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Objective: Assess the prognostic value of pre‐operative haemoglobin concentration (Hb) for identifying patients who develop severe post‐operative anaemia or require blood transfusion following primary total hip or knee, or unicompartmental knee arthroplasty (THA, TKA, UKA). Background: Pre‐operative group and save (G&S), and post‐operative Hb measurement may be unnecessary for many patients undergoing hip and knee arthroplasty provided individuals at greatest risk of severe post‐operative anaemia can be identified. Methods and Materials: Patients undergoing THA, TKA, or UKA between 2011 and 2018 were included. Outcomes were post‐operative Hb below 70 and 80 g/L, and peri‐operative blood transfusion. Logistic regression assessed the association between pre‐operative Hb and each outcome. Decision curve analysis compared strategies for selecting patients for G&S and post‐operative Hb measurement. Results: 10 015 THA, TKA and UKA procedures were performed in 8582 patients. The incidence of blood transfusion (4.5%) decreased during the study. Using procedure specific Hb thresholds to select patients for pre‐operative G&S and post‐operative Hb testing had a greater net benefit than selecting all patients, no patients, or patients with pre‐operative anaemia. Conclusions: Pre‐operative G&S and post‐operative Hb measurement may not be indicated for UKA or TKA when adopting restrictive transfusion thresholds, provided clinicians accept a 0.1% risk of patients developing severe undiagnosed post‐operative anaemia (Hb < 70 g/L). The decision to perform these blood tests for THA patients should be based on local institutional data and selection of acceptable risk thresholds. [ABSTRACT FROM AUTHOR]

Background: Errors in hospital transfusion may cause wrong (blood) components to be transfused. This study assessed the value of electronic identification systems (EISs) in reducing wrong component transfusions (WCTs).Methods: UK hospitals reporting to Serious Hazards of Transfusion were invited to complete an electronic survey about transfusion including the use of EISs. Further information was requested for WCTs and near-miss WCTs.Results: A response rate of 93 of 222 (42%) hospitals accounted for 38% of UK blood component issues in 2015 and 2016. Thirty-three of 93 (35%) hospitals employ manual procedures and 16 (17%) use EISs throughout the transfusion process; most of the remainder use EISs for blood collection only. Fifty-seven WCTs were identified in approximately two million blood components. The primary error was at blood draw and sample labeling (3), blood collection (15), and administration (2); the remainder were mostly blood bank errors. No WCTs occurred with blood draw and sample labeling or administration with use of EISs. Three WCTs occurred with EISs for blood collection due to incorrect processes for emergency transfusions of group O blood without any adverse effects. Seventeen WCTs occurred with manual processes; one was an ABO-incompatible red blood cell transfusion resulting in renal impairment. Near-miss WCTs were also more frequent with manual procedures than EISs at blood draw and sample labeling and blood collection.Conclusions: This is the first multicenter study to demonstrate a lower incidence of WCTs and near-miss WCTs with EISs compared to manual processes, and highlights some limitations of both manual and EIS procedures. [ABSTRACT FROM AUTHOR]

Collecting data for an individual participant data meta‐analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three‐step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two‐sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between‐study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method. [ABSTRACT FROM AUTHOR]