Abstract: Objectives To evaluate the accuracy of four existing risk stratification models including the Society of Thoracic Surgeons(STS) 2008 Cardiac Surgery Risk Models for Coronary Artery Bypass Grafting (CABG), the European System for Cardiac Operative Risk Evaluation (EuroSCORE), the American College of Cardiology/American Heart Association (ACC/AHA) model, and the initial Parsonnet’s score in predicting early deaths of Chinese patients after CABG procedure. Methods We collected clinical records of 1 559 consecutive patients who had undergone isolated CABG in the Fu WaiHospital from November 2006 to December 2007. There were 264 females (16.93%) and 1 295 males (83.06%) with an average age of 60.87±9.06 years. Early death was defined as death inhospital or within 30 days after CABG. Calibration was assessed by the Hosmer-Lemeshow (H-L) test, and discrimination was assessed by the receiveroperatingcharacteristic (ROC) curve. The endpoint was early death. Results Sixteen patients(1.03%) died early after the operation. STS and ACC/AHA models had a good calibration in predicting the number of early deaths for the whole group(STS: 12.06 deaths, 95% confidence interval(CI) 5.28 to 18.85; ACC/[CM(159mm]AHA:20.67deaths, 95%CI 11.82 to 29.52 ), While EuroSCORE and Parsonnet models overestimated the number of early deaths for the whole group(EuroSCORE：36.44 deaths，95%CI 24.75 to 48.14；Parsonnet：43.87 deaths，95%CI 31.07 to 56.67). For the divided groups, STS model had a good calibration of prediction(χ2=11.46, Pgt;0.1)，while the other 3 models showed poor calibration（EuroSCORE：χ2=22.07，Plt;0.005；ACC/AHA：χ2=28.85，Plt;0.005；Parsonnet：χ2=26.74，Plt;0.005）.All the four models showed poor discrimination with area under the ROC curve lower than 0.8. Conclusion The STS model may be a potential appropriate choice for Chinese patients undergoing isolated CABG procedure.
Abstract： Objective To establish a risk prediction model and risk score for inhospital mortality in heart valve surgery patients, in order to promote its perioperative safety. Methods We collected records of 4 032 consecutive patients who underwent aortic valve replacement, mitral valve repair, mitral valve replacement, or aortic and mitral combination procedure in Changhai hospital from January 1,1998 to December 31,2008. Their average age was 45.90±13.60 years and included 1 876 (46.53%) males and 2 156 (53.57%) females. Based on the valve operated on, we divided the patients into three groups including mitral valve surgery group (n=1 910), aortic valve surgery group (n=724), and mitral plus aortic valve surgery group (n=1 398). The population was divided a 60% development sample (n=2 418) and a 40% validation sample (n=1 614). We identified potential risk factors, conducted univariate analysis and multifactor logistic regression to determine the independent risk factors and set up a risk model. The calibration and discrimination of the model were assessed by the HosmerLemeshow (H-L) test and [CM(159mm]the area under the receiver operating characteristic (ROC) curve,respectively. We finally produced a risk score according to the coefficient β and rank of variables in the logistic regression model. Results The general inhospital mortality of the whole group was 4.74% (191/4 032). The results of multifactor logistic regression analysis showed that eight variables including tricuspid valve incompetence with OR=1.33 and 95%CI 1.071 to 1.648, arotic valve stenosis with OR=1.34 and 95%CI 1.082 to 1.659, chronic lung disease with OR=2.11 and 95%CI 1.292 to 3.455, left ventricular ejection fraction with OR=1.55 and 95%CI 1.081 to 2.234, critical preoperative status with OR=2.69 and 95%CI 1.499 to 4.821, NYHA ⅢⅣ (New York Heart Association) with OR=2.75 and 95%CI 1.343 to 5641, concomitant coronary artery bypass graft surgery (CABG) with OR=3.02 and 95%CI 1.405 to 6.483, and serum creatinine just before surgery with OR=4.16 and 95%CI 1.979 to 8.766 were independently correlated with inhospital mortality. Our risk model showed good calibration and discriminative power for all the groups. P values of H-L test were all higher than 0.05 (development sample: χ2=1.615, P=0.830, validation sample: χ2=2.218, P=0.200, mitral valve surgery sample: χ2=5.175,P=0.470, aortic valve surgery sample: χ2=12.708, P=0.090, mitral plus aortic valve surgery sample: χ2=3.875, P=0.380), and the areas under the ROC curve were all larger than 0.70 (development sample: 0.757 with 95%CI 0.712 to 0.802, validation sample: 0.754 and 95%CI 0.701 to 0806; mitral valve surgery sample: 0.760 and 95%CI 0.706 to 0.813, aortic valve surgery sample: 0.803 and 95%CI 0.738 to 0.868, mitral plus aortic valve surgery sample: 0.727 and 95%CI 0.668 to 0.785). The risk score was successfully established: tricuspid valve regurgitation (mild:1 point, moderate: 2 points, severe:3 points), arotic valve stenosis (mild: 1 point, moderate: 2 points, severe: 3 points), chronic lung disease (3 points), left ventricular ejection fraction (40% to 50%: 2 points, 30% to 40%: 4 points, ＜30%: 6 points), critical preoperative status (3 points), NYHA IIIIV (4 points), concomitant CABG (4 points), and serum creatinine (＞110 μmol/L: 5 points).Conclusion Eight risk factors including tricuspid valve regurgitation are independent risk factors associated with inhospital mortality of heart valve surgery patients in China. The established risk model and risk score have good calibration and discrimination in predicting inhospital mortality of heart valve surgery patients.
