Cheng Yuanyuan, He Fei, Li Songtao
Objective To investigate the differences in sleep patterns between patients with mixed features depression (MFD) and those with mixed features bipolar disorder (MFBD), and to develop predictive models. Method A cohort of 198 patients with MM/MF (n = 83) and BM/MF (n = 115) who were admitted to the hospital between January 2022 and June 2023 was selected as the modeling dataset.Use single-factor and multi-factor logistic regression to identify independent influencing factors and construct and validate predictive models. Create risk nomograms and receiver operating characteristic curves.(ROC), calibration curves, and decision curves (DCA) are used to evaluate the predictive performance of the model. A sample of cases admitted to the hospital between July 2023 and August 2024 was selected in a 7:3 ratio 85 cases of MMF and BMF patients were used as a validation set to externally validate the model. The results show that the sleep structure of MMF and BMF patients in this study exhibited characteristics of rapid eye movement (REM) sleep.There were differences in the latent period (REM), the proportion of N2-stage sleep, the proportion of N3-stage sleep, sleep efficiency (SE), and wakefulness time (P < 0.05). Multivariate logistic analysis. Results The regression analysis indicate that the body mass index (BMI), disease duration, N2 stage, N3 stage, SE, REM latency, and wakefulness duration all differ between MMf and BMf patients in terms of their sleep structure.There were significant independent factors of variability (p < 0.05). The predictive model constructed had an ROC area under the curve of 0.852 (95% CI: 0.800–0.903), with a maximum Youden index.The value is 0.558, the sensitivity is 0.739, the specificity is 0.819, and the differentiation is good; the overall trend of the model calibration curve closely approximates an ideal curve, with an average absolute error (EMA) of 0.033, the overall calibration performance is good; the net benefit level of the predictive model is high when the DCA curve threshold is between 0.40 and 1.00; Hosmer-Lemeshow test, χ.2 =5.211, P = 0.735, good fit (P > 0.05). In the external validation of this model, the ROC curve area was 0.940 (95% CI: 0.894–0.987).The maximum Youden index is 0.740, the sensitivity is 0.875, the specificity is 0.865, and the discrimination is excellent; the overall trend of the model calibration curve closely approximates an ideal curve, EMa =0.047; the overall calibration performance is good. The net benefit level of the predictive model is high when the DCA curve threshold is between 0.18 and 1.00. Høsør/Lemeshow test, χ2 =3.644, p = 0.820, good fit (p > 0.05), indicating that the model’s predictive performance is strong.Conclusion There are differences in sleep structure between patients with MMF and BMMF.IBM, disease duration, N2 stage, N3 stage, SE, REM latency, and wakefulness time are all independent factors that differentiate the sleep structure of MMF and BMMF patients.The predictive model performs well, allowing for a better understanding of patients’ sleep issues and enabling the development of more effective intervention strategies to improve patients’ quality of life.
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