Predicting Outcome of Total Knee Arthroplasty by Cluster Analysis of Patient-Reported Outcome Measures.
MCID
PROM
cluster analysis
total knee arthroplasty
Journal
The Journal of arthroplasty
ISSN: 1532-8406
Titre abrégé: J Arthroplasty
Pays: United States
ID NLM: 8703515
Informations de publication
Date de publication:
01 Oct 2024
01 Oct 2024
Historique:
received:
01
05
2024
revised:
24
09
2024
accepted:
25
09
2024
medline:
4
10
2024
pubmed:
4
10
2024
entrez:
3
10
2024
Statut:
aheadofprint
Résumé
Total knee arthroplasties (TKAs) exhibit an 8 to 30% risk of suboptimal outcomes, resulting in persistent symptoms, individual morbidity, and revision surgery, prompting a contemporary focus on risk reduction and outcome improvement. This study introduces hierarchical cluster analysis as a way of preoperatively assessing the likelihood of success/failure of TKA based on several patient-reported outcome measures, which have been analyzed both intact and with component questions as individual variables. The study utilized data on 1,433 TKAs from The Miriam Hospital's Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement (FORCE-TJR) registry. Outcomes are expressed as Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and function scores. Criteria for success/failure were developed with an integrative, anchor-based, minimum clinically important difference. Preoperative and postoperative patient-reported outcome measures (PROMs) were studied by cluster analysis. There were three sequential cluster analyses that revealed clusters of patients, based upon preoperative patient responses, that were predictive of surgical outcomes. Clusters varied most significantly in their responses to individual component questions of preoperative PROMs. Extracting and combining the clinically meaningful patient-reported component questions yielded a new, and clinically relevant, outcome measure that has the potential to preoperatively predict postoperative outcomes of total knee arthroplasty. In contrast to a single medical, psychological, or social variable, cluster analysis offers the opportunity to develop a whole-patient profile that reflects the contextual interactions of sociodemographic and clinical variables in predicting outcomes. In the context of determining clinical meaningfulness, cluster analysis has one of its major strengths.
Sections du résumé
BACKGROUND
BACKGROUND
Total knee arthroplasties (TKAs) exhibit an 8 to 30% risk of suboptimal outcomes, resulting in persistent symptoms, individual morbidity, and revision surgery, prompting a contemporary focus on risk reduction and outcome improvement. This study introduces hierarchical cluster analysis as a way of preoperatively assessing the likelihood of success/failure of TKA based on several patient-reported outcome measures, which have been analyzed both intact and with component questions as individual variables.
METHODS
METHODS
The study utilized data on 1,433 TKAs from The Miriam Hospital's Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement (FORCE-TJR) registry. Outcomes are expressed as Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and function scores. Criteria for success/failure were developed with an integrative, anchor-based, minimum clinically important difference. Preoperative and postoperative patient-reported outcome measures (PROMs) were studied by cluster analysis.
RESULTS
RESULTS
There were three sequential cluster analyses that revealed clusters of patients, based upon preoperative patient responses, that were predictive of surgical outcomes. Clusters varied most significantly in their responses to individual component questions of preoperative PROMs. Extracting and combining the clinically meaningful patient-reported component questions yielded a new, and clinically relevant, outcome measure that has the potential to preoperatively predict postoperative outcomes of total knee arthroplasty.
CONCLUSION
CONCLUSIONS
In contrast to a single medical, psychological, or social variable, cluster analysis offers the opportunity to develop a whole-patient profile that reflects the contextual interactions of sociodemographic and clinical variables in predicting outcomes. In the context of determining clinical meaningfulness, cluster analysis has one of its major strengths.
Identifiants
pubmed: 39362414
pii: S0883-5403(24)00999-9
doi: 10.1016/j.arth.2024.09.039
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Elsevier Inc. All rights reserved.