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      Sequential organ failure assessment score is an excellent operationalization of disease severity of adult patients with hospitalized community acquired pneumonia – results from the prospective observational PROGRESS study

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          Abstract

          Background

          CAP (Community acquired pneumonia) is frequent, with a high mortality rate and a high burden on health care systems. Development of predictive biomarkers, new therapeutic concepts, and epidemiologic research require a valid, reproducible, and quantitative measure describing CAP severity.

          Methods

          Using time series data of 1532 patients enrolled in the PROGRESS study, we compared putative measures of CAP severity for their utility as an operationalization. Comparison was based on ability to correctly identify patients with an objectively severe state of disease (death or need for intensive care with at least one of the following: substantial respiratory support, treatment with catecholamines, or dialysis). We considered IDSA/ATS minor criteria, CRB-65, CURB-65, Halm criteria, qSOFA, PSI, SCAP, SIRS-Score, SMART-COP, and SOFA.

          Results

          SOFA significantly outperformed other scores in correctly identifying a severe state of disease at the day of enrollment (AUC = 0.948), mainly caused by higher discriminative power at higher score values. Runners-up were the sum of IDSA/ATS minor criteria (AUC = 0.916) and SCAP (AUC = 0.868). SOFA performed similarly well on subsequent study days (all AUC > 0.9) and across age groups. In univariate and multivariate analysis, age, sex, and pack-years significantly contributed to higher SOFA values whereas antibiosis before hospitalization predicted lower SOFA.

          Conclusions

          SOFA score can serve as an excellent operationalization of CAP severity and is proposed as endpoint for biomarker and therapeutic studies.

          Trial registration

          clinicaltrials.gov NCT02782013, May 25, 2016, retrospectively registered.

          Electronic supplementary material

          The online version of this article (10.1186/s13054-019-2316-x) contains supplementary material, which is available to authorized users.

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          Most cited references20

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          SMART-COP: a tool for predicting the need for intensive respiratory or vasopressor support in community-acquired pneumonia.

          K Christiansen, Wayne J Fuller, Ingrid A Sarmiento … (2008)
          Existing severity assessment tools, such as the pneumonia severity index (PSI) and CURB-65 (tool based on confusion, urea level, respiratory rate, blood pressure, and age >or=65 years), predict 30-day mortality in community-acquired pneumonia (CAP) and have limited ability to predict which patients will require intensive respiratory or vasopressor support (IRVS). The Australian CAP Study (ACAPS) was a prospective study of 882 episodes in which each patient had a detailed assessment of severity features, etiology, and treatment outcomes. Multivariate logistic regression was performed to identify features at initial assessment that were associated with receipt of IRVS. These results were converted into a simple points-based severity tool that was validated in 5 external databases, totaling 7464 patients. In ACAPS, 10.3% of patients received IRVS, and the 30-day mortality rate was 5.7%. The features statistically significantly associated with receipt of IRVS were low systolic blood pressure (2 points), multilobar chest radiography involvement (1 point), low albumin level (1 point), high respiratory rate (1 point), tachycardia (1 point), confusion (1 point), poor oxygenation (2 points), and low arterial pH (2 points): SMART-COP. A SMART-COP score of >or=3 points identified 92% of patients who received IRVS, including 84% of patients who did not need immediate admission to the intensive care unit. Accuracy was also high in the 5 validation databases. Sensitivities of PSI and CURB-65 for identifying the need for IRVS were 74% and 39%, respectively. SMART-COP is a simple, practical clinical tool for accurately predicting the need for IRVS that is likely to assist clinicians in determining CAP severity.
          Article 0 comments Cited 159 times – based on 0 reviews     Review now
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            Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide.

            Ewout W. Steyerberg, Moniek Vedder, Jacqueline Witteman … (2014)
            The net reclassification improvement (NRI) is an increasingly popular measure for evaluating improvements in risk predictions. This article details a review of 67 publications in high-impact general clinical journals that considered the NRI. Incomplete reporting of NRI methods, incorrect calculation, and common misinterpretations were found. To aid improved applications of the NRI, the article elaborates on several aspects of the computation and interpretation in various settings. Limitations and controversies are discussed, including the effect of miscalibration of prediction models, the use of the continuous NRI and “clinical NRI,” and the relation with decision analytic measures. A systematic approach toward presenting NRI analysis is proposed: Detail and motivate the methods used for computation of the NRI, use clinically meaningful risk cutoffs for the category-based NRI, report both NRI components, address issues of calibration, and do not interpret the overall NRI as a percentage of the study population reclassified. Promising NRI findings need to be followed with decision analytic or formal cost-effectiveness evaluations.
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              Severity assessment tools for predicting mortality in hospitalised patients with community-acquired pneumonia. Systematic review and meta-analysis.

