Journal of affective disorders · 2025
Rapid screening of depression and anxiety in outpatients: Symptom-based nomograms for early identification from gastrointestinal presentations.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Zhong Likun, Xu Zhiyue, Liu Xinghuang, Wang Dongke, Tong Qiaoyun, Tian Xia, Xiong Jianguang, Hou Xiaohua, Bai Tao
Аннотация
OBJECTIVES: Depression and anxiety are highly prevalent yet frequently underdiagnosed comorbidities in gastroenterology outpatients. This multicenter study aimed to develop and validate nomograms for predicting depression and anxiety risk in this population.
METHODS: This prospective multicenter study enrolled 2942 patients from 19 Chinese hospitals, with the training cohort (n = 1079) derived from a single center and the validation cohort (n = 1507) from 18 other hospitals for model construction. Participants completed structured questionnaires assessing demographic information, gastrointestinal symptoms, and psychometric evaluations. Key predictors were identified through multivariable logistic regression analysis, with subsequent development of nomograms for depression (PHQ-9 ≥ 10) and anxiety (GAD-7 ≥ 10). Model performance was evaluated through area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA).
RESULTS: The depression nomogram incorporated female gender, sleep disturbances, ≥3 clinic visits, retrosternal discomfort, and loss of appetite, achieving AUCs of 0.787 (0.755-0.818) in training and 0.799 (0.764-0.835) in validation cohorts. The anxiety model identified female gender, sleep disturbances, ≥3 clinic visits, globus sensation, retrosternal discomfort, and diarrhea, with AUCs of 0.740 (0.700-0.780) in training and 0.831 (0.799-0.862) in validation cohorts respectively. Calibration curves showed excellent observed-predicted agreement (Hosmer-Lemeshow test p > 0.05), while DCA confirmed clinical utility across risk thresholds for these models. External validation confirmed robust generalizability.
CONCLUSIONS: The first symptom-based nomograms for rapid depression and anxiety screening in gastroenterology outpatients were successfully developed through systematic integration of 5-6 routinely documented clinical variables. Validated across multicenter cohorts, these models enable efficient risk stratification during time-limited consultations.
METHODS: This prospective multicenter study enrolled 2942 patients from 19 Chinese hospitals, with the training cohort (n = 1079) derived from a single center and the validation cohort (n = 1507) from 18 other hospitals for model construction. Participants completed structured questionnaires assessing demographic information, gastrointestinal symptoms, and psychometric evaluations. Key predictors were identified through multivariable logistic regression analysis, with subsequent development of nomograms for depression (PHQ-9 ≥ 10) and anxiety (GAD-7 ≥ 10). Model performance was evaluated through area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA).
RESULTS: The depression nomogram incorporated female gender, sleep disturbances, ≥3 clinic visits, retrosternal discomfort, and loss of appetite, achieving AUCs of 0.787 (0.755-0.818) in training and 0.799 (0.764-0.835) in validation cohorts. The anxiety model identified female gender, sleep disturbances, ≥3 clinic visits, globus sensation, retrosternal discomfort, and diarrhea, with AUCs of 0.740 (0.700-0.780) in training and 0.831 (0.799-0.862) in validation cohorts respectively. Calibration curves showed excellent observed-predicted agreement (Hosmer-Lemeshow test p > 0.05), while DCA confirmed clinical utility across risk thresholds for these models. External validation confirmed robust generalizability.
CONCLUSIONS: The first symptom-based nomograms for rapid depression and anxiety screening in gastroenterology outpatients were successfully developed through systematic integration of 5-6 routinely documented clinical variables. Validated across multicenter cohorts, these models enable efficient risk stratification during time-limited consultations.