Transcatheter aortic valves implantation have been widely used in patients with high risk of non-surgical or surgical procedures since the first implantation in 2002, and have achieved good therapeutic results. However, as one of the main complications after transcatheter aortic valve implantation, paravalvular regurgitation seriously affects the outcome of patients. This article reviews recent researches on transcatheter aortic valve paravalvular regurgitation, and summarizes the influencing factors of paravalvular regurgitation after transcatheter aortic valve implantation and the corresponding countermeasures. This review can provide guidance and reference for clinical application and research of transcatheter aortic valves.
Objective To identify the heart sounds of aortic stenosis by deep learning model based on DenseNet121 architecture, and to explore its application potential in clinical screening aortic stenosis. Methods We prospectively collected heart sounds and clinical data of patients with aortic stenosis in Tianjin Chest Hospital, from June 2021 to February 2022. The collected heart sound data were used to train, verify and test a deep learning model. We evaluated the performance of the model by drawing receiver operating characteristic curve and precision-recall curve. Results A total of 100 patients including 11 asymptomatic patients were included. There were 50 aortic stenosis patients with 30 males and 20 females at an average age of 68.18±10.63 years in an aortic stenosis group (stenosis group). And 50 patients without aortic valve disease were in a negative group, including 26 males and 24 females at an average age of 45.98±12.51 years. The model had an excellent ability to distinguish heart sound data collected from patients with aortic stenosis in clinical settings: accuracy at 91.67%, sensitivity at 90.00%, specificity at 92.50%, and area under receiver operating characteristic curve was 0.917. Conclusion The model of heart sound diagnosis of aortic stenosis based on deep learning has excellent application prospects in clinical screening, which can provide a new idea for the early identification of patients with aortic stenosis.
ObjectivesTo investigate the correlation of warfarin dose genetic and polymorphism of Han-patients after heart valve replacement, to forecast the anticoagulation therapy with warfarin reasonable dosage, and to realize individualized management of anticoagulation monitoring. MethodsWe selected 103 patients between January 1, 2011 and December 31, 2012 in West China Hospital of Sichuan University who were treated by oral warfarin after heart valve replacement with monitoring anticoagulation by international normalized ratio (INR) in Anticoagulation Therapy Database of Chinese Patients after Heart Valve Replacement. There were 32 males and 71 female at age of 21-85 (48.64± 11.66) years. All the patients' CYP2C9 and VKORC1 genetic polymorphisms were detected by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RELP) method and gene sequencing technology. Warfarin concentration in plasma was determined by high performance liquid chromatography (HPLC) method. The activity of coagulation factorⅡ, Ⅶ, Ⅸ, Ⅹwas determined by Sysmex CA7000 analyzer. ResultsThe multivariate linear regression analysis showed that age, body surface area, and coagulation factor activity had no significant effect on warfarin dosage. While the gene polymor-phisms of CYP2C9 and VKORC1, warfarin concentration, and age had significant contributions to the overall variability in warfarin dose with decisive coefficients at 1.2%, 26.5%, 43.4%, and 5.0% respectively. The final equation was:Y=1.963-0.986× (CYP2C9* 3) +0.893× (VKORC1-1639) +0.002× (warfarin concentration)-0.019× (age). ConclusionMultiple regression equation including gene polymorphisms of CYP2C9 and VKORC1, non-genetic factors of coagulation factor activity, warfarin concentration, age, and body surface area can predict reasonable dosage of warfarin for anticoagulation to achieve individualized management of anticoagulation monitoring and reduce the anticoagulation complications.
ObjectiveTo determine postoperative pain of the robotic technique for the patients with lobectomy. MethodsWe retrospectively analyzed the clinical data of 120 patients with lobectomy between October 2014 and May 2015 in our hospital. The patients are divided into two groups:a robotic group, including 40 patients with 16 males and 24 females at age of 59.7±7.2 years, undergoing robotic lobectomy, and a video-assisted thoraciscopic surgery (VATS) lobectomy group (a VATS group) including 80 patients with 29 males and 51 females at age of 61.2±8.9 years, undergoing VATS lobectomy. We used the numerical rating scale (NRS) and verbal rating scale (VRS) to assess the pain level on the first day, the 7th day and the 30th day after the surgery. The pain level of the two groups was compared. ResultsThe patients in the two groups both felt pain. There were no statistical differences in the scores of VRS and NRS on the first day, the 7th day and the 30th day after the surgery between the two group (P>0.05). The pain score of the patients in the two groups decreased with no statistical difference from the first day to the 30th day after the surgery (P>0.05). ConclusionThe patients with robotic lobectomy have similar pain level after surgery compared with the patients with VATS lobectomy.
