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10.1371/journal.pone.0177894 http://scihub22266oqcxt.onion/10.1371/journal.pone.0177894 C5436831!5436831!28542627     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28542627&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2017 ; 12 (5): ä Nephropedia Template TP {{tp|p=28542627|t=2017. Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome |pdf=|usr=}}{{28542627}} gab.com Text Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28542627 #FOAvip Chronic kidney disease (CKD) is a frequent and serious complication of atypical haemolytic uremic syndrome (aHUS). We aimed to develop a simple accurate model to predict the risk of renal dysfunction in aHUS based on clinical and biological features available at hospital admission. Renal function at 1-year follow-up, based on an estimated glomerular filtration rate < 60mL/min/1.73m2 as assessed by the Modification of Diet in Renal Disease equation, was used as an indicator of significant CKD. Prospectively collected data from a cohort of 156 aHUS patients who did not receive eculizumab were used to identify predictors of CKD. Covariates associated with renal impairment were identified by multivariate analysis. The model performance was assessed and a scoring system for clinical practice was constructed from the regression coefficient. Multivariate analyses identified three predictors of CKD: a high serum creatinine level, a high mean arterial pressure and a mildly decreased platelet count. The prognostic model had a good discriminative ability (area under the curve = .84). The scoring system ranged from 0 to 5, with corresponding risks of CKD ranging from 18% to 100%. This model accurately predicts development of 1-year CKD in patients with aHUS using clinical and biological features available on admission. After further validation, this model may assist in clinical decision making. Twit Text FOAVip Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28542627 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28542627 #FOAvip English Wikipedia <ref>{{Cite pmid|28542627}}</ref> Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome #MMPMID28542627 Jamme M; Raimbourg Q; Chauveau D; Seguin A; Presne C; Perez P; Gobert P; Wynckel A; Provôt F; Delmas Y; Mousson C; Servais A; Vrigneaud L; Veyradier A; Rondeau E; Coppo P PLoS One 2017[]; 12 (5): ä PMID28542627show ga Chronic kidney disease (CKD) is a frequent and serious complication of atypical haemolytic uremic syndrome (aHUS). We aimed to develop a simple accurate model to predict the risk of renal dysfunction in aHUS based on clinical and biological features available at hospital admission. Renal function at 1-year follow-up, based on an estimated glomerular filtration rate < 60mL/min/1.73m2 as assessed by the Modification of Diet in Renal Disease equation, was used as an indicator of significant CKD. Prospectively collected data from a cohort of 156 aHUS patients who did not receive eculizumab were used to identify predictors of CKD. Covariates associated with renal impairment were identified by multivariate analysis. The model performance was assessed and a scoring system for clinical practice was constructed from the regression coefficient. Multivariate analyses identified three predictors of CKD: a high serum creatinine level, a high mean arterial pressure and a mildly decreased platelet count. The prognostic model had a good discriminative ability (area under the curve = .84). The scoring system ranged from 0 to 5, with corresponding risks of CKD ranging from 18% to 100%. This model accurately predicts development of 1-year CKD in patients with aHUS using clinical and biological features available on admission. After further validation, this model may assist in clinical decision making. äPredictive features of chronic kidney disease in atypical haemolytic u(epmc).pdf DeepDyve Pubget Overpricing