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Background Population attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. In contrast to individual specific metrics such as relative risks and odds ratios, attributable fractions depend jointly on both risk factor prevalence and relative risk. The relative contributions of these two components is important, and usually needs to be presented in summary tables that are presented together with the attributable fraction calculation. However, representing PAF in an accessible graphical format, that captures both prevalence and relative risk, may assist interpretation. Methods Taylor-series approximations to PAF in terms of risk factor prevalence and log-odds ratio are derived that facilitate simultaneous representation of PAF, risk factor prevalence and risk-factor/disease log-odds ratios on a single co-ordinate axis. Methods are developed for binary, multi-category and continuous exposure variables. Results The methods are demonstrated using INTERSTROKE, a large international case control dataset focused on risk factors for stroke. Conclusions The described methods could be used as a complement to tables summarizing prevalence, odds ratios and PAF, and may convey the same information in a more intuitive and visually appealing manner. The suggested nomogram can also be used to visually estimate the effects of health interventions which only partially reduce risk factor prevalence. Finally, in the binary risk factor case, the approximations can also be used to quickly convert logistic regression coefficients for a risk factor into approximate PAFs.

The aim of this study was to determine if there is a difference in 6-min walk test (6MWT) distance when the assessor accompanies the patient to continuously measure peripheral oxygen saturation (SpO2) compared to the patient walking unaccompanied. We conducted a randomised crossover study to evaluate the impact of the assessor walking with the patient during the 6MWT (6MWTwith) versus the patient walking alone (6MWTwithout). At the end of a pulmonary rehabilitation programme, each patient performed two 6MWTs in random order and separated by a 30-min rest. 49 patients with chronic obstructive pulmonary disease (COPD) (Global Initiative for Obstructive Lung Disease classification II–IV) were included. In a regression model adjusting for period and subject, accompanying the patient resulted in a lower walking distance (mean difference −9.1 m, 95% CI −13.9– −4.3, p=0.0004). Notably, six patients walked more than 30 m farther (minimal important difference) in one of the two conditions (6MWTwith: n=1, 6MWTwithout: n=5). There were no between-sequence-group differences in heart rate, dyspnoea, leg fatigue and SpO2. The median (interquartile range) number and duration of SpO2 signal artefacts were high but not different between the experimental conditions (6MWTwith: 17 (4–24), 34 s (7–113 s); 6 MWTwithout: 11 (3–26), 24 s (4–62 s)). At a study population level, we observed a statistically significant difference in 6MWT distance between the two experimental conditions; however, the magnitude of difference was small and may not be clinically relevant. Nevertheless, in a clinical setting, unaccompanied walking resulted in a substantially higher walking distance in individual patients, pointing towards strictly standardised testing methodology, in particular in pre–post study designs., Accompanied walking during a 6MWT versus unaccompanied walking results in shorter walked distance (mean difference −9.1 m, 95% CI −13.9– −4.3 m). No differences in nadir SpO2 or oximetry artefacts were noted between accompanied and unaccompanied walking. https://bit.ly/3tBjfCq

Interferon-stimulated genes (ISGs) are a set of genes whose transcription is induced by interferon (IFN). The measure of the expression of ISGs enables calculating an IFN score, which gives an indirect estimate of the exposition of cells to IFN-mediated inflammation. The measure of the IFN score is proposed for the screening of monogenic interferonopathies, like the Aicardi-Gouti&egrave, res syndrome, or to stratify subjects with systemic lupus erythematosus to receive IFN-targeted treatments. Apart from these scenarios, there is no agreement on the diagnostic value of the score in distinguishing IFN-related disorders from diseases dominated by other types of cytokines. Since the IFN score is currently measured in several research hospitals, merging experiences could help define the potential of scoring IFN inflammation in clinical practice. However, the IFN score calculated at different laboratories may be hardly comparable due to the distinct sets of IFN-stimulated genes assessed and to different controls used for data normalization. We developed a reliable approach to minimize the inter-laboratory variability, thereby providing shared strategies for the IFN signature analysis and allowing different centers to compare data and merge their experiences.

Aim: The aim of the study was to assess predictors of outcome in patients hospitalized for dilated cardiomyopathy (DCM) and severe left ventricular dysfunction. Patients & methods: 83 pediatric patients hospitalized for heart failure due to DCM with coexistent left ventricular dysfunction were enrolled.Results: Overall, 5-year survival free from heart transplantation was 69.8%. Normalization of left ventricular function was achieved in 39.8% of patients during follow-up: younger age, less necessity of inotropic support and other than idiopathic DCM predicted left ventricular function, while familial history for cardiac disease or sudden death and inotropic support during hospitalization were associated with poorer outcome. Conclusion: Almost 40% of patients with DCM experienced a complete normalization of cardiac function. Outcome was extremely variable according to the type of DCM.

