Anticipating undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry: a decision tool for bronchial challenge testing.

BACKGROUND: Some patients with asthma demonstrate normal spirometry and remain undiagnosed without further testing. OBJECTIVE: To determine clinical predictors of asthma in symptomatic adults with normal spirometry, and to generate a tool to help clinicians decide who should undergo bronchial challenge testing (BCT). METHODS: Using random-digit dialling and population-based case-finding, we recruited adults from the community with respiratory symptoms and no previous history of diagnosed lung disease. Participants with normal pre- and post-bronchodilator spirometry subsequently underwent BCT. Asthma was diagnosed in those with symptoms and a methacholine provocative concentration (PC20) of < 8 mg/ml. Sputum and blood eosinophils, and exhaled nitric oxide were measured. Univariate analyses identified potentially predictive variables, which were then used to construct a multivariable logistic regression model to predict asthma. Model sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. RESULTS: Of 132 symptomatic individuals with normal spirometry, 34 (26%) had asthma. Of those ultimately diagnosed with asthma, 33 (97%) answered 'yes' to a question asking whether they experienced cough, chest tightness or wheezing provoked by exercise or cold air. Other univariate predictors of asthma included female sex, pre-bronchodilator FEV1 percentage predicted, and percent positive change in FEV1 post bronchodilator. A multivariable model containing these predictive variables yielded an AUC of 0.82 (95% CI: 0.72-0.91), a sensitivity of 82%, and a specificity of 66%. The model was used to construct a nomogram to advise clinicians which patients should be prioritized for BCT. CONCLUSIONS: Four readily available patient characteristics demonstrated a high sensitivity and AUC for predicting undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry. These characteristics can potentially help clinicians to decide which individuals with normal spirometry should be investigated with bronchial challenge testing. However, further prospective validation of our decision tool is required.

The Canadian Study of Arterial Inflammation in Patients with Diabetes and Recent Vascular Events, Evaluation of Colchicine Effectiveness (CADENCE): protocol for a randomised, double-blind, placebo-controlled trial.

BACKGROUND: Inflammation is a key mediator in the development and progression of the atherosclerotic disease process as well as its resultant complications, like myocardial infarction (MI), stroke and cardiovascular (CV) death, and is emerging as a novel treatment target. Trials involving anti-inflammatory medications have demonstrated outcome benefit in patients with known CV disease. In this regard, colchicine appears to hold great promise. However, there are potential drawbacks to colchicine use, as some studies have identified an increased risk of infection, and a non-significant trend for increased all-cause mortality. Thus, a more thorough understanding of the underlying mechanism of action of colchicine is needed to enable a better patient selection for this novel CV therapy. OBJECTIVE: The primary objective of the Canadian Study of Arterial Inflammation in Patients with Diabetes and Recent Vascular Events, Evaluation of Colchicine Effectiveness (CADENCE) trial is to assess the effect of colchicine on vascular inflammation in the carotid arteries and ascending aorta measured with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT in patients with type 2 diabetes mellitus (T2DM) or pre-diabetes who have experienced a recent vascular event (acute coronary syndrome (ACS)/MI, transient ischaemic attack (TIA) or stroke). Secondary objectives include determining colchicine's effect on inflammatory biomarkers (high-sensitivity C reactive protein (hs-CRP) and interleukin-6 (IL-6)). Additionally, we will assess if baseline inflammation imaging or biomarkers are associated with a treatment response to colchicine determined by imaging. Exploratory objectives will look at: (1) the difference in the inflammatory response to colchicine in patients with coronary events compared with patients with cerebral events; (2) the difference in the inflammatory response to colchicine in different vascular beds; (3) the relationship of FDG-PET imaging markers with serum biomarkers and (4) assessment of quality-of-life changes. METHODS AND DESIGN: CADENCE is a multicentre, prospective, randomised, double-blinded, placebo-controlled study to determine the effect of colchicine on arterial inflammation as assessed with imaging and circulatory biomarkers, specifically carotid arteries and aortic FDG uptake as well as hs-CRP and IL-6 among others. Patients with T2DM or pre-diabetes who have recently experienced a CV event (within 30-120 days after an ACS (ie, ST-elevation MI (STEMI) or non-STEMI)) or TIA/stroke with documented large vessel atherosclerotic disease will be randomised to treatment with either colchicine 0.6 mg oral daily or placebo. Participants will undergo baseline clinical evaluation including EQ5D assessment, blood work for inflammatory markers and FDG PET/CT scan of the ascending aorta and left and right carotid arteries. Patients will undergo treatment for 6 months and have repeat clinical evaluation including EQ5D assessment, blood work for inflammatory markers and FDG PET/CT scan at the conclusion of the study. The primary outcome will be the change in the maximum target to background ratio (TBRmax) in the ascending aorta (or carotid arteries) from baseline to follow-up on FDG PET/CT imaging. DISCUSSION: Colchicine is an exciting potential new therapy for CV risk reduction. However, its use is associated with side effects and greater understanding of its underlying mechanism of action is needed. Importantly, the current study will determine whether its anti-inflammatory action is an indirect systemic effect, or a more local plaque action that decreases inflammation. The results will also help identify patients who will benefit most from such therapy. TRIAL REGISTRATION NUMBER: NCT04181996.

