ObjectiveWe sought to find a bedside prognosis prediction model based on clinical and image parameters to determine the in-hospital outcomes of acute aortic dissection (AAD) in the emergency department.MethodsPatients who presented with AAD from January 2010 to December 2019 were retrospectively recruited in our derivation cohort. Then we prospectively collected patients with AAD from January 2020 to December 2021 as the validation cohort. We collected the demographics, medical history, treatment options, and in-hospital outcomes. All enrolled patients underwent computed tomography angiography. The image data were systematically reviewed for anatomic criteria in a retrospective fashion by three professional radiologists. A series of radiological parameters, including the extent of dissection, the site of the intimal tear, entry tear diameter, aortic diameter at each level, maximum false lumen diameter, and presence of pericardial effusion were collected.ResultsOf the 449 patients in the derivation cohort, 345 (76.8%) were male, the mean age was 61 years, and 298 (66.4%) had a history of hypertension. Surgical repair was performed in 327 (72.8%) cases in the derivation cohort, and the overall crude in-hospital mortality of AAD was 10.9%. Multivariate logistic regression analysis showed that predictors of in-hospital mortality in AAD included age, Marfan syndrome, type A aortic dissection, surgical repair, and maximum false lumen diameter. A final prognostic model incorporating these five predictors showed good calibration and discrimination in the derivation and validation cohorts. As for type A aortic dissection, 3-level type A aortic dissection clinical prognosis score (3ADPS) including 5 clinical and image variables scored from −2 to 5 was established: (1) moderate risk of death if 3ADPS is <0; (2) high risk of death if 3ADPS is 1–2; (3) very high risk of death if 3ADPS is more than 3. The area under the receiver operator characteristic curves in the validation cohorts was 0.833 (95% CI, 0.700–0.967).ConclusionAge, Marfan syndrome, type A aortic dissection, surgical repair, and maximum false lumen diameter can significantly affect the in-hospital outcomes of AAD. And 3ADPS contributes to the prediction of in-hospital prognosis of type A aortic dissection rapidly and effectively. As multivariable risk prediction tools, the risk models were readily available for emergency doctors to predict in-hospital mortality of patients with AAD in extreme clinical risk.
Objectives A rapid 0 h/1 h algorithm using high-sensitivity cardiac troponin T (hs-cTnT) for rule-out and rule-in of non-ST-segment elevation myocardial infarction (NSTEMI) is recommended by the European Society of Cardiology. We aim to prospectively evaluate the diagnostic performance of the algorithm in Chinese Han patients with suspected NSTEMI. Methods In this prospective diagnostic cohort study, 577 patients presenting to the emergency department with suspected NSTEMI and recent (<12 h) onset of symptoms were enrolled. The levels of serum hs-cTnT were measured on admission, 1 h later and 4–14 h later. All patients underwent the initial clinical assessment and were triaged into three groups (rule-out, rule-in and observe) according to the 0 h/1 h algorithm. The major cardiovascular events (MACE) were evaluated at the 7-day and 30-day follow-ups. Results Among 577 enrolled patients, NSTEMI was the final diagnosis for 106 (18.4%) patients. Based on the hs-cTnT 0 h/1 h algorithm, 148 patients (25.6%) were classified as rule-out, 278 patients (48.2%) as rule-in and 151 patients (26.2%) were assigned to the observe group. The rule-out approach resulted in a sensitivity of 100% and negative predictive value of 100%. The rule-in approach resulted in a specificity of 62.9% [95% CI (58.5–67.2%)] and positive predictive value of 37.1% [95%CI (31.3–42.8%)]. No MACE was observed in the rule-out group within 30-day follow-up. Conclusions The hs-cTnT 0 h/1 h algorithm is a safe tool for early rule-out of NSTEMI, while probably not an effective strategy for accurate rule-in of NSTEMI in Chinese Han population.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.