ObjectiveTo explore the risk factors affecting occurrence of arteriosclerosis obliterans (ASO) for patients with type 2 diabetes mellitus (T2DM) and to develop a nomogram predictive model using these risk factors. MethodsA case-control study was conducted. The patients with T2DM accompanied with ASO and those with T2DM alone, admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, were retrospectively collected according to the inclusion and exclusion criteria. The basic characteristics, blood, thyroid hormones, and other relevant indicators of the paitents in two groups were compared. The multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ASO in the patients with T2DM, and then a nomogram predictive model was developed. ResultsThere were 119 patients with T2DM alone and 114 patients with T2DM accompanied with lower extremity ASO in this study. The significant differences were observed between the two groups in terms of smoking history, white blood cell count, neutrophil count, lymphocyte count, platelet count, systemic immune-inflammation index, systemic inflammatory response index (SIRI), high-density lipoprotein cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein α (Apoα), serum cystatin C, free-triiodothyronine (FT3), total triiodothyronine, FT3/total triiodothyronine ratio, fibrinogen (Fib), fibrinogen degradation products, and plasma D-dimer (P<0.05). Further the results of the multivariate logistic regression analysis revealed that the history of smoking, increased Fib level and SIRI value increased the probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=2.921 (1.023, 4.227), P=0.003; OR (95%CI)=2.641 (1.810, 4.327), P<0.001; OR (95%CI)=1.020 (1.004, 1.044), P=0.018], whereas higher levels of ApoA1 and FT3 were associated with reduced probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=0.231 (0.054, 0.782), P=0.021; OR (95%CI)=0.503 (0.352, 0.809), P=0.002]. The nomogram predictive model based on these factors demonstrated a good discrimination for predicting the ASO occurrence in the T2DM patients [area under the receiver operating characteristic curve (95%CI)=0.788 (0.730, 0.846)]. The predicted curve closely matched the ideal curve (Hosmer-Lemeshow goodness-of-fit test, χ2=5.952, P=0.653). The clinical decision analysis curve showed that the clinical net benefit of intervention based on the nomogram model was higher within a threshold probability range of 0.18 to 0.80 compared to no intervention or universal intervention. ConclusionsThe analysis results indicate that T2DM patients with a smoking history, elevated Fib level and SIRI value, as well as decreased ApoA1 and FT3 levels should be closely monitored for ASO risk. The nomogram predictive model based on these features has a good discriminatory power for ASO occurrence in T2DM patients, though its value warrants further investigation.
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 explore the risk factors affecting the prognosis of patients with metastatic breast cancer (MBC) and construct a nomogram survival prediction model.MethodsThe patients with MBC from 2010 to 2013 were collected from surveillance, epidemiology, and end results (SEER) database, then were randomly divided into training group and validation group by R software. SPSS software was used to compare the survival and prognosis of MBC patients with different metastatic sites in the training group by log-rank method and construct the Kaplan-Meier survival curve. The Cox proportional hazards model was used to analyze the factors of 3-year overall survival, then construct a nomogram survival prediction model by the independent prognostic factors. The C-index was used to evaluate its predictive value and the calibration curve was used to verify the nomogram survival prediction model by internal and external calibration graph.ResultsA total of 3 288 patients with MBC were collected, including 2 304 cases in the training group and 984 cases in the validation group. The data of the two groups were comparable. The median follow-up time of training group and validation group was 34 months and 34 months, respectively. In the training group, the results of Cox proportional hazards model showed that the older, black race, higher histological grading, without operation, ER (–), PR (–), HER-2 (–), and metastases of bone, brain, liver and lung were the risk factors of survival prognosis (P<0.05) and constructed the nomogram survival prediction model with these independent prognostic factors. The nomogram survival prediction showed a good accuracy with C-index of 0.704 [95%CI (0.691, 0.717)] in internal validation (training group) and C-index of 0.691 [95%CI (0.671, 0.711)] in external validation (validation group) in predicting 3-year overall survival. All calibration curves showed excellent consistency.ConclusionNomogram for predicting 3-year overall survival of patients with MBC in this study has a good predictive capability, and it is conducive to development of individualized clinical treatment.
