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.
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.
Objective To identify risk factors that affect the verification of malignancy in patients with solitary pulmonary nodule (SPN) and verify different prediction models for malignant probability of SPN. Methods We retrospectively analyzed the clinical data of 117 SPN patients with definite postoperative pathological diagnosis who underwent surgical procedure in China-Japan Friendship Hospital from March to September 2017. There were 59 males and 58 females aged 59.10±11.31 years ranging from 24 to 83 years. Imaging features of the nodule including maximum diameter, location, spiculation, lobulation, calcification and serum level of CEA and Cyfra21-1 were assessed as potential risk factors. Univariate analysis was used to establish statistical correlation between risk factors and postoperative pathological diagnosis. Receiver operating characteristic (ROC) curve was drawn by different predictive models for the malignant probability of SPN to get areas under the curves (AUC), sensitivity, specificity, positive predictive values, negative predictive values for each model. The predictive effectiveness of each model was statistically assessed subsequently. Results Among 117 patients, 93 (79.5%) were malignant and 24 (20.5%) were benign. Statistical difference was found between the benign and malignant group in age, maximum diameter, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification of the nodules. The AUC value was 0.813±0.051 (Mayo model), 0.697±0.066 (VA model) and 0.854±0.045 (Peking University People's Hospital model), respectively. Conclusion Age, maximum diameter of the nodule, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification are potential independent risk factors associated with the malignant probability of SPN. Peking University People's Hospital model is of high accuracy and clinical value for patients with SPN. Adding serum index into the prediction model as a new risk factor and adjusting the weight of age in the model may improve the accuracy of prediction for SPN.
The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
In recent years, the potential value of clinical big data have been gradually realized, and disease prediction models have begun to become a hot spot in clinical research. Predictive models of different types of diseases play an increasingly important role in individual risk assessment. However, due to the lack of reporting specifications for studies on disease prediction model, the structure and quality of reports are mostly mixed. In 2015, BMJ published a paper entitled "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement" stated that there should be a uniform study of predictive models for disease diagnosis and prognosis. This article interprets key contents of the statement to promote research and understanding of the report specification.
ObjectiveTo investigate the predictive value of volatile organic compounds (VOCs) on pulmonary nodules in people aged less than 50 years.MethodsThe 147 patients with pulmonary nodules and aged less than 50 years who were treated in the Department of Thoracic Surgery of Sichuan Cancer Hospital from August 1, 2019 to January 15, 2020 were divided into a lung cancer group and a lung benign disease group. The lung cancer group included 36 males and 68 females, with the age of 27-49 (43.54±5.73) years. The benign lung disease group included 23 males and 20 females, with the age of 22-49 (42.49±6.83) years. Clinical data and exhaled breath samples were collected prospectively from the two groups. Exhaled breath VOCs were analyzed by gas chromatography mass spectrometry. Binary logistic regression analysis was used to select variables and establish a prediction model. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the prediction model were calculated.ResultsThere were statistically significant differences in sex (P=0.034), smoking history (P=0.047), cyclopentane (P=0.002), 3-methyl pentane (P=0.043) and ethylbenzene (P=0.009) between the two groups. The sensitivity, specificity and area under the ROC curve of the prediction model with gender, cyclopentane, 3-methyl pentane, ethylbenzene and N,N-dimethylformamide as variables were 80.8%, 60.5% and 0.781, respectively.ConclusionThe combination of VOCs and clinical characteristics has a certain predictive value for the benign and malignant pulmonary nodules in people aged less than 50 years.
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.
ObjectiveCombined with long non-coding RNA (lncRNA) to find a regression model that can be used to predict the survival rate of patients with colon cancer before operation.MethodsThe clinical information and gene expression information of patients with colon cancer were downloaded by using TCGA database. The differentially expressed lncRNAs in tumor and paracancerous tissues were screened out, and then combined with the clinical information of patients to construct Cox proportional hazard regression model.ResultsA total of 26 kinds of lncRNAs with statistical difference in gene expression between paracancerous tissues and tumor tissues were selected (P<0.05). Through repeated screening and comparison of prediction efficiency, the prediction model was finally selected, which was constructed by patients’ age, M stage, N stage, and three kinds of lncRNAs (ZFAS1, SNHG25, and SNHG7) gene expression level: age [HR=4.00, 95%CI: (1.48, 10.84), P=0.006], M stage [HR=3.96, 95%CI: (2.23, 7.04), P<0.001], N stage [HR=1.87, 95%CI: (1.24, 2.84), P=0.003], ZFAS1 gene expression level [HR=0.60, 95%CI: (0.41, 0.86), P=0.006], SNHG25 gene expression level [HR=0.85, 95%CI: (0.73, 1.00), P=0.045], and SNHG7 gene expression level [HR=2.32, 95%CI: (1.53, 3.52), P<0.001] were all independent risk factors for postoperative survival of patients with colon cancer. The area under the ROC curves for predicting 1, 3, and 5-year overall survival were 0.802, 0.828, and 0.771, respectiely, which had a good prediction ability.ConclusionThe predictive model constructed by the combination of ZFAS1, SNHG25, SNHG7 genes expression level with M stage, N stage, and age can better predict the overall survival rate of patients before operation, which can effectively guide clinical decision-making and choose the most suitable treatment method for patients.
ObjectiveTo investigate the risk factors for anastomotic leakage after McKeown esophagectomy, and to establish a risk prediction model for early clinical intervention.MethodsWe selected 469 patients including 379 males and 90 females, with a median age of 67 (42-91) years, who underwent McKeown esophagectomy in our department from 2018 to 2019. The clinical data of the patients were analyzed.ResultsAmong the 469 patients, 7.0% (33/469) patients had anastomotic leakage after McKeown esophagectomy. Logistic analysis showed that the risk factors for anastomotic leakage were operation time >4.5 h, postoperative low albumin and postoperative hypoxemia (P<0.05). A prognostic nomogram model was established based on these factors with the area under the receiver operator characteristic curve of 0.769 (95%CI 0.677-0.861), indicating a good predictive value.ConclusionOperation time >4.5 h, postoperative low albumin and postoperative hypoxemia are the independent risk factors for anastomotic leakage after McKeown esophagectomy. Through the nomogram prediction model, early detection and intervention can be achieved, and the hospital stay can be shortened.