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Development of explicit criteria for prioritization of hip and knee replacement
Author(s) -
Escobar Antonio,
Quintana José M.,
Bilbao Amaia,
Ibañez Berta,
Arenaza Juan C.,
Gutiérrez Luis,
Azkárate Jesús,
Güenaga Jose I.,
Vidaurreta Ignacio
Publication year - 2007
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2006.00733.x
Subject(s) - delphi method , physical therapy , logistic regression , knee replacement , prioritization , delphi , medicine , physical medicine and rehabilitation , linear regression , computer science , arthroplasty , surgery , machine learning , artificial intelligence , management science , economics , operating system
Rationale and aims  Among the problems to the publicly funded national health services are the waiting lists. Patients who need elective surgery generally have long waiting times before treatment. We aimed to develop a new prioritization tool for primary hip and knee replacement. Methods  Criteria were developed using a modified Delphi panel process. We convened a panel of nine members who scored the scenarios created by the research team and by patient focus groups. We studied the level of agreement among the panelists and the contribution of the variables to the ratings of the panel using linear and logistic regression models. The priority scores of the variables and their levels were synthesized using the optimal scaling and standard linear regression methods. Results  Seven variables, pain on motion, walking functional limitations, abnormal findings on physical examination, pain at rest, other functional limitations, social role, and other pathologies that could improve with joint replacement, were considered to create the different scenarios. The panel scored 192 scenarios. The disagreement among the panelists was very low (1%) with an intra‐class correlation coefficient of 0.72. Of the 192 scenarios, 45.8% were scored as urgent, 35.4% as preferred and 18.8% as ordinary. The variables that contributed most to the scores were pain on motion and walking functional limitations. When optimal scaling and regression techniques were applied, similar results were obtained. Conclusion  This tool can evaluate and prioritize patients on a waiting list for hip or knee replacement. We also provide a simple and easy way to use an algorithm to estimate the treatment priority for individual patients.

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