The Impact of Cardiac Arrhythmias on Total Knee Arthroplasty Outcomes.

BACKGROUND: Cardiac comorbidities are common in patients undergoing total knee arthroplasty (TKA). While there is an abundance of research showing an association between cardiac abnormalities and poor postoperative outcomes, relatively little is published on specific pathologies. The aim of this study was to assess the impact of cardiac arrhythmias on postoperative outcomes in the setting of TKA. METHODS: This retrospective cohort study included all patients undergoing TKA from a national database, from 2016 to 2019. Patients who had cardiac arrhythmias were identified via International Classification of Diseases, Tenth Revision, and Clinical Modification/Procedure Coding System codes and served as the cohort of interest. Multivariate regression was performed to compare postoperative outcomes. Gamma regression was performed to assess length of stay and total charges, while negative binomial regression was used to assess 30-day readmission and reoperation. Patient demographic variables and comorbidities, measured via the Elixhauser comorbidity index, were controlled in our regression analysis. Out of a total of 1,906,670 patients, 224,434 (11.76%) had a diagnosed arrhythmia and were included in our analyses. RESULTS: Those who had arrhythmias had greater odds of both medical (odds ratio [OR] 1.52; P < .001) and surgical complications (OR 2.27; P < .001). They also had greater readmission (OR 2.49; P < .001) and reoperation (OR 1.93; P < .001) within 30 days, longer hospital stays (OR 1.07; P < .001), and greater total charges (OR 1.02; P < .001). CONCLUSIONS: Cardiac arrhythmia is a common comorbidity in the TKA population and is associated with worse postoperative outcomes. Patients who had arrhythmias had greater odds of both medical and surgical complications requiring readmission or reoperation. STUDY DESIGN: Level III; Retrospective Cohort Study.

Radius additivity score: a novel combination index for tumour growth inhibition in fixed-dose xenograft studies.

The effect of combination therapies in many cancers has often been shown to be superior to that of monotherapies. This success is commonly attributed to drug synergies. Combinations of two (or more) drugs in xenograft tumor growth inhibition (TGI) studies are typically designed at fixed doses for each compound. The available methods for assessing synergy in such study designs are based on combination indices (CI) and model-based analyses. The former methods are suitable for screening exercises but are difficult to verify in in vivo studies, while the latter incorporate drug synergy in semi-mechanistic frameworks describing disease progression and drug action but are unsuitable for screening. In the current study, we proposed the empirical radius additivity (Rad-add) score, a novel CI for synergy detection in fixed-dose xenograft TGI combination studies. The Rad-add score approximates model-based analysis performed using the semi-mechanistic constant-radius growth TGI model. The Rad-add score was compared with response additivity, defined as the addition of the two response values, and the bliss independence model in combination studies derived from the Novartis PDX dataset. The results showed that the bliss independence and response additivity models predicted synergistic interactions with high and low probabilities, respectively. The Rad-add score predicted synergistic probabilities that appeared to be between those predicted with response additivity and the Bliss model. We believe that the Rad-add score is particularly suitable for assessing synergy in the context of xenograft combination TGI studies, as it combines the advantages of CI approaches suitable for screening exercises with those of semi-mechanistic TGI models based on a mechanistic understanding of tumor growth.

Nanoparticle surface stabilizing agents influence antibacterial action.

The antibacterial properties of nanoparticles are of particular interest because of their potential to serve as an alternative therapy to combat antimicrobial resistance. Metal nanoparticles such as silver and copper nanoparticles have been investigated for their antibacterial properties. Silver and copper nanoparticles were synthesized with the surface stabilizing agents cetyltrimethylammonium bromide (CTAB, to confer a positive surface charge) and polyvinyl pyrrolidone (PVP, to confer a neutral surface charge). Minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and viable plate count assays were used to determine effective doses of silver and copper nanoparticles treatment against Escherichia coli, Staphylococcus aureus and Sphingobacterium multivorum. Results show that CTAB stabilized silver and copper nanoparticles were more effective antibacterial agents than PVP stabilized metal nanoparticles, with MIC values in a range of 0.003 µM to 0.25 µM for CTAB stabilized metal nanoparticles and 0.25 µM to 2 µM for PVP stabilized metal nanoparticles. The recorded MIC and MBC values of the surface stabilized metal nanoparticles show that they can serve as effective antibacterial agents at low doses.

Determining bioequivalence possibilities of long acting injectables through population PK modelling.

Long acting injectables (LAI) products are a popular intervention for treating a number of chronic conditions, with their long drug release reducing the administration frequency and thus improving patient adherence. The extended release, however, can provide a major challenge to bioequivalence (BE) testing since the long absorption half-life results in a long washout period, meaning that a traditional BE study can be many months or years in length. The unique PK profile for LAI products also means that it is critical to have appropriate metrics to summarise the plasma concentration profile. In this work, we use paliperidone as a case study to demonstrate how Population PK modelling can be utilised to explore sensitivity of summary metrics to different products. We also determine a range of products that are bioequivalent after both multiple dosing and single dosing. Finally, we show how the modelling can be used in a (virtual) PK study as an alternative approach to determining bioequivalence. This work demonstrates the potential for Population PK modelling in bioequivalence assessment, opening doors to more streamlined product development.

Exploring a model-based analysis of patient derived xenograft studies in oncology drug development.

PURPOSE: To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. METHODS: Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). RESULTS: The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. CONCLUSIONS: The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.