Christiane Muth1, Hanna Kirchner2, Marjan van den Akker3, Martin Scherer4, Paul P Glasziou5. 1. Institute of General Practice, Johann Wolfgang Goethe University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany. Electronic address: muth@allgemeinmedizin.uni-frankfurt.de. 2. Institute of General Practice, Johann Wolfgang Goethe University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany; Department of Primary Medical Care, University Medical Centre Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany. 3. Department of Family Medicine, School CAPHRI, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of General Practice, Katholieke Universiteit Leuven, Kapucijnenvoer 33, blok J, 3000 Leuven, Belgium. 4. Department of Primary Medical Care, University Medical Centre Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany. 5. The Centre for Research in Evidence-Based Practice (CREBP), Bond University, Gold Coast, Robina, Queensland 4226, Australia.
Abstract
OBJECTIVES: To develop a framework to identify and classify interactions within and among treatments and conditions and to test this framework with guidelines on chronic heart failure (CHF) and its frequent comorbidity. STUDY DESIGN AND SETTING: Text analysis of evidence-based clinical practice guidelines on CHF and 18 conditions co-occurring in ≥5% of CHF patients (2-4 guidelines per disease). We extracted data on interactions between CHF and comorbidity and key recommendations on diagnostic and therapeutic management. From a subset of data, we derived 13 subcategories within disease-disease (Di-Di-I), disease-drug (Di-D-I), drug-drug interactions (DDI) and synergistic treatments. We classified the interactions and tested the interrater reliability, refined the framework, and agreed on the matrix of interactions. RESULTS: We included 48 guidelines; two-thirds provided information about comorbidity. In total, we identified N = 247 interactions (on average, 14 per comorbidity): 68 were Di-Di-I, 115 were Di-D-I, 12 were DDI, and 52 were synergisms. All 18 comorbidities contributed at least one interaction. CONCLUSION: The interaction matrix provides a structure to present different types of interactions between an index disease and comorbidity. Guideline developers may consider the matrix to support clinical decision making in multimorbidity. Further research is needed to show its relevance to improve guidelines and health outcomes.
OBJECTIVES: To develop a framework to identify and classify interactions within and among treatments and conditions and to test this framework with guidelines on chronic heart failure (CHF) and its frequent comorbidity. STUDY DESIGN AND SETTING: Text analysis of evidence-based clinical practice guidelines on CHF and 18 conditions co-occurring in ≥5% of CHFpatients (2-4 guidelines per disease). We extracted data on interactions between CHF and comorbidity and key recommendations on diagnostic and therapeutic management. From a subset of data, we derived 13 subcategories within disease-disease (Di-Di-I), disease-drug (Di-D-I), drug-drug interactions (DDI) and synergistic treatments. We classified the interactions and tested the interrater reliability, refined the framework, and agreed on the matrix of interactions. RESULTS: We included 48 guidelines; two-thirds provided information about comorbidity. In total, we identified N = 247 interactions (on average, 14 per comorbidity): 68 were Di-Di-I, 115 were Di-D-I, 12 were DDI, and 52 were synergisms. All 18 comorbidities contributed at least one interaction. CONCLUSION: The interaction matrix provides a structure to present different types of interactions between an index disease and comorbidity. Guideline developers may consider the matrix to support clinical decision making in multimorbidity. Further research is needed to show its relevance to improve guidelines and health outcomes.
Authors: Ferrán Catalá-López; Adolfo Alonso-Arroyo; Matthew J Page; Brian Hutton; Rafael Tabarés-Seisdedos; Rafael Aleixandre-Benavent Journal: PLoS One Date: 2018-01-03 Impact factor: 3.240
Authors: Daniel Hausmann; Vera Kiesel; Lukas Zimmerli; Narcisa Schlatter; Amandine von Gunten; Nadine Wattinger; Thomas Rosemann Journal: PLoS One Date: 2019-04-10 Impact factor: 3.240
Authors: Christiane Muth; Marjan van den Akker; Jeanet W Blom; Christian D Mallen; Justine Rochon; François G Schellevis; Annette Becker; Martin Beyer; Jochen Gensichen; Hanna Kirchner; Rafael Perera; Alexandra Prados-Torres; Martin Scherer; Ulrich Thiem; Hendrik van den Bussche; Paul P Glasziou Journal: BMC Med Date: 2014-12-08 Impact factor: 8.775