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      Plasma neutrophil gelatinase-associated lipocalin independently predicts dialysis need and mortality in critical COVID-19

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          Abstract

          Neutrophil gelatinase-associated lipocalin (NGAL) is a novel kidney injury and inflammation biomarker. We investigated whether NGAL could be used to predict continuous renal replacement therapy (CRRT) and mortality in critical coronavirus disease 2019 (COVID-19). This prospective multicenter cohort study included adult COVID-19 patients in six intensive care units (ICUs) in Sweden between May 11, 2020 and May 10, 2021. Blood was sampled at admission, days two and seven in the ICU. The samples were batch analyzed for NGAL, creatinine, and cystatin c after the end of the study period. Initiation of CRRT and 90-day survival were used as dependent variables in regression models. Of 498 included patients, 494 were analyzed regarding CRRT and 399 were analyzed regarding survival. Seventy patients received CRRT and 154 patients did not survive past 90 days. NGAL, in combination with creatinine and cystatin c, predicted the subsequent initiation of CRRT with an area under the curve (AUC) of 0.95. For mortality, NGAL, in combination with age and sex, had an AUC of 0.83. In conclusion, NGAL is a valuable biomarker for predicting subsequent initiation of CRRT and 90-day mortality in critical COVID-19. NGAL should be considered when developing future clinical scoring systems.

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          Most cited references46

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          Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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            pROC: an open-source package for R and S+ to analyze and compare ROC curves

            Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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              Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

              Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
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                Author and article information

                Contributors
                jonas.engstrom@med.lu.se
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 March 2024
                20 March 2024
                2024
                : 14
                : 6695
                Affiliations
                [1 ]Department of Clinical Sciences, Lund University, Anesthesiology and Intensive Care, ( https://ror.org/012a77v79) Lund, 221 00 Sweden
                [2 ]Department of Anesthesia and Intensive Care, Kristianstad Hospital, Kristianstad, 291 85 Sweden
                [3 ]Department of Intensive and Perioperative Care, Skåne University Hospital, ( https://ror.org/02z31g829) Malmö, 205 02 Sweden
                [4 ]Department of Medical Sciences, Clinical Chemistry, Uppsala University, ( https://ror.org/048a87296) Uppsala, 751 05 Sweden
                [5 ]Department of Intensive and Perioperative Care, Skåne University Hospital, ( https://ror.org/02z31g829) Lund, 221 85 Sweden
                Article
                57409
                10.1038/s41598-024-57409-z
                10954663
                38509165
                2a2bb37e-1a1b-44c7-b84a-a129a200a16a
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 November 2023
                : 18 March 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100009234, Centralsjukhuset Kristianstad;
                Funded by: FundRef http://dx.doi.org/10.13039/501100011077, Skånes universitetssjukhus;
                Funded by: Region Skåne
                Award ID: #2022-1256
                Award ID: #2022-1284
                Award Recipient :
                Funded by: Hjärt-Lungfonden
                Award ID: #2021-0233
                Award ID: #2022-0352
                Award Recipient :
                Funded by: Utbildningsdepartementet
                Award ID: #2022-0226
                Award ID: #2022:YF0009
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003173, Crafoordska Stiftelsen;
                Award ID: #2021-0833
                Award Recipient :
                Funded by: Lions Skåne
                Funded by: Lund University
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                predictive markers,prognostic markers,viral infection,prognosis,continuous renal replacement therapy,haemodialysis,acute kidney injury

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