纪宏文
中国医学科学院阜外医院 麻醉科
BACKGROUND:Few studies have considered outcomes among low body mass index (BMI) cohorts undergoing coronary artery bypass grafting (CABG). This study aims to investigate the effects of low body weight on blood transfusion and perioperative outcomes in patients undergoing isolated CABG.METHODS:This retrospective study enrolled consecutive cases from a single-center between January 2008 and December 2018. Low body weight/underweight was defined as a BMI < 18.5 kg/m², while normal BMI was defined as 18.5 ≤ BMI < 24.0 kg/m². The primary endpoint was the perioperative red blood cell (RBC) transfusion rate. Secondary endpoints include platelet and plasma transfusion rates, transfusion volume for all blood components, hospital length of stay, and the occurrence of adverse events including prolonged mechanical ventilation, re-intubation, re-operation, acute kidney injury, and 30-day all-cause mortality.RESULTS:A total of 7,620 patients were included in this study. After 1:1 propensity score matching, 130 pairs were formed, with 61 pairs in the on-pump group and 69 pairs in the off-pump group. Baseline characteristics were comparable between the matched groups. Low body weight independently increased the risk of RBC transfusion (on-pump: OR = 3.837, 95% CI = 1.213-12.144, p = 0.022; off-pump: OR = 3.630, 95% CI = 1.875-5.313, p < 0.001). Moreover, within the on-pump group of the original cohort, BMI of < 18.5 kg/m² was independently correlated with increased risk of re-intubation (OR = 5.365, 95% CI = 1.159 to 24.833, p = 0.032), re-operation (OR = 4.650, 95% CI = 1.019 to 21.210, p = 0.047), and 30-day all-cause mortality (OR = 10.325, 95% CI = 2.011 to 53.020, p = 0.005).CONCLUSION:BMI < 18.5 kg/m² was identified as an independent risk factor for increased perioperative RBC transfusion rate in patient underwent isolated CABG with or without CPB. Only on-pump underweight patients in the original cohort exhibited an increased risk for re-intubation, re-operation, and 30-day all-cause mortality. Physicians and healthcare systems should consider these findings to improve management for this population.
BMC anesthesiology 2023
BACKGROUND:Our previous showed that a blood management program in the cardiopulmonary bypass (CPB) department, reduced red blood cell (RBC) transfusion and complications, but assessing transfusion practice solely based on transfusion rates was insufficient. This study aimed to design a risk stratification score to predict perioperative RBC transfusion to guide targeted measures for on-pump cardiac surgery patients.STUDY DESIGN AND METHODS:We analyzed data from 42,435 adult cardiac patients. Eight predictors were entered into the final model including age, sex, anemia, New York Heart Association classification, body surface area, cardiac surgery history, emergency surgery, and surgery type. We then simplified the score to an integer-based system. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and a calibration curve were used for its performance test. The score was compared to existing scores.RESULTS:The final score included eight predictors. The AUC for the model was 0.77 (95% CI, 0.76-0.77) and 0.77 (95% CI, 0.76-0.78) in the training and test set, respectively. The calibration curves showed a good fit. The risk score was finally grouped into low-risk (score of 0-13 points), medium-risk (14-19 points), and high-risk (more than 19 points). The score had better predictive power compared to the other two existing risk scores.DISCUSSION:We developed an effective risk stratification score with eight variables to predict perioperative RBC transfusion for on-pump cardiac surgery. It assists perfusionists in proactively preparing blood conservation measures for high-risk patients before surgery.
Transfusion 2023
Background:Selecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.Methods:Participants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.Results:We identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).Conclusions:Key features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk.
Frontiers in cardiovascular medicine 2023
INTRODUCTION:Perioperative coagulopathy is common in patients undergoing aortic surgery, increasing the risk of excessive blood loss and subsequent allogeneic transfusion. Blood conservation has become a vital part of cardiovascular surgery, but measures to protect platelets from destruction by cardiopulmonary bypass (CPB) are still lacking. Autologous platelet concentrate (APC) may have potential benefits for intraoperative blood preservation, but its efficacy has not been studied extensively. This study aims to evaluate the efficacy of APC as a blood conservation technique to reduce blood transfusion in adult aortic surgery.METHODS AND ANALYSIS:This is a prospective, single-centre, single-blind randomised controlled trial. A total of 344 adult patients undergoing aortic surgery with CPB will be enrolled and randomised to either the APC group or the control group with a 1:1 randomisation ratio. Patients in the APC group will receive autologous plateletpheresis before heparinisation, while those in the control group will not. The primary outcome is the perioperative packed red blood cell (pRBC) transfusion rate. Secondary endpoints include the volume of perioperative pRBC transfusion; drainage volume within 72 hours post-surgery; postoperative coagulation and platelet function; and the incidence of adverse events. Data will be analysed according to the intention-to-treat principle.ETHICS AND DISSEMINATION:This study was approved by the institutional review board of Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (no. 2022-1806). All procedures included in this study will be performed in adherence to the Helsinki Declaration. The results of the trial will be published in an international peer-reviewed journal.TRIAL REGISTRATION NUMBER:Chinese Clinical Trial Register (ChiCTR2200065834).
