Classification algorithm for the International Classification of Diseases-11 chronic pain classification: development and results from a preliminary pilot evaluation
The International Classification of Diseases-11 (ICD-11) chronic pain classification includes about 100 chronic pain diagnoses on different diagnostic levels. Each of these diagnoses requires specific operationalized diagnostic criteria to be present. The classification comprises more than 200 diagn...
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| Veröffentlicht in: | Pain (Amsterdam) Jg. 162; H. 7; S. 2087 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.07.2021
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| ISSN: | 1872-6623, 1872-6623 |
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| Abstract | The International Classification of Diseases-11 (ICD-11) chronic pain classification includes about 100 chronic pain diagnoses on different diagnostic levels. Each of these diagnoses requires specific operationalized diagnostic criteria to be present. The classification comprises more than 200 diagnostic criteria. The aim of the Classification Algorithm for Chronic Pain in ICD-11 (CAL-CP) is to facilitate the use of the classification by guiding users through these diagnostic criteria. The diagnostic criteria were ordered hierarchically and visualized in accordance with the standards defined by the Society for Medical Decision Making Committee on Standardization of Clinical Algorithms. The resulting linear decision tree underwent several rounds of iterative checks and feedback by its developers, as well as other pain experts. A preliminary pilot evaluation was conducted in the context of an ecological implementation field study of the classification itself. The resulting algorithm consists of a linear decision tree, an introduction form, and an appendix. The initial decision trunk can be used as a standalone algorithm in primary care. Each diagnostic criterion is represented in a decision box. The user needs to decide for each criterion whether it is present or not, and then follow the respective yes or no arrows to arrive at the corresponding ICD-11 diagnosis. The results of the pilot evaluation showed good clinical utility of the algorithm. The CAL-CP can contribute to reliable diagnoses by structuring a way through the classification and by increasing adherence to the criteria. Future studies need to evaluate its utility further and analyze its impact on the accuracy of the assigned diagnoses. |
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| AbstractList | The International Classification of Diseases-11 (ICD-11) chronic pain classification includes about 100 chronic pain diagnoses on different diagnostic levels. Each of these diagnoses requires specific operationalized diagnostic criteria to be present. The classification comprises more than 200 diagnostic criteria. The aim of the Classification Algorithm for Chronic Pain in ICD-11 (CAL-CP) is to facilitate the use of the classification by guiding users through these diagnostic criteria. The diagnostic criteria were ordered hierarchically and visualized in accordance with the standards defined by the Society for Medical Decision Making Committee on Standardization of Clinical Algorithms. The resulting linear decision tree underwent several rounds of iterative checks and feedback by its developers, as well as other pain experts. A preliminary pilot evaluation was conducted in the context of an ecological implementation field study of the classification itself. The resulting algorithm consists of a linear decision tree, an introduction form, and an appendix. The initial decision trunk can be used as a standalone algorithm in primary care. Each diagnostic criterion is represented in a decision box. The user needs to decide for each criterion whether it is present or not, and then follow the respective yes or no arrows to arrive at the corresponding ICD-11 diagnosis. The results of the pilot evaluation showed good clinical utility of the algorithm. The CAL-CP can contribute to reliable diagnoses by structuring a way through the classification and by increasing adherence to the criteria. Future studies need to evaluate its utility further and analyze its impact on the accuracy of the assigned diagnoses.ABSTRACTThe International Classification of Diseases-11 (ICD-11) chronic pain classification includes about 100 chronic pain diagnoses on different diagnostic levels. Each of these diagnoses requires specific operationalized diagnostic criteria to be present. The classification comprises more than 200 diagnostic criteria. The aim of the Classification Algorithm for Chronic Pain in ICD-11 (CAL-CP) is to facilitate the use of the classification by guiding users through these diagnostic criteria. The diagnostic criteria were ordered hierarchically and visualized in accordance with the standards defined by the Society for Medical Decision Making Committee on Standardization of Clinical Algorithms. The resulting linear decision tree underwent several rounds of iterative checks and feedback by its developers, as well as other pain experts. A preliminary pilot evaluation was conducted in the context of an ecological implementation field study of the classification itself. The resulting algorithm consists of a linear decision tree, an introduction form, and an appendix. The initial decision trunk can be used as a standalone algorithm in primary care. Each diagnostic criterion is represented in a decision box. The user needs to decide for each criterion whether it is present or not, and then follow the respective yes or no arrows to arrive at the corresponding ICD-11 diagnosis. The results of the pilot evaluation showed good clinical utility of the algorithm. The CAL-CP can contribute to reliable diagnoses by structuring a way through the classification and by increasing adherence to the criteria. Future studies need to evaluate its utility further and analyze its impact on the accuracy of the assigned diagnoses. The International Classification of Diseases-11 (ICD-11) chronic pain classification includes about 100 chronic pain diagnoses on different diagnostic levels. Each of these diagnoses requires specific operationalized diagnostic criteria to be present. The classification