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.
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.
Objective To investigate the value of procalcitonin (PCT) at admission for severity stratificaton and prognosis prediction of community-acquired pneumonia (CAP), and assess the ability of the combination of PCT and the validated pneumonia risk scores (PSI and CURB-65) for predicting 30-day mortality. Methods A retrospective study was performed in 150 hospitalized CAP patients admitted in the Department of Respiratory Medicine of General Hospital of Tianjin Medical University between March 2015 and March 2016. The primary end point for this study was mortality within 30 days. Sensitivity (SEN), specificity (SPE), positive and negative predictive value (PPV, NPV) of PCT for assessing mortality was calculated and compared to validated pneumonia risk scores. Results In the 150 CAP patients enrolled, there were 77 males and 73 females with an average age of 58.4±16.3 years. Twelve (8%) patients died within 30 days. The non-survivors had significantly higher median PCT level (4.25 ng/mlvs. 0.24 ng/ml) and C-reactive protein (CRP) level (14.60 mg/dlvs. 5.10 mg/dl) compared with the survivors. The median PCT level was significantly higher in the patients with more severe disease assessed by two risk scoring systems. Combination of PCT with risk scores can improve prognostic value for predicting 30-day mortality of CAP. Conclusions The level of PCT at admission is more useful than the traditional biomarkers for the severity stratification and prognosis prediction of CAP. It can well determine patients at low risk of mortality from CAP. There is no advantage of PCT compared to PSI or CURB-65, so we recommend combination of PCT to risk sores to predict 30-day mortality of CAP.
ObjectiveTo systematically review the research status of risk prediction models for gestational diabetes mellitus (GDM). MethodsThe CNKI, WanFang Data, VIP, CBM, PubMed, JBI EBP, Ovid MEDLINE, Embase, Web of Science and Cochrane Library databases were electronically searched to collect relevant literature on risk prediction models for GDM from inception to October 2022. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies, and then qualitative description was performed. ResultsA total of 19 studies were included, involving 19 risk prediction models. The evaluation results showed that, in terms of the risk of bias, 18 studies were high risk, and 1 study was unclear. In terms of applicability, 14 studies were high risk, 2 studies were low risk, and 3 studies were unclear. The area under the receiver operating characteristic curve of the included models was 0.69 to 0.88. The most common predictors included age, weight, pre-pregnancy BMI, history of diabetes, family history of diabetes, and race. ConclusionThe overall performance of the risk prediction model for gestational diabetes mellitus is good, but the risk of bias of the model is high, and the clinical applicability of the model needs to be further verified.