ObjectiveTo construct a predictive model for acute kidney injury (AKI) after coronary artery bypass grafting (CABG) based on uromodulin (UMOD) and tumor necrosis factor receptor-associated factor 6 (TRAF6). MethodsPatients undergoing CABG treatment at Tianjin Chest Hospital from 2022 to 2024 were prospectively enrolled. Based on whether they developed AKI post-surgery, patients were divided into the an AKI group and a non-AKI group. Differences in UMOD, TRAF6, blood urea nitrogen (BUN), serum creatinine (SCr), β-N-acetylglucosaminidase (NAG), and SCr clearance rate at different time points were compared between the two groups. Predictive models for AKI after CABG were constructed at various time points, and the predictive efficacy of the models for AKI was analyzed. ResultsA total of 70 patients were included, with 22 in the AKI group [13 males and 9 females, aged 55-72 (67.91±4.91) years] and 48 in the non-AKI group [32 males and 16 females, aged 56-72 (68.07±4.67) years]. The UMOD levels in the AKI group were lower than those in the non-AKI group at various time points including before surgery (t=34.283, P<0.001), postoperative 2 h (t=29.590, P<0.001), 4 h (t=30.705, P<0.001), 8 h (t=26.620, P<0.001), 12 h (t=29.671, P<0.001), and 24 h (t=31.397, P<0.001). The TRAF6 levels in the AKI group were higher than those in the non-AKI group at all these time points (P<0.001). Multivariate analysis showed that higher levels of TRAF6, BUN, SCr, NAG, and lower levels of UMOD and SCr clearance rate were risk factors for AKI after CABG (P<0.05). The receiver operating characteristic curve analysis showed that the area under the curve of the predictive model at postoperative 12 h was significantly higher than that of the remaining models. The risk of AKI after CABG was: log (Y)=12.333−1.582×UMOD+1.270×TRAF6+1.356×BUN+1.356×SCr+1.355×NAG−1.254×SCr clearance rate. ConclusionIn the occurrence process of AKI after CABG, TRAF6 exacerbates renal injury by activating inflammatory signals and promoting cell apoptosis, while UMOD alleviates renal injury by regulating renal tubular function and protecting renal tubular epithelial cells. Through the simulation analysis of the two biomarkers combined with renal injury indicators at postoperative 12 h, the occurrence of AKI after CABG can be effectively predicted.
ObjectiveTo investigate the relationship between the changes in preoperative serum creatinine (Cr), myoglobin (Mb), alanine aminotransferase (ALT) and postoperative fibrinogen (Fib), C- reactive protein (CRP) expression levels and postoperative hypoxemia in patients with aortic dissection aneurysm (ADA), and construct a predictive model. Additionally, the study explores the role of transpulmonary pressure-guided positive end expiratory pressure (PEEP) in improving postoperative hypoxemia. MethodsA retrospective analysis was conducted on the clinical data of ADA patients admitted to Tianjin Chest Hospital from April 2021 to August 2023. Patients were divided into a hypoxemia group [partial pressure of oxygen/fraction of inspiration oxygen (PaO2/FiO2) ≤200 mm Hg] and a non-hypoxemia group (PaO2/FiO2 >200 mm Hg) based on whether they developed postoperative hypoxemia. Univariate and multivariate regression analyses were used to identify risk factors for postoperative hypoxemia in ADA patients and to construct a predictive model for postoperative hypoxemia. The receiver operating characteristic (ROC) curve was plotted, and the Hosmer-Lemeshow goodness-of-fit test was used to evaluate the predictive value of the model. Furthermore, the impact of different ventilation modes on the improvement of postoperative hypoxemia was analyzed. From April 2021 to August 2023, 16 ADA patients with postoperative hypoxemia who received conventional mechanical ventilation were included in the control group. From September 2023 to December 2024, 28 ADA patients with postoperative hypoxemia who received transpulmonary pressure-guided PEEP were included in the experimental group. ICU stay duration, mechanical ventilation duration, hospital mortality rate, and respiratory and circulatory parameters were analyzed to evaluate the effect of transpulmonary pressure-guided PEEP on patients with postoperative hypoxemia after acute aortic dissection. ResultsA total of 98 ADA patients were included, of which 79 (80.61%) were males and 19 (19.39%) were females. Their ages ranged from 32 to 79 years, with an average age of (49.4±11.2) years. Sixteen (16.3%) patients developed postoperative hypoxemia. Body mass index (BMI), smoking history, cardiopulmonary bypass (CPB) duration, preoperative serum Cr, Mb, ALT, and postoperative Fib and CRP showed a certain correlation with postoperative hypoxemia in ADA patients (P<0.05). There was no statistical difference in other baseline data between the two groups (P>0.05). Logistic regression analysis results indicated that BMI [OR=1.613, 95%CI (1.260, 2.065)] and preoperative Mb [OR=2.344, 95%CI (1.048, 5.246)], ALT [OR=1.012, 95%CI (1.000, 1.024)], Cr [OR=1.752, 95%CI (1.045, 2.940)], postoperative Fib [OR=1.165, 95%CI (1.080, 1.258)] and intraoperative CPB time [OR=1.433, 95%CI (1.017, 2.020)] were influencing factors of postoperative hypoxemia in ADA patients (P<0.05). Based on this, a prediction model for postoperative hypoxemia in ADA patients was established. The area under the curve corresponding to the optimal critical point was 0.837 [95%CI (0.799, 0.875)], with a sensitivity of 87.5% and a specificity of 79.3%. The Hosmer-Lemeshow goodness of fit test showed P=0.536. Before treatment, there were no statistical differences in respiratory and circulatory parameters between the control group and the experimental group (P>0.05). After treatment, the levels of PEEP, PaO2/FiO2, end-expiratory esophageal pressure, and end-inspiratory transpulmonary pressure in the experimental group were higher than those in the control group (P<0.05). The duration of mechanical ventilation and ICU stay in the experimental group were shorter than those in the control group (P<0.05), while there was no statistical difference in mortality between the two groups (P=0.626). ConclusionThe hypoxia prediction model based on preoperative Cr, Mb, ALT and postoperative Fib levels, combined with transpulmonary pressure-guided PEEP optimization, provides a scientific basis for the precise management of postoperative hypoxemia in ADA. This approach not only improves the predictive ability of hypoxemia risk but also significantly improves the postoperative oxygenation status of patients through personalized mechanical ventilation strategies, providing new insights into the management of postoperative complications.
Objective To investigate the impact of red blood cell suspension infusion across various perioperative periods on patients with valvular heart disease. Methods The patients with valvular heart disease admitted to Tianjin Chest Hospital from 2018 to 2020 were selected. Based on the timing of perioperative red cell suspension infusion, patients were categorized into three groups: a group 1 receiving intraoperative red cell suspension infusion, a group 2 receiving red cell suspension infusion within 24 hours after entering the ICU, and a group 3 receiving red cell suspension infusion at both time points. The laboratory results, perioperative blood component infusion volume, and other relevant parameters were retrospectively analyzed. After propensity score matching, the differences in different variables among the three groups were compared. Results After propensity score matching, 102 patients were enrolled, including 52 males and 50 females, with an average age of (61.74±10.58) years. There were 34 patients in each group. The preoperative hemoglobin (Hb) value of the group 2 was significantly higher than that of the group 1 and the group 3, and the amount of red cell suspension and autoblood transfusion was the lowest (P<0.05). In the group 1, Hb was the highest after surgery, Hb was the highest within 24 hours after surgery. HCT was the highest within 24 hours after surgery (P<0.05). The group 1 had the lowest plasma, platelet and cryoprecipitate infusion volumes, and the shortest cardiopulmonary bypass time, aortic occlusion time, postoperative ICU stay and hospital stay, and the least blood loss, total drainage volume (P<0.05). The difference between postoperative Hb and preoperative △Hb1 was significantly increased in the group 1 (P<0.05). Conclusion The intraoperative infusion of suspended red blood cells in patients with heart valves can be used to indicate to clinicians that patients have a better prognosis at discharge, review and follow-up.