ArticleBMJ open2022
Development of prediction models for complications after primary total hip and knee arthroplasty: a single-centre retrospective cohort study in the Netherlands.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.JMIR medical informatics · 2026Pooled it
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Data-Driven Approach to Development of a Risk Score for Periprosthetic Joint Infections in Total Joint Arthroplasty Using Electronic Health Records.The Journal of arthroplasty · 2025Article
- Establishment and Verification of Risk Prediction Model for Adverse Outcomes After Hip Arthroplasty in Elderly Patients.Therapeutics and clinical risk management · 2025Article
- Predicting periprosthetic joint infection: external validation of preoperative prediction models.Journal of bone and joint infection · 2024Article
- Influencing factors on the quality of recovery after total knee arthroplasty: development of a predictive model.Frontiers in medicine · 2024Article
- 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
- Preoperative Risk Factor Analysis and Dynamic Online Nomogram Development for Early Infections Following Primary Hip Arthroplasty in Geriatric Patients with Hip Fracture.Clinical interventions in aging · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to develop prediction models for patients with total hip arthroplasty (THA) and total knee arthroplasty (TKA) to predict the risk for surgical complications based on personal factors, comorbidities and medication use.
designRetrospective cohort study.
settingTertiary care in outpatient clinic of university medical centre.
participants3776 patients with a primary THA or TKA between 2004 and 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: Multivariable logistic regression models were developed for primary outcome surgical site infection (SSI), and secondary outcomes venous thromboembolism (VTE), postoperative bleeding (POB), luxation, delirium and nerve damage (NER).
resultsFor SSI, age, smoking status, body mass index, presence of immunological disorder, diabetes mellitus, liver disease and use of non-steroidal anti-inflammatory drugs were included. An area under the receiver operating characteristic curve (AUC) of 71.9% (95% CI=69.4% to 74.4%) was found. For this model, liver disease showed to be the strongest predictor with an OR of 10.7 (95% CI=2.4 to 46.6). The models for POB and NER showed AUCs of 73.0% (95% CI=70.7% to 75.4%) and 76.6% (95% CI=73.2% to 80.0%), respectively. For delirium an AUC of 85.9% (95% CI=83.8% to 87.9%) was found, and for the predictive algorithms for luxation and VTE we found least favourable results (AUC=58.4% (95% CI=55.0% to 61.8%) and AUC=66.3% (95% CI=62.7% to 69.9%)).
conclusionsDiscriminative ability was reasonable for SSI and predicted probabilities ranged from 0.01% to 51.0%. We expect this to enhance shared decision-making in considering THA or TKA since current counselling is predicated on population-based probability of risk, rather than using personalised prediction. We consider our models for SSI, delirium and NER appropriate for clinical use when taking underestimation and overestimation of predicted risk into account. For VTE and POB, caution concerning overestimation exceeding a predicted probability of 0.08 for VTE and 0.05 for POB should be taken into account. Furthermore, future studies should evaluate clinical impact and whether the models are feasible in an external population.
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