Prognostic factors and prediction models for acute aortic dissection: a systematic review

Abstract

ObjectiveOur study aimed to systematically review the methodological characteristics of studies that identified prognostic factors or developed or validated models for predicting mortalities among patients with acute aortic dissection (AAD), which would inform future work.Design/settingA methodological review of published studies.MethodsWe searched PubMed and EMBASE from inception to June 2020 for studies about prognostic factors or prediction models on mortality among patients with AAD. Two reviewers independently collected the information about methodological characteristics. We also documented the information about the performance of the prognostic factors or prediction models.ResultsThirty-two studies were included, of which 18 evaluated the performance of prognostic factors, and 14 developed or validated prediction models. Of the 32 studies, 23 (72%) were single-centre studies, 22 (69%) used data from electronic medical records, 19 (59%) chose retrospective cohort study design, 26 (81%) did not report missing predictor data and 5 (16%) that reported missing predictor data used complete-case analysis. Among the 14 prediction model studies, only 3 (21%) had the event per variable over 20, and only 5 (36%) reported both discrimination and calibration statistics. Among model development studies, 3 (27%) did not report statistical methods, 3 (27%) exclusively used statistical significance threshold for selecting predictors and 7 (64%) did not report the methods for handling continuous predictors. Most prediction models were considered at high risk of bias. The performance of prognostic factors showed varying discrimination (AUC 0.58 to 0.95), and the performance of prediction models also varied substantially (AUC 0.49 to 0.91). Only six studies reported calibration statistic.ConclusionsThe methods used for prognostic studies on mortality among patients with AAD—including prediction models or prognostic factor studies—were suboptimal, and the model performance highly varied. Substantial efforts are warranted to improve the use of the methods in this population.