A high hospital frailty risk score indicates an increased risk for complications following surgical treatment of proximal humerus fractures.

Hospital frailty risk score Plate osteosynthesis Proximal humerus fracture Quality measurement Reverse shoulder arthroplasty

Journal

Archives of gerontology and geriatrics
ISSN: 1872-6976
Titre abrégé: Arch Gerontol Geriatr
Pays: Netherlands
ID NLM: 8214379

Informations de publication

Date de publication:
05 Aug 2024
Historique:
received: 01 07 2024
revised: 01 08 2024
accepted: 04 08 2024
medline: 26 8 2024
pubmed: 26 8 2024
entrez: 25 8 2024
Statut: aheadofprint

Résumé

Approximately 70 % of proximal humerus fractures (PHF) occur after the age of 60. High complication rates have been described in correlation with the treatment of PHF. Major risk factors for the outcome might be frailty, mobility and comorbidities of patients at the time of hospital admission. The aim of this study was to create risk adjusted quality indicators for surgical treatment of proximal humerus fractures based on German claims data and to evaluate the impact of the Hospital Frailty Risk Score (HFRS) on risk adjustment. Retrospective claims data (2015-2021) were used to create risk adjusted quality indicators for eight outcomes by clustered multivariable logistic regression. The comparison of different risk adjustment model performances was done by ROC-AUC and Standardized Mortality/Morbidity Ratios. In total, N = 34,912 patients (median age 75 years, 80.3 % female) were included. The most common surgical procedure was open reduction and internal fixation with plate osteosynthesis with 39.7 %, followed by reverse shoulder arthroplasty with 25.3 %. The most influential risk factor for all outcomes was a high HFRS with an Odds Ratio of 2.0 (95 %-Confidence Interval 1.8-2.3) for any secondary surgery (365 days) up to an Odds Ratio of 17.6 (95 %-Confidence Interval 14.9-20.8) for general complications during the index stay. Comparative quality reporting for the surgical treatment of PHF appears feasible with the developed models for risk adjustment using claims data. Preoperative evaluation of HFRS in PHF can contribute to risk assessment, and individual patient management. It therefore enables personalized treatment decisions.

Sections du résumé

BACKGROUND BACKGROUND
Approximately 70 % of proximal humerus fractures (PHF) occur after the age of 60. High complication rates have been described in correlation with the treatment of PHF. Major risk factors for the outcome might be frailty, mobility and comorbidities of patients at the time of hospital admission. The aim of this study was to create risk adjusted quality indicators for surgical treatment of proximal humerus fractures based on German claims data and to evaluate the impact of the Hospital Frailty Risk Score (HFRS) on risk adjustment.
METHODS METHODS
Retrospective claims data (2015-2021) were used to create risk adjusted quality indicators for eight outcomes by clustered multivariable logistic regression. The comparison of different risk adjustment model performances was done by ROC-AUC and Standardized Mortality/Morbidity Ratios.
RESULTS RESULTS
In total, N = 34,912 patients (median age 75 years, 80.3 % female) were included. The most common surgical procedure was open reduction and internal fixation with plate osteosynthesis with 39.7 %, followed by reverse shoulder arthroplasty with 25.3 %. The most influential risk factor for all outcomes was a high HFRS with an Odds Ratio of 2.0 (95 %-Confidence Interval 1.8-2.3) for any secondary surgery (365 days) up to an Odds Ratio of 17.6 (95 %-Confidence Interval 14.9-20.8) for general complications during the index stay.
CONCLUSION CONCLUSIONS
Comparative quality reporting for the surgical treatment of PHF appears feasible with the developed models for risk adjustment using claims data. Preoperative evaluation of HFRS in PHF can contribute to risk assessment, and individual patient management. It therefore enables personalized treatment decisions.

Identifiants

pubmed: 39182348
pii: S0167-4943(24)00274-7
doi: 10.1016/j.archger.2024.105598
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105598

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest Jochen Schmitt reports institutional grants for investigator-initiated research from the German GBA, BMG, BMBF, EU, Federal State of Saxony, Novartis, Sanofi, ALK, and Pfizer. He also participated in advisory board meetings as a paid consultant for Sanofi, Lilly, and ALK. JS is a member of the Expert Council on Health and Care at the Federal Ministry of Health and a member of the government commission for modern and needs-based hospital care of the current German Coalition. All other authors report no conflicts of interest regarding the submitted work.

Auteurs

Melissa Spoden (M)

AOK Research Institute, Berlin, Germany. Electronic address: Melissa.Spoden@wido.bv.aok.de.

Patrik Dröge (P)

AOK Research Institute, Berlin, Germany.

Christian Günster (C)

AOK Research Institute, Berlin, Germany.

Thomas Datzmann (T)

Center for Evidence-based Healthcare, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

Tobias Helfen (T)

Department of Orthopaedics and Trauma Surgery, Musculoskeletal UniversityCenter Munich (MUM), LMU University Hospital, LMU Munich, Germany.

Klaus-Dieter Schaser (KD)

University Center of Orthopaedic, Trauma and Plastic Surgery, University Hospital, Technische Universität Dresden, Germany.

Jochen Schmitt (J)

Center for Evidence-based Healthcare, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

Ekkehard Schuler (E)

Helios Kliniken GmbH, Berlin, Germany.

J Christoph Katthagen (J)

Department of Trauma, Hand and Reconstructive Surgery, University Hospital Munster, Munster, Germany.

Jörg Nowotny (J)

University Center of Orthopaedic, Trauma and Plastic Surgery, University Hospital, Technische Universität Dresden, Germany.

Classifications MeSH