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.
Objective To investigate the nutritional status of hospitalized patients with chronic kidney disease (CKD), analyze the influencing factors, and construct a predictive model to provide a localized theoretical basis and more convenient risk prediction indicators and models for clinical nutrition support and intervention treatment of CKD patients in China. Methods Convenience sampling was used to select hospitalized CKD patients from Department of Nephrology, West China Hospital, Sichuan University, from January to October 2019. General information questionnaires, the Nutritional Risk Screening 2002 scale, and the Huaxi Emotional-distress Index questionnaire were used for data collection. Single factor analyses and multiple logistic regression analysis were conducted to explore the risk factors for malnutrition in CKD hospitalized patients. A predictive model was established and evaluated using receiver operating characteristic (ROC) curve analysis and bootstrap resampling. Results A total of 1059 valid copies of questionnaires were collected out of 1118 distributed. Among the 1059 CKD hospitalized patients, 207 cases (19.5%) were identified as having nutritional risk. The multiple logistic regression analysis showed that CKD stage [odds ratio (OR)=1.874, 95% confidence interval (CI) (1.631, 2.152), P<0.001], age [OR=1.015, 95%CI (1.003, 1.028), P=0.018], and the Huaxi Emotional-distress Index [OR=1.024, 95%CI (1.002, 1.048), P=0.033] were independent risk factors for malnutrition in CKD hospitalized patients, while serum albumin [OR=0.880, 95%CI (0.854, 0.907), P<0.001] was an independent protective factor. The evaluation of the multiple logistic regression analysis predictive model showed a concordance index of 0.977, standard deviation of 0.021, and P<0.05. The area under the ROC curve was 0.977. Conclusions The prevalence of malnutrition is relatively high among CKD hospitalized patients. CKD stage, age, psychological status, and serum albumin are influencing factors for malnutrition in CKD hospitalized patients. The multiple logistic regression model based on the above indicators demonstrates good predictive performance and is expected to provide assistance for early nutritional intervention to improve the clinical outcomes and quality of life for CKD patients with malnutrition in China.
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.
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 analyze the risk factors for esophagogastric anastomotic leakage (EGAL) after esophageal cancer surgery, and to establish a risk prediction model for early prevention and treatment.MethodsClinical data of patients undergoing esophagectomy in our hospital from January 2013 to October 2020 were retrospectively analyzed. The independent risk factors for postoperative EGAL were analyzed by univariate and multivariate logistic regression analyses, and a clinical nomogram prediction model was established. According to whether EGAL occurred after operation, the patients were divided into an anastomotic fistula group and a non-anastomotic fistula group.ResultsA total of 303 patiens were enrolled, including 267 males and 36 females with a mean age of 62.30±7.36 years. The incidence rate of postoperative EGAL was 15.2% (46/303). The multivariate logistic regression analysis showed that high blood pressure, chronic bronchitis, peptic ulcer, operation way, the number of lymph node dissected, anastomotic way, the number of intraoperative chest drainage tube, tumor location, no-supplementing albumin in the first three days after operation, postoperative pulmonary infection, postoperative use of bronchoscope were the independent risk factors for EGAL after esophageal cancer surgery (P<0.05). A prognostic nomogram model was established based on these factors with the area under the receiver operating characteristic curve of 0.954 (95%CI 0.924-0.975), indicating a high predictive value.ConclusionThe clinical prediction model based on 11 perioperative risk factors in the study has a good evaluation efficacy and can promote the early detection, diagnosis and treatment of EGAL.
Risk prediction models for postoperative pulmonary complications (PPCs) can assist healthcare professionals in assessing the likelihood of PPCs occurring after surgery, thereby supporting rapid decision-making. This study evaluated the merits, limitations, and challenges of these models, focusing on model types, construction methods, performance, and clinical applications. The findings indicate that current risk prediction models for PPCs following lung cancer surgery demonstrate a certain level of predictive effectiveness. However, there are notable deficiencies in study design, clinical implementation, and reporting transparency. Future research should prioritize large-scale, prospective, multi-center studies that utilize multiomics approaches to ensure robust data for accurate predictions, ultimately facilitating clinical translation, adoption, and promotion.
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.
Objective To externally validate a prediction model based on clinical and CT imaging features for the preoperative identification of high-grade patterns (HGP), such as micropapillary and solid subtypes, in early-stage lung adenocarcinoma, in order to guide clinical treatment decisions. Methods This study conducted an external validation of a previously developed prediction model using a cohort of patients with clinical stage ⅠA lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model, which incorporated factors including tumor size, density, and lobulation, was assessed for its discrimination, calibration performance, and clinical impact. Results A total of 650 patients (293 males, 357 females; age range: 30-82 years) were included. The validation showed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). After recalibration, the model's calibration performance was improved. Decision curve analysis (DCA) indicated that at a threshold probability>0.6, the number of HGP patients predicted by the model closely approximated the actual number of cases. Conclusion This study confirms the effectiveness of a clinical and imaging feature-based prediction model for identifying HGP in stage ⅠA lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patients' prognosis. Despite certain limitations, these findings provide new directions for future research.
Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
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.