Assay harmonization and use of biological standards to improve the reproducibility of the hemagglutination inhibition assay: A FLUCOP collaborative study

The hemagglutination inhibition (HAI) assay is an established technique for assessing influenza immunity, through measurement of antihemagglutinin antibodies. Improved reproducibility of this assay is required to provide meaningful data across different testing laboratories. This study assessed the impact of harmonizing the HAI assay protocol/reagents and using standards on interlaboratory variability. Human pre- and postvaccination sera from individuals (n = 30) vaccinated against influenza were tested across six laboratories. We used a design of experiment (DOE) method to evaluate the impact of assay parameters on interlaboratory HAI assay variability. Statistical and mathematical approaches were used for data analysis. We developed a consensus protocol and assessed its performance against in-house HAI testing. We additionally tested the performance of several potential biological standards. In-house testing with four reassortant viruses showed considerable interlaboratory variation (geometric coefficient of variation [GCV] range of 50% to 117%). The age, concentration of turkey red blood cells, incubation duration, and temperature were key assay parameters affecting variability. Use of a consensus protocol with common reagents, including viruses, significantly reduced GCV between laboratories to 22% to 54%. Pooled postvaccination human sera from different vaccination campaigns were effective as biological standards. Our results demonstrate that the harmonization of protocols and critical reagents is effective in reducing interlaboratory variability in HAI assay results and that pools of postvaccination human sera have potential as biological standards that can be used over multiple vaccination campaigns. Moreover, the use of standards together with in-house protocols is as potent as the use of common protocols and reagents in reducing interlaboratory variability.publishedVersio

International Geomagnetic Reference Field: the 12th generation

The 12th generation of the International Geomagnetic Reference Field (IGRF) was adopted in December 2014 by the Working Group V-MOD appointed by the International Association of Geomagnetism and Aeronomy (IAGA). It updates the previous IGRF generation with a definitive main field model for epoch 2010.0, a main field model for epoch 2015.0, and a linear annual predictive secular variation model for 2015.0-2020.0. Here, we present the equations defining the IGRF model, provide the spherical harmonic coefficients, and provide maps of the magnetic declination, inclination, and total intensity for epoch 2015.0 and their predicted rates of change for 2015.0-2020.0. We also update the magnetic pole positions and discuss briefly the latest changes and possible future trends of the Earth’s magnetic fiel

Interactive statistical monitoring to optimize review of potential clinical trial issues during study conduct

Background: Statistical monitoring involves the review of prospective study data collected in participating sites to detect intra/inter patients and sites inconsistencies. We report methods and results of statistical monitoring in a phase IV clinical trial. Method: PRO-MSACTIVE is a study evaluating ocrelizumab in active relapsing multiple sclerosis (RMS) patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot …) have been applied to a SDTM database to detect potential issues. R-Shiny application was developed to generate an interactive web application in order to ease site and/or patients identification during statistical data review meetings. Results: The PRO-MSACTIVE study enrolled 422 patients in 46 centers between July 2018 and August 2019. Three data review meetings were held between April and October 2019 and 14 standard and planned tests were run on study data, with a total of 15 (32.6%) sites identified as needing review or investigation. Overall 36 findings were identified during the meetings: duplicate records, outliers, inconsistent delays between dates. Conclusion: Statistical monitoring is useful to identify unusual or clustered data patterns that might be revealing issues that could impact the data integrity and/or may potentially impact patients’ safety. With anticipated and appropriate interactive data visualization, early signals can easily be identified or reviewed by the study team and appropriate actions be set up and assigned to the most appropriate function for a close follow-up and resolution. Interactive statistical monitoring is time consuming to initiate using R-Shiny, but is time saving after the 1st data review meeting (DRV).(ClinicalTrials.gov identifier: NCT03589105; EudraCT identifier: 2018-000780-91

Identification of profiles associated with conversions between the Alzheimer’s disease stages, using a machine learning approach

Abstract Background The identification of factors involved in the conversion across the different Alzheimer’s disease (AD) stages is crucial to prevent or slow the disease progression. We aimed to assess the factors and their combination associated with the conversion across the AD stages, from mild cognitive impairment to dementia, at a mild, moderate or severe stage and to identify profiles associated with earliest/latest conversion across the AD stages. Methods In this study conducted on the real-life MEMORA cohort data collected from January 1, 2013, and December 31, 2019, three cohorts were selected depending on the baseline neurocognitive stage from a consecutive sample of patients attending a memory center, aged between 50 and 90 years old, with a diagnosis of AD during the follow-up, and with at least 2 visits at 6 months to 1 year of interval. A machine learning approach was used to assess the relationship between factors including socio-demographic characteristics, comorbidities and history of diseases, prescription of drugs, and geriatric hospitalizations, and the censored time to conversion from mild cognitive impairment to AD dementia, from the mild stage of dementia to the moderate or severe stages of AD dementia, and from the moderate stage of AD dementia to the severe stage. Profiles of earliest/latest conversion compared to median time to conversion across stages were identified. The median time to conversion was estimated with a Kaplan-Meier estimator. Results Overall, 2891 patients were included (mean age 77±9 years old, 65% women). The median time of follow-up was 28 months for mild cognitive impairment (MCI) patients, 33 months for mild AD dementia and 30 months for moderate AD dementia. Among the 1264 patients at MCI stage, 61% converted to AD dementia (median time to conversion: 25 months). Among the 1142 patients with mild AD dementia, 59% converted to moderate/severe stage (median time: 23 months) and among the 1332 patients with moderate AD dementia, 23% converted to severe stage (Q3 time to conversion: 22 months). Among the studied factors, cardiovascular comorbidities, anxiety, social isolation, osteoporosis, and hearing disorders were identified as being associated with earlier conversion across stages. Symptomatic treatment i.e. cholinesterase inhibitors for AD was associated with later conversion from mild stage of dementia to moderate/severe stages. Conclusion This study based on a machine learning approach allowed to identify potentially modifiable factors associated with conversion across AD stages for which timely interventions may be implemented to delay disease progression

Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm.

