Objective To investigate cost and influencing factors of the economic burden of chronic respiratory disease in hospitalize elderly patients. Methods A convenience sample of 284 elderly patients with chronic respiratory disease, investigated with Geriatric Depression Scale (GDS-15) and EuroQol-5 Dimensions (EQ-5D). Results The hospitalization expenses was (8 810.80 ± 4 290.90) yuan,especially medical costs was the highest(54.18%).Correlation analysis and the outcome of multiple lineal stepwise regression reveals that durations of hospitalization (r=0.822), value of GDS-15 (r=0.558), value of EQ-5D (r=0.486), medical insurance types (r=0.290), nutrition medicine (r=0.226), prognosis of disease (r=0.188), measures of PTA (r=0.158), chronic complications (r=0.143) and reimbursement ratio (r=-0.211) were related with the direct economic burden (P<0.05). Conclusion The durations of hospitalization,degree of depression,medical insurance types,complications,nutrition medicine,level of healthy,and the antibacterial effect are the main influence factors of the direct economic burden.Among them,the durations of hospitalization is more severe.

Objective:To compare the value of NoSAS score, STOP-BANG questionnaire (SBQ) and Epworth Sleepiness Scale (ESS) in assessing the risk of obstructive sleep apnea hypopnea syndrome (OSAHS) in patients with respiratory disease (RD).Methods:The clinical data, NoSAS, SBQ and ESS scores of 190 patients who underwent overnight polysomnography (PSG) were collected. According to the receiver operating characteristic (ROC) curve, with different apnea-hypopnea index (AHI) as the judgment cutoff, the sensitivity, specificity, positive predictive value, negative predictive value, diagnostic odds ratio (DOR) and accuracy of the three scales were compared.Results:With AHI ≥5 times/h as the cutoff, the area under the ROC curve (AUC) of NoSAS and SBQ were 0.833 and 0.729, respectively, indicating that both have predictive value for mild OSAHS. Among them, NoSAS had a larger DOR value (16.150), indicating that NoSAS had the higher accuracy in assessing the risk of mild OSAHS. When AHI>15 times/h was used as the cutoff, the AUC value of NoSAS was 0.704, indicating that it has predictive value for moderate OSAHS. When AHI>30 times/h was used as the cutoff, the AUC value (0.706) and DOR value (6.527) of SBQ were high, indicating that it has predictive value and good accuracy for severe OSAHS. The SBQ is more sensitive than NoSAS and ESS when evaluating patients at high risk for OSAHS ( SBQ≥3). Conclusions:When evaluating the risk of mild and moderate OSAHS in RD patients, NoSAS is better than SBQ and ESS, and when evaluating severe OSAHS, SBQ is better than NoSAS and ESS. In clinical work, appropriate predictive tools should be selected according to the actual situation to assess the risk of OSAHS, so as to formulate and implement early intervention plans based on the assessment results.