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

Abstract

BackgroundThis 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.MethodsThis retrospective study involved patients with TBAD treated from April 2002 to December 2020 at the General Hospital of Northern Theater Command. The patients with TBAD were divided into survival and non-survival groups. The data were analyzed by univariate and multivariate logistic regression analyses. To identify independent risk factors for in-hospital mortality, multivariate logistic regression analysis, least absolute shrinkage, and selection operator regression were used. A prediction model was constructed using a nomogram based on these factors and validated using the original data set. To assess its discriminative ability, the area under the receiver operating characteristic curve (AUC) was calculated, and the calibration ability was tested using a calibration curve and the Hosmer-Lemeshow test. Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curves (CIC).ResultsOf 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, anemia, ischemic cerebrovascular disease, abnormal cTnT level, and estimated glomerular filtration rate <60 ml/min. The prediction model demonstrated good discrimination [AUC = 0.894; 95% confidence interval (CI), 0.850–0.938]. The predicted probabilities of in-hospital death corresponded well to 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)]. DCA indicated that when the risk threshold was set between 0.04 and 0.88, the predictive model could achieve larger clinical net benefits than “no intervention” or “intervention for all” options. Moreover, CIC showed good predictive ability and clinical utility for the model.ConclusionWe 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.