The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Paul Glasziou - One of the best experts on this subject based on the ideXlab platform.
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current guidelines poorly address multimorbidity pilot of the Interaction Matrix method
Journal of Clinical Epidemiology, 2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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.
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ORIGINAL ARTICLES Current guidelines poorly address multimorbidity: pilot of the Interaction Matrix method
2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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 (2e4 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 diseaseedisease (Di-Di-I), diseaseedrug (Di-D-I), drugedrug 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 5 247 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
Marjan Van Den Akker - One of the best experts on this subject based on the ideXlab platform.
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current guidelines poorly address multimorbidity pilot of the Interaction Matrix method
Journal of Clinical Epidemiology, 2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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.
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ORIGINAL ARTICLES Current guidelines poorly address multimorbidity: pilot of the Interaction Matrix method
2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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 (2e4 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 diseaseedisease (Di-Di-I), diseaseedrug (Di-D-I), drugedrug 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 5 247 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
Christiane Muth - One of the best experts on this subject based on the ideXlab platform.
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current guidelines poorly address multimorbidity pilot of the Interaction Matrix method
Journal of Clinical Epidemiology, 2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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.
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ORIGINAL ARTICLES Current guidelines poorly address multimorbidity: pilot of the Interaction Matrix method
2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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 (2e4 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 diseaseedisease (Di-Di-I), diseaseedrug (Di-D-I), drugedrug 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 5 247 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
Hanna Kirchner - One of the best experts on this subject based on the ideXlab platform.
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current guidelines poorly address multimorbidity pilot of the Interaction Matrix method
Journal of Clinical Epidemiology, 2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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.
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ORIGINAL ARTICLES Current guidelines poorly address multimorbidity: pilot of the Interaction Matrix method
2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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 (2e4 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 diseaseedisease (Di-Di-I), diseaseedrug (Di-D-I), drugedrug 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 5 247 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
Martin Scherer - One of the best experts on this subject based on the ideXlab platform.
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current guidelines poorly address multimorbidity pilot of the Interaction Matrix method
Journal of Clinical Epidemiology, 2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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.
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ORIGINAL ARTICLES Current guidelines poorly address multimorbidity: pilot of the Interaction Matrix method
2014Co-Authors: Christiane Muth, Hanna Kirchner, Marjan Van Den Akker, Martin Scherer, Paul GlasziouAbstract: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 (2e4 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 diseaseedisease (Di-Di-I), diseaseedrug (Di-D-I), drugedrug 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 5 247 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
