Objective To summarize safety and effectiveness of cryomaze ablation procedure concomitant with valve surgery. Methods We retrospectively investigated the clinical data of 62 patients (24 males and 38 females) with mean age of 49.4±14.2 years who underwent cryomaze ablation procedure concomitant with valve surgery in our hospital from August 2013 through July 2015. The heart rhythm of the patients after surgery was supervised by 12-leads electrical cardiogram respectively. Results The rate of sinus rhythm restored right after surgery was 98.4%. The rate of sinus rhythm restored at the time of discharge was 93.4%. The rate of sinus rhythm restored 3 months, 6 months, 12 months, 18 months after surgery was 90.2%, 87.3%, 85.0%, 83.3% respectively. The one-year post-operation rate of sinus rhythm restored for the group of right minimal invasive thoracoscopic assisted mitral valve surgery was 90.5%. Longer duration for atrial fibrillation (>7 years) was a risk factor for the reoccurrence of atrial fibrillation 1 year after surgery (P<0.05). Conclusion Cryomaze ablation procedure concomitant with valve surgery is quite effective in treatment of rheumatic valve disease and atrial fibrillation. This approach is associated with fewer complications, comparable atrial fibrillation reoccurrence for short-term follow-up.
Abstract： Objective To analyze risk factors associated with postoperative respiratory failure in patients with valvular surgery. Methods Between January 2001 and November 2010, clinical data of 618 patients with 339 males and 279 fameles at age of 10-74（44.01±13.95）years,undergoing valvular operations were investigated retrospectively. We divided the patients into two groups according to the presence （74 patients）or absence（544 patients）of postoperative respiratory failure. Its risk factors were evaluated by univariate and multivariate logistic regression analysis. Results The hospital mortality rate of valvular surgery was 6.1%（38/618）.The morbidity rate of respiratory failure was 12.0%（74/618） with hospital mortality rate at 17.6%（13/74） which was significantly higher than those patients without postoperative respiratory failure at 4.6%（25/544, χ2=18.994, P=0.000）. Univariate analysis showed age＞ 65 years（P=0.005）, New York Heart Association（NYHA）classⅣ（P=0.014）, election fraction＜ 50.0%（P=0.003）, cardiopulmonary bypass time＞ 3 h（P=0.001）, aortic cross clamping time＞ 2 h（P=0.008）, concomitant operation（ valvular operation with coronary artery bypass grafting, Bentall or radiofrequency ablation maze operation（P=0.000）, reoperation（P=0.012）, postoperative complications （P=0.000）, and blood transfusion＞ 2 000 ml（P=0.000） were important risk factors for postoperative respiratory failure. Multivariate logistic regression showed that concomitant operation（P=0.003）, reoperation（P=0.010）, postoperative complications（P=0.000）, and blood transfusion＞2 000 ml（P=0.012）were significant independent predictive risk factors. Conclusion This study suggest that patients with predictive risk factors of postoperative respiratory failure need more carefully treated. The morbidity of these patients would be reduced through improving perioperative management, shortening cardiopulmonary bypass time and reducing postoperative complications.
Objective Explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods From January 2022 to July 2022, screening was conducted among 118 patients based on inclusion/exclusion criteria. Fifty-eight patients were excluded, and 60 patients participated in this trial with informed consent and were randomly divided into a RIPC group (n=30) and a control group (n=30). Due to the cancellation of surgery, HRV data was missing. 7 patients in the control group were excluded, and 5 patients in the RIPC group were excluded, 23 patients in the final control group and 25 patients in the RIPC group were included in the analysis. Comparison of relevant indicators of heart rate variability (standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency component (LF), high frequency component (HF) and LF/HF) at 8 hours in the morning on the surgical day between two groups of patients. Results There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.
Objective To systematically evaluate the research quality and efficacy of prediction models for acute kidney injury (AKI) after heart valve surgery, screen key predictive factors, and provide evidence-based basis for clinical risk assessment. Methods Computer search was carried out in PubMed, Web of Science, EMBASE, Cochrane Library, Medline, China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, and VIP Database to collect studies on AKI prediction models after heart valve surgery published from January 2015 to July 2025. The PROBAST tool was used to evaluate the bias risk and applicability of the models, and the TRIPOD was used to assess the reporting quality. Meta-analysis was performed to integrate the effect sizes of high-frequency (≥3 times) predictive factors. Results A total of 24 studies (39 models) were included. Area under the curve (AUC) of the receiver operational characteristic curve was between 0.551 and 0.928, and the combined AUC was 0.77 (95%CI 0.72-0.82). The overall bias risk of the models was relatively high (100% of the studies had a high bias risk), only 2 studies conducted external validation, and the models in 10 studies were not validated. In terms of TRIPOD reporting quality, the overall reporting quality of 24 studies was low, with a compliance percentage (number of items) ranging from 36.36% to 77.27%. Meta-analysis showed that age (OR=1.041, P=0.006), diabetes (OR=1.64, P=0.001), hypertension (OR=2.529, P <0.001), blood transfusion (OR=1.49, P=0.001), cystatin C (OR=2.408, P=0.018), history of cardiac surgery (OR=2.585, P <0.001), atrial fibrillation (OR=1.33, P <0.001), and vascular complications (OR=1.22, P=0.008) were independent risk factors for postoperative AKI. Conclusion The clinical applicability of existing prediction models is limited, with high bias risk and low reporting quality, and the methodology needs to be optimized. Eight factors such as age and hypertension can be used as core indicators for postoperative AKI risk assessment. In the future, multicenter prospective studies should be carried out to develop more reliable prediction tools.
Objective To analyze and explore the risk factors of secondary tricuspid regurgitation (TR) after left-sided valve surgery (left cardiac valve replacement or valvuloplasty) using meta-analysis, so as to provide evidence for clinical diagnosis and treatment of secondary TR. Methods We electronically searched databases including PubMed, MEDLINE, CBM, CNKI, VIP, for literature on the risk factors of secondary TR after left-sided valve surgery from 1995 to 2012. According to the inclusion and exclusion criteria, we screened literature, extracted data, and assessed methodological quality. Then, meta-analysis was performed using RevMan 5.0 software. Results A total of 6 case-control studies were included, involving 437 patients and 2 102 controls. The results of meta-analysis showed that, the risk factors of progressive exacerbation of secondary TR after left-sided valve surgery included preoperative atrial fibrillation (OR=3.90, 95%CI 3.00 to 5.07; adjusted OR=3.04, 95%CI 2.21 to 4.16), age (MD=5.36, 95%CI 3.49 to 7.23), huge left atrium (OR=5.17, 95%CI 3.12 to 8.57; adjusted OR=1.91, 95%CI 1.49 to 2.44) or left atrium diameter (MD=4.85, 95%CI 3.18 to 6.53), degradation of left heart function (OR=2.97, 95%CI 1.73 to 5.08), rheumatic pathological change (OR=3.06, 95%CI 1.66 to 4.68), preoperative TR no less than 2+ (OR=3.52, 95%CI 1.26 to 9.89), and mitral valve replacement (MVR) (OR=2.35, 95%CI 1.68 to 3.30). Sex (OR=1.54, 95%CI 0.94 to 2.52) and preoperative pulmonary arterial hypertension (OR=1.28, 95%CI 0.77 to 2.12) were not associated with secondary TR after left-sided valve surgery. Conclusion The risk factors of progressive exacerbation of secondary TR after left-sided valve surgery include preoperative atrial fibrillation, age, huge left atrium or left atrium diameter, degradation of left heart function, rheumatic pathological change, preoperative TR no less than 2+, and MVR. Understanding these risk factors helps us to improve the long-time effectiveness of preventing and treating TR after left-sided valve surgery.
In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.
Objective To analyze risk factors for prolonged stay in intensive care unit (ICU) after cardiac valvular surgery. Methods Between January 2005 and May 2005, five hundred and seven consecutive patients undergone cardiac valvular surgery were divided into two groups based on if their length of ICU stay more than 5 days (prolonged stay in ICU was defined as 5 days or more). Group Ⅰ: 75 patients required prolonged ICU stay. Group Ⅱ: 432 patients did not require prolonged ICU stay. Univariate and multivariate analysis (logistic regression) were used to identify the risk factors. Results Seventyfive patients required prolonged ICU stay. Univariate risk factors showed that age, the proportion of previous heart surgery, smoking history and repeat cardiopulmonary bypass (CPB) support, cardiothoracicratio, the CPB time and aortic crossclamping time of group Ⅰ were higher or longer than those of group Ⅱ. The heart function, left ventricular ejection fraction (LVEF), pulmonary function of group Ⅰwere worse than those of group Ⅱ(Plt;0.05, 0.01). Logistic regression identified that preoperative age≥65 years (OR=4.399), LVEF≤0.50(OR=2.788),cardiothoracic ratio≥0.68(OR=2.411), maximal voluntary ventilation observed value/predicted value %lt;71%(OR=4.872), previous heart surgery (OR=3.241) and repeat CPB support during surgery (OR=18.656) were final risk factors for prolonged ICU stay. Conclusion Prolonged ICU stay after cardiac valvular surgery can be predicted through age, LVEF, cardiothoracic ratio, maximal voluntary ventilation, previous heart surgery and repeat CPB support during surgery. The patients with these risk factors need more preoperative care and postoperative care to reduce mortality, morbidity and avoid prolonged ICU stay after cardiac valvular surgery.
Abstract: Objective To evaluate the prediction validation of European system for cardiac operative risk evaluation （EuroSCORE） in prolonged intensive care unit (ICU) stay, mortality, and major postoperative complications for Chinese patients operated for acquired heart valve disease. Methods Between January 2004 and January 2006, 2 218 consecutive patients treated for acquired heart valve diseases were enrolled in Fu Wai Hospital. All these patients accepted valvular surgery. Both logistic model and additive model were applied to EuroSCORE to evaluate its ability in predicting mortality, prolonged ICU stay and major postoperative complications of patients who had undergone heart valve surgery. An receiver operating characteristic curve（ ROC) area was used to test the discrimination of the models. Calibration was assessed by HosmerLemeshow goodnessoffit statistic. Results Discriminating abilities of logistic and additive EuroSCORE algorithm were 0.710 and 0.690 respectively for mortality, 0.670 and 0.660 for prolonged ICU stay, 0.650 and 0.640 for heart failure, 0.720 and 0.710 for respiratory failure, 0.700 and 0.740 for renal failure, and 0.540 and 0.550 for reexploration for bleeding. There was significant difference between logistic and additive algorithm in predicting renal failure and heart failure (Plt;0.05). Calibration of logistic and additive algorithm in predicting mortality, prolonged ICU stay and major postoperative complications were not satisfactory. However, logistic algorithm could be used to predict postoperative respiratory failure (P=0.120). Conclusion EuroSCORE is not an accurate predictor in predicting mortality, prolonged ICU stay and major postoperative complications, but the logistic model can be used to predict postoperative respiratory failure in Chinese patients operated for acquired heart valve diseases.