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      Phenotype-specific therapeutic efficacy of ilofotase alfa in patients with sepsis-associated acute kidney injury

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          Abstract

          Background

          There is no effective treatment for sepsis-associated acute kidney injury (SA-AKI). Ilofotase alfa (human recombinant alkaline phosphatase) has been shown to exert reno-protective properties, although it remains unclear which patients might be most likely to benefit. We aimed to identify a clinical phenotype associated with ilofotase alfa's therapeutic efficacy.

          Methods

          Data from 570 out of 650 patients enrolled in the REVIVAL trial were used in a stepwise machine learning approach. First, clinical variables with increasing or decreasing risk ratios for ilofotase alfa treatment across quartiles for the main secondary endpoint, Major Adverse Kidney Events up to day 90 (MAKE90), were selected. Second, linear regression analysis was used to determine the therapeutic effect size. Finally, the top-15 variables were used in different clustering analyses with consensus assessment.

          Results

          The optimal clustering model comprised two phenotypes. Phenotype 1 displayed relatively lower disease severity scores, and less pronounced renal and pulmonary dysfunction. Phenotype 2 exhibited higher severity scores and creatinine, with lower eGFR and bicarbonate levels. Compared with placebo treatment, ilofotase alfa significantly reduced MAKE90 events for phenotype 2 patients (54% vs. 68%, p = 0.013), but not for phenotype 1 patients (49% vs. 46%, p = 0.54).

          Conclusion

          We identified a clinical phenotype comprising severely ill patients with underlying kidney disease who benefitted most from ilofotase alfa treatment. This yields insight into the therapeutic potential of this novel treatment in more homogeneous patient groups and could guide patient selection in future trials, showing promise for personalized medicine in SA-AKI and other complex conditions.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13054-024-04837-y.

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          Most cited references12

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          ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

          Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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            MissForest--non-parametric missing value imputation for mixed-type data.

            Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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              Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study.

              Current reports on acute kidney injury (AKI) in the intensive care unit (ICU) show wide variation in occurrence rate and are limited by study biases such as use of incomplete AKI definition, selected cohorts, or retrospective design. Our aim was to prospectively investigate the occurrence and outcomes of AKI in ICU patients.
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                Author and article information

                Contributors
                peter.pickkers@radboudumc.nl
                Journal
                Crit Care
                Critical Care
                BioMed Central (London )
                1364-8535
                1466-609X
                19 February 2024
                19 February 2024
                2024
                : 28
                : 50
                Affiliations
                [1 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Department of Intensive Care Medicine, , Radboud University Medical Center, ; Nijmegen, The Netherlands
                [2 ]GRID grid.487155.a, ISNI 0000 0004 0646 5466, AM-Pharma B.V., ; Utrecht, The Netherlands
                Article
                4837
                10.1186/s13054-024-04837-y
                10875769
                38373981
                284b7e34-ab71-47fd-94c5-58ed1caa34fa
                © 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 22 November 2023
                : 12 February 2024
                Funding
                Funded by: AM-Pharma B.V.
                Categories
                Brief Report
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Emergency medicine & Trauma
                sepsis,acute kidney injury,chronic kidney disease,make90,phenotypes,machine learning,cluster analysis

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