ObjectiveBy combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.
ObjectiveTo construct a model for predicting prognosis risk in patients with pancreatic malignancy (PM).MethodsThe clinicopathological data of 8 763 patients with PM undergone resection between 2010 and 2015 were collected and analyzed by SEER*Stat (v8.3.5) and R software, respectively. The univariate and multivariate Cox proportional hazard regression analysis were used to analyze the factors for predicting prognosis outcome risk and constructed the nomograms of patients with PM, respectively. Kaplan-Meier method was used to evaluate the survival of patients according to relevant factors and the high risk group and low risk group of patients with PM. The discriminative ability and calibration of the nomograms to predict overall survival were tested by using C-index, area under ROC curve (AUC) and calibration plots.ResultsThe multivariate Cox proportional hazard regression analysis showed that age, T staging, N staging, M staging, histological type, the differentiation, number of regional lymph node dissection, chemotherapy, and radiotherapy were independent factors for predicting the prognosis of patients with PM (P<0.05). Based on regression analysis of patients with PM, a nomograms model for predicting the risk of patients with PM was established, including age, T staging, N staging, M staging, histological type, the differentiation, tumor location, type of surgery, number of regional lymph node dissection, chemotherapy, and radiotherapy. The discriminative ability and calibration of the nomograms revealed good predictive ability as indicated by the C-index (0.747 for modeling group and 0.734 for verification group). The 3- and 5-year survival AUC values of the modeling group were 0.766 and 0.781, and the validation group were 0.758 and 0.783, respectively. The calibration plots showed that predictive value of the 3- and 5-year survival were close to the actual values in both modeling group and the verification group. ConclusionsIndependent predictors of survival risk after curative-intent surgery for PM were selected to create nomograms for predicting overall survival. The nomograms provide a basis for judging the prognosis of PM patients.
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.
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.
ObjectiveTo explore the risk factors of lymph node metastasis (LNM) in patients with early gastric cancer (EGC), and try to establish a risk prediction model for LNM of EGC.MethodsThe clinicopathologic data of EGC patients who underwent radical gastrectomy and lymph node dissection from January 1, 2015 to December 31, 2019 in this hospital were retrospectively analyzed. Univariate analysis and logistic regression analysis were used to determine the risk factors for LNM of EGC, and the risk prediction model for LNM of EGC was established based on the multivariate results.ResultsA total of 311 cases of EGC were included in this study, and 60 (19.3%) cases had LNM. Univariate and multivariate analysis showed that age (younger), depth of tumor invasion (submucosa), vascular invasion, and undifferentiated carcinoma were the risk factors for LNM of EGC (P<0.05). The optimal threshold for predicting LNM of EGC was 0.158 (area under the receiver operating characteristic curve was 0.864), the sensitivity was 80.0%, and the specificity was 79.3%.ConclusionsFrom results of this study, risk factors for LNM of EGC have age, depth of invasion, vascular invasion, and differentiation degree. Risk prediction model for LNM of EGC established on this results has high sensitivity and specificity, which could provide some references for treatment strategy of EGC.
The risk prediction model (RPM) can be used to predict the risks of disease for individuals, playing an extremely important role in decision-making regarding disease prevention, treatment, and prognostic management. Most of the existing RPMs only utilize a single-time cross-section of variable data, so-called static models, which fail to consider the many changes during disease progression and lead to limited prediction accuracy. Dynamic prediction models can incorporate longitudinal data such as repeated measurements of variables during follow-up to capture the longitudinal changes in individual characteristics over time, describe the dynamic trajectory of individual disease risk and improve the prediction accuracy of the models; however, their application in medical research is still relatively small. In this paper, we conducted a systematic literature search to summarize the commonly used dynamic models: joint model, landmark model, and Bayesian dynamic model. By introducing their application scenarios, advantages and disadvantages, and software implementations and conducting comparisons, we aimed to provide methodological references for the future application of dynamic prediction models in medical research.
ObjectiveTo study the predictability of dynamic prediction model of clinical pancreatic fistula in patients with or without pancreatic duct stent in laparoscopic pancreaticoduodenectomy (LPD).MethodsA total of 66 patients who underwent LPD in West China Hospital of Sichuan University from November 2019 to October 2020 were enrolled in the randomized controlled trial (registration number: ChiCTR1900026653). The perioperative data of the patients were collected in real time. The patients were divided into groups according to whether the pancreatic duct support tube was retained during the operation, and the probability prediction value was output according to the model formula. The specificity, sensitivity, accuracy, discrimination, and stability of the prediction results were analyzed.ResultsFor the group with pancreatic stent tubes, the specificity, sensitivity, and accuracy of the model at the model cut-off points on the postoperative day 2, 3 and 5 were 92.0%, 76.7% and 57.1%, 50.0%, 100% and 66.7%, and 88.8%, 78.8% and 61.3%, respectively. The areas under the ROC curve were 0.870, 0.956 and 0.702, respectively. The kappa values of the prediction result based on model cut-off point and cut-off point of ROC curve were 0.308, 0.582 and 0.744, respectively. Whereas for those who without the stent tube, the specificity, sensitivity, and prediction accuracy of the model on the postoperative day 5 were 66.7%, 100% and 72%, respectively. The area under curve at different time points were 0.304, 0.821, and 0.958, respectively. The kappa values at the last two time points were 0.465 and 0.449, respectively.ConclusionsFor patients with pancreatic duct support during LPD operation, the dynamic model of clinical pancreatic fistula can more accurately screen high-risk groups of clinical pancreatic fistula, and has better stability of prediction results. For patients without supporting tube, in the case of flexible adjustment of the boundary point, the model can also be more accurate screening on the 3rd and 5th days after operation.
