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.
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.
Breast cancer is the most common malignant tumor among Chinese females. We should focus on the research of risk assessment models of gene-environmental factors to guide primary and secondary prevention, and this public health strategy is expected to maximize the health benefits of the population. This paper introduces previous studies of risk factors and predictive models for Chinese breast cancer and provides three points for future research. Firstly, we should explore the specific risk factors related to breast cancer risk in Chinese population, such as overweight or reproductive control measures. Secondly, we should use evidence-based and machine learning methods to select environmental-genetic risk factors. Finally, we should set up an information collective platform for breast cancer risk factors to test the validity of prediction models based on a long-term follow-up cohort of Chinese females.
ObjectiveTo analyze the influencing factors of acute exacerbation readmission in elderly patients with chronic obstructive pulmonary disease (COPD) within 30 days, construct and validate the risk prediction model.MethodsA total of 1120 elderly patients with COPD in the respiratory department of 13 general hospitals in Ningxia from April 2019 to August 2020 were selected by convenience sampling method and followed up until 30 days after discharge. According to the time of filling in the questionnaire, 784 patients who entered the study first served as the modeling group, and 336 patients who entered the study later served as the validation group to verify the prediction effect of the model.ResultsEducation level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors were the influencing factors of patients’ readmission to hospital. The risk prediction model was constructed: Z=–8.225–0.310×assignment of education level+0.564×assignment of smoking status+0.873×assignment of number of acute exacerbations of COPD hospitalizations in the past 1 year+0.779×assignment of regular use of medication+0.617×assignment of rehabilitation and exercise +0.970×assignment of nutritional status+assignment of seasonal factors [1.170×spring (0, 1)+0.793×autumn (0, 1)+1.488×winter (0, 1)]. The area under ROC curve was 0.746, the sensitivity was 75.90%, and the specificity was 64.30%. Hosmer-Lemeshow test showed that P=0.278. Results of model validation showed that the sensitivity, the specificity and the accuracy were 69.44%, 85.71% and 81.56%, respectively.ConclusionsEducation level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors are the influencing factors of patients’ readmission to hospital. The risk prediction model is constructed based on these factor. This model has good prediction effect, can provide reference for the medical staff to take preventive treatment and nursing measures for high-risk patients.
ObjectiveTo systematically review the prediction models of blood-based biomarkers for non-small cell lung cancer (NSCLC). MethodsThe PubMed, Embase, Cochrane Library, Web of Science, VIP, WanFang Data and CNKI databases were electronically searched to collect studies related to the objectives from inception to June, 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4.1 software. ResultsA total of 8 studies were included and all of them were retrospective cohort studies. The models were internally validated in 2 studies and externally validated in 4 studies. The performances of the eight predictive models were stable, which was measured by the area under the curve of receiver operating characteristic curve lying between 0.664 and 0.783. However, the risk of bias was high, which may mainly be reflected in data processing, model validation and performance adjustment. Meta-analysis showed that LDH (HR=1.86, 95%CI 41.32 to 2.63, P<0.01), dNLR (HR=2.15, 95%CI 1.56 to 2.96, P<0.01) and NLR (HR=1.71, 95%CI 1.08 to 2.69, P=0.02) were independent factors of prognosis for NSCLC patients. Conclusion Current evidence shows that the NSCLC prediction models based on peripheral blood biomarkers are still in the development stage, and the models have a high risk of bias.