Zimmer Biomet (France)

Since 2019, death data published by the Institute of Statistics and Economic Studies (INSEE) are available, raising questions regarding methodology and potential biases in overall survival analyses. We conducted a simulation study to quantify biases and formulate recommendations for using these data for research. We compared several approaches for estimating overall survival by (i) including only hospital data (EMR), (ii) adding deaths known to the INSEE (EMR_INSEE), or (iii) considering patients without reported death as "alive" (EMR_INSEE_IMP). We conducted simulation studies by varying the mortality risk of the disease studied, rate of loss to follow-up, and death capture rate from INSEE. With the EMR_INSEE approach, the risk of bias appeared to be significant in all clinical scenarios, with a large underestimation of overall survival. On comparing two survival curves, the hazard ratio estimate was highly biased, and type-I and II errors were inflated. With the EMR_INSEE_IMP approach, the risk of bias seemed low and acceptable for clinical situations involving low mortality, especially if loss to follow-up was low. However, some clinical situations seemed to require greater vigilance because of risk of bias when mortality was intermediate or high, especially when the risk of loss to follow-up was high. To our knowledge, this is the first study to assess the impact of using INSEE data in addition to hospital data on vital status. Various simulated scenarios enabled us to quantify the biases involved and thus make recommendations on the various possible strategies for using these data.

Empiric combination antibiotic therapy (ECAT) is commonly used to treat healthcare-associated bloodstream infections (HA-BSIs) and sepsis. However, the level of supporting evidence is low and clinical practice varies significantly. We conducted a post hoc analysis using the EUROBACT-2 international cohort study database, which contained data on 2406 adult patients from 328 intensive care units (ICUs) across 52 countries, collected between June 2019 and January 2021. The main outcome was the proportion of patients receiving ECAT for HA-BSIs. Patient and institutional factors influencing the use of ECAT were examined using Markov-Chain Monte Carlo estimation. Three quarters of patients (75.2%; n = 1810) received empiric antibiotic therapy, with ECAT used in approximately half of cases (52.5%; n = 950). Most patients receiving ECAT (70.4%; n = 669) were treated with two antibiotics, beta-lactams plus glycopeptides being the most common combination (40.2%; n = 382). The odds of ECAT were increased by immune deficiency (OR 1.35 [95% CrI 1.03-1.75]), SOFA scores > 11 (OR 1.77 [95% CrI 1.28-2.46]), uncommon sources of infection (OR 1.63 [95% CrI 1.02-2.59]), and admission to ICUs where > 25% of Enterobacteriaceae isolates produce carbapenemases (OR 2.46 [95% CrI 1.37-4.41). The intra-class correlation coefficients at the ICU and country levels were 23.2% and 4.4%, respectively. In conclusion, factors at the individual, institutional, and national levels may affect the use of ECAT to treat HA-BSIs. Given the impact of institutional variables on the use of ECAT and the inconclusive evidence regarding its potential risks, it is of great importance that treatment is tailored based on local antibiotic stewardship programs and the needs of the individual patient.

BackgroundMatching Adjusted Indirect Comparison (MAIC) is a statistical method used to adjust for potential biases when comparing treatment effects between separate data sources, with aggregate data in one arm, and individual patients data in the other. However, acceptance of MAIC in health technology assessment (HTA) is challenging because of the numerous biases that can affect the estimates of treatment effects – especially with small sample sizes, increasing the risk of convergence issues. We suggest statistical approaches to address some of the challenges in supporting evidence from MAICs, applied to a case study.MethodsThe proposed approaches were illustrated with a case study comparing an integrated analysis of three single-arm trials of entrectinib with the French standard of care using the Epidemio-Strategy and Medical Economics (ESME) Lung Cancer Data Platform, in metastatic ROS1-positive Non-Small Cell Lung Cancer (NSCLC) patients. To obtain convergent models with balanced treatment arms, a transparent predefined workflow for variable selection in the propensity score model, with multiple imputation of missing data, was used. To assess robustness, multiple sensitivity analyses were conducted, including Quantitative Bias Analyses (QBA) for unmeasured confounders (E-value, bias plot), and for missing at random assumption (tipping-point analysis).ResultsThe proposed workflow was successful in generating satisfactory models for all sub-populations, that is, without convergence problems and with effectively balanced key covariates between treatment arms. It also gave an indication of the number of models tested. Sensitivity analyses confirmed the robustness of the results, including to unmeasured confounders. The QBA performed on the missing data allowed to exclude the potential impact of the missing data on the estimate of comparative effectiveness, even though approximately half of the ECOG Performance Status data were missing.ConclusionsTo the best of our knowledge, we present the first in-depth application of QBA in the context of MAIC. Despite the real-world data limitations, with this MAIC, we show that it is possible to confirm the robustness of the results by using appropriate statistical methods.Trial registrationNA.

