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Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models


Fernandez-Felix, Borja M; Barca, Laura Varela; Garcia-Esquinas, Esther; Correa-Pérez, Andrea; Fernández-Hidalgo, Nuria; Muriel, Alfonso; López-Alcalde, Jesús; Álvarez-Diaz, Noelia; Pijoan, Jose I; Ribera, Aida; Elorza, Enrique Navas; Muñoz, Patricia; Fariñas, María del Carmen; Goenaga, Miguel Ángel; Zamora, Javier (2021). Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models. Clinical Microbiology and Infection, 27(10):1422-1430.

Abstract

Background: There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain.
Objective: We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model.
Data sources: We searched Medline and EMBASE databases from inception to June 2020.
Study eligibility criteria: We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE.
Methods: We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism.
Results: We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76–0.82), calibration slope 0.98 (95% CI 0.86–1.13) and calibration-in-the-large –0.05 (95% CI –0.20 to 0.11).
Conclusions: The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE.

Abstract

Background: There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain.
Objective: We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model.
Data sources: We searched Medline and EMBASE databases from inception to June 2020.
Study eligibility criteria: We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE.
Methods: We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism.
Results: We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76–0.82), calibration slope 0.98 (95% CI 0.86–1.13) and calibration-in-the-large –0.05 (95% CI –0.20 to 0.11).
Conclusions: The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Complementary Medicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Microbiology (medical)
Health Sciences > Infectious Diseases
Uncontrolled Keywords:Aggregation; Infective endocarditis; Meta-model; Prognostic models; Systematic review; Validation
Language:English
Date:1 October 2021
Deposited On:14 Jul 2021 15:26
Last Modified:25 Jun 2024 01:41
Publisher:Elsevier
ISSN:1198-743X
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.cmi.2021.05.051
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