BMJ open 2023
Background:Previous studies have found atrial fibrillation (AF) is associated with valvular heart disease (VHD). However, whether there is a causal relationship between these two diseases or it is just a result of bias caused by confounding factors is uncertain. This study aims to examine the potential causal association between AF and VHD by using Mendelian randomization.Methods:In order to examine the causal relationship between AF and VHD, we performed a two-sample Mendelian randomization study by collecting exposure and outcome data from genome-wide association study (GWAS) datasets. We utilized data from FinnGen project (FinnGen, 11,258 cases for VHD including rheumatic fever, 3,108 cases for non-rheumatic VHD, and 75,137 cases for participants) and European Bio-informatics Institute database (EBI, 55,114 cases for AF and 482,295 cases for participants). Inverse-variance weighted (IVW), MR-Egger, and weighted median approaches were performed to estimate the causal effect.Results:The Mendelian randomization analysis indicated that AF increased the risk of VHD by all three MR methods [For VHD including rheumatic fever: IVW, odds ratio (OR) = 1.255; 95% confidence interval (CI), 1.191~1.322; p = 1.23 × 10-17; Weighted median, OR = 1.305, 95% CI, 1.216~1.400, p = 1.57 × 10-13; MR-Egger, OR = 1.250, 95% CI, 1.137~1.375, p = 1.69 × 10-5; For non-rheumatic VHD: IVW, OR = 1.267; 95% CI, 1.169~1.372; p = 6.73 × 10-9; Weighted median, OR = 1.400; 95% CI, 1.232~1.591; p = 2.40 × 10-7; MR-Egger, OR = 1.308; 95% CI, 1.131~1.513; p = 5.34 × 10-4]. After the one outlier SNP was removed by heterogeneity test, the results remained the same. No horizontal pleiotropic effects were observed between AF and VHD.Conclusions:Our study provides strong evidence of a causal relationship between AF and VHD. Early intervention for AF patients may reduce the risk of developing into VHD.
Frontiers in cardiovascular medicine 2022
[This corrects the article DOI: 10.3389/fcvm.2021.771246.].
Frontiers in cardiovascular medicine 2022
[This corrects the article DOI: 10.3389/fcvm.2021.771246.].
Frontiers in cardiovascular medicine 2022
Background: This study intended to use a machine learning model to identify critical preoperative and intraoperative variables and predict the risk of several severe complications (myocardial infarction, stroke, renal failure, and hospital mortality) after cardiac valvular surgery. Study Design and Methods: A total of 1,488 patients undergoing cardiac valvular surgery in eight large tertiary hospitals in China were examined. Fifty-four perioperative variables, such as essential demographic characteristics, concomitant disease, preoperative laboratory indicators, operation type, and intraoperative information, were collected. Machine learning models were developed and validated by 10-fold cross-validation. In each fold, Recursive Feature Elimination was used to select key variables. Ten machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC), accuracy (ACC), Youden index, sensitivity, specificity, F1-score, positive predictive value (PPV), and negative predictive value (NPV) were used to compare the prediction performance of different models. The SHapley Additive ex Planations package was applied to interpret the best machine learning model. Finally, a model was trained on the whole dataset with the merged key variables, and a web tool was created for clinicians to use. Results: In this study, 14 vital variables, namely, intraoperative total input, intraoperative blood loss, intraoperative colloid bolus, Classification of New York Heart Association (NYHA) heart function, preoperative hemoglobin (Hb), preoperative platelet (PLT), age, preoperative fibrinogen (FIB), intraoperative minimum red blood cell volume (Hct), body mass index (BMI), creatinine, preoperative Hct, intraoperative minimum Hb, and intraoperative autologous blood, were finally selected. The eXtreme Gradient Boosting algorithms (XGBOOST) algorithm model presented a significantly better predictive performance (AUROC: 0.90) than the other models (ACC: 81%, Youden index: 70%, sensitivity: 89%, specificity: 81%, F1-score:0.26, PPV: 15%, and NPV: 99%). Conclusion: A model for predicting several severe complications after cardiac valvular surgery was successfully developed using a machine learning algorithm based on 14 perioperative variables, which could guide clinical physicians to take appropriate preventive measures and diminish the complications for patients at high risk.