Heart valve disease is one of the three most common cardiac diseases,and the patients undergoing valve surgery have been increasing every year. Due to the high mortality,increasing number of valve surgeries,and increasing economic burdens on public health, a lot of risk models for valve surgery have been developed by various countries based on their own clinical data all over the world,which aimed to regulate the preoperative risk assessment and decrease the perioperative mortality. Over the last 10 years, a number of excellent risk models for valve surgery have finally been developed including the Society of Thoracic Surgeons(STS), the Society of Thoracic Surgeons’ National Cardiac Database (STS NCD),New York Cardiac Surgery Reporting System(NYCSRS),the European System for Cardiac Operative Risk Evaluation(EuroSCORE),the Northern New England Cardiovascular Disease Study Group(NNECDSG),the Veterans Affairs Continuous Improvement in Cardiac Surgery Study(VACICSP),Database of the Society of Cardiothoracic Surgeons of Great Britain and Ireland(SCTS), and the North West Quality Improvement Programme in Cardiac Interventions(NWQIP). In this article, we reviewed these risk models which had been developed based on the multicenter database from 1999 to 2009, and summarized these risk models in terms of the year of publication, database, valve categories, and significant risk predictors.
Objective To explore the risk factors of female’s breast cancer in secondary cities of the west and establish a risk prediction model to identify high-risk groups， and provide the basis for the primary and secondary preve-ntion of breast cancer. Methods Random sampling （method of random digits table） 1 700 women in secondary cities of the west （including 1 020 outpatient cases and 680 physical examination cases） were routinely accept the questionnaire survey. Sixty-two patients were confirmed breast cancer with pathologically. Based on the X-image of the mammary gland patients and questionnaire survey to put mammographic density which classificated into high- and low-density groups. The relationships between the mammographic density， age， body mass index （BMI）， family history of breast cancer， socio-economic status （SES）， lifestyle， reproductive fertility situation， and breast cancer were analyzed， then a risk prediction model of breast cancer which fitting related risk factors was established. Results Univariate analysis showed that risk factors for breast cancer were age （P=0.006）， BMI （P=0.007）， age at menarche （P=0.039）， occupation （P=0.001）， domicile place （P=0.000）， educational level （P=0.001）， health status compared to the previous year （P=0.046）， age at first birth （P=0.014）， whether menopause （P=0.003）， and age at menopause （P=0.006）. The unconditional logistic regr-ession analysis showed that the significant risk factors were age （P=0.003）， age at first birth （P=0.000）， occupation （P=0.010）， and domicile place （P=0.000）， and the protective factor was age at menarche （P=0.000）. The initially established risk prediction model in the region which fitting related risk factors was y=-5.557＋0.042x1-0.375x2＋1.206x3＋0.509x4＋2.135x5. The fitting coefficient （R square）=0.170， it could reflect 17% of the actual situation. Conclusions The breast cancer risk prediction model which established by using related risk factors analysis and epidemiological investigation could guide the future clinical work，but there is still need the validation studies of large populations for the model.
Surgical risk prediction is to predict postoperative morbidity and mortality with internationally authoritative mathematical models. For patients undergoing high-risk cardiac surgery, surgical risk prediction is helpful for decision-making on treatment strategies and minimization of postoperative complications, which has gradually arouse interest of cardiac surgeons. There are many risk prediction models for cardiac surgery in the world, including European System for Cardiac Operative Risk Evaluation (EuroSCORE), Ontario Province Risk (OPR)score, Society of Thoracic Surgeons (STS)score, Cleveland Clinic risk score, Quality Measurement and Management Initiative (QMMI), American College of Cardiology/American Heart Association (ACC/AHA)Guidelines for Coronary Artery Bypass Graft Surgery, and Sino System for Coronary Operative Risk Evaluation (SinoSCORE). All these models are established from the database of thousands or ten thousands patients undergoing cardiac surgery in a specific region. As different sources of data and calculation imparities exist, there are probably bias and heterogeneities when the models are applied in other regions. How to decrease deviation and improve predicting effects had become the main research target in the future. This review focuses on the progress of risk prediction models for patients undergoing cardiac surgery.