Global Comparison of Readmission Rates for Patients With Heart Failure.

BACKGROUND: Heart failure (HF) readmission rates are low in some jurisdictions. However, international comparisons are lacking and could serve as a foundation for identifying regional patient management strategies that could be shared to improve outcomes. OBJECTIVES: This study sought to summarize 30-day and 1-year all-cause readmission and mortality rates of hospitalized HF patients across countries and to explore potential differences in rates globally. METHODS: We performed a systematic review and meta-analysis using MEDLINE, Embase, and CENTRAL for observational reports on hospitalized adult HF patients at risk for readmission or mortality published between January 2010 and March 2021. We conducted a meta-analysis of proportions using a random-effects model, and sources of heterogeneity were evaluated with meta-regression. RESULTS: In total, 24 papers reporting on 30-day and 23 papers on 1-year readmission were included. Of the 1.5 million individuals at risk, 13.2% (95% CI: 10.5%-16.1%) were readmitted within 30 days and 35.7% (95% CI: 27.1%-44.9%) within 1 year. A total of 33 papers reported on 30-day and 45 papers on 1-year mortality. Of the 1.5 million individuals hospitalized for HF, 7.6% (95% CI: 6.1%-9.3%) died within 30 days and 23.3% (95% CI: 20.8%-25.9%) died within 1 year. Substantial variation in risk across countries was unexplained by countries' gross domestic product, proportion of gross domestic product spent on health care, and Gini coefficient. CONCLUSIONS: Globally, hospitalized HF patients exhibit high rates of readmission and mortality, and the variability in readmission rates was not explained by health care expenditure, risk of mortality, or comorbidities.

Trends in glucose testing among individuals without diabetes in Ontario between 2010 and 2017: a population-based cohort study.

BACKGROUND: Early identification of people with diabetes or prediabetes enables greater opportunities for glycemic control and management strategies to prevent related complications. To identify gaps in screening for these conditions, we examined population trends in receipt of timely glucose testing overall and in specific clinical subgroups. METHODS: Using linked administrative databases, we conducted a retrospective cohort study of people aged 40 years and older without diabetes at baseline. Our primary outcome was up-to-date glucose testing, defined as having received testing at least once in the 3 years before each index year from 2010 to 2017, using linked administrative databases of people residing in Ontario, Canada. We calculated rates of up-to-date testing by age group, sex, ethnicity (South Asian, Chinese, general population) and comorbidities (hypertension, hyperlipidemia, cardiovascular disease). RESULTS: Over the 8-year study period, up-to-date glucose testing rates were stable at 67% for men and 77% for women (both relative risk 1.00 per year; 95% confidence interval 1.00-1.00). Testing rates were significantly lower in men than in women (all age groups p < 0.001) and lower in younger than older age groups (except those aged ≥ 80 yr). South Asian people had the highest testing rates, although among people aged 70 years or older, testing was highest in the general population (p < 0.001). Among people with hypertension, hyperlipidemia and cardiovascular disease, annual testing rates were also stable, but only 58% overall among people with hypertension. INTERPRETATION: We found lower glucose testing rates in younger men and people with hypertension. Our findings reinforce the need for initiatives to increase awareness of glycemic testing.

