The accuracy of multiple regression models for predicting the individual risk of recurrent lateral patellar dislocation.


Journal

BMC musculoskeletal disorders
ISSN: 1471-2474
Titre abrégé: BMC Musculoskelet Disord
Pays: England
ID NLM: 100968565

Informations de publication

Date de publication:
06 Dec 2023
Historique:
received: 20 09 2023
accepted: 04 12 2023
medline: 11 12 2023
pubmed: 7 12 2023
entrez: 6 12 2023
Statut: epublish

Résumé

Recurrent lateral patellar dislocation (RLPD) poses a significant threat to patients' quality of life due to knee pain, patellofemoral cartilage damage, and potential traumatic arthritis. Predictive scoring systems have been developed to assess the risk of RLPD; however, their relative accuracy remains uncertain. To investigate the accuracy of the multiple regression models to predict the individual risk of recurrent LPD. The Patellar Instability probability calculator (PIP), Recurrent Instability of the Patella Score (RIP), and Patellar Instability Severity Score (PIS) scoring rules were measured in 171 patients with a history of patellar dislocation and 171 healthy individuals. Three prediction models were calculated based on the data to predict the risk of recurrent lateral patellar dislocation. The inter-observer and intra-observer reliability of each measurement parameter was evaluated. The predictive capacity of the three-prediction model was investigated using the receiver operating characteristic curve. In the case group of 171 patients, PIS accurately predicted recurrent lateral Patella dislocation in 143 patients. RIP was 96, and PIP was 83. The positive predictive values were 92.9%, 64%, and 68% respectively. In the control group of 171 patients, the PIS was validated in 160 patients who would not experience dislocations. RIP was 117, and PIP was 50. The negative predictive values were 85.1%, 60.9%, and 36.2%, respectively. The area under the curve score for the PIS was 0.866, and the RIP was 0.673. the PIP was 0.678. RIP and PIP did not work to predict LPD. PIS can accurately predict recurrent lateral patellar dislocation. It can aid doctors in making treatment decisions. Level III, retrospective comparative study.

Sections du résumé

BACKGROUND BACKGROUND
Recurrent lateral patellar dislocation (RLPD) poses a significant threat to patients' quality of life due to knee pain, patellofemoral cartilage damage, and potential traumatic arthritis. Predictive scoring systems have been developed to assess the risk of RLPD; however, their relative accuracy remains uncertain.
PURPOSE OBJECTIVE
To investigate the accuracy of the multiple regression models to predict the individual risk of recurrent LPD.
METHODS METHODS
The Patellar Instability probability calculator (PIP), Recurrent Instability of the Patella Score (RIP), and Patellar Instability Severity Score (PIS) scoring rules were measured in 171 patients with a history of patellar dislocation and 171 healthy individuals. Three prediction models were calculated based on the data to predict the risk of recurrent lateral patellar dislocation. The inter-observer and intra-observer reliability of each measurement parameter was evaluated. The predictive capacity of the three-prediction model was investigated using the receiver operating characteristic curve.
RESULTS RESULTS
In the case group of 171 patients, PIS accurately predicted recurrent lateral Patella dislocation in 143 patients. RIP was 96, and PIP was 83. The positive predictive values were 92.9%, 64%, and 68% respectively. In the control group of 171 patients, the PIS was validated in 160 patients who would not experience dislocations. RIP was 117, and PIP was 50. The negative predictive values were 85.1%, 60.9%, and 36.2%, respectively. The area under the curve score for the PIS was 0.866, and the RIP was 0.673. the PIP was 0.678.
CONCLUSION CONCLUSIONS
RIP and PIP did not work to predict LPD. PIS can accurately predict recurrent lateral patellar dislocation. It can aid doctors in making treatment decisions.
LEVEL OF EVIDENCE METHODS
Level III, retrospective comparative study.

Identifiants

pubmed: 38057697
doi: 10.1186/s12891-023-07094-2
pii: 10.1186/s12891-023-07094-2
pmc: PMC10701990
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

948

Informations de copyright

© 2023. The Author(s).

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Auteurs

Jiang Yu (J)

Department of Orthopedic Surgery/Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China.

Yijin Li (Y)

Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.

Kaibo Zhang (K)

Department of Orthopedic Surgery/Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China.

Runze Yang (R)

Department of Orthopedic Surgery/Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China.

Xiaolong Yang (X)

Department of Orthopedic Surgery/Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China.

Meng Gong (M)

Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.

Cheng Long (C)

Department of Orthopedic Surgery/Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China. hxlongcheng@163.com.

Weili Fu (W)

Department of Orthopedic Surgery/Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China. foxwin2008@163.com.

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