Predicting Progression of IgA Nephropathy: New Clinical Progression Risk Score
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{"title"=>"Predicting progression of IgA nephropathy: New clinical progression risk score", "type"=>"journal", "authors"=>[{"first_name"=>"Jingyuan", "last_name"=>"Xie", "scopus_author_id"=>"36115691100"}, {"first_name"=>"Krzysztof", "last_name"=>"Kiryluk", "scopus_author_id"=>"11641412400"}, {"first_name"=>"Weiming", "last_name"=>"Wang", "scopus_author_id"=>"57013913800"}, {"first_name"=>"Zhaohui", "last_name"=>"Wang", "scopus_author_id"=>"56667809800"}, {"first_name"=>"Shanmai", "last_name"=>"Guo", "scopus_author_id"=>"37095018400"}, {"first_name"=>"Pingyan", "last_name"=>"Shen", "scopus_author_id"=>"7201767625"}, {"first_name"=>"Hong", "last_name"=>"Ren", "scopus_author_id"=>"56666552500"}, {"first_name"=>"Xiaoxia", "last_name"=>"Pan", "scopus_author_id"=>"7401930809"}, {"first_name"=>"Xiaonong", "last_name"=>"Chen", "scopus_author_id"=>"36128265600"}, {"first_name"=>"Wen", "last_name"=>"Zhang", "scopus_author_id"=>"56146561500"}, {"first_name"=>"Xiao", "last_name"=>"Li", "scopus_author_id"=>"23469678400"}, {"first_name"=>"Hao", "last_name"=>"Shi", "scopus_author_id"=>"57199074970"}, {"first_name"=>"Yifu", "last_name"=>"Li", "scopus_author_id"=>"37018426400"}, {"first_name"=>"Ali G.", "last_name"=>"Gharavi", "scopus_author_id"=>"7005583929"}, {"first_name"=>"Nan", "last_name"=>"Chen", "scopus_author_id"=>"7401912126"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84862261279", "pui"=>"365003233", "doi"=>"10.1371/journal.pone.0038904", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84862261279", "pmid"=>"22719981"}, "id"=>"f2ca1882-11a6-3a44-9e80-75614b670059", "abstract"=>"IgA nephropathy (IgAN) is a common cause of end-stage renal disease (ESRD) in Asia. In this study, based on a large cohort of Chinese patients with IgAN, we aim to identify independent predictive factors associated with disease progression to ESRD. We collected retrospective clinical data and renal outcomes on 619 biopsy-diagnosed IgAN patients with a mean follow-up time of 41.3 months. In total, 67 individuals reached the study endpoint defined by occurrence of ESRD necessitating renal replacement therapy. In the fully adjusted Cox proportional hazards model, there were four baseline variables with a significant independent effect on the risk of ESRD. These included: eGFR [HR = 0.96(0.95-0.97)], serum albumin [HR = 0.47(0.32-0.68)], hemoglobin [HR = 0.79(0.72-0.88)], and SBP [HR = 1.02(1.00-1.03)]. Based on these observations, we developed a 4-variable equation of a clinical risk score for disease progression. Our risk score explained nearly 22% of the total variance in the primary outcome. Survival ROC curves revealed that the risk score provided improved prediction of ESRD at 24th, 60th and 120th month of follow-up compared to the three previously proposed risk scores. In summary, our data indicate that IgAN patients with higher systolic blood pressure, lower eGFR, hemoglobin, and albumin levels at baseline are at a greatest risk of progression to ESRD. The new progression risk score calculated based on these four baseline variables offers a simple clinical tool for risk stratification.", "link"=>"http://www.mendeley.com/research/predicting-progression-iga-nephropathy-new-clinical-progression-risk-score", "reader_count"=>20, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>3, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>3, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>3, "Mathematics"=>1, "Medicine and Dentistry"=>12, "Agricultural and Biological Sciences"=>1, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>12}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/623279"], "description"=>"<p>the distributions of hemoglobin levels for IgAN patients and healthy controls; <b>(b)</b> hemoglobin levels by the degree of renal impairment; <b>(c)</b> serum albumin distributions in IgAN patients and healthy controls; <b>(d)</b> serum albumin levels by the degree of renal impairment; <b>(e)</b> correlation between serum albumin and urine protein excretion by three different groups of proteinuria. Significance code: * p<0.05, ** p<0.01, *** p<0.001.</p>", "links"=>[], "tags"=>["hemoglobin", "serum", "albumin"], "article_id"=>293760, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Detailed_Analysis_of_Hemoglobin_and_Serum_Albumin_Levels_a_/293760", "title"=>"Detailed Analysis of Hemoglobin and Serum Albumin Levels. (a)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-14 01:02:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/323759", "https://ndownloader.figshare.com/files/323804", "https://ndownloader.figshare.com/files/323850", "https://ndownloader.figshare.com/files/323909", "https://ndownloader.figshare.com/files/323957", "https://ndownloader.figshare.com/files/324009"], "description"=>"<div><p>IgA nephropathy (IgAN) is a common cause of end-stage renal disease (ESRD) in Asia. In this study, based on a large cohort of Chinese patients with IgAN, we aim to identify independent predictive factors associated with disease progression to ESRD. We collected retrospective clinical data and renal outcomes on 619 biopsy-diagnosed IgAN patients with a mean follow-up time of 41.3 months. In total, 67 individuals reached the study endpoint defined by occurrence of ESRD necessitating renal replacement therapy. In the fully adjusted Cox proportional hazards model, there were four baseline variables with a significant independent effect on the risk of ESRD. These included: eGFR [HR = 0.96(0.95–0.97)], serum albumin [HR = 0.47(0.32–0.68)], hemoglobin [HR = 0.79(0.72–0.88)], and SBP [HR = 1.02(1.00–1.03)]. Based on these observations, we developed a 4-variable equation of a clinical risk score for disease progression. Our risk score explained nearly 22% of the total variance in the primary outcome. Survival ROC curves revealed that the risk score provided improved prediction of ESRD at 24th, 60th and 120th month of follow-up compared to the three previously proposed risk scores. In summary, our data indicate that IgAN patients with higher systolic blood pressure, lower eGFR, hemoglobin, and albumin levels at baseline are at a greatest risk of progression to ESRD. The new progression risk score calculated based on these four baseline variables offers a simple clinical tool for risk stratification.