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Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.
Groot, Olivier Q; Bindels, Bas J J; Ogink, Paul T; Kapoor, Neal D; Twining, Peter K; Collins, Austin K; Bongers, Michiel E R; Lans, Amanda; Oosterhoff, Jacobien H F; Karhade, Aditya V; Verlaan, Jorrit-Jan; Schwab, Joseph H.
Afiliación
  • Groot OQ; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Bindels BJJ; Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands.
  • Ogink PT; Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands.
  • Kapoor ND; Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands.
  • Twining PK; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Collins AK; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Bongers MER; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Lans A; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Oosterhoff JHF; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Karhade AV; Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands.
  • Verlaan JJ; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • Schwab JH; Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
Acta Orthop ; 92(4): 385-393, 2021 Aug.
Article en En | MEDLINE | ID: mdl-33870837
ABSTRACT
Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines.Material and methods - We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting.Results - We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43-89), with 6 items being reported in less than 4/18 of the studies.Interpretation - Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos / Técnicas de Apoyo para la Decisión / Procedimientos Ortopédicos / Aprendizaje Automático Tipo de estudio: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Acta Orthop Asunto de la revista: ORTOPEDIA Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos / Técnicas de Apoyo para la Decisión / Procedimientos Ortopédicos / Aprendizaje Automático Tipo de estudio: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Acta Orthop Asunto de la revista: ORTOPEDIA Año: 2021 Tipo del documento: Article