ObjectiveTo investigate the independent risk factors for acute biliary pancreatitis (ABP) induced by gallstones, and to develop a nomogram prediction model for the onset of ABP, thereby enabling early identification of high-risk patients and the formulation of individualized management plans, so as to provide a scientific basis for improving the clinical management of ABP. MethodsThe patients with gallstones who were admitted to the Department of Hepatobiliary and Pancreatic Surgery, Suining Central Hospital from January 2022 to January 2024 were collected. The patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate and multivariate logistic regression analyses were applied to identify independent risk factors for ABP and to develop a nomogram prediction model. The discriminative ability, calibration, and clinical utility of the prediction model were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The accuracy and stability of the prediction model were further tested using the validation set. ResultsA total of 1 103 patients with gallstones were included, comprising 772 in the training set and 331 in the validation set. Among them, 137 (17.75%) and 60 (18.13%) patients in the training and validation sets, respectively, were complicated with ABP. Multivariate logistic regression analysis in the training set revealed that age [OR (95%CI)=1.03 (1.01, 1.04), P=0.002], normal gallbladder size [OR (95%CI)=5.36 (2.70, 10.65), P<0.001], gallstone diameter ≤1 cm [OR (95%CI)=6.26 (3.23, 12.14), P<0.001], round-shaped gallstones [OR (95%CI)=11.29 (5.42, 23.51), P<0.001], multiple gallstones [OR (95%CI)=1.37 (1.17, 1.81), P=0.013], and common bile duct diameter ≥1 cm [OR (95%CI)=4.44 (1.37, 14.41), P=0.013] were independent risk factors for ABP induced by gallstones. Based on these risk factors, a nomogram prediction model was constructed. The model demonstrated area under the ROC curve for distinguishing patients with ABP induced by gallstones were 0.873 [95%CI (0.842, 0.905)] in the training set and 0.858 [95%CI (0.810, 0.905)] in the validation set, respectively. The calibration of the model by the Hosmer-Lemeshow test indicated a good fit between predicted and actual probabilities (training set: χ2=14.061, P=0.080; validation set: χ2=7.656, P=0.468). Internal validation via the Bootstrap method (1 000 resamples) yielded calibration curves for both the training and validation sets that closely aligned with the ideal diagonal line. In both the training and validation sets, the patients with gallstone would benefit from intervention according to the nomogram at threshold probabilities of 0.03–0.88 and 0.89–0.91, respectively. ConclusionsThis study confirms that age, normal gallbladder size, gallstone characteristics (diameter ≤1 cm, round shape, and multiple stones), and common bile duct dilation (diameter ≥1 cm) are independent risk factors for ABP induced by gallstones. The nomogram prediction model constructed based on these factors demonstrates good discrimination and calibration in both the training and validation sets, and decision curve analysis confirmes its ideal clinical utility. This model provides clinicians with an intuitive and reliable quantitative tool for early identification of high-risk ABP patients and the development of individualized prevention and management strategies.
Objective To investigate the risk factors for postoperative loss of reduction in unstable distal radius fractures and to develop a predictive model, providing clinicians with a more precise risk assessment tool to support the formulation of individualized treatment plans. MethodsA retrospective analysis was conducted on the clinical data of 209 patients with unstable distal radius fractures who met the selection criteria between January 2018 and December 2023. There were 97 males and 112 females, aged 44-81 years with a mean age of 57.1 years. Univariate analysis was performed to identify factors associated with postoperative loss of reduction. Multivariate logistic regression analysis was then used to screen risk factors and construct a nomogram prediction model. The Hosmer-Lemeshow test was applied to assess model fit, while the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to evaluate the predictive performance. Additionally, decision curve analysis was employed to assess the clinical utility of the model. ResultsAt 6 months after operation, radiographic evaluation showed loss of reduction in 68 cases. Univariate analysis identified the following as influencing factors for postoperative loss of reduction (P<0.05): age, fracture displacement, fracture classification, dorsal metaphyseal comminuted fracture, osteoporosis, operation time, and preoperative serum calcium level. Multivariate analysis confirmed the following as risk factors for postoperative loss of reduction (P<0.05): older age, more severe fracture type (higher AO/OTA classification), presence of fracture displacement, dorsal metaphyseal comminuted fracture, low preoperative serum calcium level, osteoporosis, and prolonged operation time. The nomogram prediction model constructed based on these factors demonstrated high accuracy in assessing the risk of loss of reduction, with an AUC of 0.946 (95%CI: 0.917, 0.975). The calibration curve showed good agreement between predicted and observed probabilities (χ2=4.735, P=0.785). Decision curve analysis indicated that when the predicted risk of postoperative loss of reduction exceeds 0.1, timely intervention can yield substantial net clinical benefit. ConclusionOlder age, AO/OTA type C fractures, fracture displacement, dorsal metaphyseal comminuted fracture, prolonged operation time, low preoperative serum calcium level, and comorbid osteoporosis are the main risk factors for postoperative loss of reduction in patients with unstable distal radius fractures. The established predictive nomogram model enables clinicians to more accurately assess the risk of postoperative loss of reduction and provides valuable support for personalized treatment decisions.