ArticleThe Journal of arthroplasty2018
Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis.
Article in The Journal of arthroplasty, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating methodological quality of prognostic prediction models on patient-reported outcome measurements after total hip and total knee arthroplasty : a systematic review.Bone & joint open · 2026Pooled it
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- [Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2025Article
- Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model.Frontiers in bioengineering and biotechnology · 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
- External Validation of Prediction Models for Surgical Complications in People Considering Total Hip or Knee Arthroplasty Was Successful for Delirium but Not for Surgical Site Infection, Postoperative Bleeding, and Nerve Damage: A Retrospective Cohort Study.Journal of personalized medicine · 2023Article
- Article
- Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol.Systematic reviews · 2022Article
- Association of Social Behaviors With Community Discharge in Patients with Total Hip and Knee Replacement.Journal of the American Medical Directors Association · 2021Article
- A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.Arthroplasty today · 2021Article
- Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.The Journal of arthroplasty · 2021Article
- Article
- Reply to the Letter to the Editor: How Accurate Are the Surgical Risk Preoperative Assessment System (SURPAS) Universal Calculators in Total Joint Arthroplasty?Clinical orthopaedics and related research · 2020Article
- Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics.Indian journal of orthopaedics · 2020Review
- Surgical selection criteria compliance is associated with a lower risk of periprosthetic joint infection in total hip arthroplasty.Arthroplasty today · 2019Article
- Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.BMC health services research · 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
- American Joint Replacement Registry Risk Calculator Does Not Predict 90-day Mortality in Veterans Undergoing Total Joint Replacement.Clinical orthopaedics and related research · 2018Article
- 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
- Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty.Journal of orthopaedicsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundStatistical models to preoperatively predict patients' risk of death and major complications after total joint arthroplasty (TJA) could improve the quality of preoperative management and informed consent. Although risk models for TJA exist, they have limitations including poor transparency and/or unknown or poor performance. Thus, it is currently impossible to know how well currently available models predict short-term complications after TJA, or if newly developed models are more accurate. We sought to develop and conduct cross-validation of predictive risk models, and report details and performance metrics as benchmarks.
methodsOver 90 preoperative variables were used as candidate predictors of death and major complications within 30 days for Veterans Health Administration patients with osteoarthritis who underwent TJA. Data were split into 3 samples-for selection of model tuning parameters, model development, and cross-validation. C-indexes (discrimination) and calibration plots were produced.
resultsA total of 70,569 patients diagnosed with osteoarthritis who received primary TJA were included. C-statistics and bootstrapped confidence intervals for the cross-validation of the boosted regression models were highest for cardiac complications (0.75; 0.71-0.79) and 30-day mortality (0.73; 0.66-0.79) and lowest for deep vein thrombosis (0.59; 0.55-0.64) and return to the operating room (0.60; 0.57-0.63).
conclusionsModerately accurate predictive models of 30-day mortality and cardiac complications after TJA in Veterans Health Administration patients were developed and internally cross-validated. By reporting model coefficients and performance metrics, other model developers can test these models on new samples and have a procedure and indication-specific benchmark to surpass.
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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.