BMC nursing · 2026
Development of a visualized calculation nomogram for comorbid depression and anxiety symptoms among nurses based on second victim experience and support-A cross-sectional study.
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Zhang Ke, Gu ZhiHui
Аннотация
BACKGROUND: This cross-sectional study aimed to explore factors associated with comorbid depression and anxiety symptoms (CADS) among nurses who had experienced adverse medical events, and to develop a visualized calculation nomogram for estimating the probability of CADS in individuals.
METHODS: A total of 2,457 nurses from the Northeastern Three Provinces China were enrolled based on Wenjuanxing from March to July 2024. Depression and anxiety were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), and CADS was defined using prespecified clinical thresholds (PHQ-9 ≥ 10 and GAD-7 ≥ 7). Latent class analysis (LCA) was conducted to identify subgroups of second victim experience and support (SVES). Multivariate logistic regression was performed to screen independent associated factors, and a visualized calculation nomogram was constructed and internally validated using a 70/30 split sample and bootstrap resampling.
RESULTS: 40% of the nurses exhibited CADS. Four classes were identified: "Low SVE and Low Support" (21.6%), "Low SVE and High Support" (10.2%), "Moderate SVE and Moderate Support" (50.4%), and "High SVE and Low Support" (17.8%). Independent risk factors for CADS included Moderate/High SVE with corresponding Support, age 30 ~ 40 years and >40 years, ≥3 weekly coffee drinks, weekly working hours 45 ~ 50 and >50 hours, and ≥6 monthly night shifts. Protective factors included psychological detachment, psychological empowerment, weekly physical exercise, and monthly income ≥ 8001 CNY. Internal evaluation of the nomogram showed good discriminative ability (AUC = 0.734), calibration, and clinical applicability, and the validation cohort confirmed robust performance (AUC = 0.938) with excellent calibration and clinical value.
CONCLUSIONS: CADS is prevalent among nurses. This study provides a user-friendly visualized calculation nomogram to help non-specialists quickly estimate individual probabilities of CADS in a cross-sectional sample.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: A total of 2,457 nurses from the Northeastern Three Provinces China were enrolled based on Wenjuanxing from March to July 2024. Depression and anxiety were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), and CADS was defined using prespecified clinical thresholds (PHQ-9 ≥ 10 and GAD-7 ≥ 7). Latent class analysis (LCA) was conducted to identify subgroups of second victim experience and support (SVES). Multivariate logistic regression was performed to screen independent associated factors, and a visualized calculation nomogram was constructed and internally validated using a 70/30 split sample and bootstrap resampling.
RESULTS: 40% of the nurses exhibited CADS. Four classes were identified: "Low SVE and Low Support" (21.6%), "Low SVE and High Support" (10.2%), "Moderate SVE and Moderate Support" (50.4%), and "High SVE and Low Support" (17.8%). Independent risk factors for CADS included Moderate/High SVE with corresponding Support, age 30 ~ 40 years and >40 years, ≥3 weekly coffee drinks, weekly working hours 45 ~ 50 and >50 hours, and ≥6 monthly night shifts. Protective factors included psychological detachment, psychological empowerment, weekly physical exercise, and monthly income ≥ 8001 CNY. Internal evaluation of the nomogram showed good discriminative ability (AUC = 0.734), calibration, and clinical applicability, and the validation cohort confirmed robust performance (AUC = 0.938) with excellent calibration and clinical value.
CONCLUSIONS: CADS is prevalent among nurses. This study provides a user-friendly visualized calculation nomogram to help non-specialists quickly estimate individual probabilities of CADS in a cross-sectional sample.
CLINICAL TRIAL NUMBER: Not applicable.