Frontiers in cardiovascular medicine 2021
BACKGROUND AND OBJECTIVES:Haemovigilance involves surveillance of the whole chain of blood transfusion with the aim of identifying adverse events and errors and improving outcomes for patients. The Chinese Haemovigilance Network, founded in August 2017, has witnessed a rapid development in the last three years.MATERIALS AND METHODS:Based on the 1,022 cases in 2019, we analysed the adverse reactions (ARs) by blood component, clinical outcome severity and demography of recipients in an effort to publish the first annual Chinese haemovigilance report.RESULTS:The AR rate associated with blood transfusion in 2019 was 0·2% in China. Allergic reactions and FNHTR were the two most common adverse symptoms, accounting for 97·7% of the reports. Two-thirds of the TAD, AHTR and TACO and all of the HTR and DHTR resulted in hospitalization or prolongation of hospitalization. Plasma and AP were usually associated with allergic reaction (81·1%), whereas red cells more commonly cause FNHTR (68·8%) and all the AHTR, HTR, DSTR and DHTR. 84·1% of patients were aged 16 years or over, and the majority of the TAD, AHTR, TACO and HTR involved patients aged 60 and above. The ratio of serious adverse reactions (SARs) was 8·2%. Allergic reaction and FNHTR were top two (85·7%) SARs. The first case related to anti-D immunoglobulin was detected in a DHTR report.CONCLUSION:This report provides the world's first overview of transfusion-related adverse reactions in China. This report is useful for better understanding transfusion risks in China.
Vox sanguinis 2021
OBJECTIVES:To investigate the association of adenosine diphosphate (ADP)-induced platelet maximum amplitude (MAADP) with postoperative bleeding and blood product transfusions in patients undergoing coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB).DESIGN:This single-center observational study recruited 200 patients who underwent elective, first-time, isolated CABG with CPB. A rapid thromboelastography with platelet mapping test was conducted for all patients before the surgery. Patients were categorized by the preoperative MAADP into ≤50 mm (MAADP ≤50 group [n = 87]) and MAADP >50 mm (MAADP >50 group [n = 113]). The primary outcome was postoperative bleeding at 6 and 24 hours as measured by chest tube drainage volume. The perioperative blood product transfusions, postoperative complications, postoperative time course, and in-hospital mortality also were evaluated.SETTING:University hospital.PARTICIPANTS:Adult patients scheduled to undergo isolated primary CABG with CPB.INTERVENTIONS:None.MEASUREMENTS AND MAIN RESULTS:The study included 200 patients who underwent CABG with CPB. MAADP was >50 mm in 113 (56.5%) patients (MAADP >50 group). Compared with the MAADP >50 group, the postoperative chest tube drainage volume at 6 and 24 hours was significantly greater in the patients with MAADP ≤50 mm (476.90 ± 156.36 mL v 403.36 ± 133.24 mL; p < 0.001 and 935.86 ± 318.43 mL v 667.21 ± 222.75 mL; p < 0.001, respectively). The consumption of blood products in patients with MAADP ≤50 mm was significantly more than those with MAADP >50 mm. The durations of intensive care unit stay and length of postoperative hospital stay were markedly longer in the MAADP ≤50 group than in the MAADP >50 group (p = 0.001 and p = 0.005; respectively). There were no significant differences in adverse outcomes between the 2 groups except for the postoperative atrial fibrillation, which occurred more in the MAADP ≤50 group than in the MAADP >50 group (8.05% v 1.77%; p = 0.043). MAADP (area under the receiver operating characteristic curve of 0.767; p < 0.001) was demonstrated to have significant ability to predict bleeding tendency, with a sensitivity of 76.2% and a specificity of 69.0%.CONCLUSIONS:Preoperative MAADP may play a potential role in the prediction of postoperative bleeding and allogeneic blood transfusions and guide clinicians in perioperative management of patients undergoing CABG with CPB.
Journal of cardiothoracic and vascular anesthesia 2021
