Renal Function Trajectories in Patients with Prior Improved eGFR Slopes and Risk of Death
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{"title"=>"Renal function trajectories in patients with prior improved EGFR slopes and risk of death", "type"=>"journal", "authors"=>[{"first_name"=>"Yan", "last_name"=>"Xie", "scopus_author_id"=>"56953880200"}, {"first_name"=>"Benjamin", "last_name"=>"Bowe", "scopus_author_id"=>"56955129500"}, {"first_name"=>"Hong", "last_name"=>"Xian", "scopus_author_id"=>"7007014406"}, {"first_name"=>"Sumitra", "last_name"=>"Balasubramanian", "scopus_author_id"=>"35285641100"}, {"first_name"=>"Ziyad", "last_name"=>"Al-Aly", "scopus_author_id"=>"9738161500"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84960532929", "sgr"=>"84960532929", "doi"=>"10.1371/journal.pone.0149283", "pui"=>"608919513", "pmid"=>"26900691", "isbn"=>"1932-6203 (Electronic)\r1932-6203 (Linking)", "issn"=>"19326203"}, "id"=>"e606e99b-36ea-38a5-8d66-845daacb2d1b", "abstract"=>"BACKGROUND: Multiple prior studies demonstrated that patients with early Chronic Kidney Disease (CKD) and positive estimated Glomerular Filtration Rate (eGFR) slopes experience increased risk of death. We sought to characterize patients with positive eGFR slopes, examine the renal function trajectory that follows the time period where positive slope is observed, and examine the association between different trajectories and risk of death. METHODS AND FINDINGS: We built a cohort of 204,132 United States veterans with early CKD stage 3; eGFR slopes were defined based on Bayesian mixed-effects models using outpatient eGFR measurements between October 1999 and September 2004; to build renal function trajectories, patients were followed longitudinally thereafter (from October 2004) until September 2013. There were 41,410 (20.29%) patients with positive eGFR slope and they exhibited increased risk of death compared to patients with stable eGFR slope (HR = 1.33, CI:1.31-1.35). There was an inverse graded association between severity of albuminuria and the odds of positive eGFR slope (OR = 0.94, CI:0.90-0.98, and OR = 0.76, CI:0.69-0.84 for microalbuminuria and albuminuria; respectively). Following the time period where positive eGFR slope is observed, we characterized 4 trajectory phenotypes: high eGFR intercept and positive trajectory (HIPT) (12.42%), intermediate intercept and mild negative trajectory (IIMNT) (60.04%), low intercept and fast negative trajectory (LIFNT)(23.33%), and high intercept and fast negative trajectory (HIFNT) (4.20%). Compared to IIMNT (reference group), HIPT is associated with younger age, dementia, HIV, chronic lung disease, peripheral artery disease, weight loss, and inversely associated with albuminuria; LIFNT and HIFNT were associated with diabetes, hypertension, cardiovascular disease, peripheral artery disease, and albuminuria. The risk of death at 9 years was lowest in IIMNT (HR = 1.12, CI:1.09-1.14), highest in HIPT (HR = 1.71, CI:1.63-1.79), and intermediate in LIFNT (HR = 1.36, CI:1.32-1.40) and HIFNT (HR = 1.56, CI:1.45-1.68). CONCLUSIONS: Our results demonstrate that patients with positive eGFR slopes, when followed over longer period of time, follow 4 distinct trajectory phenotypes that have distinct demographic and clinical correlates and are differentially associated with risk of death.", "link"=>"http://www.mendeley.com/research/renal-function-trajectories-patients-prior-improved-egfr-slopes-risk-death", "reader_count"=>14, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Nursing and Health Professions"=>1, "Materials Science"=>1, "Medicine and Dentistry"=>9}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Italy"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4406209"], "description"=>"<p>Model adjusted for age, race, gender, diabetes mellitus, hypertension, cardiovascular disease, hyperlipidemia, peripheral artery disease, cerebrovascular disease, chronic lung disease, hepatitis C, HIV, dementia, and initial eGFR. Reference group is patients with stable eGFR slope.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730148, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Adjusted_associations_of_improved_eGFR_slope_and_declining_eGFR_slopes_/2730148", "title"=>"Adjusted associations of improved eGFR slope, and declining eGFR slopes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406377"], "description"=>"<p>HIFNT = High Intercept and Fast Negative Trajectory includes trajectory F. HIPT = High Intercept Positive Trajectory and includes trajectory A and B; IIMNT = Intermediate Intercept Mild Negative Trajectory and includes trajectory C, and D; LIFNT = Low Intercept Fast Negative Trajectory and includes trajectory E and G; Model adjusted for age, race, gender, for age, race, gender, diabetes mellitus, hypertension, cardiovascular disease, hyperlipidemia, peripheral artery disease, cerebrovascular disease, chronic lung disease, hepatitis C, HIV, dementia, and eGFR at time of cohort entry (T0). Reference group is IIMNT.