</p> </div>", "links"=>[], "tags"=>["predicting", "progression", "iga"], "article_id"=>123906, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0038904.s001", "https://dx.doi.org/10.1371/journal.pone.0038904.s002", "https://dx.doi.org/10.1371/journal.pone.0038904.s003", "https://dx.doi.org/10.1371/journal.pone.0038904.s004", "https://dx.doi.org/10.1371/journal.pone.0038904.s005", "https://dx.doi.org/10.1371/journal.pone.0038904.s006"], "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predicting_Progression_of_IgA_Nephropathy_New_Clinical_Progression_Risk_Score/123906", "title"=>"Predicting Progression of IgA Nephropathy: New Clinical Progression Risk Score", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-06-14 01:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/623506"], "description"=>"<p>The ROC curves for predicting renal outcomes within (a) 24 months, (b) 60 months, and (c) 120 months of follow-up. The Risk Score from this study (black) is contrasted against the Goto et al. score (blue), RENAAL score (red) and the Berthoux et al. score (green); (d) comparisons of AUCs (and their 95% CIs) and R<sup>2</sup> for the four risk score prediction models.</p>", "links"=>[], "tags"=>["published", "esrd"], "article_id"=>293987, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_Published_ESRD_Prediction_Scores_/293987", "title"=>"Performance of the Published ESRD Prediction Scores.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-14 01:06:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/623646"], "description"=>"<p>SBP: systolic blood pressure;</p><p>DBP: diastolic blood pressure;</p><p>MAP: mean arterial pressure;</p><p>UA: uric acid;</p><p>WBC: white blood cell count.</p><p># p<0.05.</p>", "links"=>[], "tags"=>["baseline", "variables", "renal", "points"], "article_id"=>294129, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.t002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Univariate_analysis_of_baseline_variables_with_renal_end_points_for_ESRD_/294129", "title"=>"Univariate analysis of baseline variables with renal end points for ESRD.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-14 01:08:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/623732"], "description"=>"<p>SBP: systolic blood pressure;</p><p>DBP: diastolic blood pressure;</p><p>MAP: mean arterial pressure;</p><p>UA: uric acid;</p><p>WBC: white blood cell count.</p>", "links"=>[], "tags"=>["characteristics", "igan"], "article_id"=>294210, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.t001", "stats"=>{"downloads"=>7, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Baseline_characteristics_of_IgAN_patients_/294210", "title"=>"Baseline characteristics of IgAN patients.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-14 01:10:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/623168"], "description"=>"<p><b>(a)</b> low (red) versus high (black) baseline eGFR group; <b>(b)</b> patients with a baseline diagnosis of anemia (red) versus no anemia (black); <b>(c)</b> patients with hypoalbuminemia (red) versus normoalbuminemia (black); <b>(d)</b> patients with systolic hypertension (red) versus normotensives (black). Censor points are denoted by vertical tick lines.</p>", "links"=>[], "tags"=>["outcome-free"], "article_id"=>293649, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.g001", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kaplan_Meier_Outcome_free_Survival_Curves_/293649", "title"=>"Kaplan-Meier Outcome-free Survival Curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-14 01:00:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/623399"], "description"=>"<p>Kaplan-Meier outcome-free survival curves by risk score tertiles; <b>(b)</b> the Risk Score’s ROC curves for predicting ESRD at 24 months, 60 months and 120 months.</p>", "links"=>[], "tags"=>["roc", "curves"], "article_id"=>293878, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Survival_and_Survival_ROC_curves_for_the_Risk_Score_a_/293878", "title"=>"Survival and Survival ROC curves for the Risk Score. (a)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-14 01:04:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/623691"], "description"=>"<p>SBP: systolic blood pressure.</p>*<p>Renal outcome defined as end-stage renal disease (ESRD).</p>**<p>Renal outcome defined as 50% decline from baseline eGFR.</p><p># The risk score was calculated from the coefficients of independent risk factors in model 1.</p>", "links"=>[], "tags"=>["cox", "regression", "stepwise"], "article_id"=>294168, "categories"=>["Physiology", "Marine Biology"], "users"=>["Jingyuan Xie", "Krzysztof Kiryluk", "Weiming Wang", "Zhaohui Wang", "Shanmai Guo", "Pingyan Shen", "Hong Ren", "Xiaoxia Pan", "Xiaonong Chen", "Wen Zhang", "Xiao Li", "Hao Shi", "Yifu Li", "Ali G. Gharavi", "Nan Chen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0038904.t003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multivariate_Cox_Regression_with_Stepwise_Selection_n_8202_8202_619_/294168", "title"=>"Multivariate Cox Regression with Stepwise Selection (n = 619).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-14 01:09:28"}

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Relative Metric

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