Variability in MRI vs. ultrasound measures of prostate volume and its impact on treatment recommendations for favorable-risk prostate cancer patients: a case series

Background: Prostate volume can affect whether patients qualify for brachytherapy (desired size ≥20 mL and ≤60 mL) and/or active surveillance (desired PSA density ≤0.15 for very low risk disease). This study examines variability in prostate volume measurements depending on imaging modality used (ultrasound versus MRI) and volume calculation technique (contouring versus ellipsoid) and quantifies the impact of this variability on treatment recommendations for men with favorable-risk prostate cancer. Methods: We examined 70 patients who presented consecutively for consideration of brachytherapy for favorable-risk prostate cancer who had volume estimates by three methods: contoured axial ultrasound slices, ultrasound ellipsoid (height × width × length × 0.523) calculation, and endorectal coil MRI (erMRI) ellipsoid calculation. Results: Average gland size by the contoured ultrasound, ellipsoid ultrasound, and erMRI methods were 33.99, 37.16, and 39.62 mLs, respectively. All pairwise comparisons between methods were statistically significant (all p < 0.015). Of the 66 patients who volumetrically qualified for brachytherapy on ellipsoid ultrasound measures, 22 (33.33%) did not qualify on ellipsoid erMRI or contoured ultrasound measures. 38 patients (54.28%) had PSA density ≤0.15 ng/dl as calculated using ellipsoid ultrasound volumes, compared to 34 (48.57%) and 38 patients (54.28%) using contoured ultrasound and ellipsoid erMRI volumes, respectively. Conclusions: The ultrasound ellipsoid and erMRI ellipsoid methods appeared to overestimate ultrasound contoured volume by an average of 9.34% and 16.57% respectively. 33.33% of those who qualified for brachytherapy based on ellipsoid ultrasound volume would be disqualified based on ultrasound contoured and/or erMRI ellipsoid volume. As treatment recommendations increasingly rely on estimates of prostate size, clinicians must consider method of volume estimation

Implementation and testing of the first prompt search for gravitational wave transients with electromagnetic counterparts

Aims. A transient astrophysical event observed in both gravitational wave (GW) and electromagnetic (EM) channels would yield rich scientific rewards. A first program initiating EM follow-ups to possible transient GW events has been developed and exercised by the LIGO and Virgo community in association with several partners. In this paper, we describe and evaluate the methods used to promptly identify and localize GW event candidates and to request images of targeted sky locations. Methods. During two observing periods (Dec 17 2009 to Jan 8 2010 and Sep 2 to Oct 20 2010), a low-latency analysis pipeline was used to identify GW event candidates and to reconstruct maps of possible sky locations. A catalog of nearby galaxies and Milky Way globular clusters was used to select the most promising sky positions to be imaged, and this directional information was delivered to EM observatories with time lags of about thirty minutes. A Monte Carlo simulation has been used to evaluate the low-latency GW pipeline's ability to reconstruct source positions correctly. Results. For signals near the detection threshold, our low-latency algorithms often localized simulated GW burst signals to tens of square degrees, while neutron star/neutron star inspirals and neutron star/black hole inspirals were localized to a few hundred square degrees. Localization precision improves for moderately stronger signals. The correct sky location of signals well above threshold and originating from nearby galaxies may be observed with ~50% or better probability with a few pointings of wide-field telescopes.Comment: 17 pages. This version (v2) includes two tables and 1 section not included in v1. Accepted for publication in Astronomy & Astrophysic

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Mobile Applications and Wearables for Chronic Respiratory Disease Monitoring

Mobile health (mHealth) has tremendous potential to benefit patients, providers, and the entire healthcare system. Benefits for patients to adopt mHealth include more effective access to health providers, reduced costs of care, and better health control. For physicians and the healthcare system, reasons to embrace mHealth are enhanced health outcomes, facilitated access to patients for care, and decreased time required for administrative tasks. Currently, some mobile apps and wearables dedicated to respiratory health provide medical education and messaging services, enable diary logs, aid with disease self-management, and include educational games. Major challenges for mHealth to be widely adopted include lack of studies demonstrating effectiveness, limited access to technology by all patients, decreased adoption over time, high costs, and data privacy concerns

Using resource modelling to inform decision making and service planning: the case of colorectal cancer screening in Ireland

Background - Organised colorectal cancer screening is likely to be cost-effective, but cost-effectiveness results alone may not help policy makers to make decisions about programme feasibility or service providers to plan programme delivery. For these purposes, estimates of the impact on the health services of actually introducing screening in the target population would be helpful. However, these types of analyses are rarely reported. As an illustration of such an approach, we estimated annual health service resource requirements and health outcomes over the first decade of a population-based colorectal cancer screening programme in Ireland. Methods - A Markov state-transition model of colorectal neoplasia natural history was used. Three core screening scenarios were considered: (a) flexible sigmoidoscopy (FSIG) once at age 60, (b) biennial guaiac-based faecal occult blood tests (gFOBT) at 55–74 years, and (c) biennial faecal immunochemical tests (FIT) at 55–74 years. Three alternative FIT roll-out scenarios were also investigated relating to age-restricted screening (55–64 years) and staggered age-based roll-out across the 55–74 age group. Parameter estimates were derived from literature review, existing screening programmes, and expert opinion. Results were expressed in relation to the 2008 population (4.4 million people, of whom 700,800 were aged 55–74). Results - FIT-based screening would deliver the greatest health benefits, averting 164 colorectal cancer cases and 272 deaths in year 10 of the programme. Capacity would be required for 11,095-14,820 diagnostic and surveillance colonoscopies annually, compared to 381–1,053 with FSIG-based, and 967–1,300 with gFOBT-based, screening. With FIT, in year 10, these colonoscopies would result in 62 hospital admissions for abdominal bleeding, 27 bowel perforations and one death. Resource requirements for pathology, diagnostic radiology, radiotherapy and colorectal resection were highest for FIT. Estimates depended on screening uptake. Alternative FIT roll-out scenarios had lower resource requirements. Conclusions - While FIT-based screening would quite quickly generate attractive health outcomes, it has heavy resource requirements. These could impact on the feasibility of a programme based on this screening modality. Staggered age-based roll-out would allow time to increase endoscopy capacity to meet programme requirements. Resource modelling of this type complements conventional cost-effectiveness analyses and can help inform policy making and service planning

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests