Redo Coronary Artery Bypass Grafting: On-Pump and Off-Pump Coronary Artery Bypass Grafting Revascularization Techniques

Objective To analyze the short-term outcomes of redo coronary artery bypass grafting(CABG) using on-pump and off-pump CABG techniques. Methods From January 2003 to August 2013, non-randomized 80 patients were treated with redo CABG in the Department of Cardiac Surgery, Peking University Third Hospital. Among these patients, 40 underwent on-pump CABG technique(redo-ONCAB group) and 40 underwent off-pump CABG technique(redo-OPCAB group). Furthermore, transmyocardial laser revascularization was performed in high-risk patients who were not suitable to conventional grafting. Clinical data of the two groups were recorded and analyzed including operation time, coronary grafts, incomplete revascularization, postoperative ventilation, perioperative stroke, and low output syndrome, etc. Results There were no significantly differences in age, gender distribution, incidences of hypertension, stroke, and other clinical characteristics between redo-OPCAB group and redo-ONCAB group(all P>0.05), except for incidences of renal dysfunction and pulmonary disease(all P<0.05). The number of grafting vessels in the redo-ONCAB and redo-OPCAB groups was 2.1 ± 0.74 and 1.4 ±0.52 respectively. There was significant difference between the two groups(P=0.0243). Compared with the redo-ONCAB group, there was shorter operation time(P=0.0045), postoperative ventilation(P=0.0211) and intensive care unit stay(P=0.0400), as well as fewer use of platelet(P=0.0338) and blood transfusion(P=0.0034) in the redo-OPCAB group. The incidence of incomplete revascularization(P=0.0253) and the use of transmyocardial laser revascularization(P=0.0052) were higher in the redo-OPCAB group than those in the redo-ONCAB group(all P<0.05). However, no significant differences were showed for the incidence of the use of intra aortic balloon pump and continuous renal replacement therapy, perioperative stroke, low output syndrome, and in-hospital mortality between the two groups(all P>0.05). Conclusion Redo CABG is the safety and efficacy surgical procedure, and redo-OPCAB technique with better outcomes is commended especially in high-risk patients.

Sternal Reconstruction of Deep Sternal Wound Infections Following Median Sternotomy by Single-stage Muscle Flaps Transposition

Objective To assess clinical effectiveness of using bilateral pectoralis major or plus rectus abdominis muscle flaps in treating deep sternal wound infection(DSWI) following median sternotomy. Methods Between January 2009 and December 2013, 19 patients with DSWI after median sternotomy for cardiac surgery were admitted to our hospital, including 14 males(73.7%) and 5 females(26.3%), aged 55±13(18-78) years. According to the Pairolero classification of infected median sternotomies, 3(15.8%) patients were type II, and the other 16(84.2%) were type III. Surgical procedure consisted of adequate debridement of infected sternum, costal cartilage, granulation, steel wires, suture residues and other foreign substances. Sternal reconstruction used the bilateral pectoralis major or plus rectus abdominis muscle flaps to obliterate dead space. The drainage tubes were placed and connected to a negative pressure generator for adequate drainage. Results There were no intraoperative deaths. In 15 patients(78.9%), bilateral pectoral muscle flaps were mobilized sufficiently to cover and stabilize the defect created by wound debridement. 4 patients(21.0%) needed bilateral pectoral muscle flaps plus rectus abdominis muscle flaps because their pectoralis major muscle flaps could not reach the lowest portion of the wound. 2 patients(10.5%) presented with subcutaneous infection, and 3 patients(15.8%) had hematoma. They recovered following local debridement and medication. 17 patients(89.5%) were examined at follow-up 12 months later, all healed and having stable sternum. No patients showed infection recurrence during the follow-up period over 12 months. Conclusion DSWI following median sternotomy may be effectively managed with adequate debridement of infected tissues and reconstruction with bilateral pectoralis major muscle or plus rectus abdominis muscle flap transposition.

Predicting lymph node metastasis in colorectal cancer:An analysis of influencing factors to develop a risk model

BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.