Connective tissue disease related interstitial lung diseases and idiopathic pulmonary fibrosis: provisional core sets of domains and instruments for use in clinical trials

Rationale Clinical trial design in interstitial lung diseases (ILDs) has been hampered by lack of consensus on appropriate outcome measures for reliably assessing treatment response. In the setting of connective tissue diseases (CTDs), some measures of ILD disease activity and severity may be confounded by non-pulmonary comorbidities. Methods The Connective Tissue Disease associated Interstitial Lung Disease (CTD-ILD) working group of Outcome Measures in Rheumatology—a non-profit international organisation dedicated to consensus methodology in identification of outcome measures—conducted a series of investigations which included a Delphi process including >248 ILD medical experts as well as patient focus groups culminating in a nominal group panel of ILD experts and patients. The goal was to define and develop a consensus on the status of outcome measure candidates for use in randomised controlled trials in CTD-ILD and idiopathic pulmonary fibrosis (IPF). Results A core set comprising specific measures in the domains of lung physiology, lung imaging, survival, dyspnoea, cough and health-related quality of life is proposed as appropriate for consideration for use in a hypothetical 1-year multicentre clinical trial for either CTD-ILD or IPF. As many widely used instruments were found to lack full validation, an agenda for future research is proposed. Conclusion Identification of consensus preliminary domains and instruments to measure them was attained and is a major advance anticipated to facilitate multicentre RCTs in the field

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

<p>Abstract</p> <p>Background</p> <p>Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.</p> <p>Methods</p> <p>Daily measures of twelve ambient air pollutants were analyzed: NO₂, NO_x, O₃, SO₂, CO, PM₁₀mass, PM_2.5mass, and PM_2.5components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.</p> <p>Results</p> <p>Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.</p> <p>Conclusions</p> <p>For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.</p

Oral medicine acceptance in infants and toddlers: measurement properties of the caregiver-administered Children’s acceptance tool (CareCAT)

BACKGROUND: Developing age-appropriate medications remains a challenge in particular for the population of infants and toddlers, as they are not able to reliably self-report if they would accept and consequently take an oral medicine. Therefore, it is common to use caregivers as proxies when assessing medicine acceptance. The outcome measures used in this research field differ and most importantly lack validation, implying a persisting gap in knowledge and controversy in the field. The newly developed Caregiver-administered Children’s Acceptance Tool (CareCAT) is based on a 5-point nominal scale, with descriptors of medication acceptance behavior. This crosssectional study assessed the measurement properties of the tool with regards to the user’s understanding and its intra- and inter-rater reliability. METHODS: Participating caregivers were enrolled at a primary healthcare facility where their children (median age 6 months) had been prescribed oral antibiotics. Caregivers, trained observers and the tool developer observed and scored on the CareCAT tool what behavior children exhibited when receiving the medicine (n = 104). The videorecords of this process served as replicate observations (n = 69). After using the tool caregivers were asked to explain their observations and the tool descriptors in their own words. The tool’s reliability was assessed by percentage agreement and Cohen’s unweighted kappa coefficients of agreement for nominal scales. RESULTS: The study found that caregivers using CareCAT had a satisfactory understanding of the tool’s descriptors. Using its dichotomized scores the tool reliably was strong for acceptance behavior (agreement inter-rater 84–88%, kappa 0.66–0.76; intra-rater 87–89%, kappa 0.68–0.72) and completeness of medicine ingestion (agreement inter-rater 82–86%, kappa 0.59–0.67; intra-rater 85–93%, kappa 0.50–0.70). CONCLUSIONS: The CareCAT is a low-cost, easy-to-use and reliable instrument, which is relevant to assess acceptance behavior and completeness of medicine ingestion, both of which are of significant importance for developing age-appropriate medications in infants and toddlers

Risk of malnutrition is associated with mental health symptoms in community living elderly men and women: The Tromsø Study

<p>Abstract</p> <p>Background</p> <p>Little research has been done on the relationship between malnutrition and mental health in community living elderly individuals. In the present study, we aimed to assess the associations between mental health (particularly anxiety and depression) and both the risk of malnutrition and body mass index (BMI, kg/m²) in a large sample of elderly men and women from Tromsø, Norway.</p> <p>Methods</p> <p>In a cross-sectional survey, with 1558 men and 1553 women aged 65 to 87 years, the risk of malnutrition was assessed by the Malnutrition Universal Screening Tool ('MUST'), and mental health was measured by the Symptoms Check List 10 (SCL-10). BMI was categorised into six groups (< 20.0, 20.0-22.4, 22.5-24.9, 25.0-27.4, 27.5-29.9, ≥ 30.0 kg/m²).</p> <p>Results</p> <p>The risk of malnutrition (combining medium and high risk) was found in 5.6% of the men and 8.6% of the women. Significant mental health symptoms were reported by 3.9% of the men and 9.1% of the women. In a model adjusted for age, marital status, smoking and education, significant mental health symptoms (SCL-10 score ≥ 1.85) were positively associated with the risk of malnutrition (odds ratio 3.9 [95% CI 1.7-8.6] in men and 2.5 [95%CI 1.3-4.9] in women), the association was positive also for subthreshold mental health symptoms. For individuals with BMI < 20.0 the adjusted odds ratio for significant mental health symptoms was 2.0 [95% CI 1.0-4.0].</p> <p>Conclusions</p> <p>Impaired mental health was strongly associated with the risk of malnutrition in community living elderly men and women and this association was also significant for subthreshold mental health symptoms.</p

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

BACKGROUND: Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings

Cancer recurrence times from a branching process model

As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer related mortality. Here we investigate a conceptually simple model of metastasis formation where metastatic lesions are initiated at a rate which depends on the size of the primary tumor. The evolution of each metastasis is described as an independent branching process. We assume that the primary tumor is resected at a given size and study the earliest time at which any metastasis reaches a minimal detectable size. The parameters of our model are estimated independently for breast, colorectal, headneck, lung and prostate cancers. We use these estimates to compare predictions from our model with values reported in clinical literature. For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable. Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery can significantly increase the recurrence probability.Comment: 26 pages, 9 figures, 4 table

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Frequently asked questions about chlorophyll fluorescence, the sequel

[EN] Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122: 121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additionalChl a fluorescence-related topics are discussed again in a question and answer format. 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