Predictive modeling to identify undiagnosed COPD sufferers
Dr. med. Andrea Gillessen MBA / Dr. Sportwiss. Manfred Ramme / Prof. Dr. med. Claus Franz Vogelmeier
The most comprehensive study on the prevalence of COPD to date is the international Burden of Obstructive Lung Disease (BOLD) study (Buist & McBurnie, 2007). In Germany, the Hanover region took part in this work. The lung function was examined in 683 volunteers over the age of 40 and a survey was carried out as to their state of health. Overall, more than 13 percent of the study participants in the Hanover area were diagnosed with COPD, a good 7 percent had a mild, 5 percent a moderate and almost 1 percent a severe disease stage. Since the test subjects came from the Hanover metropolitan region, it cannot be said with certainty whether the numbers are representative for the whole of Germany. However, the overall evaluation of the BOLD study suggests this. According to this, every tenth of the more than 9,000 study participants in the twelve countries involved suffers from chronic obstructive pulmonary disease in stage II or higher. The current figures on the development of COPD in Germany were based on 6.8 million COPD cases in 2010. By 2030, the number of people affected is expected to rise to 7.9 million. (Geldmacher & Biller, 2008). COPD is also important from an economic perspective. One study determined the total costs of COPD by weighting the costs per level of severity. The main part of the costs is caused by hospital stays (26%), medications (23%) and early retirement (17%). (Herse & Kiljander, 2015); (Grønseth & S.-A., 2014); (Nowak & Dietrich, 2004). Early detection of COPD is of great importance, as timely cessation of exposure to the triggering noxa can at least partially prevent disease progression. (Vogelmeier & Buhl, 2018). There is also evidence that the natural course of the disease can be significantly influenced by the early onset of drug treatment. (Troosters & Celli, 2010).
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First publication date: 04.06.2018
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Abstract: Predictive Modeling zur Identifikation von nichtdiagnostizierten COPD-Erkrankten
Hintergrund: COPD ist eine chronische Lungenkrankheit mit progredientem Verlauf und hoher Morbidität. Die Früherkennung hat große Bedeutung, da der Krankheitsprogress in frühen Stadien partiell verhindert werden kann. Methode: Durch die Entwicklung eines Prädiktionsmodells zur Vorhersage der COPD-Inzidenz auf Basis von Sekundärdaten können Versicherte der Techniker Krankenkasse identifiziert werden, die im Folgejahr eine COPD-Diagnose erhalten werden, aber für die aktuell noch keine COPD dokumentiert wurde. Ergebnisse: Die wichtigsten Prädiktoren sind Alter, Medikamente aus dem Bereich der inhalativen Sympathomemetika sowie Bronchitis. Die Trefferquote des Modells liegt im 1. Perzentil zwischen 30 und 36% (Grundgesamtheit 6%), was einem Lift von 5 bis 6 entspricht (ROC-Index 0,69). Dies hat die praktische Konsequenz, dass Die Techniker über 3.000 Versicherte ohne dokumentierte COPD-Diagnose identifizieren kann, von denen für ca. 1.000 Versicherte im Folgejahr eine COPD-Diagnose vorliegen wird.Schlussfolgerung: Die Anwendung eines Prädiktionsmodells könnte möglicherweise zur Früherkennung von COPD-Erkrankten hilfreich sein.
Abstract: Identification of underdiagnosed COPD patients via predictive modelling
Background: Chronic obstructive pulmonary disease (COPD) is a disease with a progressive course and a high level of morbidity. Early recognition and therefore early diagnosis are of utmost importance because the progress of the disease can partially be prevented in the early stages. Methods: A newly developed prediction model for COPD incidence was developed and applied to secondary data of subjects insured with Die Techniker. The main goal was the early identification of high-risk subjects with no current record of COPD, but a positive future COPD diagnosis, i.e. in the subsequent year.Results: The most important predictors that could be identified were age, medications like inhaled sympathomimetics, as well as bronchitis. The hit rate of the prediction model lies in the 1st percentile between 30% and 36% (population 6%), which translates to a lift of 5 to 6 (ROC index 0.69). The practical consequence is that the Techniker Krankenkasse can identify more than 3,000 insured persons without currently documented COPD diagnosis of whom approx. 1,000 persons will be diagnosed with COPD in the following year.Conclusion: Using a prediction model can be useful for the early detection of COPD.
Keywords (English)
chronic obstructive pulmonary disease, early detection, predictive modeling, Screening
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