Predicting in-hospital death in patients with type B acute aortic dissection. Issue 32 (August 2019)

• Record Type: Journal Article
• Authors: Zhang, Jing; Cheng, Baoshan; Yang, Mengsi; Pan, Jianyuan; Feng, Jun; Cheng, Ziping
• Other Names: Chen. Robert section editor.
• Is Part Of: Medicine. Volume 98:Issue 32(2019)
• Publication Date: 2019-08
• Subjects: acute -- aortic dissection -- in-hospital death -- predicting -- type B; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000016462
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: The outcome of patients with acute type B aortic dissection (BAAD) is largely dictated by whether or not the case is "complicated." The purpose of this study was to investigate the risk factors leading to in-hospital death among patients with BAAD and then to develop a predictive model to estimate individual risk of in-hospital death. A total of 188 patients with BAAD were enrolled. Risk factors for in-hospital death were investigated with univariate and multivariable logistic regression analysis. Significant risk factors were used to develop a predictive model. The in-hospital mortality rate was 9% (17 of 188 patients). Univariate analysis revealed 7 risk factors to be statistically significant predictors of in-hospital death ( P < .1). In multivariable analysis, the following variables at admission were independently associated with increased in-hospital mortality: hypotension (odds ratio [OR], 4.85; 95% confidence interval [CI], 1.12–18.90; P = .04), ischemic complications (OR, 8.24; 95% CI, 1.25–33.85; P < .001), renal dysfunction (OR, 12.32; 95% CI, 10.63–76.66; P < .001), and neutrophil percentage ≥80% (OR, 5.76; 95% CI, 2.58–12.56; P = .03). Based on these multivariable results, a reliable and simple prediction model was developed, a total score of 4 offered the best point value. Independent risk factors associated with in-hospital death can be predicted in BAAD patients. The prediction model could be used to identify the prognosis for BAAD patients and assist physicians in their choice of management.