Urinary Biomarkers TIMP-2 and IGFBP7 Early Predict Acute Kidney Injury after Major Surgery
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{"title"=>"Urinary biomarkers TIMP-2 and IGFBP7 early predict acute kidney injury after major surgery", "type"=>"journal", "authors"=>[{"first_name"=>"Ivan", "last_name"=>"Gocze", "scopus_author_id"=>"55224858100"}, {"first_name"=>"Matthias", "last_name"=>"Koch", "scopus_author_id"=>"56677233000"}, {"first_name"=>"Philipp", "last_name"=>"Renner", "scopus_author_id"=>"16029448600"}, {"first_name"=>"Florian", "last_name"=>"Zeman", "scopus_author_id"=>"34882385000"}, {"first_name"=>"Bernhard M.", "last_name"=>"Graf", "scopus_author_id"=>"7102995809"}, {"first_name"=>"Marc H.", "last_name"=>"Dahlke", "scopus_author_id"=>"55880587900"}, {"first_name"=>"Michael", "last_name"=>"Nerlich", "scopus_author_id"=>"35444906600"}, {"first_name"=>"Hans J.", "last_name"=>"Schlitt", "scopus_author_id"=>"7005572464"}, {"first_name"=>"John A.", "last_name"=>"Kellum", "scopus_author_id"=>"7103325757"}, {"first_name"=>"Thomas", "last_name"=>"Bein", "scopus_author_id"=>"7005979732"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84925727334", "sgr"=>"84925727334", "pui"=>"603282107", "isbn"=>"1932-6203", "pmid"=>"25798585", "doi"=>"10.1371/journal.pone.0120863"}, "id"=>"01101f00-03d1-3227-ae1a-8780fc37c2bd", "abstract"=>"OBJECTIVE: To assess the ability of the urinary biomarkers IGFBP7 (insulin-like growth factor-binding protein 7) and TIMP-2 (tissue inhibitor of metalloproteinase 2) to early predict acute kidney injury (AKI) in high-risk surgical patients.\\n\\nINTRODUCTION: Postoperative AKI is associated with an increase in short and long-term mortality. Using IGFBP7 and TIMP-2 for early detection of cellular kidney injury, thus allowing the early initiation of renal protection measures, may represent a new concept of evaluating renal function.\\n\\nMETHODS: In this prospective study, urinary [TIMP-2]×[IGFBP7] was measured in surgical patients at high risk for AKI. A predefined cut-off value of [TIMP-2]×[IGFBP7] >0.3 was used for assessing diagnostic accuracy. Perioperative characteristics were evaluated, and ROC analyses as well as logistic regression models of risk assessment were calculated with and without a [TIMP-2]×[IGFBP7] test.\\n\\nRESULTS: 107 patients were included in the study, of whom 45 (42%) developed AKI. The highest median values of biomarker were detected in septic, transplant and patients after hepatic surgery (1.24 vs 0.45 vs 0.47 ng/l²/1000). The area under receiving operating characteristic curve (AUC) for the risk of any AKI was 0.85, for early use of RRT 0.83 and for 28-day mortality 0.77. In a multivariable model with established perioperative risk factors, the [TIMP-2]×[IGFBP7] test was the strongest predictor of AKI and significantly improved the risk assessment (p<0.001).\\n\\nCONCLUSIONS: Urinary [TIMP-2]×[IGFBP7] test sufficiently detect patients with risk of AKI after major non-cardiac surgery. Due to its rapid responsiveness it extends the time frame for intervention to prevent development of AKI.", "link"=>"http://www.mendeley.com/research/urinary-biomarkers-timp2-igfbp7-early-predict-acute-kidney-injury-after-major-surgery", "reader_count"=>56, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>8, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>8, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Medicine and Dentistry"=>43, "Agricultural and Biological Sciences"=>3, "Chemistry"=>2, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>43}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Canada"=>1, "Spain"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1981965"], "description"=>"<p>Data are mean (SD) or n (%). Fluid 24 = Fluid balance in the first 24 hours after ICU admission; MAP = mean \"mean arterial pressure\" over the first 24 hours; Hemoglobin = mean hemoglobin level in the first 24 hours; Urine output = mean urine output ml/kg/h in first 24 hours.</p><p>Baseline characteristics of the study subgroups.</p>", "links"=>[], "tags"=>["risk assessment", "perioperative risk factors", "biomarkers IGFBP 7", "roc", "timp", "Predict Acute Kidney Injury", "aki", "auc", "rrt", "Major Surgery ObjectiveTo", "IGFBP 7", "kidney injury"], "article_id"=>1350605, "categories"=>["Biological Sciences"], "users"=>["Ivan Gocze", "Matthias Koch", "Philipp Renner", "Florian Zeman", "Bernhard M. Graf", "Marc H. Dahlke", "Michael Nerlich", "Hans J. Schlitt", "John A. Kellum", "Thomas Bein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120863.t002", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Baseline_characteristics_of_the_study_subgroups_/1350605", "title"=>"Baseline characteristics of the study subgroups.