ObjectiveTo explore the combined application of neutrophil to lymphocyte ratio (NLR) and systemic immune inflammation index (SII) on the prognosis of hepatitis B-related hepatocellular carcinoma after resection.MethodsRetrospectively collected data of 180 patients with hepatitis B-related hepatocellular carcinoma who were hospitalized in the Department of Infectious Diseases and Hepatobiliary Surgery of the Affiliated Hospital of Southwest Medical University and received surgical treatment from January 2013 to December 2019, including general information, laboratory examination and abdominal CT or MRI results. NLR and SII values were measured at one week before operation, and their critical values of NLR and SII were determined by ROC curve analysis. Univariate and multivariate analysis were performed to determine the risk factors to predict the survival status of patients with hepatitis B-related hepatocellular carcinoma after hepatectomy.ResultsUnivariate analysis showed that AFP, platelets, TNM staging, portal vein tumor thrombus, tumor differentiation, NLR, SII, and NLR+SII combined score were significantly correlated with the prognosis of patients with hepatitis B-related hepatocellular carcinoma (P<0.05). Multivariate analysis showed that PLT [HR=1.791, 95%CI (1.124, 2.854), P=0.014], NLR [HR=4.289, 95%CI (2.571, 7.156), P<0.001], SII [HR=5.317, 95%CI (3.016, 9.374), P<0.001], and NLR+SII combined score [HR=7.901, 95%CI (4.124, 15.138), P<0.001] were independently correlated with the survival of patients with hepatitis B-related hepatocellular carcinoma.ConclusionsThe preoperative NLR+SII combined score can be used to evaluate the postoperative prognosis of patients with hepatitis B-related hepatocellular carcinoma. The higher the score, the lower the postoperative survival rate.
Objective To determine the risk factors of anastomotic leakage after elective colectomy in elderly patients with colon cancer, and to establish a model for predicting the risk of postoperative anastomotic leakage based on these factors. Methods The clinical data of 122 over 65 years old elderly patients who underwent colon cancer surgery in the First Hospital of Lanzhou University from January 2018 to December 2021 were analyzed retrospectively. Single factor analysis and multivariate logistic regression were used to analyze the potential risk factors for anastomotic leakage. A nomogram predictive model was established based on the determined independent risk factors, and the predictive performance of the model was evaluated by the receiver operating characteristic curve. Results Among the 122 patients included in this study, 10 had postoperative anastomotic leakage and 112 had no anastomotic leakage. Single factor analysis results showed that the occurrence of anastomotic leakage was associated with body mass index, smoking, combined diabetes, age-adjusted Charlson comorbidity index, intraoperative and postoperative blood transfusion within 2 days, preoperative hemoglobin, preoperative albumin, and preoperative prognostic nutritional index (P＜0.05). The results of multivariate logistic regression analysis showed that smoking [OR=15.529, 95%CI (1.529, 157.690), P=0.020], age-adjusted Charlson comorbidity index [OR=1.742, 95%CI (1.024, 2.966), P=0.041], and intraoperative and postoperative blood transfusion within 2 days [OR=82.223, 95%CI (1.265, 5 343.025), P=0.038] were independent risk factors for anastomotic leakage. A nomogram predictive model was established based on three independent risk factors. The area under the receiver operating characteristic curve of the model was 0.897 [95%CI (0.804, 0.990)], and its corrected C-index value was 0.881, indicating that the model had good predictive ability for the risk of anastomotic leakage. Conclusions Smoking, higher age-adjusted Charlson comorbidity index, and intraoperative and postoperative blood transfusion within 2 days are important risk factors for anastomotic leak in elderly patients undergoing elective colon cancer resection. This nomogram predictive model based on the combination of the three factors is helpful for surgeons to optimize treatment decisions and postoperative monitoring.
