Aims and objectives: To investigate and analyse the prevalence of depression among patients with lung cancer, identify risk factors of depression, and develop a visual, non-invasive, and straightforward clinical prediction model that can be used to predict the risk probability of depression in patients with lung cancer quantitatively.Background: Depression is one of the common concomitant symptoms of patients with lung cancer, which can increase the risk of suicide. However, the current assessment tools cannot combine multiple risk factors to predict the risk probability of depression in patients.
Design:A cross-sectional study.
Methods:The clinical data from 297 patients with lung cancer in China were collected and analysed in this cross-sectional study. The clinical prediction model was constructed according to the results of the Chi-square test and the logistic regression analysis, evaluated by discrimination, calibration, and decision curve analysis, and visualised by a nomogram. This study was reported using the TRIPOD checklist.Results: 130 patients with lung cancer had depressive symptoms with a prevalence of 43.77%. A visual prediction model was constructed based on age, disease duration, exercise, stigma, and resilience. This model showed good discrimination at an AUC of 0.842. Calibration curve analysis indicated a good agreement between experimental and predicted values, and the decision curve analysis showed a high clinical utility.
Conclusions:The visual prediction model developed in this study has excellent performance, which can accurately predict the occurrence of depression in patients with lung cancer at an early stage and assist the medical staff in taking targeted preventative measures.
Relevance to clinical practice:The visual, non-invasive, and simple nomogram can help clinical medical staff to calculate the risk probability of depression among patients with lung cancer, formulate personalised preventive care measures for high-risk groups as soon as possible, and improve the quality of life of patients.
The purpose of our study was to evaluate the application value of the GOAL questionnaire in screening obstructive sleep apnea (OSA) and to compare it with the other three questionnaires in sleep clinics. A cross-sectional study was conducted in 436 patients who had undergone nocturnal polysomnography in the sleep unit of the First Hospital of Shanxi Medical University between September 2021 and May 2022, and all patients completed the four questionnaires (GOAL questionnaire, STOP-Bang questionnaire, NoSAS score and No-Apnea score) truthfully, and the patients were divided into 3 groups: AHI ≥ 5 events/h group, AHI ≥ 15 events/h group and AHI ≥ 30 events/h group. The predictive effect of the questionnaire on different AHI cut-off values was calculated, and performance of four questionnaires was assessed by the discriminatory ability. This study ultimately included 410 patients, and there were statistically significant differences in gender, age, BMI, neck circumference, clinical symptoms, hypertension, diabetes, AHI, and minimum oxygen saturation between OSA and non-OSA groups (P < 0.05). The AUC for No-Apnea score was 0.79, the AUC for STOP-Bang questionnaire was 0.86, the AUC for NoSAS score was 0.81, and the AUC for GOAL questionnaire was 0.77. These four questionnaires were effective in screening OSA when AHI ≥ 15 events/h. Similar to No-Apnea score, STOP-Bang score and NoSAS score, GOAL questionnaire has a good predictive value for OSA, which is a questionnaire suitable for primary health-care centers and clinics.
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