Matesanz-Fernández María, Seoane-Pillado Teresa, Iñiguez-Vázquez Iria, Suárez-Gil Roi, Pértega-Díaz Sonia, Casariego-Vales Emilio
Medicina Interna, Hospital Universitario Lucus Augusti, Lugo, Spain
Área de Medicina Preventiva y Salud pública, Departamento de Ciencias de la Salud, Universidade da Coruña, A Coruña, Spain.
Postgrad Med J. 2022 Apr;98(1158):294-299. doi: 10.1136/postgradmedj-2020-139361. Epub 2021 Feb 5.
We aim to identify patterns of disease clusters among inpatients of a general hospital and to describe the characteristics and evolution of each group.
We used two data sets from the CMBD (Conjunto mínimo básico de datos - Minimum Basic Hospital Data Set (MBDS)) of the Lucus Augusti Hospital (Spain), hospitalisations and patients, realising a retrospective cohort study among the 74 220 patients discharged from the Medic Area between 01 January 2000 and 31 December 2015. We created multimorbidity clusters using multiple correspondence analysis.
We identified five clusters for both gender and age. Cluster 1: alcoholic liver disease, alcoholic dependency syndrome, lung and digestive tract malignant neoplasms (age under 50 years). Cluster 2: large intestine, prostate, breast and other malignant neoplasms, lymphoma and myeloma (age over 70, mostly males). Cluster 3: malnutrition, Parkinson disease and other mobility disorders, dementia and other mental health conditions (age over 80 years and mostly women). Cluster 4: atrial fibrillation/flutter, cardiac failure, chronic kidney failure and heart valve disease (age between 70-80 and mostly women). Cluster 5: hypertension/hypertensive heart disease, type 2 diabetes mellitus, ischaemic cardiomyopathy, dyslipidaemia, obesity and sleep apnea, including mostly men (age range 60-80). We assessed significant differences among the clusters when gender, age, number of chronic pathologies, number of rehospitalisations and mortality during the hospitalisation were assessed (p<0001 in all cases).
We identify for the first time in a hospital environment five clusters of disease combinations among the inpatients. These clusters contain several high-incidence diseases related to both age and gender that express their own evolution and clinical characteristics over time.
我们旨在识别一家综合医院住院患者中的疾病聚集模式,并描述每组的特征和演变情况。
我们使用了西班牙卢库斯·奥古斯蒂医院的CMBD(Conjunto mínimo básico de datos - 最小基本医院数据集(MBDS))中的两个数据集,即住院数据和患者数据,对2000年1月1日至2015年12月31日期间从医疗区出院的74220名患者进行了回顾性队列研究。我们使用多重对应分析创建了多病共患聚集组。
我们针对性别和年龄分别识别出了五个聚集组。聚集组1:酒精性肝病、酒精依赖综合征、肺和消化道恶性肿瘤(年龄在50岁以下)。聚集组2:大肠、前列腺、乳腺和其他恶性肿瘤、淋巴瘤和骨髓瘤(年龄在70岁以上,以男性为主)。聚集组3:营养不良、帕金森病和其他运动障碍、痴呆和其他心理健康状况(年龄在80岁以上,以女性为主)。聚集组4:心房颤动/扑动、心力衰竭、慢性肾衰竭和心脏瓣膜病(年龄在70 - 80岁之间,以女性为主)。聚集组5:高血压/高血压性心脏病、2型糖尿病、缺血性心肌病、血脂异常、肥胖和睡眠呼吸暂停,主要为男性(年龄范围60 - 80岁)。当评估性别、年龄、慢性疾病数量、再次住院次数和住院期间死亡率时,我们评估了各聚集组之间的显著差异(所有情况下p<0.001)。
我们首次在医院环境中识别出住院患者中的五个疾病组合聚集组。这些聚集组包含几种与年龄和性别相关的高发病,随着时间的推移呈现出各自的演变和临床特征。