Objective To evaluate the diagnostic accuracy of Wilson score for predicating difficult intubation. Methods Such databases as PubMed, EMbase, CNKI, WanFang Data and VIP were searched to collect the studies about Wilson score for predicating difficult intubation published from inception to January 2013. Two reviewers independently screened the studies, extracted the data, and assessed the methodological quality by QUADAS. The analysis was conducted by using Meta-Disc 1.4 software, and the random effect model was chosen to calculate the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the 95%CI. The summary receiver operating characteristic (SROC) curve was drawn and the area under the curve (AUC) was calculated in order to comprehensively assess the total diagnostic accuracy of Wilson score for predicating difficult intubation. Results A total of 9 studies involving 6 506 subjects were included. The results of meta-analysis showed that: the pooled sensitivity was 0.57 (95%CI 0.53 to 0.62), specificity was 0.89 (95%CI 0.88 to 0.90), positive likelihood ratio was 6.11 (95%CI 4.63 to 8.07), negative likelihood ratio was 0.52 (95%CI 0.41 to 0.66), diagnostic odds ratio was 12.76 (95%CI 8.60 to 18.93), and the AUC of SROC was 0.84. Conclusion Wilson score plays a role in predicating difficult intubation, while some other clinical indicators also need to be taken into consideration in its application.
Objective To analyze the imaging features of solitary pulmonary nodules ( SPNs) , and compare the two types of lung cancer prediction models in distinguishing malignancy of SPNs.Methods A retrospective study was performed on the patients admitted to Ruijin Hospital between 2002 and 2009 with newly discovered SPNs. The patients all received pathological diagnosis. The clinical and imaging characteristics were analyzed. Then the diagnostic accuracy of two lung cancer prediction models for distinguishing malignancy of SPNs was evaluated and compared.Results A total of 90 patients were enrolled, of which 32 cases were with benign SPNs, 58 cases were with malignant SPNs. The SPNs could be identified between benign and maligant by the SPN edge features of lobulation ( P lt;0. 05) . The area under ROC curve of VA model was 0. 712 ( 95% CI 0. 606 to 0. 821) . The area under ROC curve of Mayo Clinic model was 0. 753 ( 95% CI 0. 652 to 0. 843) , which was superior to VA model. Conclusions It is meaningful for the identification of benign and maligant SPNs by the obulation sign in CT scan. We can integrate the clinical features and the lung cancer predicting models to guide clinical work.
Abstract： Objective To validate the value of Cleveland Clinical Score to predict acute renal failure（ARF） requiring renal replacement therapy （RRT） and in-hospital death in Chinese adult patients after cardiac surgery. Methods A retrospective analysis was conducted for all the patients who underwent cardiac surgery from January 2005 to December 2009 in Renji Hospital of School of Medicine, Shanghai Jiaotong University. A total of 2 153 adult patients, 1 267 males and 886 females，were included. Their age ranged from 18 to 99 years with an average age of 58.70 years. Cleveland Clinical Score was used to predict ARF after cardiac surgery. ARF was defined as the need for RRT. Based on Cleveland Clinical Score, the patients were divided into four risk categories of increasing severity：0 to 2 point（n=979）, 3 to 5 point （n=1 116）, 6 to 8 point（n=54）, 9 to 13 point（n=4）. The rates of ARF, multiple organ system failure （MOSF）, and mortality were compared among the 4 categories. The predictive accuracy of postoperative ARF and hospital mortality was assessed by area under the receiver operating characteristic curve （AUC-ROC）. Results In the four categories, the rate of postoperative ARF was 0.92%, 1.88%, 12.96%, and 25.00%, respectively； MOSF rate was 1.23%, 1.88%, 3.70%, and 25.00%, respectively; mortality was 0.92%, 4.21%, 25.93%, and 50.00%, respectively. There was significant dif ference among the four categories in ARF rate （χ2=55.635, P=0.000），MOSF rate（χ2=16.080, P=0.001）, and mortality （χ2=71.470, P=0.000）. The AUC-ROC for Cleveland Clinical Score predicting ARF rate and hospital mortality was 0.775 （95%CI 0.713 to 0.837, P=0.000）and 0.764（95%CI, 0.711 to 0.817, P=0.000）, respectively. Conclusion Cleveland Clinical Score can accurately predict postoperative ARF and hospital mortality in a large, unselected Chinese cohort of adult patients after cardiac surgery. It can be used to provide evidence for effective preventive measures for patients at high risk of postoperative ARF.
