Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

Zhong, Jingyu; Si, Lin; Zhang, Guangcheng; Huo, Jiayu; Xing, Yue; Hu, Yangfan; Zhang, Huan; Yao, Weiwu

doi:10.21203/rs.3.rs-70145/v1

Cited by 3 publications

(3 citation statements)

References 56 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We are unaware of any other previously published review of multivariable risk prediction models for OA incidence with which to compare our findings, although we note a published protocol of a review in development relating to prognostic models for knee OA (56). Reviews in other fields have found similar concerns over methodologic quality and applicability of multivariable prediction models for disease incidence (57)(58)(59)(60).…”

Section: Discussionmentioning

confidence: 94%

Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review

Appleyard

Thomas

Antcliff

et al. 2023

Arthritis Care & Research

View full text Add to dashboard Cite

Objective. To evaluate the performance and applicability of multivariable prediction models for osteoarthritis (OA). Methods. This was a systematic review and narrative synthesis using 3 databases (EMBASE, PubMed, and Web of Science) from inception to December 2021. We included general population longitudinal studies reporting derivation, comparison, or validation of multivariable models to predict individual risk of OA incidence, defined by recognized clinical or imaging criteria. We excluded studies reporting prevalent OA and joint arthroplasty outcome. Paired reviewers independently performed article selection, data extraction, and risk-of-bias assessment. Model performance, calibration, and retained predictors were summarized.Results. A total of 26 studies were included, reporting 31 final multivariable prediction models for incident knee (23), hip (4), hand (3) and any-site OA (1), with a median of 121.5 (range 27-12,803) outcome events, a median prediction horizon of 8 years (range 2-41), and a median of 6 predictors (range 3-24). Age, body mass index, previous injury, and occupational exposures were among the most commonly included predictors. Model discrimination after validation was generally acceptable to excellent (area under the curve = 0.70-0.85). Either internal or external validation processes were used in most models, although the risk of bias was often judged to be high with limited applicability to mass application in diverse populations.Conclusion. Despite growing interest in multivariable prediction models for incident OA, focus remains predominantly on the knee, with reliance on data from a small pool of appropriate cohort data sets, and concerns over general population applicability.

show abstract

Section: Discussionmentioning

confidence: 94%

Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review

Appleyard

Thomas

Antcliff

et al. 2023

Arthritis Care & Research

View full text Add to dashboard Cite

show abstract

“…Además, las anomalías en la alineación de la rodilla, el empuje del varo al caminar y el aumento del momento de aducción y flexión de la rodilla son factores de riesgo para la progresión de la artrosis de rodilla, lo que pone de relieve la importancia de comprender estos factores para prevenir la enfermedad [17]. Faltan métodos de detección temprana y fármacos modificadores de la enfermedad, lo que subraya la necesidad de contar con modelos de predicción pronóstica que guíen la toma de decisiones clínicas en el tratamiento de la artrosis [18].…”

Section: Pronósticounclassified

Actualización en Traumatología Vol. 9

Quinchi Maicelo,

Crespo Cordero,

Maldonado García

et al. 2024

View full text Add to dashboard Cite

"Actualización en Traumatología Vol. 9" es la novena entrega de una serie reconocida por proporcionar análisis detallados y actualizaciones esenciales en el campo de la traumatología. Este volumen recoge los avances más recientes en el diagnóstico, tratamiento y manejo de lesiones musculoesqueléticas. La colaboración de un grupo selecto de expertos asegura una cobertura exhaustiva y actualizada de los temas más relevantes para los profesionales de la traumatología.

show abstract

“…Data will be synthesized by a meta-analysis if the number (at least 5) of studies included is large enough in a subset with an analogous clinical question appraised repeatedly [33]. A meta-analysis of validation studies for a common prediction model will also be performed if feasible.…”

Section: Quantitative Synthesesmentioning

confidence: 99%

Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

Zhang¹,

Zhou²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

show abstract

Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

Cited by 3 publications

References 56 publications

Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review

Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review

Actualización en Traumatología Vol. 9

Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

Contact Info

Product

Resources

About