ArticleClinical orthopaedics and related research2018
American Joint Replacement Registry Risk Calculator Does Not Predict 90-day Mortality in Veterans Undergoing Total Joint Replacement.
Article in Clinical orthopaedics and related research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Readmission, Complication, and Disposition Calculators in Total Joint Arthroplasty: A Systemic Review.The Journal of arthroplasty · 2021Pooled it
- The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review.Arthroplasty today · 2025Article
- Predicting discharge destination and length of stay after open reduction internal fixation of distal femur fractures.OTA international : the open access journal of orthopaedic trauma · 2025Article
- Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.Medical & biological engineering & computing · 2025Article
- Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.Medical & biological engineering & computing · 2023Review
- Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future.International orthopaedics · 2022Review
- Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review.Arthroplasty (London, England) · 2022Review
- Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.Journal of shoulder and elbow arthroplasty · 2022Article
- A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.Arthroplasty today · 2021Article
- Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.World neurosurgery · 2021Article
- Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.The Journal of arthroplasty · 2021Article
- Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery.Current reviews in musculoskeletal medicine · 2020Review
- Establishing a risk prediction model for acute kidney injury: methodology is important.Chinese medical journal · 2019Article
- Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?Clinical orthopaedics and related research · 2019Article
- CORR Insights®: American Joint Replacement Registry Risk Calculator Does Not Predict 90-day Mortality in Veterans Undergoing Total Joint Replacement.Clinical orthopaedics and related research · 2018Article
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7 authors at 2 institutions in 3 countries.
Funding
Abstract
backgroundThe American Joint Replacement Registry (AJRR) Total Joint Risk Calculator uses demographic and clinical parameters to provide risk estimates for 90-day mortality and 2-year periprosthetic joint infection (PJI). The tool is intended to help surgeons counsel their Medicare-eligible patients about their risk of death and PJI after total joint arthroplasty (TJA). However, for a predictive risk model to be useful, it must be accurate when applied to new patients; this has yet to be established for this calculator. QUESTIONS/PURPOSES: To produce accuracy metrics (ie, discrimination, calibration) for the AJRR mortality calculator using data from Medicare-eligible patients undergoing TJA in the Veterans Health Administration (VHA), the largest integrated healthcare system in the United States, where more than 10,000 TJAs are performed annually.
methodsWe used the AJRR calculator to predict risk of death within 90 days of surgery among 31,214 VHA patients older than 64 years of age who underwent primary TJA; data was drawn from the Veterans Affairs Surgical Quality Improvement Project (VASQIP) and VA Corporate Data Warehouse (CDW). We then used VHA mortality data to evaluate the extent to which the AJRR calculator estimates distinguished individuals who died compared with those who did not (C-statistic), and graphically depicted the relationship between estimated risk and observed mortality (calibration). As a secondary evaluation of the calculator, a sample of 39,300 patients younger than 65 years old was assigned to the youngest age group available to the user (65-69 years) as might be done in real-world practice.
resultsC-statistics for 90-day mortality for the older samples were 0.62 (95% CI, 0.60-0.64) and for the younger samples they were 0.46 (95% CI, 0.43-0.49), suggesting poor discrimination. Calibration analysis revealed poor correspondence between deciles of predicted risk and observed mortality rates. Poor discrimination and calibration mean that patients who died will frequently have a lower estimated risk of death than surviving patients.
conclusionsFor Medicare-eligible patients receiving TJA in the VA, the AJRR risk calculator had a poor performance in the prediction of 90-day mortality. There are several possible reasons for the model's poor performance. Veterans Health Administration patients, 97% of whom were men, represent only a subset of the broader Medicare population. However, applying the calculator to a subset of the target population should not affect its accuracy. Other reasons for poor performance include a lack of an underlying statistical model in the calculator's implementation and simply the challenge of predicting rare events. External validation in a more representative sample of Medicare patients should be conducted to before assuming this tool is accurate for its intended use. LEVEL OF EVIDENCE: Level I, diagnostic study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.