Abstract： Objective To evaluate the incidence and prognosis of postoperative acute kidney injury （AKI） in patients after cardiovascular surgery， and analyse the value of AKI criteria and classification using the Acute Kidney Injury Network （AKIN） definition to predict their in-hospital mortality. Methods A total of 1 056 adult patients undergoing cardiovascular surgery in Renji Hospital of School of Medicine， Shanghai Jiaotong University from Jan. 2004 to Jun. 2007 were included in this study. AKI criteria and classification under AKIN definition were used to evaluate the incidence and in-hospital mortality of AKI patients. Univariate and multivariate analyses were used to evaluate preoperative， intraoperative， and postoperative risk factors related to AKI. Results Among the 1 056 patients， 328 patients（31.06%） had AKI. In-hospital mortality of AKI patients was significantly higher than that of non-AKI patients （11.59% vs. 0.69%， P＜0.05）. Multivariate logistic regression analysis suggested that advanced age （OR=1.40 per decade）， preoperative hyperuricemia（OR=1.97）， preoperative left ventricular failure （OR=2.53）， combined CABG and valvular surgery （OR=2.79）， prolonged operation time （OR=1.43 per hour）， postoperative hypovolemia （OR=11.08） were independent risk factors of AKI after cardiovascular surgery. The area under the ROC curve of AKIN classification to predict in-hospital mortality was 0.865 （95% CI 0.801-0.929）. Conclusion Higher AKIN classification is related to higher in-hospital mortality after cardiovascular surgery. Advanced age， preoperative hyperuricemia， preoperative left ventricular failure， combined CABG and valvular surgery， prolonged operation time， postoperative hypovolemia are independent risk factors of AKI after cardiovascular surgery. AKIN classification can effectively predict in-hospital mortality in patients after cardiovascular surgery， which provides evidence to take effective preventive and interventive measures for high-risk patients as early as possible.
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.
Abstract: Objective To investigate the application value of the Clinical Score developed by Cleveland University in predicting the occurrence ratio of acute renal failure in Chinese patients after cardiac surgery. Methods A total of 456 adult patients , 230 males and 226 females , with cardiac surgery during August 2008 to July 2009 were included in our study. Their age ranged from 18 to 88 years with an average age of 56.7 years. Before the surgery, Clinical Score was used to predict acute renal failure after cardiac surgery. Based on the score of ≤5, 610, or ≥11, the patients were divided into group Ⅰ (n=401), group Ⅱ (n=42) and group Ⅲ (n=13). The occurrence rate of acute kidney injury (AKI), continuous renal replacement therapy in hospital, multiple organ failure, mortality and other clinical indexes were compared among the 3 groups. Results Occurrence ratio of AKI of group Ⅰ, Ⅱ, Ⅲ was respectively 2.74%, 28.57% and 76.92% (χ2=73.004, P=0.000). Continuous renal replacement therapy rate was respectively 0.50%, 9.52%, and 38.46% (χ2=36.939, P=0.000). Multiple organ failure rate was respectively 0.50%, 4.76%, and 23.08% (χ2=19.694, P=0.000). Mortality rate was respectively 0.25%, 2.38%, and 15.38% (χ2=14.061, P=0.001). There were significant differences among the three groups. Conclusion The Clinical Score to Predict Acute Renal Failure developed by Cleveland University can effectively predict the occurrence rate of acute renal failure in the Chinese patients after cardiac surgery before the operation. Therefore, corresponding preventive methods can be taken for highrisk patients.