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A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients.

ABSTRACT: Background:Although there are several biomarkers for identifying in-hospital mortality in acute type A aortic dissection (AAD), timely as well as perfect prediction in-hospital mortality is still not attained. Herein, we intend to develop as well to validate an in-hospital mortality risk independent predictive nomogram for AAD patients. Methods:From January 2014 to December 2018, 703 individuals with AAD were involved in this study. They were indiscriminately categorized into training (n=520) and validation (n=183) sets. The univariate and multivariate analyses were used to screen in-hospital mortality predictors from the entire training set data. The predictors were used to establish a nomogram which was confirmed via internal as well as external authentication. This validation included discriminative capacity defined by the receiver operating characteristic (ROC) curve area under the curve (AUC) and the predictive precision via calibration curves. Results:There was 33.43% in-hospital mortality overall incidence. The uric acid, D-dimer, C-reactive protein and management were individually related to in-hospital mortality as per multivariate logistic regression. On the basis of four variables with internal of AUC 0.901 and external validation of AUC 0.903, a nomogram was established. Calibration plots showed that the predicted and actual in-hospital mortality probabilities were fitted well on both internal and external validation. Conclusions:This recommended nomogram can calculate the specific possibility of in-hospital mortality with good precision, high discrimination, and probable clinical application in AAD patients.

SUBMITTER: Yang G

PROVIDER: S-EPMC7139052 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients.

Yang Guifang G Zhou Yang Y He Huaping H Pan Xiaogao X Li Xizhao X Chai Xiangping X

Journal of thoracic disease 20200301 3

<h4>Background</h4>Although there are several biomarkers for identifying in-hospital mortality in acute type A aortic dissection (AAD), timely as well as perfect prediction in-hospital mortality is still not attained. Herein, we intend to develop as well to validate an in-hospital mortality risk independent predictive nomogram for AAD patients.<h4>Methods</h4>From January 2014 to December 2018, 703 individuals with AAD were involved in this study. They were indiscriminately categorized into trai ...[more]

PMID: 32274093

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Project description:Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

Project description:BackgroundSurgery for type A aortic dissection (TAAD) is associated with high risk of mortality. Current risk scoring methods have a limited predictive accuracy.MethodsSubjects were patients who underwent surgery for acute TAAD at 18 European centers of cardiac surgery from the European Registry of Type A Aortic Dissection (ERTAAD).ResultsOut of 3,902 patients included in the ERTAAD, 2,477 fulfilled the inclusion criteria. In the validation dataset (2,229 patients), the rate of in-hospital mortality was 18.4%. The rate of composite outcome (in-hospital death, stroke/global ischemia, dialysis, and/or acute heart failure) was 41.2%, and 10-year mortality rate was 47.0%. Logistic regression identified the following patient-related variables associated with an increased risk of in-hospital mortality [area under the curve (AUC), 0.755, 95% confidence interval (CI), 0.729-0.780; Brier score 0.128]: age; estimated glomerular filtration rate; arterial lactate; iatrogenic dissection; left ventricular ejection fraction ≤50%; invasive mechanical ventilation; cardiopulmonary resuscitation immediately before surgery; and cerebral, mesenteric, and peripheral malperfusion. The estimated risk score was associated with an increased risk of composite outcome (AUC, 0.689, 95% CI, 0.667-0.711) and of late mortality [hazard ratio (HR), 1.035, 95% CI, 1.031-1.038; Harrell's C 0.702; Somer's D 0.403]. In the validation dataset (248 patients), the in-hospital mortality rate was 16.1%, the composite outcome rate was 41.5%, and the 10-year mortality rate was 49.1%. The estimated risk score was predictive of in-hospital mortality (AUC, 0.703, 95% CI, 0.613-0.793; Brier score 0.121; slope 0.905) and of composite outcome (AUC, 0.682, 95% CI, 0.614-0.749). The estimated risk score was predictive of late mortality (HR, 1.035, 95% CI, 1.031-1.038; Harrell's C 0.702; Somer's D 0.403), also when hospital deaths were excluded from the analysis (HR, 1.024, 95% CI, 1.018-1.031; Harrell's C 0.630; Somer's D 0.261).ConclusionsThe present analysis identified several baseline clinical risk factors, along with preoperative estimated glomerular filtration rate and arterial lactate, which are predictive of in-hospital mortality and major postoperative adverse events after surgical repair of acute TAAD. These risk factors may be valuable components for risk adjustment in the evaluation of surgical and anesthesiological strategies aiming to improve the results of surgery for TAAD.Clinical trial registrationhttps://clinicaltrials.gov, identifier NCT04831073.

Dataset Information

A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients.

Publications

A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients.

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