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      T1 mapping and survival in systemic light-chain amyloidosis

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          Abstract

          Aims

          To assess the prognostic value of myocardial pre-contrast T1 and extracellular volume (ECV) in systemic amyloid light-chain (AL) amyloidosis using cardiovascular magnetic resonance (CMR) T1 mapping.

          Methods and results

          One hundred patients underwent CMR and T1 mapping pre- and post-contrast. Myocardial ECV was calculated at contrast equilibrium (ECV i) and 15 min post-bolus (ECV b). Fifty-four healthy volunteers served as controls. Patients were followed up for a median duration of 23 months and survival analyses were performed. Mean ECV i was raised in amyloid (0.44 ± 0.12) as was ECV b (mean 0.44 ± 0.12) compared with healthy volunteers (0.25 ± 0.02), P < 0.001. Native pre-contrast T1 was raised in amyloid (mean 1080 ± 87 ms vs. 954 ± 34 ms, P < 0.001). All three correlated with pre-test probability of cardiac involvement, cardiac biomarkers, and systolic and diastolic dysfunction. During follow-up, 25 deaths occurred. An ECV i of >0.45 carried a hazard ratio (HR) for death of 3.84 [95% confidence interval (CI): 1.53–9.61], P = 0.004 and pre-contrast T1 of >1044 ms = HR 5.39 (95% CI: 1.24–23.4), P = 0.02. Extracellular volume after primed infusion and ECV b performed similarly. Isolated post-contrast T1 was non-predictive. In Cox regression models, ECV i was independently predictive of mortality (HR = 4.41, 95% CI: 1.35–14.4) after adjusting for E:E′, ejection fraction, diastolic dysfunction grade, and NT-proBNP.

          Conclusion

          Myocardial ECV (bolus or infusion technique) and pre-contrast T1 are biomarkers for cardiac AL amyloid and they predict mortality in systemic amyloidosis.

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          Most cited references26

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Time-dependent ROC curves for censored survival data and a diagnostic marker.

            ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.
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              Relaxing the rule of ten events per variable in logistic and Cox regression.

              The rule of thumb that logistic and Cox models should be used with a minimum of 10 outcome events per predictor variable (EPV), based on two simulation studies, may be too conservative. The authors conducted a large simulation study of other influences on confidence interval coverage, type I error, relative bias, and other model performance measures. They found a range of circumstances in which coverage and bias were within acceptable levels despite less than 10 EPV, as well as other factors that were as influential as or more influential than EPV. They conclude that this rule can be relaxed, in particular for sensitivity analyses undertaken to demonstrate adequate control of confounding.
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                Author and article information

                Journal
                Eur Heart J
                Eur. Heart J
                eurheartj
                ehj
                European Heart Journal
                Oxford University Press
                0195-668X
                1522-9645
                21 January 2015
                17 November 2014
                17 November 2014
                : 36
                : 4
                : 244-251
                Affiliations
                [1 ]The Heart Hospital , 16-18 Westmoreland Street, London W1G 8PH, UK
                [2 ]The National Amyloidosis Centre, Division of Medicine, UCL Medical School, Royal Free Hospital , Rowland Hill Street, London NW3 2PF, UK
                [3 ]Institute of Cardiovascular Science, University College London , Gower Street, London WC1E 6BT, UK
                [4 ]Biostatistics Unit, UCL Eastman Dental Institute , 256 Grays Inn Road, London WC1X 8LD, UK
                [5 ]Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford , Oxford OX3 9DU, UK
                Author notes
                [* ]Corresponding author. Tel: +44 203 456 3081, Fax: +44 203 456 3086, Email: james.moon@ 123456uclh.nhs.uk
                Article
                ehu444
                10.1093/eurheartj/ehu444
                4301598
                25411195
                dfa22600-b2bb-4c49-8a73-bde92591a504
                © The Author 2014. Published by Oxford University Press on behalf of the European Society of Cardiology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 April 2014
                : 20 October 2014
                : 21 October 2014
                Categories
                Clinical Research
                Imaging
                Editor's choice

                Cardiovascular Medicine
                ecv,amyloid,cmr,cardiomyopathy,heart failure,t1 mapping
                Cardiovascular Medicine
                ecv, amyloid, cmr, cardiomyopathy, heart failure, t1 mapping

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