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SeonCheol Park,HwaYoung Lee,DongWoo Lee,SangWoo Hahn,SangHo Park,YeoJu Kim,JaeSung Choi,HoSung Lee,SoyoungIrene Lee,KyoungSae Na,SungWon Jung,SeHoon Shim,KiWon Kim,JongWoo Paik,YoungJoon Kwon 대한신경정신의학회 2017 PSYCHIATRY INVESTIGATION Vol.14 No.3
Objective-We aimed to identify depressive symptom profiles that indicated the presence of depressive disorder and present optimal cut-off sub-scores for depressive symptom profiles for detecting depressive disorder in elderly subjects with chronic physical diseases including diabetes, chronic obstructive pulmonary disease/asthma, and coronary artery disease, using the Patient Health Questionnaire-9 (PHQ-9). Methods-Two hundred and thirty-one elderly patients with chronic physical diseases were recruited consecutively from a university-affiliated general hospital in South Korea. Results-Greater severities of all 9 depressive symptoms in the PHQ-9 were presented in those with depressive disorder rather than those without depressive disorder. A binary logistic regression modeling presented that little interest [adjusted odds ratio (aOR)=4.648, p<0.001], reduced/increased sleep (aOR=3.269, p<0.001), psychomotor retardation/agitation (aOR=2.243, p=0.004), and concentration problem (aOR=16.116, p<0.001) were independently associated with increased likelihood of having depressive disorder. Receiver operating characteristics (ROC) curve analysis presented that the optimal cut-off value of score on the items for little interest, reduced/increased sleep, psychomotor retardation/agitation and concentration problem (PHQ-9) for detecting depressive disorder was 4 with 61.9% of sensitivity and 91.5% of specificity [area under curve (AUC)=0.937, p<0.001]. Conclusion-Our findings suggested that the diagnostic weighting of little interest, reduced/increased sleep, psychomotor retardation/agitation, and concentration problem is needed to detect depressive disorder among the elderly patients with chronic physical diseases.