ObjectiveTo evaluate existing predictive models for surgical site infection (SSI) following colorectal cancer (CRC) surgery, aiming to provide a scientific basis for refining risk prediction models and developing clinically practical and widely applicable screening tools. MethodA comprehensive review of existing literature on predictive models for SSI following CRC surgery, both domestically and internationally, were conducted. ResultsThe determination of SSI following CRC surgery primarily relied on the Centers for Disease Control and Prevention standard of USA, which presented issues of consistency and accuracy. Various predictive models had been developed, including traditional statistical models and machine learning models, with 0.991 of an area under the operating characteristic curve of predictive model. However, most studies were based on retrospective and single-center data, which limited their applicability and accuracy. ConclusionsAlthough existing models provide strong support for predicting SSI following CRC surgery, there is a need for multi-center, prospective studies to enhance the generalizability and accuracy of these models. Additionally, future research should focus on improving model interpretability to better apply them in clinical practice, providing personalized risk assessments and intervention strategies for patients.
Objective To evaluate the predictive effect of three machine learning methods, namely support vector machine (SVM), K-nearest neighbor (KNN) and decision tree, on the daily number of new patients with ischemic stroke in Chengdu. Methods The numbers of daily new ischemic stroke patients from January 1st, 2019 to March 28th, 2021 were extracted from the Third People’s Hospital of Chengdu. The weather and meteorological data and air quality data of Chengdu came from China Weather Network in the same period. Correlation analyses, multinominal logistic regression, and principal component analysis were used to explore the influencing factors for the level of daily number of new ischemic stroke patients in this hospital. Then, using R 4.1.2 software, the data were randomly divided in a ratio of 7∶3 (70% into train set and 30% into validation set), and were respectively used to train and certify the three machine learning methods, SVM, KNN and decision tree, and logistic regression model was used as the benchmark model. F1 score, the area under the receiver operating characteristic curve (AUC) and accuracy of each model were calculated. The data dividing, training and validation were repeated for three times, and the average F1 scores, AUCs and accuracies of the three times were used to compare the prediction effects of the four models. Results According to the accuracies from high to low, the prediction effects of the four models were ranked as SVM (88.9%), logistic regression model (87.5%), decision tree (85.9%), and KNN (85.1%); according to the F1 scores, the models were ranked as SVM (66.9%), KNN (62.7%), decision tree (59.1%), and logistic regression model (57.7%); according to the AUCs, the order from high to low was SVM (88.5%), logistic regression model (87.7%), KNN (84.7%), and decision tree (71.5%). Conclusion The prediction result of SVM is better than the traditional logistic regression model and the other two machine learning models.
Objective To construct a risk prediction score model for serious adverse event (SAE) after cardiac catheterization in patients with adult congenital heart disease (ACHD) and pulmonary hypertension (PH) and verify its predictive effect. Methods The patients with PH who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected. The patients were randomly divided into a model group and a validation group according to the order of admission. The model group was divided into a SAE group and a non-SAE group according to whether SAE occurred after the catheterization. The data of the two groups were compared, and the risk prediction score model was established according to the results of multivariate logistic regression analysis. The discrimination and calibration of the model were evaluated using the area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test, respectively. Results A total of 758 patients were enrolled, including 240 (31.7%) males and 518 (68.3%) females, with a mean age of 43.1 (18.0-81.0) years. There were 530 patients in the model group (47 patients in the SAE group and 483 patients in the non-SAE group) and 228 patients in the validation group. Univariate analysis showed statistical differences in age, smoking history, valvular disease history, heart failure history, N-terminal pro-B-type natriuretic peptide, and other factors between the SAE and non-SAE groups (P<0.05). Multivariate analysis showed that age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, surgical general anesthesia, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients (P<0.05). The risk prediction score model had a total score of 0-139 points and patients who had a score>50 points were high-risk patients. Model validation results showed an area under the ROC curve of 0.937 (95%CI 0.897-0.976). Hosmer-Lemeshow goodness-of-fit test: χ2=3.847, P=0.797. Conclusion Age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, general anesthesia for surgery, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients. The risk prediction model based on these factors has a high predictive value and can be applied to the risk assessment of SAE after interventional therapy in ACHD-PH patients to help clinicians perform early intervention.
