Background-Worsening renal function (WRF), traditionally defined as an increase in serum creatinine levels Ն0.3 mg/dL, is a frequent finding in patients with acute heart failure (AHF) and has been associated with poorer outcomes in some but not all studies. We hypothesized that these discrepancies may be caused by the interaction between WRF and congestion in AHF patients. Methods and Results-We measured serum creatinine levels on a daily basis during the hospitalization and assessed the persistence of signs of congestion at discharge in 599 consecutive patients admitted at our institute for AHF. They had a postdischarge mortality and mortality or AHF readmission rates of 13% and 43%, respectively, after 1 year. Patients were subdivided into 4 groups according to the development or not of WRF and the persistence of Ն1 sign of congestion at discharge. Patients with WRF and no congestion had similar outcomes compared with those with no WRF and no congestion, whereas the risk of death or of death or AHF readmission was increased in the patients with persistent congestion alone and in those with both WRF and congestion (hazard ratio, 5.35; 95% confidence interval, 3.0 -9.55 at univariable analysis; hazard ratio, 2.44; 95% confidence interval, 1.24 -4.18 at multivariable analysis for mortality; hazard ratio, 2.14; 95% confidence interval, 1.39 -3.3 at univariable analysis; and hazard ratio, 1.39; 95% confidence interval, 0.88 -2.2 at multivariable analysis for mortality and rehospitalizations). Conclusions-WRF alone, when detected using serial serum creatinine measurements, is not an independent determinant of outcomes in patients with AHF. It has an additive prognostic value when it occurs in patients with persistent signs of congestion. (Circ Heart Fail. 2012;5:54-62.)

Background: Renal function is a powerful prognostic variable in patients with heart failure (HF). Hospitalisations for acute HF (AHF) may be associated with further worsening of renal function (WRF). Methods and results: We analysed the clinical significance of WRF in 318 consecutive patients admitted at our institute for AHF. WRF was defined as the occurrence, at any time during the hospitalisation, of both a ≥25% and a ≥ 0.3 mg/dL increase in serum creatinine (s-Cr) from admission (WRF-Abs-%). Results: Patients were followed for 480 ± 363 days. Fifty-three patients (17%) died and 132 (41%) were rehospitalised for HF. WRF-Abs-% occurred in 107 (34%) patients. At multivariable survival analysis, WRF-Abs-% was an independent predictor of death or HF rehospitalisation (adjusted HR, 1.47; 95%CI, 1.13-1.81; p = 0.024). The independent predictors of WRF-Abs-%, evaluated using multivariable logistic regression, were history of chronic kidney disease (p = 0.002), LV ejection fraction (p = 0.012), furosemide daily dose (p = 0.03) and NYHA class (p = 0.05) on admission. Conclusion: WRF is a frequent finding in patients hospitalised for AHF and is associated with a poor prognosis. Severity of HF and daily furosemide dose are the most important predictors of the occurrence of WRF.

No abstract

Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.

No abstract