Classification of exacerbation episodes in chronic obstructive pulmonary disease patients.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive disease affecting the airways, which constitutes a major cause of chronic morbidity and a significant economic and social burden throughout the world. Despite the fact that in COPD patients exacerbations are common acute events causing significant and often fatal worsening of symptoms, an accurate prognostication continues to be difficult. OBJECTIVES: To build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers. METHODS: We recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period we recorded six exacerbation episodes in the patients, accounting for 37 days. We were able to analyse data for 52 patients (369 patient days), and extracted three distinct sets of features from the data, one set of basic features such as average, one set based on the frequency domain and the last exploring the cross-information among sensors pairs. These were used by three machine-learning techniques (logarithmic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days. RESULTS: The support vector machine classifier achieved an AUC of 90% ± 9, when supplied with a set of features resulting from sequential feature selection method. Neu- ral networks achieved an AUC of 83% ± 16 and the logarithmic regression an AUC of 67% ± 15. CONCLUSIONS: None of the individual feature sets provided robust for reasonable classification of PA recording days. Our results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.

[Multimorbidity and successful aging: the population-based KORA-Age study]. / Multimorbidität und erfolgreiches Altern: Ein Blick auf die Bevölkerung im Rahmen der KORA-Age-Studie.

BACKGROUND: The objective of the KORA-Age research consortium is to assess the determinants and consequences of multimorbidity in the elderly and to look into reasons for successful aging in the general public. PATIENTS AND METHODS: In the KORA-Age cohort study 9,197 persons were included who where born in the year 1943 or before and participants of previous KORA cohort studies conducted between 1984 and 2001 (KORA: Cooperative Health Research in the Region of Augsburg). The randomized intervention study KORINNA (Coronary infarct follow-up treatment in the elderly) tested a nurse-based case management program with 338 patients with myocardial infarct and included an evaluation in health economics. RESULTS: A total of 2,734 deaths were registered, 4,565 participants submitted a postal health status questionnaire and 4,127 participants were interviewed by telephone (response 76.2% and 68.9% respectively). A gender and age-stratified random sample of the cohort consisting of 1,079 persons took part in a physical examination (response 53.8%). CONCLUSION: The KORA-Age consortium was able to collect data in a large population-based sample and is contributing to the understanding of multimorbidity and successful aging.