Serum uric acid might be associated with aortic dissection in Chinese men

Biomarkers in aortic dissection: Diagnostic and prognostic value from clinical research

ABSTRACT

Aortic dissection is a life-threatening condition for which diagnosis mainly relies on imaging examinations, while reliable biomarkers to detect or monitor are still under investigation. Recent advances in technologies provide an unprecedented opportunity to yield the identification of clinically valuable biomarkers, including proteins, ribonucleic acids (RNAs), and deoxyribonucleic acids (DNAs), for early detection of pathological changes in susceptible patients, rapid diagnosis at the bedside after onset, and a superior therapeutic regimen primarily within the concept of personalized and tailored endovascular therapy for aortic dissection.

Post-operative uric acid: a predictor for 30-days mortality of acute type A aortic dissection repair

Background

Hyperuricemia is associated with aortic dissection and cardiovascular diseases. The implication of high serum uric acid (UA) level after acute aortic dissection repair remains unknown. The aim of this study is to explore the role of peri-operative serum UA level in predicting 30-days mortality with acute type A aortic dissection (AAAD) patients, who underwent surgery.

Methods

This study retrospectively enrolled 209 consecutive patients with AAAD, who underwent surgery in Xiangya Hospital from 2017 to 2020. Post-operative laboratory examinations were measured within 24 h after surgery. Univariate analysis and logistic regression analysis were used for predictor finding.

Results

209 consecutive AAAD patients were included, 14.3% (n = 30) were dead within 30 days after surgery. By univariate analysis, we found AAAD repair patients with 30-days mortality had a higher prevalence of cerebral malperfusion, lower pre-operative fibrinogen, longer cardiopulmonary bypass and aortic crossclamp time, and higher post-operative day 1 (POD1) creatinine and urea levels. Both pre-operative (433.80 ± 152.59 vs. 373.46 ± 108.31 mmol/L, p = 0.038) and POD1 (559.78 ± 162.23 vs. 391.29 ± 145.19 mmol/L, p < 0.001) UA level were higher in mortality group than in survival group. In regression model, only cerebral malperfusion (OR, 7.938, 95% CI 1.252–50.323; p = 0.028) and POD1 UA level (OR, 2.562; 95% CI 1.635–4.014; p < 0.001) were independent predictors of 30-days mortality in AAAD repair patients. According to the ROC curve, the POD1 UA level provided positive value for 30-days mortality in AAAD repair patients with 0.799 areas under the curve. The optimum cutoff value selected by ROC curve was 500.15 mmol/L, with a sensitivity of 65% and a specificity of 86%.

Conclusion

Pre- and post-operative hyperuricemia are potentially associated with worsened outcomes in AAAD surgery patients. The POD1 UA level has a predictive role in 30-days mortality in AAAD repair patients.

Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning

Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can hardly offer practical help to patients. Meanwhile, the artificial intelligence technology may provide a cheap but effective approach to building an auxiliary diagnosis model for improving the early AD diagnosis rate by taking advantage of the data of the general conditions of AD patients, such as the data about the basic inspection information. Therefore, this study proposes to hybrid five types of machine learning operators into an integrated diagnosis model, as an auxiliary diagnostic approach, to cooperate with the AD-clinical analysis. To improve the diagnose accuracy, the participating rate of each operator in the proposed model may adjust adaptively according to the result of the data learning. After a set of experimental evaluations, the proposed model, acting as the preliminary AD-discriminant, has reached an accuracy of over 80%, which provides a promising instance for medical colleagues.