ObjectiveTo develop and validate a nomogram for predicting the risk of peripheral artery disease (PAD) in patients with type 2 diabetes mellitus (T2DM) and assess its clinical application value.MethodsClinical data were retrospectively collected from 474 patients with T2DM at the Air Force Medical Center between January 2019 and April 2022. The patients were divided into training and validation sets using the random number table method in a ratio of 7:3. Multivariate logistic regression analysis was performed to identify the independent risk factors for PAD in patients with T2DM. A nomogram prediction model was developed based on the independent risk factors. The predictive efficacy of the prediction model was evaluated using the consistency index (C-index), area under the curve (AUC), receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (HL) test, and calibration curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the prediction model’s performance during clinical application.ResultsAge, disease duration, blood urea nitrogen (BUN), and hemoglobin (P<0.05) were observed as independent risk factors for PAD in patients with T2DM. The C-index and the AUC were 0.765 (95% CI: 0.711-0.819) and 0.716 (95% CI: 0.619-0.813) for the training and validation sets, respectively, indicating that the model had good discriminatory power. The calibration curves showed good agreement between the predicted and actual probabilities for both the training and validation sets. In addition, the P-values of the HL test for the training and validation sets were 0.205 and 0.414, respectively, indicating that the model was well-calibrated. Finally, the DCA curve indicated that the model had good clinical utility.ConclusionA simple nomogram based on three independent factors–duration of diabetes, BUN, and hemoglobin levels–may help clinicians predict the risk of developing PAD in patients with T2DM.
ObjectiveTo analyze the prognostic factors of patients with cholangiocarcinoma (CCA) who were unresected and received radiotherapy to establish a nomogram model for the prediction of patient cancer-specific survival (CSS).MethodsSuitable patient cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database, survival rates were calculated using the Kaplan-Meier method, prognostic factors were analyzed by Lasso, Cox regression, and nomogram was developed based on independent prognostic factors to predict 6 and 12 months CSS. The consistency index (C-index), calibration curve, and decision curve analysis (DCA) were tested for the predictive efficacy of the model, respectively.ResultsThe primary site, tumor size, T-stage, M-stage, and chemotherapy (P < 0.05) were identified as independent risk factors after Cox and Lasso regression analysis. Patients in training cohort had a 6 months CSS rates was 68.6 ± 2.6%, a 12-month CSS rates was 49.0 ± 2.8%. The median CSS time of 12.00 months (95% CI: 10.17–13.83 months). The C-index was 0.664 ± 0.039 for the training cohort and 0.645 ± 0.042 for the validation cohort. The nomogram predicted CSS and demonstrated satisfactory and consistent predictive performance in 6 (73.4 vs. 64.9%) and 12 months (72.2 vs. 64.9%), respectively. The external validation calibration plot is shown AUC for 6- and 12-month compared with AJCC stage was (71.2 vs. 63.0%) and (65.9 vs. 59.8%). Meanwhile, the calibration plot of the nomogram for the probability of CSS at 6 and 12 months indicates that the actual and nomogram predict that the CSS remains largely consistent. DCA showed that using a nomogram to predict CSS results in better clinical decisions compared to the AJCC staging system.ConclusionA nomogram model based on clinical prognostic characteristics can be used to provide CSS prediction reference for patients with CCA who have not undergone surgery but have received radiotherapy.
BackgroundThere is a lack of studies regarding radiotherapy (RT) in patients with gallbladder cancer (GBC) on the survival benefit after surgery and nonsurgical treatment. Therefore, this study evaluated the impact of external beam RT on the overall survival (OS) of patients with GBC in a real-world setting.MethodsPatients with GBC enrolled from the Surveillance, Epidemiology, and End Results (SEER) database were examined through Kaplan–Meier survival curves and multivariable Cox regression analyses.ResultsA total of 7,866 patients with GBC were screened for the current analysis, of whom 2,130 (27.1%) did not undergo RT or surgery, 209 (2.7%) underwent RT, 4,511 (57.3%) underwent surgery, and 1,016 (12.9%) underwent both RT and surgery. The median OS times were 4 months, 8 months, 16 months, and 21 months (p < 0.0001). OS was significantly different between adjuvant RT (p = 0.0002) and palliative RT (p < 0.0001). Multifactorial analysis (controlling for age, sex, year of diagnosis, marital status, race, grade, and stage) showed that both adjuvant RT (surgery and adjuvant RT vs. surgery alone; HR, 0.75; 95% CI, 0.69–0.82, p < 0.001) and palliative RT (RT alone vs. no treatment; HR, 0.80; 95% CI, 0.69–0.92, p = 0.003) had a significant impact on patient OS. The results remained stable following sensitivity analyses.ConclusionThe study results indicate that adjuvant and palliative radiation treatment was associated with a survival benefit. GBC patients can derive a survival benefit from external beam RT.
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