AUTHOR=Yang Lin , Wang Yasong , He Xiaofeng , Liu Xuanze , Sui Honggang , Wang Xiaozeng , Wang Mengmeng 

TITLE=Develop ment and validation of a prognostic dynamic nomogram for in-hospital mortality in patients with Stanford type B aortic dissection

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1099055

DOI=10.3389/fcvm.2022.1099055

ISSN=2297-055X

ABSTRACT=Background: This study aimed to identify the risk factors for in-hospital mortality in patients with Stanford type B aortic dissection (TBAD) and develop and validate a prognostic dynamic nomogram for in-hospital mortality in these patients.
Methods: This retrospective study included patients with TBAD treated between April 2002 and December 2020 at the General Hospital of Northern Theater Command. They were divided into survival and non-survival groups. The data were analyzed using univariate and multivariate logistic regression analyses. Multivariate logistic regression analysis and least absolute shrinkage and selection operator regression were performed to identify independent risk factors for in-hospital mortality. A prediction model was constructed using a nomogram based on these factors and validated using the original dataset. To assess its discriminative ability, the area under the receiver operating characteristic curve (AUC) was measured, and the calibration ability was tested using a calibration curve and the Hosmer-Lemeshow test. Decision curve analysis and clinical impact curves were used to evaluate clinical utility.
Results: Of the 978 included patients, 52 (5.3%) died in hospital. The following variables helped predict in-hospital mortality: pleural effusion, systolic blood pressure ≥ 160 mmHg, heart rate > 100 bpm, anaemia, ischemic cerebrovascular disease, abnormal cTnT level, and estimated glomerular filtration rate < 60 mL/min. The prediction model revealed good discrimination [AUC = 0.894; 95% confidence interval (CI), 0.850–0.938]. The predicted probabilities of in-hospital death fitted well with the actual prevalence rate [calibration curve: via 1,000 bootstrap resamples, a bootstrap-corrected Harrell’s concordance index of 0.905 (95% CI, 0.865-0.945), and the Hosmer–Lemeshow test (χ2=8.3334, P = 0.4016)]. Decision curve analysis demonstrated that compared with “no intervention” or “intervention for all” strategies, the predictive model could achieve more clinical net benefits when the risk threshold was set between 0.04 and 0.88. Moreover, the clinical impact curve showed good predictive ability and clinical utility for the model.
Conclusion: We developed and validated prediction nomograms, including a simple bed nomogram and online dynamic nomogram, that could be used to identify patients with TBAD at higher risk of in-hospital mortality, thereby better enabling clinicians to provide individualized patient management and timely and effective interventions.