Open AccessA critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?
Author(s) -
BeneytoRipoll Concepción,
PalazónBru Antonio,
LlópezEspinós Patricia,
MartínezDíaz Ana María,
GilGuillén Vicente Francisco,
CarbonellTorregrosa María
Publication year - 2021
Publication title -
international journal of clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 98
eISSN - 1742-1241
pISSN - 1368-5031
DOI - 10.1111/ijcp.14044
Subject(s) - medicine , data extraction , critical appraisal , checklist , meta analysis , selection bias , medline , sample size determination , missing data , systematic review , statistics , risk assessment , publication bias , receiver operating characteristic , predictive modelling , intensive care medicine , alternative medicine , computer science , pathology , political science , law , psychology , mathematics , computer security , cognitive psychology
Background Sepsis 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. Methods In 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). Results A 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. Discussion Despite 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.