              James Chalmers, Aran Singanayagam, Ahsan Akram … (2010)
              International guidelines recommend a severity-based approach to management in community-acquired pneumonia. CURB65, CRB65 and the Pneumonia Severity Index (PSI) are the most widely recommended severity scores. The aim of this study was to compare the performance characteristics of these scores for predicting mortality in community-acquired pneumonia. A systematic review and meta-analysis was conducted according to MOOSE (meta-analysis of observational studies in epidemiology) guidelines. PUBMED and EMBASE were searched (1980-2009). 40 studies reporting prognostic information for the PSI, CURB65 and CRB65 severity scores were identified. Performance characteristics were pooled using a random effects model. Relationships between sensitivity and specificity were plotted using summary receiver operator characteristic (sROC) curves. All three scores predicted 30 day mortality. The PSI had the highest area under the sROC curve, 0.81 (SE 0.008), compared with CURB65, 0.80 (SE 0.008), p=0.1, and CRB65, 0.79 (0.01), p=0.09. These differences were not statistically significant. Performance characteristics were similar across comparable cut-offs for low, intermediate and high risk for each score. In identifying low risk patients, PSI (groups I and II) had the best negative likelihood ratio 0.08 (0.06-0.12) compared with CURB65 (score 0-1) 0.21 (0.15-0.30) and CRB65 (score 0), 0.15 (0.10-0.22). There were no significant differences in overall test performance between PSI, CURB65 and CRB65 for predicting mortality from community-acquired pneumonia.
              Article 0 comments Cited 113 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Peter Ahnert:
                ORCID: http://orcid.org/0000-0002-1771-0856
                peter.ahnert@imise.uni-leipzig.de
                Petra Creutz: petra.creutz@charite.de
                Katrin Horn: katrin.horn@imise.uni-leipzig.de
                Fabian Schwarzenberger: schwarzenberger@htw-dresden.de
                Michael Kiehntopf: michael.kiehntopf@med.uni-jena.de
                Hamid Hossain: hamid.hossain@lse.thm.de
                Michael Bauer: michael.bauer@med.uni-jena.de
                Frank Martin Brunkhorst: frank.brunkhorst@med.uni-jena.de
                Konrad Reinhart: konrad.reinhart@med.uni-jena.de
                Uwe Völker: voelker@uni-greifswald.de
                Trinad Chakraborty: trinad.chakraborty@mikrobio.med.uni-giessen.de
                Martin Witzenrath: martin.witzenrath@charite.de
                Markus Löffler: markus.loeffler@imise.uni-leipzig.de
                Norbert Suttorp: norbert.suttorp@charite.de
                Markus Scholz: markus.scholz@imise.uni-leipzig.de
                : progress-study-group@imise.uni-leipzig.de
                Journal
                Journal ID (nlm-ta): Crit Care
                Title: Critical Care
                Publisher: BioMed Central (London )
                ISSN (Print): 1364-8535
                ISSN (Electronic): 1466-609X
                Publication date (Electronic): 4 April 2019
                Publication date PMC-release: 4 April 2019
                Publication date Collection: 2019
                Volume: 23
                Electronic Location Identifier: 110
                Affiliations
                [1 ]ISNI 0000 0001 2230 9752, GRID grid.9647.c, University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology (IMISE), ; Härtelstr. 16-18, 04107 Leipzig, Germany
                [2 ]Department of Infectious Disease and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Virchowklinikum, Augustenburgerplatz 1, 13353 Berlin, Germany
                [3 ]ISNI 0000 0004 0643 2840, GRID grid.434947.9, Faculty of Informatics / Mathematics, HTW Dresden University of Applied Sciences, ; Friedrich-List-Platz 1, 01069 Dresden, Germany
                [4 ]ISNI 0000 0000 8517 6224, GRID grid.275559.9, Jena University Hospital, Integrated Biobank Jena (IBBJ) and Institute of Clinical Chemistry and Laboratory Diagnostics, ; Am Klinikum 1, 07740 Jena, Germany
                [5 ]Technische Hochschule Mittelhessen, University of Applied Sciences, Life Science Engineering, Wiesenstr. 14, 35390 Gießen, Germany
                [6 ]ISNI 0000 0000 8517 6224, GRID grid.275559.9, Department of Anesthesiology and Intensive Care Medicine, , Jena University Hospital, ; Am Klinikum 1, 07747 Jena, Germany
                [7 ]ISNI 0000 0000 8517 6224, GRID grid.275559.9, Center for Clinical Studies and Department of Anesthesiology and Intensive Care Medicine, , Jena University Hospital, ; Am Klinikum 1, 07747 Jena, Germany
                [8 ]ISNI 0000 0000 8517 6224, GRID grid.275559.9, Jena University Hospital, ; Am Klinikum 1, 07747 Jena, Germany
                [9 ]GRID grid.5603.0, Department Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, , University Medicine Greifswald, ; Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany
                [10 ]ISNI 0000 0000 8584 9230, GRID grid.411067.5, University Hospital Giessen, Institute for Medical Microbiology, ; Schubertstr. 81, 35392 Gießen, Germany
                [11 ]Department of Infectious Disease and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
                Author information
                http://orcid.org/0000-0002-1771-0856
                Article
                Publisher ID: 2316
                DOI: 10.1186/s13054-019-2316-x
                PMC ID: 6450002
                PubMed ID: 30947753
                SO-VID: c18a1522-2f39-4564-81db-a4a4b1b1c21b
                Copyright © © The Author(s). 2019
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), 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 ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 5 September 2018
                Date accepted : 7 January 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 01KI07110
                Award ID: 01KI07111
                Award ID: 01KI07113
                Award ID: 01KI07114
                Award ID: 01KI1010I
                Award ID: 01KI1010D
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010564, Deutsche Zentrum für Lungenforschung;
                Award ID: 82DZLJ19A2
                Award Recipient :
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © The Author(s) 2019

                ScienceOpen disciplines: Emergency medicine & Trauma
                Keywords: clinical epidemiology,biomarker,severity score,prospective clinical study,infectious disease,lung disease
                Data availability:
                ScienceOpen disciplines: Emergency medicine & Trauma
                Keywords: clinical epidemiology, biomarker, severity score, prospective clinical study, infectious disease, lung disease

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