• Record: found
  • Abstract: found
  • Article: found
Is Open Access

Identifying on admission patients likely to develop acute kidney injury in hospital

Read this article at

      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



      The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.


      Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation.

      Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).


      Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64–0.77); FLS II (AUC 0.77, 95% CI: 0.69–0.85) and MLR II (AUC 0.74, 95% CI: 0.65–0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92–0.98).


      FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.

      Electronic supplementary material

      The online version of this article (10.1186/s12882-019-1237-x) contains supplementary material, which is available to authorized users.

      Related collections

      Most cited references 22

      • Record: found
      • Abstract: found
      • Article: found

      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 under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
        • Record: found
        • Abstract: not found
        • Article: not found

        Fuzzy identification of systems and its applications to modeling and control

          • Record: found
          • Abstract: found
          • Article: not found


          SECTION I: USE OF THE CLINICAL PRACTICE GUIDELINE This Clinical Practice Guideline document is based upon the best information available as of February 2011. It is designed to provide information and assist decision-making. It is not intended to define a standard of care, and should not be construed as one, nor should it be interpreted as prescribing an exclusive course of management. Variations in practice will inevitably and appropriately occur when clinicians take into account the needs of individual patients, available resources, and limitations unique to an institution or type of practice. Every health-care professional making use of these recommendations is responsible for evaluating the appropriateness of applying them in the setting of any particular clinical situation. The recommendations for research contained within this document are general and do not imply a specific protocol. SECTION II: DISCLOSURE Kidney Disease: Improving Global Outcomes (KDIGO) makes every effort to avoid any actual or reasonably perceived conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the Work Group. All members of the Work Group are required to complete, sign, and submit a disclosure and attestation form showing all such relationships that might be perceived or actual conflicts of interest. This document is updated annually and information is adjusted accordingly. All reported information is published in its entirety at the end of this document in the Work Group members' Biographical and Disclosure Information section, and is kept on file at the National Kidney Foundation (NKF), Managing Agent for KDIGO.

            Author and article information

            [1 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, Centre for Implementation Science, Faculty of Health Sciences, , University of Southampton, ; Southampton, SO17 1BJ UK
            [2 ]ISNI 0000 0004 1936 8024, GRID grid.8391.3, College of Engineering, Mathematics, and Physical Sciences, , University of Exeter, ; Penryn, Cornwall, TR10 9FE UK
            [3 ]ISNI 0000 0004 0474 4488, GRID grid.412944.e, Research, Development, and Innovation, , Royal Cornwall Hospitals NHS Trust, ; Truro, TR1 3HD UK
            +44 (0)23 8059 8307 ,
            BMC Nephrol
            BMC Nephrol
            BMC Nephrology
            BioMed Central (London )
            14 February 2019
            14 February 2019
            : 20
            30764796 6376785 1237 10.1186/s12882-019-1237-x
            © The Author(s). 2019

            Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

            Funded by: Duchy Health Charity
            Research Article
            Custom metadata
            © The Author(s) 2019


            Comment on this article