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730277, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t006", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Adjusted_associations_of_trajectory_phenotypes_/2730277", "title"=>"Adjusted associations of trajectory phenotypes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406104"], "description"=>"<p>Renal function trajectory phenotypes.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730055, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Renal_function_trajectory_phenotypes_/2730055", "title"=>"Renal function trajectory phenotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406074"], "description"=>"<p>Renal function trajectories.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730028, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Renal_function_trajectories_/2730028", "title"=>"Renal function trajectories.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406263"], "description"=>"<p>Demographic and clinical characteristics according to renal function trajectory.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730190, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t004", "stats"=>{"downloads"=>3, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Demographic_and_clinical_characteristics_according_to_renal_function_trajectory_/2730190", "title"=>"Demographic and clinical characteristics according to renal function trajectory.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4405990", "https://ndownloader.figshare.com/files/4405996", "https://ndownloader.figshare.com/files/4406002", "https://ndownloader.figshare.com/files/4406014", "https://ndownloader.figshare.com/files/4406017"], "description"=>"<div><p>Background</p><p>Multiple prior studies demonstrated that patients with early Chronic Kidney Disease (CKD) and positive estimated Glomerular Filtration Rate (eGFR) slopes experience increased risk of death. We sought to characterize patients with positive eGFR slopes, examine the renal function trajectory that follows the time period where positive slope is observed, and examine the association between different trajectories and risk of death.</p><p>Methods and Findings</p><p>We built a cohort of 204,132 United States veterans with early CKD stage 3; eGFR slopes were defined based on Bayesian mixed-effects models using outpatient eGFR measurements between October 1999 and September 2004; to build renal function trajectories, patients were followed longitudinally thereafter (from October 2004) until September 2013. There were 41,410 (20.29%) patients with positive eGFR slope and they exhibited increased risk of death compared to patients with stable eGFR slope (HR = 1.33, CI:1.31–1.35). There was an inverse graded association between severity of albuminuria and the odds of positive eGFR slope (OR = 0.94, CI:0.90–0.98, and OR = 0.76, CI:0.69–0.84 for microalbuminuria and albuminuria; respectively). Following the time period where positive eGFR slope is observed, we characterized 4 trajectory phenotypes: high eGFR intercept and positive trajectory (HIPT) (12.42%), intermediate intercept and mild negative trajectory (IIMNT) (60.04%), low intercept and fast negative trajectory (LIFNT)(23.33%), and high intercept and fast negative trajectory (HIFNT) (4.20%). Compared to IIMNT (reference group), HIPT is associated with younger age, dementia, HIV, chronic lung disease, peripheral artery disease, weight loss, and inversely associated with albuminuria; LIFNT and HIFNT were associated with diabetes, hypertension, cardiovascular disease, peripheral artery disease, and albuminuria. The risk of death at 9 years was lowest in IIMNT (HR = 1.12, CI:1.09–1.14), highest in HIPT (HR = 1.71, CI:1.63–1.79), and intermediate in LIFNT (HR = 1.36, CI:1.32–1.40) and HIFNT (HR = 1.56, CI:1.45–1.68).</p><p>Conclusions</p><p>Our results demonstrate that patients with positive eGFR slopes, when followed over longer period of time, follow 4 distinct trajectory phenotypes that have distinct demographic and clinical correlates and are differentially associated with risk of death.