Background and purpose - Antibiotic-loaded bone cement (ALBC) and systemic antibiotic prophylaxis (SAP) have been used to reduce periprosthetic joint infection (PJI) rates. We investigated the use... Show moreBackground and purpose - Antibiotic-loaded bone cement (ALBC) and systemic antibiotic prophylaxis (SAP) have been used to reduce periprosthetic joint infection (PJI) rates. We investigated the use of ALBC and SAP in primary total knee arthroplasty (TKA).Patients and methods - This observational study is based on 2,971,357 primary TKAs reported in 2010-2020 to national/regional joint arthroplasty registries in Australia, Den-mark, Finland, Germany, Italy, the Netherlands, New Zealand, Norway, Romania, South Africa, Sweden, Switzerland, the UK, and the USA. Aggregate-level data on trends and types of bone cement, antibiotic agents, and doses and duration of SAP used was extracted from participating registries.Results - ALBC was used in 77% of the TKAs with variation ranging from 100% in Norway to 31% in the USA. Palacos R+G was the most common (62%) ALBC type used. The primary antibiotic used in ALBC was gentamicin (94%). Use of ALBC in combination with SAP was common prac-tice (77%). Cefazolin was the most common (32%) SAP agent. The doses and duration of SAP used varied from one single preoperative dosage as standard practice in Bolzano, Italy (98%) to 1-day 4 doses in Norway (83% of the 40,709 TKAs reported to the Norwegian arthroplasty register). Conclusion - The proportion of ALBC usage in pri-mary TKA varies internationally, with gentamicin being the most common antibiotic. ALBC in combination with SAP was common practice, with cefazolin the most common SAP agent. The type of ALBC and type, dose, and duration of SAP varied among participating countries. Show less
It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of... Show moreIt is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy. Show less