Although Vancouver B2 periprosthetic fractures (PPFs) have been historically managed with revision total hip arthroplasty (rTHA), open reduction and internal fixation (ORIF) has been proposed as an alternative option for reasons including lower cost and surgical time. The purpose of this study was to, therefore, create a Markov model to assess the cost effectiveness of ORIF versus rTHA for Vancouver B2 periprosthetic femur fractures and evaluate various inflection points for varying costs and outcome measures. A Markov model was built using discrete and mutually exclusive health states of the hypothetical patient with Vancouver B2 PPF. A decision tree was created on possible outcomes for each health state, using probabilities defined in the recent PPF literature. Direct costs and quality-adjusted life-years for each surgery and complication state were also collected. One-way and two-way deterministic sensitivity analyses were conducted to better understand the effect of 1 to 2 variables on the incremental cost-effectiveness ratio. The hypothetical patient with a Vancouver B2 PPF that was treated with rTHA incurred a cost of $52,559.64 with an effectiveness of 0.71. When treated with ORIF, the cost was $47,371.97 with an effectiveness of 0.38. The incremental cost and effectiveness of rTHA over ORIF were found to be $5,187.67 and 0.33, respectively. The cost of rTHA, the cost of ORIF, and the effectiveness of ORIF were the most influential variables in the model. On two-way sensitivity analysis, rTHA was more cost effective than ORIF within realistic healthcare parameters. From the payer perspective, we estimate that rTHA is more cost effective than ORIF for the treatment of Vancouver B2 PPFs within certain cost and quality-of-life parameters.

BackgroundProstate cancer is the most common cancer in men and represents a major public health problem. The current method for the diagnosis or screening of prostate cancer is invasive and costly. There have been renewed and innovative studies searching for urinary biomarkers to aid in the diagnosis of prostate cancer, especially with technologies based on urinary exosomes. However, technologies based on urine exosomes usually need expensive machines such as an ultracentrifuge and they are difficult to standardize, which hinder their application in clinical laboratories. We have optimized and standardized the isolation of urinary exosomes with the precipitation method. We have found that urinary exosomal prostate-specific antigen (PSA) can be quantified by automatic Elecsys total PSA technique.ObjectiveIn this study, our objective is to utilize urinary exosomes from prostate cancer for the development of a test to aid in its diagnosis.MethodsExosomes from the prostate cancer cell line LNCaP was used to set up the technique. To analyze urine samples from patients, the methods include the collection of first-void urine using the Colli-Pee device, the isolation of urine exosomes using the optimized precipitation method, and the quantification of exosomal PSA by Elecsys total PSA.ResultsThis will be a 2-year study. We will start including patients and controls in the last quarter of 2024. We expect the results to be published in the second quarter of 2027.ConclusionsThis is the first study to quantify urinary exosomal PSA using the Elecsys total PSA technique for the diagnosis of prostate cancer. This study emphasizes techniques that are suitable for implementation in clinical laboratories, which will facilitate the application of urinary exosomes to simplify and improve the diagnosis and screening of prostate cancer.International Registered Report Identifier (IRRID)PRR1-10.2196/63551

In October 2023, TMT International Observatory contracted AMOS to develop the Secondary Mirror Support System and Positioner (M2SSP). The M2SSP was identified by TIO as a critical sub-system for which early design would substantially reduce technical, schedule and cost risks to the overall TMT project development plan. In this paper, we present the preliminary design of the M2SSP elaborated by AMOS to comply with the demanding performance requirements imposed by the operational cases and the environment of TMT. The M2SSP is composed of a semi-active mirror support assembly (M2CA) and a positioner (M2POS) that consists of a hexapod with tracking capabilities.