Examining anti-inflammatory therapies in the prevention of cardiovascular events: protocol for a systematic review and network meta-analysis of randomised controlled trials.

INTRODUCTION: Inflammation is emerging as an important risk factor for atherosclerotic cardiovascular disease and has been a recent target for many novel therapeutic agents. However, comparative evidence regarding efficacy of these anti-inflammatory treatment options is currently lacking. METHODS AND ANALYSIS: This systematic review will include randomised controlled trials evaluating the effect of anti-inflammatory agents on cardiovascular outcomes in patients with known cardiovascular disease. Studies will be retrieved from Medline, Embase, the Cochrane Central Register of Controlled Trials, as well as clinical trial registry websites, Europe PMC and conference abstract handsearching. No publication date or language restrictions will be imposed. Eligible interventions must have some component of anti-inflammatory agent. These include (but are not limited to): non-steroidal anti-inflammatory drugs (NSAIDs), colchicine, prednisone, methotrexate, canakinumab, pexelizumab, anakinra, succinobucol, losmapimod, inclacumab, atreleuton, LP-PLA2 (darapladib) and sPLA2 (varespladib). The primary outcomes will include major adverse cardiac events (MACE), and each individual component of MACE (myocardial infarction, stroke and cardiovascular death). Key secondary outcomes will include unstable angina, heart failure, all-cause mortality, cardiac arrest and revascularisation. Screening, inclusion, data extraction and quality assessment will be performed independently by two reviewers. Network meta-analysis based on the random effects model will be conducted to compare treatment effects both directly and indirectly. The quality of the evidence will be assessed with appropriate tools including the Grading of Recommendations, Assessment, Development and Evaluation profiler or Confidence in Network Meta-Analysis tool. ETHICS AND DISSEMINATION: Ethics approval is not required for this systematic review. The findings will be disseminated through a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022303289.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality.

AIMS: This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling. METHODS AND RESULTS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full-text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time-point comparisons, ML-derived c-indices were higher than CSM-derived c-indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in-hospital mortality to 1 year survival; of these nine, seven reported higher c-indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c-indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c-indices 0.913 vs. 0.835). CONCLUSIONS: ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML-based studies of prediction modelling. We suggest that ML-based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867.

APP21 transgenic rats develop age-dependent cognitive impairment and microglia accumulation within white matter tracts.

BACKGROUND: Most of the animal models commonly used for preclinical research into Alzheimer's disease (AD) largely fail to address the pathophysiology, including the impact of known risk factors, of the widely diagnosed sporadic form of the disease. Here, we use a transgenic rat (APP21) that does not develop AD-like pathology spontaneously with age, but does develop pathology following vascular stress. To further the potential of this novel rat model as a much-needed pre-clinical animal model of sporadic AD, we characterize APP21 transgenic rats behaviorally and histologically up to 19 months of age. METHODS: The open field test was used as a measure of activity; and the Morris water maze was used to assess learning, memory, and strategy shift. Neuronal loss and microglia activation were also assessed throughout the brain. RESULTS: APP21 transgenic rats showed deficits in working memory from an early age, yet memory recall performance after 24 and 72 h was equal to that of wildtype rats and did not deteriorate with age. A deficit in strategy shift was observed at 19 months of age in APP21 transgenic rats compared to Fischer wildtype rats. Histologically, APP21 transgenic rats demonstrated accelerated white matter inflammation compared to wildtype rats, but interestingly no differences in neuron loss were observed. CONCLUSIONS: The combined presence of white matter pathology and executive function deficits mirrored what is often found in patients with mild cognitive impairment or early dementia, and suggests that this rat model will be useful for translationally meaningful studies into the development and prevention of sporadic AD. The presence of widespread white matter inflammation as the only observed pathological correlate for cognitive deficits raises new questions as to the role of neuroinflammation in cognitive decline.