How to incorporate patient and public perspectives into the design and conduct of research

International government guidance recommends patient and public involvement (PPI) to improve the relevance and quality of research. PPI is defined as research being carried out ‘with’ or ‘by’ patients and members of the public rather than ‘to’, ‘about’ or ‘for’ them (http://www.invo.org.uk/). Patient involvement is different from collecting data from patients as participants. Ethical considerations also differ. PPI is about patients actively contributing through discussion to decisions about research design, acceptability, relevance, conduct and governance from study conception to dissemination. Occasionally patients lead or do research. The research methods of PPI range from informal discussions to partnership research approaches such as action research, co-production and co-learning. This article discusses how researchers can involve patients when they are applying for research funding and considers some opportunities and pitfalls. It reviews research funder requirements, draws on the literature and our collective experiences as clinicians, patients, academics and members of UK funding panels

An Approach to Mapping Forest Growth Stages in Queensland, Australia through Integration of ALOS PALSAR and Landsat Sensor Data

Whilst extensive clearance of forests in the eastern Australian Brigalow Belt Bioregion (BBB) has occurred since European settlement, appropriate management of those that are regenerating can facilitate restoration of biomass (carbon) and biodiversity to levels typical of relatively undisturbed or remnant formations. However, maps of forests are different stages of regeneration are needed to facilitate restoration planning, including prevention of further re-clearing. Focusing on the Tara Downs subregion of the BBB and on forests with brigalow (Acacia harpophylla) as a component, this research establishes a method for differentiating and mapping early, intermediate and remnant growth stages from Japan Aerospace Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phased-Array L-band Synthetic Aperture Radar (PALSAR) Fine Beam Dual (FBD) L-band HH- and HV-polarisation backscatter and Landsat-derived Foliage Projective Cover (FPC). Using inventory data collected from 74 plots, located in the Tara Downs subregion, forests were assigned to one of three regrowth stages based on their height and cover relative to that of undisturbed stands. The image data were then segmented into objects with each assigned to a growth stage by comparing the distributions of L-band HV and HH polarisation backscatter and FPC to that of reference distributions using a z-test. Comparison with independent assessments of growth stage, based on time-series analysis of aerial photography and SPOT images, established an overall accuracy of > 70%, with this increasing to 90% when intermediate regrowth was excluded and only early-stage regrowth and remnant classes were considered. The proposed method can be adapted to respond to amendments to user-definitions of growth stage and, as regional mosaics of ALOS PALSAR and Landsat FPC are available for Queensland, has application across the state

Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived foliage projective cover data

Differentiation of forest growth stages through classification of single date or time-series of Landsat sensor data is limited because of insensitivity to their three-dimensional structure. This study therefore evaluated the benefits of integrating the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) L-band HH and HV polarisation response from the woody components of vegetation with Landsat-derived foliage projective cover (FPC). Focus was on 12 regional ecosystems (REs) distributed across the Brigalow Belt Bioregion (BRB) of Queensland, Australia, where different stages of growth dominated by brigalow (Acacia harpophylla) were widespread. From remnant areas of brigalow-dominated forests mapped previously for each RE by the Queensland Herbarium through field visits and interpretations of aerial imagery, frequency distributions of all three channels were extracted and compared to those of image segments generated using FPC and PALSAR data. For woody vegetation (with an FPC threshold of ≥ 9%) outside of the remnant areas, mature (non-remnant) forests were associated with segments where the HH and HV backscatter thresholds were within one standard deviation of the mean extracted for remnant forest. Early-stage regrowth was differentiated using an L-band HH threshold o

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Identifying priority questions regarding rapid systematic reviews’ methods: protocol for an eDelphi study

Introduction Rapid systematic reviews (RRs) have the potential to provide timely information to decision-makers, thus directly impacting healthcare. However, consensus regarding the most efficient approaches to performing RRs and the presence of several unaddressed methodological issues pose challenges. With such a large potential research agenda for RRs, it is unclear what should be prioritised.Objective To elicit a consensus from RR experts and interested parties on what are the most important methodological questions (from the generation of the question to the writing of the report) for the field to address in order to guide the effective and efficient development of RRs.Methods and analysis An eDelphi study will be conducted. Researchers with experience in evidence synthesis and other interested parties (eg, knowledge users, patients, community members, policymaker, industry, journal editors and healthcare providers) will be invited to participate. The following steps will be taken: (1) a core group of experts in evidence synthesis will generate the first list of items based on the available literature; (2) using LimeSurvey, participants will be invited to rate and rank the importance of suggested RR methodological questions. Questions with open format responses will allow for modifications to the wording of items or the addition of new items; (3) three survey rounds will be performed asking participants to re-rate items, with items deemed of low importance being removed at each round; (4) a list of items will be generated with items believed to be of high importance by ≥75% of participants being included and (5) this list will be discussed at an online consensus meeting that will generate a summary document containing the final priority list. Data analysis will be performed using raw numbers, means and frequencies.Ethics and dissemination This study was approved by the Concordia University Human Research Ethics Committee (#30015229). Both traditional, for example, scientific conference presentations and publication in scientific journals, and non-traditional, for example, lay summaries and infographics, knowledge translation products will be created

Additional Files for the articles: "Improving the quality of healthcare: a cross-sectional study of the features of successful clinical networks" and “The EXpert PANel Decision (EXPAND) method: a way to measure the impact of diverse quality improvement activities of clinical networks”

Additional files providing more detail about the methods and analysis for the article "Improving the quality of healthcare: a cross-sectional study of the features of successful clinical networks" accepted for publication in Public Health Research and Practice. Additional File 1: Summary of outcome variables, indicators, and data collection methods Additional file 2: Summary of explanatory variables, indicators, and data collection methods Additional File 3: Explanatory factors associated with impact on quality of care and system-wide change (unadjusted Spearman’s correlation coefficients