Analysis of predictive factors for late recurrence of atrial fibrillation after surgical ablation in patients undergoing rheumatic valve surgery.

OBJECTIVES: To identify independent predictors of late recurrence of atrial fibrillation (AF) after surgical ablation in patients undergoing rheumatic valve surgery. METHODS: A total of 258 patients who underwent surgical ablation for AF with rheumatic heart disease at our hospital between January 2019 and June 2022 were retrospectively included. The patients were followed up for 12 months. Late recurrence was defined as any AF recurrence longer than 30 s between 3 and 12 months. Patients with or without late recurrence were divided into non-recurrence and recurrence groups. Univariate and multivariate analyses were performed to identify the predictors of late recurrence. RESULTS: The in-hospital mortality rate was 0.8% (2/258), and the late recurrence rate of AF was 38.4%, including 152 and 95 cases in the non-recurrent and recurrent groups respectively, with a follow-up completion rate of 96.5% (247/256). There were no deaths during follow-up, two patients (0.8%) experienced a stroke, and one patient (0.4%) experienced gastrointestinal hemorrhage. The results of the univariate and multivariate analyses of the preoperative risk factors for late recurrence showed a left atrial (LA) anteroposterior diameter ≥ 52.9 mm (odds ratio [OR] = 2.366, 95% confidence interval [CI] = 1.089-5.138, P = 0.030], ratio of the superoinferior to the anteroposterior diameters of LA (S-AR) < 1.19 (OR = 4.639, 95% CI = 2.181-9.865, P < 0.001), and AF duration ≥ 39 months (OR = 6.152, 95% CI = 2.897-13.061, P < 0.001), and cardiothoracic ratio ≥ 0.63 (OR = 2.716, 95% CI = 1.314-5.612, P = 0.007) were the most significant independent risk factors. CONCLUSIONS: LA anteroposterior diameter ≥ 52.9 mm, S-AR < 1.19, and AF duration ≥ 36 months and cardiothoracic ratio ≥ 0.63 are independent predictors for late recurrence of AF after surgical ablation in patients undergoing rheumatic valve surgery.

Clinical usefulness of psoas muscle thickness for the prognosis of acute type A aortic dissection patients undergoing total arch replacement.

Background: Sarcopenia has emerged as a comprehensive predictor of mortality in diseased populations. The aim of this study was to evaluate the prognostic and predictive value of psoas muscle thickness/height (PMTH) measurement in patients with acute type A aortic dissection (AAAD). Methods: A retrospective analysis of patients (from January 2020 to December 2020) who underwent AAAD surgery at our institution was conducted. PMTH, as a measure of sarcopenia, was measured by preoperative computed tomography. Patients were classified into two groups according to the cut-off value of PMTH. To balance potential bias, a 1:1 propensity score matching (PSM) with a caliper 0.05 was conducted. Results: PSM analysis created 68 pairs of patients. In short-term outcomes, a lower PMTH value was strongly correlated with higher in-hospital mortality and renal failure. Receiver operating characteristic (ROC) analysis suggested that sarcopenia had good predictive capabilities in in-hospital mortality, with the area under curve (AUC) of 0.81 [95% confidence interval (CI): 0.64-0.97]. During a median follow-up of 37 months, 24 (19.4%) patients died, including 16 in low PMTH group and 8 in high PMTH group. Kaplan-Meier analysis indicated the sarcopenia significantly affected long-term survival [log-rank P=0.02; hazard ratio (HR) 2.53 (95% CI: 1.13-5.66)]. Multivariable Cox regression analysis revealed that sarcopenia was an independent predictor for decreased survival [HR 2.73 (95% CI: 1.15-8.78)]. Conclusions: Sarcopenia defined from the PMTH may be a useful tool for predicting short- and long-term mortality in patients after AAAD surgery.

Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition.

Objective: This study aims to develop a predictive model for the risk of major adverse events (MAEs) in type A aortic dissection (AAAD) patients with malnutrition after surgery, utilizing machine learning (ML) algorithms. Methods: We retrospectively collected clinical data from AAAD patients with malnutrition who underwent surgical treatment at our center. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we screened for preoperative and intraoperative characteristic variables. Based on the random forest (RF) algorithm, we constructed a ML predictive model, and further evaluated and interpreted this model. Results: Through LASSO regression analysis and univariate analysis, we ultimately selected seven feature variables for modeling. After comparing six different ML models, we confirmed that the RF model demonstrated the best predictive performance in this dataset. Subsequently, we constructed a model using the RF algorithm to predict the risk of postoperative MAEs in AAAD patients with malnutrition. The test set results indicated that this model has excellent predictive efficacy and clinical applicability. Finally, we employed the Shapley additive explanations (SHAP) method to further interpret the predictions of this model. Conclusion: We have successfully constructed a risk prediction model for postoperative MAEs in AAAD patients with malnutrition using the RF algorithm, and we have interpreted the model through the SHAP method. This model aids clinicians in early identification of high-risk patients for MAEs, thereby potentially mitigating adverse clinical outcomes associated with malnutrition.