ObjectiveTo explore the CT imaging features and independent risk factors for cystic pulmonary nodules and establish a malignant probability prediction model. Methods The patients with cystic pulmonary nodules admitted to the Department of Thoracic Surgery of the First People's Hospital of Neijiang from January 2017 to February 2022 were retrospectively enrolled. They were divided into a malignant group and a benign group according to the pathological results. The clinical data and preoperative chest CT imaging features of the two groups were collected, and the independent risk factors for malignant cystic pulmonary nodules were screened out by logistic regression analysis, so as to establish a prediction model for benign and malignant cystic pulmonary nodules. ResultsA total of 107 patients were enrolled. There were 76 patients in the malignant group, including 36 males and 40 females, with an average age of 59.65±11.74 years. There were 31 patients in the benign group, including 16 males and 15 females, with an average age of 58.96±13.91 years. Multivariate logistic analysis showed that the special CT imaging features such as cystic wall nodules [OR=3.538, 95%CI (1.231, 10.164), P=0.019], short burrs [OR=4.106, 95%CI (1.454, 11.598), P=0.008], cystic wall morphology [OR=6.978, 95%CI (2.374, 20.505), P<0.001], and the number of cysts [OR=4.179, 95%CI (1.438, 12.146), P=0.009] were independent risk factors for cystic lung cancer. A prediction model was established: P=ex/(1+ex), X=–2.453+1.264×cystic wall nodules+1.412×short burrs+1.943×cystic wall morphology+1.430×the number of cysts. The area under the receiver operating charateristic curve was 0.830, the sensitivity was 82.9%, and the specificity was 74.2%. ConclusionCystic wall nodules, short burrs, cystic wall morphology, and the number of cysts are the independent risk factors for cystic lung cancer, and the established prediction model can be used as a screening method for cystic pulmonary nodules.
ObjectiveTo establish and validate a nomogram model for predicting the risk of new-onset postoperative atrial fibrillation (POAF) after isolated aortic valve replacement (AVR). MethodsThe clinical data of patients without atrial fibrillation (AF) who underwent isolated AVR in the General Hospital of the Northern Theater of Command from June 2020 to June 2022 were retrospectively collected. Patients with AVR were divided into a POAF group and a non-POAF group according to whether POAF occurred within 7 days after surgery. The preoperative baseline data, blood indexes, color Doppler echocardiography and the heart rate variability (HRV) in 7 days before surgery were analyzed. Logistic regression was used to analyze the preoperative risk factors for POAF and R language was used to construct a nomogram to predict POAF. The results were compared with the established AF model (POAF-AF score). ResultsA total of 191 patients were enrolled in this study, and 66 (35%) of them developed POAF within 7 days after the surgery. The age of the patients in the POAF group was (60.97±8.41) years and 16 (24%) were female, while the age of the patients in the non-POAF group was (54.65±11.85) years and 59 (47%) were female. Univariate and multivariate logistic regression analysis showed that age, sex, drinking history, chronic obstructive pulmonary disease, plateletocrit and high frequency power were independently associated with POAF after the AVR. The nomogram of POAF was constructed by combining the above independent risk factors. We predicted the area under receiver operating characteristic curve (AUC=0.812) in the nomogram of POAF after simple aortic valve replacement. The model was internally verified by a 10-fold cross-validation resampling (AUC=0.757, Kappa=0.438). Compared with the POAF-AF score, the nomogram had a superior discrimination performance. ConclusionAge, sex, drinking history, chronic obstructive pulmonary disease, plateletocrit, and high frequency power are independent predictors for POAF after isolated AVR. The nomogram can be used as a practical tool to help clinicians predict the probability of individual POAF occurrence and take necessary preventive measures.
ObjectiveTo analyze the factors affecting the prognosis of patients with primary tracheal malignancy, and establish a nomogram model for prediction its prognosis.MethodsA total of 557 patients diagnosed with primary tracheal malignancy from 1975 to 2016 in the Surveillance, Epidemiology, and End Results Data were collected. The factors affecting the overall survival rate of primary tracheal malignancy were screened and modeled by univariate and multivariate Cox regression analysis. The nomogram prediction model was performed by R 3.6.2 software. Using the C-index, calibration curves and receiver operating characteristic (ROC) curve to evaluate the consistency and predictive ability of the nomogram prediction model.ResultsThe median survival time of 557 patients with primary tracheal malignancy was 21 months, and overall survival rates of the 1-year, 3-year and 5-year were 59.1%±2.1%, 42.5%±2.1%, and 35.4%±2.2%. Univariate and multivariate Cox regression analysis showed that age, histology, surgery, radiotherapy, tumor size, tumor extension and the range of lymph node involvement were independent risk factors affecting the prognosis of patients with primary tracheal malignancy (P<0.05). Based on the above 7 risk factors to establish the nomogram prediction model, the C-index was 0.775 (95%CI 0.751-0.799). The calibration curve showed that the prediction model established in this study had a good agreement with the actual survival rate of the 1 year, 3 year and 5 years. The area under curve of 1-year, 3-year and 5-year predicting overall survival rates was 0.837, 0.827 and 0.836, which showed that the model had a high predictive power.ConclusionThe nomogram prediction model established in this study has a good predictive ability, high discrimination and accuracy, and high clinical value. It is useful for the screening of high-risk groups and the formulation of personalized diagnosis and treatment plans, and can be used as an evaluation tool for prognostic monitoring of patients with primary tracheal malignancy.