</p></div>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2729971, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0149283.s001", "https://dx.doi.org/10.1371/journal.pone.0149283.s002", "https://dx.doi.org/10.1371/journal.pone.0149283.s003", "https://dx.doi.org/10.1371/journal.pone.0149283.s004", "https://dx.doi.org/10.1371/journal.pone.0149283.s005"], "stats"=>{"downloads"=>5, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Renal_Function_Trajectories_in_Patients_with_Prior_Improved_eGFR_Slopes_and_Risk_of_Death/2729971", "title"=>"Renal Function Trajectories in Patients with Prior Improved eGFR Slopes and Risk of Death", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406158"], "description"=>"<p>Model 1 adjusted for age, race, gender, diabetes mellitus, hypertension, cardiovascular disease, hyperlipidemia, peripheral artery disease, cerebrovascular disease, chronic lung disease, hepatitis C, HIV, dementia and eGFR at time of cohort entry (T0). Reference group is patients with stable kidney function (eGFR slope).</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730100, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t002", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Change_in_kidney_function_and_the_risk_of_death_/2730100", "title"=>"Change in kidney function and the risk of death.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406044"], "description"=>"<p>Cohort design and timeline.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730007, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.g001", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Cohort_design_and_timeline_/2730007", "title"=>"Cohort design and timeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406134"], "description"=>"<p>Demographic and clinical characteristics of those with improved, stable, and declining eGFR slope.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730073, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t001", "stats"=>{"downloads"=>3, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Demographic_and_clinical_characteristics_of_those_with_improved_stable_and_declining_eGFR_slope_/2730073", "title"=>"Demographic and clinical characteristics of those with improved, stable, and declining eGFR slope.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406317"], "description"=>"<p>Model adjusted for age, race, gender, for age, race, gender, diabetes mellitus, hypertension, cardiovascular disease, hyperlipidemia, peripheral artery disease, cerebrovascular disease, chronic lung disease, hepatitis C, HIV, dementia, and eGFR at time of cohort entry (T0). Reference group is patients with stable kidney function before cohort entry (T0).</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730235, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Risk_of_death_by_trajectory_/2730235", "title"=>"Risk of death by trajectory.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/4406428"], "description"=>"<p>HIFNT = High Intercept and Fast Negative Trajectory includes trajectory F. HIPT = High Intercept Positive Trajectory and includes trajectory A and B; IIMNT = Intermediate Intercept Mild Negative Trajectory and includes trajectory C, and D; LIFNT = Low Intercept Fast Negative Trajectory and includes trajectory E and G; Model adjusted for age, race, gender, for age, race, gender, diabetes mellitus, hypertension, cardiovascular disease, hyperlipidemia, peripheral artery disease, cerebrovascular disease, chronic lung disease, hepatitis C, HIV, dementia, and eGFR at time of cohort entry (T0). Reference group is patients with stable kidney function before T0.</p>", "links"=>[], "tags"=>["CKD", "HIFNT", "Renal Function Trajectories", "albuminuria", "HR", "Glomerular Filtration Rate", "IIMNT", "eGFR slopes", "intercept", "time period", "4 trajectory phenotypes", "outpatient eGFR measurements", "eGFR slope", "LIFNT", "CI", "artery disease", "Chronic Kidney Disease", "HIV", "HIPT"], "article_id"=>2730301, "categories"=>["Medicine", "Biotechnology", "Marine Biology", "Cancer", "Infectious Diseases", "Computational Biology"], "users"=>["Yan Xie", "Benjamin Bowe", "Hong Xian", "Sumitra Balasubramanian", "Ziyad Al-Aly"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0149283.t007", "stats"=>{"downloads"=>3, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Risk_of_death_of_trajectory_phenotypes_/2730301", "title"=>"Risk of death of trajectory phenotypes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-22 09:06:52"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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