Risk prediction and prognostic analysis of post-implantation syndrome after thoracic endovascular aortic repair.

This study aimed to establish a predictive model for the risk of post-thoracic endovascular aortic repair (TEVAR) post-implantation syndrome (PIS) in type B aortic dissection (TBAD) patients, assisting clinical physicians in early risk stratification and decision management for high-risk PIS patients. This study retrospectively analyzed the clinical data of 547 consecutive TBAD patients who underwent TEVAR treatment at our hospital. Feature variables were selected through LASSO regression and logistic regression analysis to construct a nomogram predictive model, and the model's performance was evaluated. The optimal cutoff value for the PIS risk nomogram score was calculated through receiver operating characteristic (ROC) curve analysis, further dividing patients into high-risk group (HRG) and low-risk group (LRG), and comparing the short to midterm postoperative outcomes between the two groups. In the end, a total of 158 cases (28.9%) experienced PIS. Through LASSO regression analysis and multivariable logistic regression analysis, variables including age, emergency surgery, operative time, contrast medium volume, and number of main prosthesis stents were selected to construct the nomogram predictive model. The model achieved an area under the curve (AUC) of 0.86 in the training set and 0.82 in the test set. Results from calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated that the predictive model exhibited good performance and clinical utility. Furthermore, after comparing the postoperative outcomes of HRG and LRG patients, we found that the incidence of postoperative PIS significantly increased in HRG patients. The duration of ICU stay and mechanical assistance time was prolonged, and the incidence of postoperative type II entry flow and acute kidney injury (AKI) was higher. The risk of aortic-related adverse events (ARAEs) and major adverse events (MAEs) at the first and twelfth months of follow-up also significantly increased. However, there was no significant difference in the mortality rate during hospitalization. This study established a nomogram model for predicting the risk of PIS in patients with TBAD undergoing TEVAR. It serves as a practical tool to assist clinicians in early risk stratification and decision-making management for patients.

The prognostic value of preoperative systemic inflammatory response index in predicting outcomes of acute type A aortic dissection patients underwent surgical treatment.

Background: The systemic inflammatory response index (SIRI) is a novel inflammatory-immune biological marker that has prognostic value in various cardiovascular diseases. This study aims to investigate the relationship between SIRI and short-term and long-term prognosis in patients with acute type A aortic dissection (AAAD) underwent surgical treatment. Methods: We conducted a retrospective analysis of patients with AAAD who underwent emergency surgical treatment at our center. Through multifactorial logistics regression analysis and cox proportional hazards regression analysis, we identified SIRI as an independent risk factor for major adverse events (MAEs) and long-term aorta-related adverse events (ARAEs) post-surgery. The optimal cutoff value of preoperative SIRI was determined using receiver operating characteristic (ROC) curve analysis, and patients were divided into low SIRI group and high SIRI group. The prognostic outcomes at different time points post-surgery for the two groups of patients were analyzed using Kaplan-Meier survival analysis, and the significance was determined by log-rank test. Results: A total of 691 AAAD patients were included in this study. Among them, 50 patients (7.2%) died within 30 days post-surgery, and 175 patients (25.3%) experienced MAEs. A total of 641 patients were followed up, with an average follow-up time of 33.5 ± 17.5 months, during which 113 patients (17.6%) experienced ARAEs. The results of multifactorial logistics regression analysis and cox proportional hazards regression analysis showed that SIRI was an independent risk factor for postoperative MAEs (OR=3.148, 95%CI[1.650-6.006], p<0.001) and ARAEs (HR=2.248, 95%CI[1.050-4.809], p<0.037). Kaplan-Meier analysis demonstrated that the MAEs-free survival in the high SIRI group was significantly lower than that in the low SIRI group, and a similar trend was observed in the ARAEs-free survival during follow-up (log-rank test, p<0.001). Conclusion: Preoperative SIRI is significantly associated with the short-term and long-term prognosis of AAAD patients underwent emergency open surgery, demonstrating its valuable prognostic value. Therefore, preoperative SIRI is a reliable biological marker that can serve as a valuable tool for preoperative risk stratification and decision management.

A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning.

Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner.

Quality of life in young patients with acute type a aortic dissection in China: comparison with Marfan syndrome and non-Marfan syndrome.

