Predictive Model of Hospital Admission for COPD Exacerbation. 2015

Josep Montserrat-Capdevila, and Pere Godoy, and Josep Ramon Marsal, and Ferran Barbé
Biomedical Research Institute (IRB) of Lleida, the Public Health Agency of Catalonia, Health Department, and the Catalan Institute of Health (ICS), Catalonia, Spain. jmontser@alumni.unav.es.

BACKGROUND The objective of this work was to determine predictive factors of hospital admission for exacerbation during primary care visits in patients with COPD. METHODS A retrospective cohort study was undertaken to assess risk of hospital admission for COPD exacerbation in primary care patients from November 1, 2010 to October 31, 2013. Data sources were primary care electronic medical records and the hospital discharge minimum data set. A total of 2,501 subjects >40 y of age with a spirometry-based COPD diagnosis were included and followed up for 3 y. The dependent variable was hospital admission for exacerbation; independent variables were: clinical parameters, spirometry results, and severity of disease (according to Global Initiative for Chronic Obstructive Lung Disease criteria). The association of these variables with hospital admission was analyzed with the adjusted odds ratio using a logistic regression model. RESULTS Mean age of subjects at the beginning of the study was 68.4 y (SD = 11.6), and 75% were men. Severity was mild in 50.8% of subjects, moderate in 35.3%, severe in 9.4%, and very severe in 4.4%. After 3 y, 32.5% of subjects had been admitted for exacerbation. Predictive values for hospital admission were: age, sex, previous exacerbations, number of visits to the primary care center, comorbidities, smoking, severity (Global Initiative for Chronic Obstructive Lung Disease), and influenza immunization. The area under the receiving operator characteristic curve was 0.72. CONCLUSIONS This model can identify patients at high risk of hospital admission for COPD exacerbation in our setting. Further studies are needed to validate the model in different populations and settings.

UI MeSH Term Description Entries
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D009819 Office Visits Visits made by patients to health service providers' offices for diagnosis, treatment, and follow-up. Office Visit,Visit, Office,Visits, Office
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D011320 Primary Health Care Care which provides integrated, accessible health care services by clinicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients, and practicing in the context of family and community. (JAMA 1995;273(3):192) Primary Care,Primary Healthcare,Care, Primary,Care, Primary Health,Health Care, Primary,Healthcare, Primary
D005260 Female Females
D005500 Follow-Up Studies Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease. Followup Studies,Follow Up Studies,Follow-Up Study,Followup Study,Studies, Follow-Up,Studies, Followup,Study, Follow-Up,Study, Followup
D006760 Hospitalization The confinement of a patient in a hospital. Hospitalizations
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly

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