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      Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology

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          Abstract

          Objective

          To provide an overview and critical appraisal of early warning scores for adult hospital patients.

          Design

          Systematic review.

          Data sources

          Medline, CINAHL, PsycInfo, and Embase until June 2019.

          Eligibility criteria for study selection

          Studies describing the development or external validation of an early warning score for adult hospital inpatients.

          Results

          13 171 references were screened and 95 articles were included in the review. 11 studies were development only, 23 were development and external validation, and 61 were external validation only. Most early warning scores were developed for use in the United States (n=13/34, 38%) and the United Kingdom (n=10/34, 29%). Death was the most frequent prediction outcome for development studies (n=10/23, 44%) and validation studies (n=66/84, 79%), with different time horizons (the most frequent was 24 hours). The most common predictors were respiratory rate (n=30/34, 88%), heart rate (n=28/34, 83%), oxygen saturation, temperature, and systolic blood pressure (all n=24/34, 71%). Age (n=13/34, 38%) and sex (n=3/34, 9%) were less frequently included. Key details of the analysis populations were often not reported in development studies (n=12/29, 41%) or validation studies (n=33/84, 39%). Small sample sizes and insufficient numbers of event patients were common in model development and external validation studies. Missing data were often discarded, with just one study using multiple imputation. Only nine of the early warning scores that were developed were presented in sufficient detail to allow individualised risk prediction. Internal validation was carried out in 19 studies, but recommended approaches such as bootstrapping or cross validation were rarely used (n=4/19, 22%). Model performance was frequently assessed using discrimination (development n=18/22, 82%; validation n=69/84, 82%), while calibration was seldom assessed (validation n=13/84, 15%). All included studies were rated at high risk of bias.

          Conclusions

          Early warning scores are widely used prediction models that are often mandated in daily clinical practice to identify early clinical deterioration in hospital patients. However, many early warning scores in clinical use were found to have methodological weaknesses. Early warning scores might not perform as well as expected and therefore they could have a detrimental effect on patient care. Future work should focus on following recommended approaches for developing and evaluating early warning scores, and investigating the impact and safety of using these scores in clinical practice.

          Systematic review registration

          PROSPERO CRD42017053324.

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          Most cited references117

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          Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks.

          The area under the time-dependent ROC curve (AUC) may be used to quantify the ability of a marker to predict the onset of a clinical outcome in the future. For survival analysis with competing risks, two alternative definitions of the specificity may be proposed depending of the way to deal with subjects who undergo the competing events. In this work, we propose nonparametric inverse probability of censoring weighting estimators of the AUC corresponding to these two definitions, and we study their asymptotic properties. We derive confidence intervals and test statistics for the equality of the AUCs obtained with two markers measured on the same subjects. A simulation study is performed to investigate the finite sample behaviour of the test and the confidence intervals. The method is applied to the French cohort PAQUID to compare the abilities of two psychometric tests to predict dementia onset in the elderly accounting for death without dementia competing risk. The 'timeROC' R package is provided to make the methodology easily usable. Copyright © 2013 John Wiley & Sons, Ltd.
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            Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

            The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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              • Article: not found

              A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

              The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature.
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                Author and article information

                Contributors
                Role: senior medical statistician
                Role: consultant intensive care physician
                Role: senior medical statistician
                Role: senior research information specialist
                Role: medical statistician
                Role: associate professor of intensive care medicine
                Role: professor of medical statistics
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2020
                20 May 2020
                : 369
                : m1501
                Affiliations
                [1 ]Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
                [2 ]Critical Care Division, University College London Hospitals NHS Trust, London, UK
                [3 ]Oxford University Hospitals NHS Foundation Trust, Oxford, UK
                [4 ]Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
                Author notes
                Correspondence to: S Gerry stephen.gerry@ 123456csm.ox.ac.uk
                Author information
                http://orcid.org/0000-0003-4654-7311
                Article
                gers053616
                10.1136/bmj.m1501
                7238890
                32434791
                a5d98ab8-9d70-4ca2-a1d2-bf017808f548
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 March 2020
                Categories
                Research

                Medicine
                Medicine

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