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-23 04:37:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1981964"], "description"=>"<p>BMI, body mass index; SAPS II, Simplified Acute Physiology Score II; ICU, intensive care unit; AKI, acute kidney injury; RRT, renal replacement therapy; SD, standard deviation; IGFBP7, insulin-like growth factor-binding protein 7; TIMP-2, tissue inhibitor of metalloproteinase.</p><p>Patient characteristics (n = 107).</p>", "links"=>[], "tags"=>["risk assessment", "perioperative risk factors", "biomarkers IGFBP 7", "roc", "timp", "Predict Acute Kidney Injury", "aki", "auc", "rrt", "Major Surgery ObjectiveTo", "IGFBP 7", "kidney injury"], "article_id"=>1350604, "categories"=>["Biological Sciences"], "users"=>["Ivan Gocze", "Matthias Koch", "Philipp Renner", "Florian Zeman", "Bernhard M. Graf", "Marc H. Dahlke", "Michael Nerlich", "Hans J. Schlitt", "John A. Kellum", "Thomas Bein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120863.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patient_characteristics_n_107_/1350604", "title"=>"Patient characteristics (n = 107).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-23 04:37:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1981967"], "description"=>"<p>*Likelihood-ratio test (comparing the fit of both models)</p><p>AKI, acute kidney injury; RRT, renal replacement therapy; IGFBP7, insulin-like growth factor-binding protein 7; TIMP-2, tissue inhibitor of metalloproteinase; MAP, mean arterial pressure, AUC, area under the curve; CI, coincidence interval; OR, odds ratio.</p><p>Multivariable logistic regression model of bedside postoperative parameters at the time of biomarker assessment with and without the (TIMP-2)<sup><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120863#t004fn001\" target=\"_blank\">*</a></sup>(IGFBP7) for risk assessment of AKI, use of RRT, 28-days mortality and AKI 2,3.</p>", "links"=>[], "tags"=>["risk assessment", "perioperative risk factors", "biomarkers IGFBP 7", "roc", "timp", "Predict Acute Kidney Injury", "aki", "auc", "rrt", "Major Surgery ObjectiveTo", "IGFBP 7", "kidney injury"], "article_id"=>1350607, "categories"=>["Biological Sciences"], "users"=>["Ivan Gocze", "Matthias Koch", "Philipp Renner", "Florian Zeman", "Bernhard M. Graf", "Marc H. Dahlke", "Michael Nerlich", "Hans J. Schlitt", "John A. Kellum", "Thomas Bein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120863.t004", "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multivariable_logistic_regression_model_of_bedside_postoperative_parameters_at_the_time_of_biomarker_assessment_with_and_without_the_TIMP_2_IGFBP7_for_risk_assessment_of_AKI_use_of_RRT_28_days_mortality_and_AKI_2_3_/1350607", "title"=>"Multivariable logistic regression model of bedside postoperative parameters at the time of biomarker assessment with and without the (TIMP-2)<sup>*</sup>(IGFBP7) for risk assessment of AKI, use of RRT, 28-days mortality and AKI 2,3.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-23 04:37:27"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1981963"], "description"=>"<p>Area under the curve (AUC) and the best cut-offs for predicting AKI, AKI Stage 2 and 3, early use of RRT and 28-days mortality.</p>", "links"=>[], "tags"=>["risk assessment", "perioperative risk factors", "biomarkers IGFBP 7", "roc", "timp", "Predict Acute Kidney Injury", "aki", "auc", "rrt", "Major Surgery ObjectiveTo", "IGFBP 7", "kidney injury"], "article_id"=>1350603, "categories"=>["Biological Sciences"], "users"=>["Ivan Gocze", "Matthias Koch", "Philipp Renner", "Florian Zeman", "Bernhard M. Graf", "Marc H. Dahlke", "Michael Nerlich", "Hans J. Schlitt", "John A. Kellum", "Thomas Bein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120863.g002", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_under_the_curve_AUC_and_the_best_cut_offs_for_predicting_AKI_AKI_Stage_2_and_3_early_use_of_RRT_and_28_days_mortality_/1350603", "title"=>"Area under the curve (AUC) and the best cut-offs for predicting AKI, AKI Stage 2 and 3, early use of RRT and 28-days mortality.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 04:37:27"}
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