• Concepción Beneyto-Ripoll, Antonio Palazón-Bru, Patricia Llópez-Espinós, Ana María Martínez-Díaz, Vicente Francisco Gil-Guillén, and de Los Ángeles Carbonell-TorregrosaMaríaMDepartment of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.Emergency Services, General University Hospital of Elda, Elda, Alicante, Spain..
• Emergency Services, General Hospital of Almansa, Almansa, Albacete, Spain.
• Int. J. Clin. Pract. 2021 Aug 1; 75 (8): e14044.

BackgroundSepsis is associated with high mortality and predictive models can help in clinical decision-making. The objective of this study was to carry out a systematic review of these models.MethodsIn 2019, we conducted a systematic review in MEDLINE and EMBASE (CDR42018111121:PROSPERO) of articles that developed predictive models for mortality in septic patients (inclusion criteria). We followed the CHARMS recommendations (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), extracting the information from its 11 domains (Source of data, Participants, etc). We determined the risk of bias and applicability (participants, outcome, predictors and analysis) through PROBAST (Prediction model Risk Of Bias ASsessment Tool).ResultsA total of 14 studies were included. In the CHARMS extraction, the models found showed great variability in its 11 domains. Regarding the PROBAST checklist, only one article had an unclear risk of bias as it did not indicate how missing data were handled while the others all had a high risk of bias. This was mainly due to the statistical analysis (inadequate sample size, handling of continuous predictors, missing data and selection of predictors), since 13 studies had a high risk of bias. Applicability was satisfactory in six articles. Most of the models integrate predictors from routine clinical practice. Discrimination and calibration were assessed for almost all the models, with the area under the ROC curve ranging from 0.59 to 0.955 and no lack of calibration. Only three models were externally validated and their maximum discrimination values in the derivation were from 0.712 and 0.84. One of them (Osborn) had undergone multiple validation studies.DiscussionDespite most of the studies showing a high risk of bias, we very cautiously recommend applying the Osborn model, as this has been externally validated various times.© 2021 John Wiley & Sons Ltd.

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