BACKGROUND: There is a paucity of Chinese studies evaluating the quality of life (QoL) in young acute type A aortic dissection (AAAD) patients with Marfan syndrome. METHODS: Young adult AAAD patients (younger than 45 years old) underwent surgical treatment at our institution from January 2017 to December 2020 were consecutive enrolled. The hospital survivors completed 1 year of follow up. Patients were divided into two groups according to the presence or absence of Marfan syndrome (MFS). A 1:1 propensity score matching (PSM) with a caliper 0.2 was conducted to balance potential bias in baseline. The follow-up data were analyzed primarily for change in quality of life and anxiety status. RESULTS: After PSM, 32 comparable pairs were matched. The baseline data were comparable and postoperative complications were similar between groups. In terms of SF-36 scale, the role physical, bodily pain, role emotional and mental health subscales were no significantly improved in MFS patients over time. At 1 year after discharged, the subscale of mental health and bodily pain were significantly lower in the MFS group than in the non-MFS group. In terms of HADS assessments, the level of anxiety in MFS patients was significantly higher than in non-MFS patients at 1 year after discharged. CONCLUSIONS: The QoL in young AAAD patients with MFS is lower than those without MFS after surgery. This may be associated with the uncontrollable persistent chronic pain and the uncertainty and concerns for the disease's progression.

Outcomes of post-implantation syndrome after endovascular repair for Stanford type B aortic dissection.

OBJECTIVE: The aim of this study was to investigate the correlation between post-implantation syndrome (PIS) and long-term prognosis in patients with Stanford type B aortic dissection (TBAD) undergoing thoracic endovascular aortic repair (TEVAR). METHODS: This retrospective study included 547 consecutive patients diagnosed with TBAD who underwent TEVAR at our institution between January 2014 and December 2019. Patients were categorized into two groups: the PIS group (patients with post-TEVAR PIS) and the non-PIS group (patients without post-TEVAR PIS). In-hospital and follow-up data were analyzed. RESULTS: The incidence of PIS was 28.9% (158/547 patients). No baseline differences were observed between the PIS (n = 158) and the non-PIS (n = 389) groups. The proportion of emergency surgery in the PIS group was higher than that in the non-PIS group (44.9% vs 26.0%; P < .001), the operation time was longer (median, 65.0; interquartile range [IQR], 56.0-75.0 minutes vs 56.0; IQR, 45.0-66.0 minutes; P < .001), the volume of contrast medium used (median, 65.0; IQR, 56.0-75.0 mL vs 56.0; IQR, 45.0-66.0 mL; P < .001), and the average number of trunk stents (1.85 ± 0.4 vs 1.34 ± 0.5 pieces; P < .001) and branch stents (0.7 ± 0.7 vs 0.2 ± 0.5 pieces; P < .001) used were more in the PIS group than in the non-PIS group. The incidence of supra-aortic branch procedures was higher in the PIS group than in the non-PIS group. There was no significant difference in device-related complications (DRCs) or 30-day mortality between the two groups (2.5% vs 4.4%; P = .442 and 1.3% vs 1.3%; P = .688, respectively). Univariate and multivariable logistic regression analysis showed that emergency surgery, number of trunk stents >1, operation time >58.5 minutes, and contrast medium volume >75 mL were risk factors for PIS, and the odds ratios of emergency operation, number of trunk stents >1 piece, operation time >58.5 minutes, and contrast medium volume >75 mL were 2.526 (95% confidence interval [CI], 1.530-4.173), 4.651 (95% CI, 2.838-7.624), 3.577 (95% CI, 2.201-5.815), and 7.356 (95% CI, 4.111-13.160), respectively. Follow-up was completed in 98.5% (532/540) of the patients, with a median follow-up of 67 months (IQR, 50-86 months). There was no significant difference in survival between the PIS and non-PIS groups (12.4% vs 10.3%; P = .476) during follow-up. The incidences of DRCs (7.8% vs 11.6%; P = .200) and aortic false lumen thrombosis (75.8% vs 79.2%; P = .399) were comparable between the PIS and non-PIS groups. Univariate logistic regression analysis showed that PIS had no effect on long-term follow-up mortality, DRCs, entry flow, or aortic false lumen thrombosis rate. CONCLUSIONS: PIS is relatively common after TEVAR and emergency surgery; number of trunk stents >1, operation time >58.5 minutes, and contrast medium volume >75 mL are of high predictive value for the assessment of PIS after TEVAR. However, PIS had little effect on early and late postoperative mortality or DRCs.

A nomogram-based model to predict postoperative transient neurological dysfunctions in patients receiving acute type A aortic dissection surgery.