comprises more than 200 diagnostic criteria. The aim of the Classification Algorithm for Chronic Pain in ICD-11 (CAL-CP) is to facilitate the use of the classification by guiding users through these diagnostic criteria. The diagnostic criteria were ordered hierarchically and visualized in accordance with the standards defined by the Society for Medical Decision Making Committee on Standardization of Clinical Algorithms. The resulting linear decision tree underwent several rounds of iterative checks and feedback by its developers, as well as other pain experts. A preliminary pilot evaluation was conducted in the context of an ecological implementation field study of the classification itself. The resulting algorithm consists of a linear decision tree, an introduction form, and an appendix. The initial decision trunk can be used as a standalone algorithm in primary care. Each diagnostic criterion is represented in a decision box. The user needs to decide for each criterion whether it is present or not, and then follow the respective yes or no arrows to arrive at the corresponding ICD-11 diagnosis. The results of the pilot evaluation showed good clinical utility of the algorithm. The CAL-CP can contribute to reliable diagnoses by structuring a way through the classification and by increasing adherence to the criteria. Future studies need to evaluate its utility further and analyze its impact on the accuracy of the assigned diagnoses. |
| Author | Benoliel, Rafael Perrot, Serge Cohen, Milton Treede, Rolf-Detlef Aziz, Qasim Rief, Winfried Smith, Blair H Nicholas, Michael Korwisi, Beatrice Vlaeyen, Johan W S Kaasa, Stein Giamberardino, Maria Adele Bennett, Michael I Barke, Antonia Evers, Stefan Kosek, Eva Lavand'homme, Patricia Wang, Shuu-Jiun Attal, Nadine Svensson, Peter Schug, Stephan Hay, Ginea |
| Author_xml | – sequence: 1 givenname: Beatrice surname: Korwisi fullname: Korwisi, Beatrice organization: Division of Clinical Psychology and Psychotherapy, Department of Psychology, Philipps-University Marburg, Marburg, Germany INSERM U-987, Centre d'Evaluation et de Traitement de la Douleur, CHU Ambroise Paré, Boulogne-Billancourt, France Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, United Kingdom Academic Unit of Palliative Care, University of Leeds, Leeds, United Kingdom Department of Diagnostic Sciences, Rutgers School of Dental Medicine, Rutgers, Newark, NJ, United States St Vincent's Clinical School, UNSW, Sydney, New South Wales, Australia Department of Neurology, Krankenhaus Lindenbrunn, Coppenbrügge, Germany Department of Medicine, University of Münster, Münster, Germany Department of Medicine and Science of Aging, CAST, G D'Annunzio University of Chieti, Chieti, Italy European Palliative Care Research Centre (PRC),Department of Cancer Treatment, University Hospital Oslo, Oslo, Norway Department of Oncology, Oslo University Hospital, Oslo, Norway Institute of Cancer Research and Molecular Medicine, Department of Medicine, Norwegian University of Science and Technology, Trondheim, Norway Department of Clinical Neuroscience, Karolinska Institute, and Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden Department of Surgical Sciences Uppsala University, Uppsala, Sweden Department of Anesthesiology, Acute Postoperative Pain Service, Saint Luc Hospital, Catholic University of Louvain, Brussels, Belgium Pain Management Research Institute, Faculty of Medicine and Health, University of Sydney and Royal North Shore Hospital, Sydney, Australia Pain Clinic, Cochin Hospital, Paris University, INSERM U987, Paris, France Department of Anaesthesiology and Pain Medicine, Medical School, University of Western Australia, Perth, Australia Division of Population Health and Genomics, University of Dundee, Dundee, Scotland Department of Dentistry and Oral Health, Section of Orofacial Pain and Jaw Function, Aarhus University, Aarhus, Denmark Research Group Health Psychology, Department of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium TRACE, Center for Translational Health Research, KU Leuven, Ziekenhuis Oost-Limburg, Genk, Belgium Department of Clinical Psychological Science, Maastricht University, Maastricht, the Netherlands The Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan Brain Research Center and School of Medicine, National Yang-Ming University, Taipei, Taiwan Department of Neurophysiology, Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim of Heidelberg University, Mannheim, Germany, Division of Clinical and Biological Psychology, Department of Psychology, Catholic University of Eichstätt-Ingolstadt, Eichstätt, Germany, Université Paris Saclay, Versailles, France – sequence: 2 givenname: Ginea surname: Hay fullname: Hay, Ginea – sequence: 3 givenname: Nadine surname: Attal fullname: Attal, Nadine – sequence: 4 givenname: Qasim surname: Aziz fullname: Aziz, Qasim – sequence: 5 givenname: Michael I surname: Bennett fullname: Bennett, Michael I – sequence: 6 givenname: Rafael surname: Benoliel fullname: Benoliel, Rafael – sequence: 7 givenname: Milton surname: Cohen fullname: Cohen, Milton – sequence: 8 givenname: Stefan surname: Evers fullname: Evers, Stefan – sequence: 9 givenname: Maria Adele surname: Giamberardino fullname: Giamberardino, Maria Adele – sequence: 10 givenname: Stein surname: Kaasa fullname: Kaasa, Stein – sequence: 11 givenname: Eva surname: Kosek fullname: Kosek, Eva – sequence: 12 givenname: Patricia surname: Lavand'homme fullname: Lavand'homme, Patricia – sequence: 13 givenname: Michael surname: Nicholas fullname: Nicholas, Michael – sequence: 14 givenname: Serge surname: Perrot fullname: Perrot, Serge – sequence: 15 givenname: Stephan surname: Schug fullname: Schug, Stephan – sequence: 16 givenname: Blair H surname: Smith fullname: Smith, Blair H – sequence: 17 givenname: Peter surname: Svensson fullname: Svensson, Peter – sequence: 18 givenname: Johan W S surname: Vlaeyen fullname: Vlaeyen, Johan W S – sequence: 19 givenname: Shuu-Jiun surname: Wang fullname: Wang, Shuu-Jiun – sequence: 20 givenname: Rolf-Detlef surname: Treede fullname: Treede, Rolf-Detlef – sequence: 21 givenname: Winfried surname: Rief fullname: Rief, Winfried – sequence: 22 givenname: Antonia surname: Barke fullname: Barke, Antonia |
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