Abstract:Aim To build a model based on general clinical variables and machine learning method to predict the risk of in-hospital major adverse events (MAE) in patients with Stanford a type aortic dissection (TAAD) after surgery. Methods A total of 1 641 patients with TAAD who underwent surgical treatment in Beijing Anzhen Hospital from January 2013 to December 2017 were included in this study. The individual characteristic variables, clinical signs and the first clinical serum markers on admission were collected. The outcome was defined as in-hospital MAE, including in-hospital death, new acute heart failure after mezzanine, respiratory failure, nervous system disorders, acute renal failure, infection, and unplanned secondary chest opening. The model was constructed after using machine learning to screen variables. Receiver operating characteristic curve (ROC) was used to analyze the ability of the model to predict in-hospital MAE. Net reclassification index (NRI) and integrated discrimination index (IDI) were used to compare the new model with the commonly used clinical models to evaluate the improvement effect of the new model in predicting the prognosis of postoperative TAAD. Finally, the nomogram was established to predict the risk of MAE in patients after TAAD operation. Results The risk prediction model of in-hospital MAE after TAAD operation was determined by using machine learning screening variables, which consisted of D-dimer, creatine kinase isoenzyme, urea, leukocyte count, age, abnormal electrocardiogram and operation time. The area under curve of ROC of in-hospital MAE predicted by the model was 0.776 (95%CI 0.718-0.734, P＜0.001). Compared with the commonly used clinical models, the NRI of our model was 0.654 (95%CI 0.540-0.750, P＜0.001), and the IDI was 0.136 (95%CI 0.117-0.155, P＜0.001), which improved the predictive ability for in-hospital MAE after TAAD operation. The model was presented in the form of nomogram, and the score of nomogram model could evaluate the risk of in-hospital MAE after TAAD operation. ConclusionsBased on machine learning, a model is constructed by using clinical variables of patients. The model can comprehensively evaluate individual characteristic variables, inflammation level, organ damage status and operation status of patients, which has predictive value for postoperative in-hospital MAE of patients with TAAD.

Abstract:Aim To explore the circulating levels of long intergenic non-coding RNA predicting cardiac remodeling (LIPCAR) in patients with chronic heart failure (CHF) and combined renal insufficiency. Methods Venous blood samples were collected from 80 control subjects, 188 patients with CHF, including 80 patients with renal insufficiency, LIPCAR was evaluated by real-time PCR. Results The expression of LIPCAR was significantly different between the control group and the CHF group (1.08±0.28 vs.0.79±0.31, P＜0.01). In addition, the subgroup analysis of CHF showed that the patients complicated with renal insufficiency exhibited a tendency of lower levels of LIPCAR. Furthermore, LIPCAR was negatively correlated with NT-proBNP, whereas positively correlated with ejection fraction (P＜0.01). AUC was 0.777(95%CI＝0.720～0.835) for LIPCAR to judge CHF. Conclusion Circulating LIPCAR levels were significantly downregulated in patients with CHF, especially in those complicated with renal insufficiency. It may be valuable as a potential biomarker for CHF.