Objective To construct, validate and evaluate a nomogram prediction model based on triglyceride-glucose index for predicting the risk of type 2 diabetes mellitus (T2DM) in patients with obstructive sleep apnea (OSA). Methods A total of 414 patients diagnosed with OSA who were hospitalized in the Second Affiliated Hospital of Kunming Medical University from July 2013 to July 2023 were retrospectively analyzed. They were randomly divided into training set (n=289) and validation set (n=125) at a ratio of 7:3 using R software. In the training set, univariate logistic regression, best subsets regression (BSR) and multivariate Logistic regression were used to determine the independent predictors of OSA combined with T2DM and construct a nomogram. The area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow goodness of fit test, decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the discrimination, calibration and clinical applicability of the nomogram prediction model. Finally, the internal validation of the nomogram prediction model was carried out on the validation set. Results In the training set, the results of univariate logistic regression, BSR and multivariate logistic regression analysis showed that hypertension (OR=2.413, 95%CI 1.276-4.563, P=0.007), apnea hypopnea index (OR=1.034, 95%CI 1.014-1.053, P=0.001), triglyceride-glucose index( OR=12.065, 95%CI 5.735-25.379, P<0.001), triglyceride/high density lipoprotein cholesterol (OR=0.736, 95%CI 0.634-0.855, P<0.001) were independent predictors of T2DM in OSA patients. A nomogram prediction model was constructed based on the above four predictors. In the training set and validation set, the AUC, sensitivity, and specificity of the nomogram prediction model for predicting the risk of T2DM in OSA patients were 0.820 (95%CI 0.771-0.869), 75.7%, 75.9% and 0.778 (95%CI 0.696-0.861), 74.5%, 73.0%, respectively, indicating that the nomogram had good discrimination. The calibration curve showed that the nomogram had a good calibration for predicting T2DM in OSA patients. DCA and CIC also showed that the nomogram prediction model had certain clinical utility. Conclusions A simple, fast and effective nomogram prediction model with good discrimination, calibration and clinical applicability was successfully constructed, validated and evaluated. It can be used to predict the risk of T2DM in OSA patients and help clinicians to identify patients with high risk of T2DM in OSA patients.
Objective To analyze the influencing factors of anxiety in adults with obstructive sleep apnea (OSA), establish and validate a nomgram model for predicting the risk of anxiety in OSA patients. Methods From January 2022 to December 2024, a total of 461 OSA patients diagnosed at the Second People's Hospital of Yibin were collected and divided into a model group (345 cases) and an internal validation group (116 cases) according to a 3:1 ratio. The model group was divided into an anxiety group and a non-anxiety group based on the Hamilton Anxiety Scale (HAMA) score of 7. Univariate and multivariate analyses were conducted to identify the influencing factors of anxiety in OSA patients. A nomogram model was established by R language. The discriminant, calibration, and net benefit of the model were validated by area under receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and decision curve analysis (DCA). ResultsThe incidence of anxiety in 461 OSA patients was 45.77%, and there were statistically significant differences (P<0.05) in 17 factors including body mass index (BMI) between the anxiety group and the non-anxiety group. Lasso regression identified 8 meaningful variables, and logistic regression found that BMI, the Epworth sleeping scale (ESS) score, medical insurance type, pooled hypertension and 5-hydroxytryptamine (5-HT) were influencing factors of anxiety in the OSA patients, with OR values (95%CI) of 0.673 (0.562-0.806), 0.801 (0.754-0.851), 0.377 (0.237-0.597), 0.363 (0.150-0.874) and 0.708 (0.575-0.872), respectively. The nomogram model based on these indicators has good discrimination, calibration, and net profit in the model group, internal validation group, and external validation group, with AUC of 0.893, 0.894, and 0.792, sensitivity of 0.902, 0.857 and 0.832, specificity of 0.830, 0.809 and 0.795, respectively. The maximum offsets of the model group, internal validation group, and external validation group were 0.049, 0.026, and 0.025, respectively (P=0.917, 0.881, and 0.823, respectively). The model group had a higher net profit at the low to medium risk threshold (0-0.7). Conclusions BMI, ESS, medical insurance type, pooled hypertension, OSA-18 score, 5-HT concentration are the influencing factors of anxiety in OSA patients. The nomogram model based on these indicators can effectively predict the risk of anxiety in OSA patients.