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      A simplified pneumonia severity index (PSI) for clinical outcome prediction in COVID-19

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          Abstract

          Background

          The Pneumonia Score Index (PSI) was developed to estimate the risk of dying within 30 days of presentation for community-acquired pneumonia patients and is a strong predictor of 30-day mortality after COVID-19. However, three of its required 20 variables (skilled nursing home, altered mental status and pleural effusion) are not discreetly available in the electronic medical record (EMR), resulting in manual chart review for these 3 factors. The goal of this study is to compare a simplified 17-factor version (PSI-17) to the original (denoted PSI-20) in terms of prediction of 30-day mortality in COVID-19.

          Methods

          In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20–5/28/20 were identified to compare the predictive performance between PSI-17 and PSI-20. Correlation was assessed between PSI-17 and PSI-20, and logistic regressions were performed for 30-day mortality. The predictive abilities were compared by discrimination, calibration, and overall performance.

          Results

          Based on 1,138 COVID-19 patients, the correlation between PSI-17 and PSI-20 was 0.95. Univariate logistic regression showed that PSI-17 had performance similar to PSI-20, based on AUC, ICI and Brier Score. After adjusting for confounding variables by multivariable logistic regression, PSI-17 and PSI-20 had AUCs (95% CI) of 0.85 (0.83–0.88) and 0.86 (0.84–0.89), respectively, indicating no significant difference in AUC at significance level of 0.05.

          Conclusion

          PSI-17 and PSI-20 are equally effective predictors of 30-day mortality in terms of several performance metrics. PSI-17 can be obtained without the manual chart review, which allows for automated risk calculations within an EMR. PSI-17 can be easily obtained and may be a comparable alternative to PSI-20.

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          Most cited references17

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          Assessing the performance of prediction models: a framework for traditional and novel measures.

          The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
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            A prediction rule to identify low-risk patients with community-acquired pneumonia.

            There is considerable variability in rates of hospitalization of patients with community-acquired pneumonia, in part because of physicians' uncertainty in assessing the severity of illness at presentation. From our analysis of data on 14,199 adult inpatients with community-acquired pneumonia, we derived a prediction rule that stratifies patients into five classes with respect to the risk of death within 30 days. The rule was validated with 1991 data on 38,039 inpatients and with data on 2287 inpatients and outpatients in the Pneumonia Patient Outcomes Research Team (PORT) cohort study. The prediction rule assigns points based on age and the presence of coexisting disease, abnormal physical findings (such as a respiratory rate of > or = 30 or a temperature of > or = 40 degrees C), and abnormal laboratory findings (such as a pH or = 30 mg per deciliter [11 mmol per liter] or a sodium concentration <130 mmol per liter) at presentation. There were no significant differences in mortality in each of the five risk classes among the three cohorts. Mortality ranged from 0.1 to 0.4 percent for class I patients (P=0.22), from 0.6 to 0.7 percent for class II (P=0.67), and from 0.9 to 2.8 percent for class III (P=0.12). Among the 1575 patients in the three lowest risk classes in the Pneumonia PORT cohort, there were only seven deaths, of which only four were pneumonia-related. The risk class was significantly associated with the risk of subsequent hospitalization among those treated as outpatients and with the use of intensive care and the number of days in the hospital among inpatients. The prediction rule we describe accurately identifies the patients with community-acquired pneumonia who are at low risk for death and other adverse outcomes. This prediction rule may help physicians make more rational decisions about hospitalization for patients with pneumonia.
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              Applied logistic regression

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 May 2024
                2024
                : 19
                : 5
                : e0303899
                Affiliations
                [1 ] Providence St. Joseph Health, Portland, Oregon, United States of America
                [2 ] Division of Cardiothoracic Surgery, Oregon Health & Science University, Portland, OR, United States of America
                [3 ] Division of Infectious Diseases, Swedish Medical Center, Seattle, WA, United States of America
                [4 ] Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA, United States of America
                [5 ] Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, United States of America
                [6 ] ClinChoice, Portland, OR, United States of America
                [7 ] Providence Heart Institute, Providence St. Joseph Health, Portland, Oregon, United States of America
                [8 ] Institute for Systems Biology, Seattle, Washington, United States of America
                [9 ] Division of Medicine, Section of Infectious Diseases, Providence Regional Medical Center Everett, Everett, WA, United States of America
                [10 ] Washington State University Elson S. Floyd College of Medicine, Spokane, WA, United States of America
                [11 ] Providence Research Network, Renton, WA, United States of America
                Taipei Medical University, TAIWAN
                Author notes

                Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: G.A.D reported receipt of clinical trial research support from Gilead Sciences, Regeneron, Roche, Boehringer Ingelheim, Edesa Biotech and NeuroBo Pharmaceuticals. J.D.G reports contracted research from Helix, Gilead, Eli Lilly, and Regeneron, grants from Merck (BARDA) and Gilead, and collaborative services agreements with Adaptive Biotechnologies, Monogram Biosciences and Labcorp; and serving as a consultant, speaker or advisory board member for Gilead, and Eli Lilly. All other authors have nothing to disclose.

                [¤]

                Current address: The Society of Thoracic Surgeons (STS), Chicago, IL, United States of America

                Author information
                https://orcid.org/0000-0002-6534-0124
                https://orcid.org/0000-0001-6103-7606
                Article
                PONE-D-24-00824
                10.1371/journal.pone.0303899
                11108185
                38771892
                5013ed2a-ba16-4471-9f94-312bc292bd89
                © 2024 Chang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 January 2024
                : 2 May 2024
                Page count
                Figures: 4, Tables: 1, Pages: 10
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Medicine and Health Sciences
                Pulmonology
                Pneumonia
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Health Care
                Health Information Technology
                Electronic Medical Records
                Computer and Information Sciences
                Information Technology
                Health Information Technology
                Electronic Medical Records
                Medicine and Health Sciences
                Health Care
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                Outpatients
                Medicine and Health Sciences
                Epidemiology
                Pandemics
                Biology and life sciences
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                Viruses
                RNA viruses
                Coronaviruses
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                SARS CoV 2
                Biology and life sciences
                Microbiology
                Medical microbiology
                Microbial pathogens
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                SARS coronavirus
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                Medicine and health sciences
                Pathology and laboratory medicine
                Pathogens
                Microbial pathogens
                Viral pathogens
                Coronaviruses
                SARS coronavirus
                SARS CoV 2
                Biology and life sciences
                Organisms
                Viruses
                Viral pathogens
                Coronaviruses
                SARS coronavirus
                SARS CoV 2
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                All relevant data are within the manuscript and its Supporting Information files.
                COVID-19

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