Objective To summarize risk factors of pancreatic fistula after pancreaticoduodenectomy and to investigate clinical application of pancreatic fistula risk prediction system. Method The literatures of the risk factors and risk prediction of pancreatic fistula after the pancreaticoduodenectomy were collected to make a review. Results There were many risk factors for pancreatic fistula after pancreatoduodenectomy, including the patient’s own factors (gender, age, underlying diseases, etc.), disease related factors (pancreatic texture, diameter of pancreatic duct, pathological type, etc.), and surgical related factors (operation time, intraoperative blood loss, anastomosis, pancreatic duct drainage, etc.). The fistula risk prediction system after the pancreatoduodenectomy had a better forecast accuracy. Conclusions Occurrence of pancreatic fistula after pancreaticoduodenectomy is related to softness of pancreas and small diameter of pancreatic duct. Pancreatic fistula risk prediction system is helpful for prevention of pancreatic fistula after pancreaticoduodenectomy.
ObjectiveTo summarise the application research progress of clinical prediction models in postoperative complications of gastric cancer, in order to reduce the risk of complications after gastric cancer surgery. MethodThe literature on the study of postoperative complications of gastric cancer at home and abroad was read and reviewed. ResultsAt present, the main way of treating gastric cancer was still radical resection, and the occurrence of complications after surgical treatment seriously affected the recovery and survival quality of patients. With the deepening of research, the prediction models of postoperative complications in gastric cancer were constantly constructed, and these models provided strong evidence for the early judgement of postoperative complications in gastric cancer, and provided a scientific basis for the improvement of patients’ life quality. ConclusionClinical predictive models are expected to become risk screening tools for predicting the risk of postoperative complications of gastric cancer with clinical utility.
Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.
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 investigate the risk factors affecting severe postoperative complications (Clavien-Dindo classification Ⅲa or higher) in patients with end-stage hepatic alveolar echinococcosis (HAE) underwent ex vivo liver resection and autotransplantation (ELRA), and to develop a nomogram prediction model. MethodsThe clinical data of end-stage HAE patients who underwent ELRA at the West China Hospital of Sichuan University from January 2014 to June 2024 were retrospectively analyzed. The logistic regression was used to analyze the risk factors affecting severe postoperative complications. A nomogram prediction model was established basing on LASSO regression and its efficiency was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Simultaneously, a generalized linear model regression was used to explore the preoperative risk factors affecting the total surgery time. Test level was α=0.05. ResultsA total of 132 end-stage HAE patients who underwent ELRA were included. The severe postoperative complications occurred in 47 (35.6%) patients. The multivariate logistic analysis results showed that the patients with invasion of the main trunk of the portal vein or the first branch of the contralateral portal vein (type P2) had a higher risk of severe postoperative complications compared to those with invasion of the first branch of the ipsilateral portal vein (type P1) [odds ratio (OR) and 95% confidence interval (CI)=8.24 (1.53, 44.34), P=0.014], the patients with albumin bilirubin index (ALBI) grade 1 had a lower risk of severe postoperative complications compared to those with grade 2 or higher [OR(95%CI)=0.26(0.08, 0.83), P=0.023]. Additionally, an increased total surgery time or the autologous blood reinfusion was associated with an increased risk of severe postoperative complications [OR(95%CI)=1.01(1.00, 1.01), P=0.009; OR(95%CI)=1.00(1.00, 1.00), P=0.043]. The nomogram prediction model constructed with two risk factors, ALBI grade and total surgery time, selected by LASSO regression, showed a good discrimination for the occurrence of severe complications after ELRA [area under the ROC curve (95%CI) of 0.717 (0.625, 0.808)]. The generalized linear regression model analysis identified the invasion of the portal vein to extent type P2 and more distant contralateral second portal vein branch invasion (type P3), as well as the presence of distant metastasis, as risk factors affecting total surgery time [β (95%CI) for type P2/type P1=110.26 (52.94, 167.58), P<0.001; β (95%CI) for type P3/type P1=109.25 (50.99, 167.52), P<0.001; β (95%CI) for distant metastasis present/absent=61.22 (4.86, 117.58), P=0.035]. ConclusionsFrom the analysis results of this study, for the end-stage HAE patients with portal vein invasion degree type P2, ALBI grade 2 or above, longer total surgery time, and more autologous blood transfusion need to be closely monitored. Preoperative strict evaluation of the first hepatic portal invasion and distant metastasis is necessary to reduce the risk of severe complications after ELRA. The nomogram prediction model constructed based on ABLI grade and total surgery time in this study demonstrates a good predictive performance for severe postoperative complications, which can provide a reference for clinical intervention decision-making.