AIMS Diabetic ketoacidosis (DKA) is a serious and potentially fatal complication of type 1 diabetes and it is difficult to identify individuals at increased risk. The aim of this study was to identify predictive factors for DKA by retrospective analysis of registry data and use of a subgroup discovery algorithm. MATERIALS AND METHODS Data from adults and children with type 1 diabetes and >2 diabetes-related visits were analyzed from the Diabetes Prospective Follow-up Registry. Q-Finder®, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH <7.3 during a hospitalization event. RESULTS Data for 108,223 adults and children, of whom 5,609 (5.2%) had DKA, were studied. Q-Finder® analysis identified 11 profiles associated with increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; HbA1c ≥8.87 [73 mmol/mol]; no fast-acting insulin intake; age <15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycemia; hypoglycemic coma; and autoimmune thyroiditis. Risk of DKA increased with number of risk profiles matching patients' characteristics. CONCLUSIONS Q-Finder® confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA. This article is protected by copyright. All rights reserved

Retrospective analysis of real-world data to evaluate actionability of a comprehensive molecular profiling panel in solid tumor tissue samples (REALM study).

IntroductionConsidering the growing interest in matched cancer treatment, our aim was to evaluate the ability of a comprehensive genomic profiling (CGP) assay to propose at least one targeted therapy given an identified genomic alteration or signature (actionability), and to collect the treatment modifications based on the CGP test results in clinical practise for solid tumors.MethodsThis retrospective, multicentre French study was conducted among 25 centres that participated in a free of charge program between 2017 and 2019 for a tissue CGP test. Data were collected on the patient, disease, tumor genomic profile, treatment suggested in the report (related to the genomic profile results) and subsequent therapeutic decisions according to the physician's declaration.ResultsAmong the 416 patients, most had lung cancer (35.6%), followed by biliary tract cancer (11.5%) or rare cancers (11.1%); 75% had a metastatic disease. The actionability was 75.0% (95% CI [70.6%-78.9%]) for all patients, 85.1% and 78.4%, respectively in lung cancer and metastatic patients. After exclusion of clinical trial suggestions, the actionability decreased to 62.3% (95% CI [57.5%-66.8%]). Treatment modification based on the test results was observed in 17.3% of the patients and was more frequent in metastatic disease (OR = 2.73, 95% CI [1.31-5.71], p = 0.007). The main reasons for no treatment modification were poor general condition (33.2%) and stable disease or remission (30.2%). The genomic-directed treatment changes were performed mostly during the first six months after the CGP test, and interestingly a substantial part was observed from six to 24 months after the genomic profiling.ConclusionThis French study provides information on the real-life actionability of a CGP test based on tissue samples, and trends to confirm its utility in clinical practice across the course of the disease, in particularly for patients with lung cancer and/or advanced disease

Assay harmonization and use of biological standards to improve the reproducibility of the hemagglutination inhibition assay: A FLUCOP collaborative study

The hemagglutination inhibition (HAI) assay is an established technique for assessing influenza immunity, through measurement of antihemagglutinin antibodies. Improved reproducibility of this assay is required to provide meaningful data across different testing laboratories. This study assessed the impact of harmonizing the HAI assay protocol/reagents and using standards on interlaboratory variability. Human pre- and postvaccination sera from individuals (n = 30) vaccinated against influenza were tested across six laboratories. We used a design of experiment (DOE) method to evaluate the impact of assay parameters on interlaboratory HAI assay variability. Statistical and mathematical approaches were used for data analysis. We developed a consensus protocol and assessed its performance against in-house HAI testing. We additionally tested the performance of several potential biological standards. In-house testing with four reassortant viruses showed considerable interlaboratory variation (geometric coefficient of variation [GCV] range of 50% to 117%). The age, concentration of turkey red blood cells, incubation duration, and temperature were key assay parameters affecting variability. Use of a consensus protocol with common reagents, including viruses, significantly reduced GCV between laboratories to 22% to 54%. Pooled postvaccination human sera from different vaccination campaigns were effective as biological standards. Our results demonstrate that the harmonization of protocols and critical reagents is effective in reducing interlaboratory variability in HAI assay results and that pools of postvaccination human sera have potential as biological standards that can be used over multiple vaccination campaigns. Moreover, the use of standards together with in-house protocols is as potent as the use of common protocols and reagents in reducing interlaboratory variability

Clinical characteristics and F1CDx test results in metastatic patients.

Clinical characteristics and F1CDx test results in metastatic patients.</p

Cumulative incidence of test-informed (genomic-directed) treatment, from the time when F1CDx results were available (Kalbfleisch and Prentice curve).

Cumulative incidence of test-informed (genomic-directed) treatment, from the time when F1CDx results were available (Kalbfleisch and Prentice curve).</p