Prevalence, patterns and prognosis of multimorbidity among middle-aged and elderly inpatients with chronic obstructive pulmonary disease / 中华预防医学杂志

Objective: To explore the characteristics, patterns of multimorbidity and the impact on quality of life and the prognosis of middle-aged and elderly patients with chronic obstructive pulmonary disease (COPD). Methods: This is a cross-sectional study. From January 2012 to December 2021, 939 middle-aged and elderly COPD patients hospitalized in Beijing Hospital were selected by the convenient sampling method. The basic data of patients and the date of 16 common chronic diseases were collected. Patterns of multimorbidity were depicted by cluster analysis. Generalized linear regression model and logistic regression were used to evaluate the multimorbidity patterns and their prognosis. Results: At least one multimorbidity existed among 93.40% of COPD patients, and the median number of multimorbidity was 3. The top five multimorbidity among the patients were hypertension (57.93%, 544/939), coronary heart disease (33.76%,317/939), heart failure (31.95%,300/939), hyperlipidemia (31.63%,297/939) and arrhythmia (27.37%,257/939). Four multimorbidity patterns were identified, cardiometabolic and metabolic multimorbidity, kidney disease multimorbidity, respiratory-digestive-tumor multimorbidity and other multimorbidity. Cardiometabolic and metabolic multimorbidity was most common (590/939, 62.83%). Compared with non-cardiometabolic and metabolic multimorbidity, the incharge ADL score of patients with this multimorbidity decreased by 7 points (95%CI:-11.22- -3.34), Correspondingly, patients with kidney disease multimorbidity decreased by 14 points (95%CI:-24.12- -3.30) on the incharge score. The presence or absence of kidney disease multimorbidity had the greatest impact on discharge score, which was reduced by 12 points in comparison with patients without this multimorbidity (95%CI:-22.43- -2.40). ICU admission is mostly affected by the presence of cardiometabolic and metabolic multimorbidity (OR=2.44, 95%CI: 1.51-3.92) and kidney disease multimorbidity (OR=2.58, 95%CI: 1.01-6.60). The risk of death is the highest for cardiometabolic and metabolic multimorbidity (OR=2.24, 95%CI: 1.19-4.21). Conclusion: Multimorbidity is common in COPD patients. The most common pattern is cardiometabolic and metabolic multimorbidity. Cardiometabolic and metabolic multimorbidity and kidney disease multimorbidity significantly affect the quality of life and often associate with a poor prognosis.

Investigation of distinct clinical phenotypes of airways disease in the elderly based on hierarchical cluster analysis / 中华老年医学杂志

Objective To explore the clinical phenotype of airways disease in elderly patients using hierarchical cluster analysis.Methods A total of 67 elderly patients with respiratory symptoms were enrolled in a prospective study.Demographic and clinical data,such as respiratory symptoms,cumulative tobacco cigarette consumption,acute exacerbation,atopic symptoms and peak flow diary were collected.Pulmonary function tests,blood tests (total serum IgE level and blood eosinophil level) were performed in each patient during the stable stage.Then patients with different clinical phenotype were identified by hierarchical cluster analysis.Results Four clusters were identified with the following characteristics by hierarchical cluster analysis:cluster 1,atopic patients with no smoking,normal lung function,but increased total serum IgE levels and asthma symptom;cluster 2,patients with no smoking and normal pulmonary function with wheezing but without chronic cough;cluster 3,patients with chronic obstructive pulmonary disease and smoking,severe airflow limitation and poor quality of life;cluster 4,patients with asthma-chronic obstructive pulmonary disease overlap syndrome and smoking,airflow limitation and increased total serum IgE levels.The forced expiratory volume in 1 second (FEV1) / forced vital capacity (FVC) ratio,FEV1/predicted value,rate of FEV1 change,maximal mid-expiratory flow (MMEF)/ predicted value,the diffusion lung capacity for carbon monoxide (DLCO)/alveolar volume (VA)/predicted value,residual volume (RV)/ predicted value,total serum Ig E levels,cumulative tobacco cigarette consumption,the St.George's Respiratory Questionnaire (SGRQ) score had significant differences in patients before versus after treatment (all P＜0.05 or P＜0.01).Conclusions Based on hierarchical cluster analysis,distinct clinical phenotypes of airways disease in elderly patients can be identified.Conclusions With patients having asthma or COPD alone,patients with Asthma-COPD overlap syndrome (ACOS) always experience a more rapid decline in lung function and frequent exacerbations,having poor health-related quality-of-life (HRQOL) outcomes,which deserve our high attention.

Study of the clinical phenotype of symptomatic chronic airways disease by hierarchical cluster analysis and two-step cluster analyses / 中华内科杂志

Objective To study the distinct clinical phenotype of chronic airway diseases by hierarchical cluster analysis and two-step cluster analysis.Methods A population sample of adult patients in Donghuamen community,Dongcheng district and Qinghe community,Haidian district,Beijing from April 2012 to January 2015,who had wheeze within the last 12 months,underwent detailed investigation,including a clinical questionnaire,pulmonary function tests,total serum IgE levels,blood eosinophil level and a peak flow diary.Nine variables were chosen as evaluating parameters,including pre-salbutamol forced expired volume in one second(FEV1)/forced vital capacity (FVC) ratio,pre-salbutamol FEV1,percentage of post-salbutamol change in FEV1,residual capacity,diffusing capacity of the lung for carbon monoxide/alveolar volume adjusted for haemoglobin level,peak expiratory flow (PEF) variability,serum IgE level,cumulative tobacco cigarette consumption (pack-years) and respiratory symptoms (cough and expectoration).Subjects' different clinical phenotype by hierarchical cluster analysis and two-step cluster analysis was identified.Results (1) Four clusters were identified by hierarchical cluster analysis.Cluster 1 was chronic bronchitis in smokers with normal pulmonary function.Cluster 2 was chronic bronchitis or mild chronic obstructive pulmonary disease (COPD) patients with mild airflow limitation.Cluster 3 included COPD patients with heavy smoking,poor quality of life and severe airflow limitation.Cluster 4 recognized atopic patients with mild airflow limitation,elevated serum IgE and clinical features of asthma.Significant differences were revealed regarding pre-salbutamol FEV1/FVC％,pre-salbutamol FEV1％ pred,postsalbutamol change in FEV1 ％,maximal mid-expiratory flow curve (MMEF)％ pred,carbon monoxide diffusing capacity per liter of alveolar(DLCO)/(VA)％ pred,residual volume(RV)％ pred,total serum IgE level,smoking history (pack-years),St.George' s respiratory questionnaire (SGRQ) score,acute exacerbation in the past one year,PEF variability and allergic dermatitis (P ＜ 0.05).(2) Four clusters were also identified by two-step cluster analysis as followings,cluster 1,COPD patients with moderate to severe airflow limitation;cluster 2,asthma and COPD patients with heavy smoking,airflow limitation and increased airways reversibility;cluster 3,patients having less smoking and normal pulmonary function with wheezing but no chronic cough;cluster 4,chronic bronchitis patients with normal pulmonary function and chronic cough.Significant differences were revealed regarding gender distribution,respiratory symptoms,pre-salbutamol FEV1/FVC％,pre-salbutamol FEV1 ％ pred,post-salbutamol change in FEV1 ％,MMEF％ pred,DLCO/VA％ pred,RV％ pred,PEF variability,total serum IgE level,cumulative tobacco cigarette consumption (pack-years),and SGRQ score (P ＜ 0.05).Conclusion By different cluster analyses,distinct clinical phenotypes of chronic airway diseases are identified.Thus,individualized treatments may guide doctors to provide based on different phenotypes.