Stratified care in hip arthroscopy: can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models

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Standard

Stratified care in hip arthroscopy : can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models. / Ishøi, Lasse; Thorborg, Kristian; Kallemose, Thomas; Kemp, Joanne L.; Reiman, Michael P.; Nielsen, Mathias Fabricius; Hölmich, Per.

I: British Journal of Sports Medicine, Bind 57, Nr. 16, 2023, s. 1025-1034.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Ishøi, L, Thorborg, K, Kallemose, T, Kemp, JL, Reiman, MP, Nielsen, MF & Hölmich, P 2023, 'Stratified care in hip arthroscopy: can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models', British Journal of Sports Medicine, bind 57, nr. 16, s. 1025-1034. https://doi.org/10.1136/bjsports-2022-105534

APA

Ishøi, L., Thorborg, K., Kallemose, T., Kemp, J. L., Reiman, M. P., Nielsen, M. F., & Hölmich, P. (2023). Stratified care in hip arthroscopy: can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models. British Journal of Sports Medicine, 57(16), 1025-1034. https://doi.org/10.1136/bjsports-2022-105534

Vancouver

Ishøi L, Thorborg K, Kallemose T, Kemp JL, Reiman MP, Nielsen MF o.a. Stratified care in hip arthroscopy: can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models. British Journal of Sports Medicine. 2023;57(16):1025-1034. https://doi.org/10.1136/bjsports-2022-105534

Author

Ishøi, Lasse ; Thorborg, Kristian ; Kallemose, Thomas ; Kemp, Joanne L. ; Reiman, Michael P. ; Nielsen, Mathias Fabricius ; Hölmich, Per. / Stratified care in hip arthroscopy : can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models. I: British Journal of Sports Medicine. 2023 ; Bind 57, Nr. 16. s. 1025-1034.

Bibtex

@article{ad2b218d95274694ac0dab0acc425dce,
title = "Stratified care in hip arthroscopy: can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models",
abstract = "Objective Although hip arthroscopy is a widely adopted treatment option for hip-related pain, it is unknown whether preoperative clinical information can be used to assist surgical decision-making to avoid offering surgery to patients with limited potential for a successful outcome. We aimed to develop and validate clinical prediction models to identify patients more likely to have an unsuccessful or successful outcome 1 year post hip arthroscopy based on the patient acceptable symptom state. Methods Patient records were extracted from the Danish Hip Arthroscopy Registry (DHAR). A priori, 26 common clinical variables from DHAR were selected as prognostic factors, including demographics, radiographic parameters of hip morphology and self-reported measures. We used 1082 hip arthroscopy patients (surgery performed 25 April 2012 to 4 October 2017) to develop the clinical prediction models based on logistic regression analyses. The development models were internally validated using bootstrapping and shrinkage before temporal external validation was performed using 464 hip arthroscopy patients (surgery performed 5 October 2017 to 13 May 2019). Results The prediction model for unsuccessful outcomes showed best and acceptable predictive performance on the external validation dataset for all multiple imputations (Nagelkerke R 2 range: 0.25-0.26) and calibration (intercept range: -0.10 to -0.11; slope range: 1.06-1.09), and acceptable discrimination (area under the curve range: 0.76-0.77). The prediction model for successful outcomes did not calibrate well, while also showing poor discrimination. Conclusion Common clinical variables including demographics, radiographic parameters of hip morphology and self-reported measures were able to predict the probability of having an unsuccessful outcome 1 year after hip arthroscopy, while the model for successful outcome showed unacceptable accuracy. The externally validated prediction model can be used to support clinical evaluation and shared decision making by informing the orthopaedic surgeon and patient about the risk of an unsuccessful outcome, and thus when surgery may not be appropriate. ",
keywords = "Arthroscopy, Groin, Hip, Sports medicine, Surveys and Questionnaires",
author = "Lasse Ish{\o}i and Kristian Thorborg and Thomas Kallemose and Kemp, {Joanne L.} and Reiman, {Michael P.} and Nielsen, {Mathias Fabricius} and Per H{\"o}lmich",
note = "Publisher Copyright: {\textcopyright} 2023 BMJ Publishing Group. All rights reserved.",
year = "2023",
doi = "10.1136/bjsports-2022-105534",
language = "English",
volume = "57",
pages = "1025--1034",
journal = "British Journal of Sports Medicine",
issn = "0306-3674",
publisher = "B M J Group",
number = "16",

}

RIS

TY - JOUR

T1 - Stratified care in hip arthroscopy

T2 - can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models

AU - Ishøi, Lasse

AU - Thorborg, Kristian

AU - Kallemose, Thomas

AU - Kemp, Joanne L.

