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      A combined clinical and biomarker approach to predict diuretic response in acute heart failure

      Clinical Research in Cardiology
      Springer
      diuretic response, heart failure, biomarkers, prediction

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          Abstract

          Background Poor diuretic response in acute heart failure is related to poor clinical outcome. The underlying mechanisms and pathophysiology behind diuretic resistance are incompletely understood. We evaluated a combined approach using clinical characteristics and biomarkers to predict diuretic response in acute heart failure (AHF). Methods and results We investigated explanatory and predictive models for diuretic response—weight loss at day 4 per 40 mg of furosemide—in 974 patients with AHF included in the PROTECT trial. Biomarkers, addressing multiple pathophysiological pathways, were determined at baseline and after 24 h. An explanatory baseline biomarker model of a poor diuretic response included low potassium, chloride, hemoglobin, myeloperoxidase, and high blood urea nitrogen, albumin, triglycerides, ST2 and neutrophil gelatinase-associated lipocalin (r 2 = 0.086). Diuretic response after 24 h (early diuretic response) was a strong predictor of diuretic response (β = 0.467, P < 0.001; r 2  = 0.523). Addition of diuretic response after 24 h to biomarkers and clinical characteristics significantly improved the predictive model (r 2  = 0.586, P < 0.001). Conclusions Biomarkers indicate that diuretic unresponsiveness is associated with an atherosclerotic profile with abnormal renal function and electrolytes. However, predicting diuretic response is difficult and biomarkers have limited additive value. Patients at risk of poor diuretic response can be identified by measuring early diuretic response after 24 h. Electronic supplementary material The online version of this article (doi:10.1007/s00392-015-0896-2) contains supplementary material, which is available to authorized users.

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          Cardio-renal syndromes: report from the consensus conference of the Acute Dialysis Quality Initiative

          A consensus conference on cardio-renal syndromes (CRS) was held in Venice Italy, in September 2008 under the auspices of the Acute Dialysis Quality Initiative (ADQI). The following topics were matter of discussion after a systematic literature review and the appraisal of the best available evidence: definition/classification system; epidemiology; diagnostic criteria and biomarkers; prevention/protection strategies; management and therapy. The umbrella term CRS was used to identify a disorder of the heart and kidneys whereby acute or chronic dysfunction in one organ may induce acute or chronic dysfunction in the other organ. Different syndromes were identified and classified into five subtypes. Acute CRS (type 1): acute worsening of heart function (AHF–ACS) leading to kidney injury and/or dysfunction. Chronic cardio-renal syndrome (type 2): chronic abnormalities in heart function (CHF-CHD) leading to kidney injury and/or dysfunction. Acute reno-cardiac syndrome (type 3): acute worsening of kidney function (AKI) leading to heart injury and/or dysfunction. Chronic reno-cardiac syndrome (type 4): chronic kidney disease leading to heart injury, disease, and/or dysfunction. Secondary CRS (type 5): systemic conditions leading to simultaneous injury and/or dysfunction of heart and kidney. Consensus statements concerning epidemiology, diagnosis, prevention, and management strategies are discussed in the paper for each of the syndromes.
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            Diuretic response in acute heart failure: clinical characteristics and prognostic significance.

            Diminished diuretic response is common in patients with acute heart failure, although a clinically useful definition is lacking. Our aim was to investigate a practical, workable metric for diuretic response, examine associated patient characteristics and relationships with outcome. We examined diuretic response (defined as Δ weight kg/40 mg furosemide) in 1745 hospitalized acute heart failure patients from the PROTECT trial. Day 4 response was used to allow maximum differentiation in responsiveness and tailoring of diuretic doses to clinical response, following sensitivity analyses. We investigated predictors of diuretic response and relationships with outcome. The median diuretic response was -0.38 (-0.80 to -0.13) kg/40 mg furosemide. Poor diuretic response was independently associated with low systolic blood pressure, high blood urea nitrogen, diabetes, and atherosclerotic disease (all P < 0.05). Worse diuretic response independently predicted 180-day mortality (HR: 1.42; 95% CI: 1.11-1.81, P = 0.005), 60-day death or renal or cardiovascular rehospitalization (HR: 1.34; 95% CI: 1.14-1.59, P < 0.001) and 60-day HF rehospitalization (HR: 1.57; 95% CI: 1.24-2.01, P < 0.001) in multivariable models. The proposed metric-weight loss indexed to diuretic dose-better captures a dose-response relationship. Model diagnostics showed diuretic response provided essentially the same or slightly better prognostic information compared with its individual components (weight loss and diuretic dose) in this population, while providing a less biased, more easily interpreted signal. Worse diuretic response was associated with more advanced heart failure, renal impairment, diabetes, atherosclerotic disease and in-hospital worsening heart failure, and predicts mortality and heart failure rehospitalization in this post hoc, hypothesis-generating study. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.
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              To Explain or to Predict?

              Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.
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                Author and article information

                Journal
                26280875
                4735256
                10.1007/s00392-015-0896-2
                http://creativecommons.org/licenses/by/4.0/

                Cardiovascular Medicine
                diuretic response,heart failure,biomarkers,prediction
                Cardiovascular Medicine
                diuretic response, heart failure, biomarkers, prediction

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