heredity in OC patients.</p

Accurate evaluation of the interstitial KKR-Green function

It is shown that the Brillouin zone integral for the interstitial KKR-Green function can be evaluated accurately by taking proper care of the free-electron singularities in the integrand. The proposed method combines two recently developed methods, a supermatrix method and a subtraction method. This combination appears to provide a major improvement compared with an earlier proposal based on the subtraction method only. By this the barrier preventing the study of important interstitial-like defects, such as an electromigrating atom halfway along its jump path, can be considered as being razed.Comment: 23 pages, RevTe

Differential analysis of genome-wide methylation and gene expression in mesenchymal stemcells of patients with fractures and osteoarthritis

Insufficient activity of the bone-forming osteoblasts leads to low bone mass and predisposes to fragility fractures. The functional capacity of human mesenchymal stem cells (hMSCs), the precursors of osteoblasts, may be compromised in elderly individuals, in relation with the epigenetic changes associated with aging. However, the role of hMSCs in the pathogenesis of osteoporosis is still unclear. Therefore, we aimed to characterize the genome-wide methylation and gene expression signatures and the differentiation capacity of hMSCs from patients with hip fractures. We obtained hMSCs from the femoral heads of women undergoing hip replacement due to hip fractures and controls with hip osteoarthritis. DNA methylation was explored with the Infinium 450K bead array. Transcriptome analysis was done by RNA sequencing. The genomic analyses revealed that most differentially methylated loci were situated in genomic regions with enhancer activity, distant from gene bodies and promoters. These regions were associated with differentially expressed genes enriched in pathways related to hMSC growth and osteoblast differentiation. hMSCs from patients with fractures showed enhanced proliferation and upregulation of the osteogenic drivers RUNX2/OSX. Also, they showed some signs of accelerated methylation aging. When cultured in osteogenic medium, hMSCs from patients with fractures showed an impaired differentiation capacity, with reduced alkaline phosphatase activity and poor accumulation of a mineralized matrix. Our results point to 2 areas of potential interest for discovering new therapeutic targets for low bone mass disorders and bone regeneration: the mechanisms stimulating MSCs proliferation after fracture and those impairing their terminal differentiation

Comparative Performance Information Plays No Role in the Referral Behaviour of GPs

Comparative performance information (CPI) about the quality of hospital care is information used to identify high-quality hospitals and providers. As the gatekeeper to secondary care, the general practitioner (GP) can use CPI to reflect on the pros and cons of the available options with the patient and choose a provider best fitted to the patient’s needs. We investigated how GPs view their role in using CPI to choose providers and support patients. Method: We used a mixed-method, sequential, exploratory design to conduct explorative interviews with 15 GPs about their referral routines, methods of referral consideration, patient involvement, and the role of CPI. Then we quantified the qualitative results by sending a survey questionnaire to 81 GPs affiliated with a representative national research network. Results: Seventy GPs (86% response rate) filled out the questionnaire. Most GPs did not know where to find CPI (87%) and had never searched for it (94%). The GPs reported that they were not motivated to use CPI due to doubts about its role as support information, uncertainty about the effect of using CPI, lack of faith in better outcomes, and uncertainty about CPI content and validity. Nonetheless, most GPs believed that patients would like to be informed about quality-of- care differences (62%), and about half the GPs discussed quality-of-care differences with their patients (46%), though these discussions were not based on CPI. Conclusion: Decisions about referrals to hospital care are not based on CPI exchanges during GP consultations. As a gatekeeper, the GP is in a good position to guide patients through the enormous amount of quality information that is available. Nevertheless, it is unclear how and whether the GP’s role in using information about quality of care in the referral process can grow, as patients hardly ever initiate a discussion based on CPI, though they seem to be increasingly more critical about differences in quality of care. Future research should address the conditions needed to support GPs’ ability and willingness to use CPI to guide their patients in the referral process

The FLAMINGO project: cosmological hydrodynamical simulations for large-scale structure and galaxy cluster surveys

We introduce the Virgo Consortium's FLAMINGO suite of hydrodynamical simulations for cosmology and galaxy cluster physics. To ensure the simulations are sufficiently realistic for studies of large-scale structure, the subgrid prescriptions for stellar and AGN feedback are calibrated to the observed low-redshift galaxy stellar mass function and cluster gas fractions. The calibration is performed using machine learning, separately for three resolutions. This approach enables specification of the model by the observables to which they are calibrated. The calibration accounts for a number of potential observational biases and for random errors in the observed stellar masses. The two most demanding simulations have box sizes of 1.0 and 2.8 Gpc and baryonic particle masses of $1\times10^8$ and $1\times10^9 \text{M}_\odot$, respectively. For the latter resolution the suite includes 12 model variations in a 1 Gpc box. There are 8 variations at fixed cosmology, including shifts in the stellar mass function and/or the cluster gas fractions to which we calibrate, and two alternative implementations of AGN feedback (thermal or jets). The remaining 4 variations use the unmodified calibration data but different cosmologies, including different neutrino masses. The 2.8 Gpc simulation follows $3\times10^{11}$ particles, making it the largest ever hydrodynamical simulation run to $z=0$. Lightcone output is produced on-the-fly for up to 8 different observers. We investigate numerical convergence, show that the simulations reproduce the calibration data, and compare with a number of galaxy, cluster, and large-scale structure observations, finding very good agreement with the data for converged predictions. Finally, by comparing hydrodynamical and `dark-matter-only' simulations, we confirm that baryonic effects can suppress the halo mass function and the matter power spectrum by up to $\approx20$ per cent.Comment: 44 pages, 23 figures. Accepted for publication in MNRAS. V3 includes changes made in published version: jet simulations were redone to fix a bug, but the differences are nearly invisible. For visualizations, see the FLAMINGO website at https://flamingo.strw.leidenuniv.nl

FLAMINGO: Calibrating large cosmological hydrodynamical simulations with machine learning

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller-volume simulations, we model how the galaxy stellar mass function and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the stellar mass function. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters.Comment: 24 pages, 10 figures (Including the appendix). Submitted to MNRAS. For visualisations, see the FLAMINGO website at https://flamingo.strw.leidenuniv.nl