AU - Reiman, Michael P.

AU - Nielsen, Mathias Fabricius

AU - Hölmich, Per

N1 - Publisher Copyright: © 2023 BMJ Publishing Group. All rights reserved.

PY - 2023

Y1 - 2023

N2 - Objective Although hip arthroscopy is a widely adopted treatment option for hip-related pain, it is unknown whether preoperative clinical information can be used to assist surgical decision-making to avoid offering surgery to patients with limited potential for a successful outcome. We aimed to develop and validate clinical prediction models to identify patients more likely to have an unsuccessful or successful outcome 1 year post hip arthroscopy based on the patient acceptable symptom state. Methods Patient records were extracted from the Danish Hip Arthroscopy Registry (DHAR). A priori, 26 common clinical variables from DHAR were selected as prognostic factors, including demographics, radiographic parameters of hip morphology and self-reported measures. We used 1082 hip arthroscopy patients (surgery performed 25 April 2012 to 4 October 2017) to develop the clinical prediction models based on logistic regression analyses. The development models were internally validated using bootstrapping and shrinkage before temporal external validation was performed using 464 hip arthroscopy patients (surgery performed 5 October 2017 to 13 May 2019). Results The prediction model for unsuccessful outcomes showed best and acceptable predictive performance on the external validation dataset for all multiple imputations (Nagelkerke R 2 range: 0.25-0.26) and calibration (intercept range: -0.10 to -0.11; slope range: 1.06-1.09), and acceptable discrimination (area under the curve range: 0.76-0.77). The prediction model for successful outcomes did not calibrate well, while also showing poor discrimination. Conclusion Common clinical variables including demographics, radiographic parameters of hip morphology and self-reported measures were able to predict the probability of having an unsuccessful outcome 1 year after hip arthroscopy, while the model for successful outcome showed unacceptable accuracy. The externally validated prediction model can be used to support clinical evaluation and shared decision making by informing the orthopaedic surgeon and patient about the risk of an unsuccessful outcome, and thus when surgery may not be appropriate.

AB - Objective Although hip arthroscopy is a widely adopted treatment option for hip-related pain, it is unknown whether preoperative clinical information can be used to assist surgical decision-making to avoid offering surgery to patients with limited potential for a successful outcome. We aimed to develop and validate clinical prediction models to identify patients more likely to have an unsuccessful or successful outcome 1 year post hip arthroscopy based on the patient acceptable symptom state. Methods Patient records were extracted from the Danish Hip Arthroscopy Registry (DHAR). A priori, 26 common clinical variables from DHAR were selected as prognostic factors, including demographics, radiographic parameters of hip morphology and self-reported measures. We used 1082 hip arthroscopy patients (surgery performed 25 April 2012 to 4 October 2017) to develop the clinical prediction models based on logistic regression analyses. The development models were internally validated using bootstrapping and shrinkage before temporal external validation was performed using 464 hip arthroscopy patients (surgery performed 5 October 2017 to 13 May 2019). Results The prediction model for unsuccessful outcomes showed best and acceptable predictive performance on the external validation dataset for all multiple imputations (Nagelkerke R 2 range: 0.25-0.26) and calibration (intercept range: -0.10 to -0.11; slope range: 1.06-1.09), and acceptable discrimination (area under the curve range: 0.76-0.77). The prediction model for successful outcomes did not calibrate well, while also showing poor discrimination. Conclusion Common clinical variables including demographics, radiographic parameters of hip morphology and self-reported measures were able to predict the probability of having an unsuccessful outcome 1 year after hip arthroscopy, while the model for successful outcome showed unacceptable accuracy. The externally validated prediction model can be used to support clinical evaluation and shared decision making by informing the orthopaedic surgeon and patient about the risk of an unsuccessful outcome, and thus when surgery may not be appropriate.

KW - Arthroscopy

KW - Groin

KW - Hip

KW - Sports medicine

KW - Surveys and Questionnaires

U2 - 10.1136/bjsports-2022-105534

DO - 10.1136/bjsports-2022-105534

M3 - Journal article

C2 - 37001982

AN - SCOPUS:85152665258

VL - 57

SP - 1025

EP - 1034

JO - British Journal of Sports Medicine

JF - British Journal of Sports Medicine

SN - 0306-3674

IS - 16

ER -

ID: 370477439