ObjectiveTo assess the accuracy of European System for Cardiac Operative Risk Evaluation (EuroSCORE) model in predicting the in-hospital mortality of Uyghur patients and Han nationality patients undergoing heart valve surgery. MethodsClinical data of 361 consecutive patients who underwent heart valve surgery at our center from September 2012 to December 2013 were collected, including 209 Uyghur patients and 152 Han nationality patients. According to the score for additive and logistic EuroSCORE models, the patients were divided into 3 subgroups including a low risk subgroup, a moderate risk subgroup, and a high risk subgroup. The actual and predicted mortality of each risk subgroup were studied and compared. Calibration of the EuroSCORE model was assessed by the test of goodness of fit, discrimination was tested by calculating the area under the receiver operating characteristic (ROC) curve. ResultsThe actual mortality was 8.03% for overall patients, 6.70% for Uyghur patients,and 9.87% for Han nationality patients. The predicted mortality by additive EuroSCORE and logistic EuroSCORE for Uyghur patients were 4.03% and 3.37%,for Han nationality patients were 4.43% and 3.77%, significantly lower than actual mortality (P<0.01). The area under the ROC curve of additive EuroSCORE and logistic EuroSCORE for overall patients were 0.606 and 0.598, for Han nationality patients were 0.574 and 0.553,and for Uyghur patients were 0.609 and 0.610. ConclusionThe additive and logistic EuroSCORE are unable to predict the in-hospital mortality accurately for Uyghur and Han nationality patients undergoing heart valve surgery. Clinical use of these model should be considered cautiously.
ObjectiveThe Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) model and its Portsmouth (P-POSSUM) modification are used extensively to predict post-operative mortality and morbidity in general surgery. The aim was to analysis the predictive value of these models in patients undergoing hepatobiliary surgery. MethodsEligible articles were identified by searching such electronic databases as PubMed, The Cochrane Library (Issue 10, 2013), Science Citation Index, CNKI, WanFang Data and CBM from 1991 to October 2013. Each study was assessed according to the inclusion and exclusion criteria. Then data were extracted, pooled, and analyzed using Comprehensive Meta Analysis Version 2. ResultsTen studies were included. The morbidity analysis included five studies and 683 patients on POSSUM with a weighted O/E ratio 0.71 (95%CI 0.60 to 0.81). The mortality analysis included seven studies with 1 291 patients on POSSUM and six studies with 1 793 patients on P-POSSUM. Weighted O/E ratios for mortality were 0.42 (95%CI 0.27 to 0.57) for POSSUM and 0.74 (95%CI 0.53 to 0.95) for P-POSSUM. ConclusionPOSSUM significantly overestimates postoperative morbidity in patients undergoing hepatobiliary surgery. Compared with the original POSSUM, P-POSSUM is more accurate for predicting post-operative mortality. Modifications to POSSUM and P-POSSUM are needed for audit in hepatobiliary surgery.
Objective To summarize risk factors of pancreatic fistula after pancreaticoduodenectomy and to investigate clinical application of pancreatic fistula risk prediction system. Method The literatures of the risk factors and risk prediction of pancreatic fistula after the pancreaticoduodenectomy were collected to make a review. Results There were many risk factors for pancreatic fistula after pancreatoduodenectomy, including the patient’s own factors (gender, age, underlying diseases, etc.), disease related factors (pancreatic texture, diameter of pancreatic duct, pathological type, etc.), and surgical related factors (operation time, intraoperative blood loss, anastomosis, pancreatic duct drainage, etc.). The fistula risk prediction system after the pancreatoduodenectomy had a better forecast accuracy. Conclusions Occurrence of pancreatic fistula after pancreaticoduodenectomy is related to softness of pancreas and small diameter of pancreatic duct. Pancreatic fistula risk prediction system is helpful for prevention of pancreatic fistula after pancreaticoduodenectomy.
Acute kidney injury (AKI) is a complication with high morbidity and mortality after cardiac surgery. In order to predict the incidence of AKI after cardiac surgery, many risk prediction models have been established worldwide. We made a detailed introduction to the composing features, clinical application and predictive capability of 14 commonly used models. Among the 14 risk prediction models, age, congestive heart failure, hypertension, left ventricular ejection fraction, diabetes, cardiac valve surgery, coronary artery bypass grafting (CABG) combined with cardiac valve surgery, emergency surgery, preoperative creatinine, preoperative estimated glomerular filtration rate (eGFR), preoperative New York Heart Association (NYHA) score>Ⅱ, previous cardiac surgery, cadiopulmonary bypass (CPB) time and low cardiac output syndrome (LCOS) are included in many risks prediction models (>3 times). In comparison to Mehta and SRI models, Cleveland risk prediction model shows the best discrimination for the prediction of renal replacement therapy (RRT)-AKI and AKI in the European. However, in Chinese population, the predictive ability of the above three risk prediction models for RRT-AKI and AKI is poor.