The purposes of this study were to develop and validate a nomogram for predicting postoperative transient neurological dysfunctions (TND) in patients with acute type A aortic dissection (AAAD) who underwent modified triple-branched stent graft implantation. This retrospective study developed a nomogram-based model in a consecutive cohort of 146 patients. Patient characteristics, preoperative clinical indices, and operative data were analyzed. Univariate and multivariable analyses were applied to identify the most useful predictive variables for constructing the nomogram. Discrimination and the calibration of the model was evaluated through the receiver operating characteristic curve (ROC), the Hosmer-Lemeshow goodness-of-fit test and the decision curve analysis (DCA). At the same time, to identify and compare long-term cumulative survival rate, Kaplan-Meier survival curve was plotted. The incidence rate of postoperative TND observed in our cohort were 40.9%. Supra-aortic dissection with or without thrombosis, creatinine >115 µmol and albumin <39.7 g/L, selective antegrade cerebral perfusion (SACP) time >7 min and total operation time >303 min, were confirmed as independent predictors that enhanced the likelihood of TND. Internal validation showed good discrimination of the model with under the ROC curve (AUC) of 0.818 and good calibration (Hosmer-Lemeshow test, p > .05). DCA revealed that the nomogram was clinically useful. In the long-term survival there was no significant difference between patients with or without TND history. The results showed the predict model based on readily available predictors has sufficient validity to identify TND risk in this population, that maybe useful for clinical decision-making.

Efficacy of prognostic nutrition index in combination with D-dimer in predicting postoperative clinical adverse events after acute type A aortic dissection: a single center retrospective study.

Background: The aim of this study was to identify the predictive factors for adverse clinical events after surgery in patients with acute type A aortic dissection (AAAD), and to explore the predictive value of preoperative prognostic nutritional index (PNI) combined with D-dimer for these events. Methods: This study was a retrospective analysis of clinical data of 153 patients with AAAD who underwent emergency surgery at our center from January 2019 to January 2022. Patients were divided into adverse event group and non-adverse event group based on whether they experienced adverse clinical events after surgery. Univariate and multivariable logistic regression analyses were performed to identify the risk factors for adverse events, and the predictive efficacy was evaluated by the area under the receiver operating characteristic curve (ROC-AUC). Results: A total of 153 AAAD patients were included in the study, and were divided into the adverse event group (n = 46) and the non-adverse events group (n = 107) based on whether or not they experienced clinical adverse events after surgery. The optimal cutoff value was determined using ROC curves, and multivariate logistic regression analysis was performed. Ultimately, it was found that preoperative PNI < 42.45 and D-dimer > 15.05 were independent predictors of postoperative clinical adverse events in AAAD patients. The odd ratios (OR) value for preoperative PNI < 42.45 is 3.596 [95% Confidence Interval (CI): 1.508-8.923, p = 0.004], while the OR value for D-dimer > 15.05 is 7.572 [95% CI: 3.094-20.220, p < 0.001]. The combination of these two indicators has a high predictive value (AUC = 0.843, 95% CI: 0.774-0.912, p < 0.001) and is superior to using either variable alone. Conclusion: Preoperative PNI < 42.45 and D-dimer > 15.05 are independent predictive factors for postoperative adverse events during hospitalization in patients with AAAD. The combination of these two indicators can improve the predictive accuracy, which is superior to using either variable alone.

Early Clinical Outcomes of Thoracoscopic Mitral Valvuloplasty: The First 90 Cases.

BACKGROUND: We reported 90 cases of thoracoscopic mitral valvuloplasty in its early stages and sought to analyze early clinical outcomes. METHODS: Ninety consecutive patients, who underwent thoracoscopic mitral valvuloplasty at our institute between April 2020 and December 2021, were assessed for outcomes. Clinical data, including baseline characteristics, operative data, postoperative data, and early follow-up results, were collected. The early clinical outcomes were used to assess the reliability and efficiency of this technique. RESULTS: No in-hospital death occurred. One patient underwent a median sternotomy for bleeding. Intraoperative transesophageal echocardiography revealed no mitral regurgitation in 82 patients and mitral regurgitation of 0-2 cm2 in six. The remaining two patients with mitral regurgitation >2 cm2 experienced serious systolic anterior motion but underwent successful re-valvuloplasty during a second pump-up. the mean cardiopulmonary bypass time was 177.1±54.8 min and aortic clamping time, 114.0±44.9 min. Each patient received a prosthetic ring (CG Future™), and 64 patients received artificial chordae with an average of 2.7±1.5 (ranging from 1 to 6) pairs. The mean follow up was 8.8±7.0 (range, 1-22 months), while two patients were lost to follow up. Recurrent severe mitral regurgitation was observed in one patient three months after the operation, and mitral valve replacement was performed via median sternotomy. During follow up, one patient died of upper respiratory tract infection, and one suffered from low cardiac output. CONCLUSIONS: Thoracoscopic mitral valvuloplasty is safe and effective and, once surgeons overcome the learning curve, can achieve excellent early clinical outcomes.