HIV-1 mother-to-child transmission (HIV-1 MTCT), is an important cause of children mortality worldwide. Brazil has been traditionally praised by its HIV/Aids program, which provides free-of-charge care for people living with HIV-1. Using public epidemiology and demographic databases, we aimed at modeling HIV-1 MTCT prevalence in Brazil through the years (1994–2016) and elaborate a statistical model for forecasting, contributing to HIV-1 epidemiologic surveillance and healthcare decision-making. We downloaded sets of live births and mothers’ data alongside HIV-1 cases notification in children one year old or less. Through time series modeling, we estimated prevalence along the years in Brazil, and observed a remarkable decrease of HIV-1 MTCT between 1994 (10 cases per 100,000 live births) and 2016 (five cases per 100,000 live births), a reduction of 50%. Using our model, we elaborated a prognosis for each Brazilian state to help HIV-1 surveillance decision making, indicating which states are in theory in risk of experiencing a rise in HIV-1 MTCT prevalence. Ten states had good (37%), nine had mild (33%), and eight had poor prognostics (30%). Stratifying the prognostics by Brazilian region, we observed that the Northeast region had more states with poor prognosis, followed by North and Midwest, Southeast and South with one state of poor prognosis each. Brazil undoubtedly advanced in the fight against HIV-1 MTCT in the past two decades. We hope our model will help indicating where HIV-1 MTCT prevalence may rise in the future and support government decision makers regarding HIV-1 surveillance and prevention. Keywords: HIV-1, Vertical transmission, Perinatal transmission, Biostatistics, Epidemiology, Forecasting

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions-similarity, transitivity, and consistency-should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.

Objective The aim of this study was to establish when a second-stage diagnostic test may be of value in cases where a primary diagnostic test has given an uncertain diagnosis of the benign or malignant nature of an adnexal mass. Methods The diagnostic performance with regard to discrimination between benign and malignant adnexal masses for mathematical models including ultrasound variables and for subjective evaluation of ultrasound findings by an experienced ultrasound examiner was expressed as area under the receiver-operating characteristics curve (AUC), sensitivity and specificity. These were calculated for the total study population of 1938 patients with an adnexal mass as well as for sub-populations defined by the certainty with which the diagnosis of benignity or malignancy was made. The effect of applying a second-stage test to the tumors where risk estimation was uncertain was determined. Results The best mathematical model (LR1) had an AUC of 0.95, sensitivity of 92% and specificity of 84% when applied to all tumors. When model LR1 was applied to the 10% of tumors in which the calculated risk fell closest to the risk cut-off of the model, the AUC was 0.59, sensitivity 90% and specificity 21%. A strategy where subjective evaluation was used to classify these 10% of tumors for which LR1 performed poorly and where LR1 was used in the other 90% of tumors resulted in a sensitivity of 91% and specificity of 90%. Applying subjective evaluation to all tumors yielded an AUC of 0.95, sensitivity of 90% and specificity of 93%. Sensitivity was 81% and specificity 47% for those patients where the ultrasound examiner was uncertain about the diagnosis (n = 115; 5.9%). No mathematical model performed better than did subjective evaluation among the 115 tumors where the ultrasound examiner was uncertain. Conclusion When model LR1 is used as a primary test for discriminating between benign and malignant adnexal masses, the use of subjective evaluation of ultrasound findings by an experienced examiner as a second-stage test in the 10% of cases for which the model yields a risk of malignancy closest to its risk cut-off will improve specificity without substantially decreasing sensitivity. However, none of the models tested proved suitable as a second-stage test in tumors where subjective evaluation yielded an uncertain result. Copyright (C) 2010 ISUOG. Published by John Wiley & Sons, Ltd.

Objective Two logistic regression models have been developed for the characterization of adnexal masses. The goal of this prospective analysis was to see whether these models perform differently according to the prevalence of malignancy and whether the cut-off levels of risk assessment for malignancy by the models require modification in different centers. Methods Centers were categorized into those with a prevalence of malignancy below 15%, between 15 and 30% and above 30%. The areas under the receiver–operating characteristics curves (AUC) were compared using bootstrapping. The optimal cut-off level of risk assessment for malignancy was chosen per center, corresponding to the highest sensitivity level possible while still keeping a good specificity. Results Both models performed better in centers with a lower prevalence of malignant cases. The AUCs of the two models for centers with fewer than 15% malignant cases were 0.97 and 0.95, those of centers with 15–30% malignancy were 0.95 and 0.93 and those of centers with more than 30% malignant cases were 0.94 and 0.92. This decrease in performance was due mainly to the decrease in specificity from over 90 to around 76%. In the centers with a higher percentage of malignant cases, a sensitivity of at least 90% with a good specificity could not be obtained by choosing a different cut-off level. Conclusions Overall the models performed well in all centers. The performance of the logistic regression models worsened with increasing prevalence of malignancy, due to a case mix with more borderline and complex benign masses seen in those centers. Because the cut-off of 0.10 is optimal for all three types of center, it seems reasonable to use this cut-off for both models in all centers. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd.