ArticleBMC musculoskeletal disorders2020
Development and validation of a nomogram to predict perioperative blood transfusion in patients undergoing total knee arthroplasty.
Article in BMC musculoskeletal disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Clinical prediction models for postoperative blood transfusion after total knee arthroplasty: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Effect of severity and cause of preoperative anemia on the transfusion rate after total knee arthroplasty.Scientific reports · 2022Article
- Development and validation of a nomogram to predict complications in patients undergoing simultaneous bilateral total knee arthroplasty: A retrospective study from two centers.Frontiers in surgery · 2022Article
- Predict Postoperative Anemia of Patients: Nomogram Construction and Validation.Frontiers in surgery · 2022Article
- Efficacy and Safety of Intravenous Ferric Carboxymaltose in Patients with Postoperative Anemia Following Same-Day Bilateral Total Knee Arthroplasty: A Randomized Controlled Trial.Journal of clinical medicine · 2021Article
- Establishment and Verification of a Perioperative Blood Transfusion Model After Posterior Lumbar Interbody Fusion: A Retrospective Study Based on Data From a Local Hospital.Frontiers in surgery · 2021Article
- The probability of allogeneic blood transfusion and its influencing factors in patients undergoing total knee arthroplasty: A retrospective cohort study.Asian journal of transfusion scienceArticle
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Authors and funding
10 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe need for a transfusion is one of the adverse events following total knee arthroplasty (TKA), and accurately predicting this need remains challenging for arthroplasty surgeons. The purpose of the present research is to study the preoperative predictors of transfusion risk in patients following TKA and develop a nomogram.
methodsThe nomogram was developed based on a training set of 5402 patients who underwent TKA at the Affiliated Hospital of Qingdao University between September 2013 and November 2018. The independent predictors of transfusion were identified by univariate, LASSO, and binary logistic regression analyses. Then, a nomogram was established based on these independent predictors. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were selected to evaluate the nomogram. The results were validated using an independent set of 1116 patients who underwent TKA between December 2018 and September 2019. In addition, we also carried out subgroup analyses in the training and testing sets based on the independent predictors.
resultsFive independent predictors were identified by multivariate analysis and were used to establish the nomogram. The AUCs of the nomogram were 0.884 (95% CI: 0.865-0.903) and 0.839 (95% CI, 0.773-0.905) in the training and testing sets, respectively. In both the training and testing sets, the calibration curve indicated that the prediction by the nomogram was highly consistent with the actual observation, and the DCA indicated that the nomogram had a favorable level of clinical usefulness. In addition, the AUC of the nomogram was significantly higher than the AUC of any independent predictor for predicting transfusion risk following TKA, and the subgroup analysis showed good performance in 20 subgroups.
conclusionLower preoperative Hb levels, simultaneous bilateral TKA, lower BMI, older age, and coronary heart disease were identified as independent predictors of postoperative transfusion in patients following TKA. A nomogram incorporating the above five predictors could accurately predict the transfusion risk.
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