Objective:To analyze the risk factors of non-suicidal self-injury behavior (NSSI) in women of childbearing age with depression, and construct a nomogram prediction model.Methods:Adopting cross-sectional research method and retrospective cohort study design, a total of 278 female patients of childbearing age with depression admitted to Shaoxing Seventh People′s Hospital from January 2022 to July 2024 were selected as the modeling group by convenience sampling method, and were divided into the NSSI group and the non-NSSI group according to whether the patients had NSSI. Another 104 cases of female depression patients of childbearing age admitted to hospital during the same period were selected as the verification group. The influencing factors were screened by multi-factor Logistic regression analysis, and the nomogram prediction model was constructed by R software "rms" package.Results:In the modeling group, there were 98 cases in the NSSI group with an age of (32.58 ± 6.96) years, and 180 cases in the non-NSSI group with an age of (32.73 ± 7.08) years. In the verification group, there were 36 cases in the NSSI group with an age of (32.92 ± 7.76) years, and 68 cases in the non-NSSI group with an age of (33.18 ± 7.59) years. Multi-factor Logistic regression analysis showed that depression degree ( OR=4.834, 95% CI 2.089-11.185), family relationships ( OR=5.121, 95% CI 1.987-13.197), sleep disorders ( OR=2.302, 95% CI 1.203-4.408), childhood trauma ( OR=2.332, 95% CI 1.235-4.402), impulsivity ( OR=2.227, 95% CI 1.168-4.248), and Defeat Scale score ( OR=1.144, 95% CI 1.085-1.206) were influence factors for NSSI in female depression patients of childbearing age (all P<0.05). The C-index of the prediction model constructed based on this was 0.847 (0.800-0.895), and the calibration curve was close to the ideal curve. The results of the decision curve showed that the predictive model provided a higher clinical net benefit. Conclusions:The nomogram prediction model based on depression degree, family relationship, sleep disorder, childhood trauma, impulsivity and Defeat Scale score provides important strategic guidance for predicting and evaluating the occurrence of NSSI in women of childbearing age with depression and clinical nursing intervention.

Objective:To analyze the risk factors of non-suicidal self-injury behavior (NSSI) in women of childbearing age with depression, and construct a nomogram prediction model.Methods:Adopting cross-sectional research method and retrospective cohort study design, a total of 278 female patients of childbearing age with depression admitted to Shaoxing Seventh People′s Hospital from January 2022 to July 2024 were selected as the modeling group by convenience sampling method, and were divided into the NSSI group and the non-NSSI group according to whether the patients had NSSI. Another 104 cases of female depression patients of childbearing age admitted to hospital during the same period were selected as the verification group. The influencing factors were screened by multi-factor Logistic regression analysis, and the nomogram prediction model was constructed by R software "rms" package.Results:In the modeling group, there were 98 cases in the NSSI group with an age of (32.58 ± 6.96) years, and 180 cases in the non-NSSI group with an age of (32.73 ± 7.08) years. In the verification group, there were 36 cases in the NSSI group with an age of (32.92 ± 7.76) years, and 68 cases in the non-NSSI group with an age of (33.18 ± 7.59) years. Multi-factor Logistic regression analysis showed that depression degree ( OR=4.834, 95% CI 2.089-11.185), family relationships ( OR=5.121, 95% CI 1.987-13.197), sleep disorders ( OR=2.302, 95% CI 1.203-4.408), childhood trauma ( OR=2.332, 95% CI 1.235-4.402), impulsivity ( OR=2.227, 95% CI 1.168-4.248), and Defeat Scale score ( OR=1.144, 95% CI 1.085-1.206) were influence factors for NSSI in female depression patients of childbearing age (all P<0.05). The C-index of the prediction model constructed based on this was 0.847 (0.800-0.895), and the calibration curve was close to the ideal curve. The results of the decision curve showed that the predictive model provided a higher clinical net benefit. Conclusions:The nomogram prediction model based on depression degree, family relationship, sleep disorder, childhood trauma, impulsivity and Defeat Scale score provides important strategic guidance for predicting and evaluating the occurrence of NSSI in women of childbearing age with depression and clinical nursing intervention.

Objective:The Nomogram prediction model of secondary insomnia in adolescent depression patients was constructed to provide reference and basis for clinical prevention of secondary insomnia in adolescent depression patients.Methods:A total of 158 adolescent depression patients who were treated in the Department of Pediatric Psychiatry of Shaoxing Seventh People′s Hospital from May 2021 to March 2022 were selected as the research objects by convenient sampling method. According to the Pittsburgh Sleep Quality Index (PSQI) score, the group was divided into a normal sleep group (PSQI ≤ 7 points) and a secondary insomnia group (PSQI>7 points). A cross-sectional survey was conducted using the Hamilton Depression Scale, Hamilton Anxiety Scaleand Social Support Rating Scale. Multivariate binary Logistic regression was used to analyze the influencing factors of secondary insomnia and construct a Nomogram prediction model for secondary insomnia in adolescents with depression. The accuracy of the model was judged by receiver operating characteristic curve. In addition, 70 adolescent depression patients who were treated in Shaoxing Seventh People′s Hospital from May 2022 to March 2023 were selected for success rate prediction. The results were compared with the actual observed results, and the fitting degree of the model was evaluated by Hosmer-Lemeshow test.Results:There were 72 cases in the normal sleep group, including 40 males and 32 females, aged (15.34 ± 2.62) years old, and 86 cases in the secondary insomnia group, including 51 males and 35 females, aged (14.89 ± 2.13) years old. The results of multivariate analysis showed that depression ( OR=6.381, 95 % CI 1.548-26.295, P<0.05), anxiety ( OR=6.248, 95 % CI 1.445-27.005, P<0.05) and social support ( OR=0.586, 95 % CI 0.346-0.994, P<0.05) were independent influencing factors for the occurrence of secondary insomnia in the two groups. On this basis, a risk prediction model was established. The AUC was 0.999, the sensitivity was 98.80 %, the specificity was 98.60 %, and the Youden index was 0.974. The total prediction accuracy of the external validation model was 99.80 %. Conclusions:The Nomogram prediction model of secondary insomnia in adolescent depression patients has excellent predictive efficacy. The degree of depression, anxiety and social support are the main influencing factors of secondary insomnia in patients. Medical staff should provide more effective psychological support for patients.