Cognitive Impairment and Dementia Data Model: Quality Evaluation and Improvements

Recently, datasets with various factors and indicators of cognitive diseases have been available for clinical research. Although the transformation of information to a particular data model is straightforward, many challenges arise if data from different repositories have to be integrated. Since each data source keeps entities with different names and relationships at different levels of granularity and format, the information can be partially lost or not properly presented. It is therefore important to have a common data model that provides a unified description of different factors and indicators related to cognitive diseases. Thus, in our previous work, we proposed a hierarchical cognitive impairment and dementia data model that keeps the semantics of the data in a human-readable format and accelerates the interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. This paper extends our previous work by evaluating and improving the data model by adapting the methodology proposed by D. Moody and G. Shanks. The completeness, simplicity, correctness and integrity of the data model are assessed and based on the results a new, improved version of the model is generated. The understandability of the improved model is evaluated using an online questionnaire. Simplicity and integrity are also considered as well as the factors that may influence the flexibility of the data model

Cognitive Impairment and Dementia Data Model: Quality Evaluation and Improvements

Recently, datasets with various factors and indicators of cognitive diseases have been available for clinical research. Although the transformation of information to a particular data model is straightforward, many challenges arise if data from different repositories have to be integrated. Since each data source keeps entities with different names and relationships at different levels of granularity and format, the information can be partially lost or not properly presented. It is therefore important to have a common data model that provides a unified description of different factors and indicators related to cognitive diseases. Thus, in our previous work, we proposed a hierarchical cognitive impairment and dementia data model that keeps the semantics of the data in a human-readable format and accelerates the interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. This paper extends our previous work by evaluating and improving the data model by adapting the methodology proposed by D. Moody and G. Shanks. The completeness, simplicity, correctness and integrity of the data model are assessed and based on the results a new, improved version of the model is generated. The understandability of the improved model is evaluated using an online questionnaire. Simplicity and integrity are also considered as well as the factors that may influence the flexibility of the data model

Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach.

IntroductionThe Amyloid/Tau/Neurodegeneration (ATN) framework was proposed to identify the preclinical biological state of Alzheimer's disease (AD). We investigated whether ATN phenotype can be predicted using routinely collected research cohort data.Methods927 EPAD LCS cohort participants free of dementia or Mild Cognitive Impairment were separated into 5 ATN categories. We used machine learning (ML) methods to identify a set of significant features separating each neurodegeneration-related group from controls (A-T-(N)-). Random Forest and linear-kernel SVM with stratified 5-fold cross validations were used to optimize model whose performance was then tested in the ADNI database.ResultsOur optimal results outperformed ATN cross-validated logistic regression models by between 2.2% and 8.3%. The optimal feature sets were not consistent across the 4 models with the AD pathologic change vs controls set differing the most from the rest. Because of that we have identified a subset of 10 features that yield results very close or identical to the optimal.DiscussionOur study demonstrates the gains offered by ML in generating ATN risk prediction over logistic regression models among pre-dementia individuals

Estimation of antimicrobial resistance of Mycoplasma genitalium, Belgium, 2022

Background: Antimicrobial resistance (AMR) of Mycoplasma genitalium (MG) is a growing concern worldwide and surveillance is needed. In Belgium, samples are sent to the National Reference Centre of Sexually Transmitted Infections (NRC-STI) on a voluntary basis and representative or robust national AMR data are lacking. Aim: We aimed to estimate the occurrence of resistant MG in Belgium. Methods: Between July and November 2022, frozen remnants of MG -positive samples from 21 Belgian laboratories were analysed at the NRC-STI. Macrolide and fluoroquinolone resistance -associated mutations (RAMs) were assessed using Sanger sequencing of the 23SrRNA and parC gene. Differences in resistance patterns were correlated with surveillance methodology, sociodemographic and behavioural variables via Fisher's exact test and logistic regression analysis. Results: Of the 244 MG -positive samples received, 232 could be sequenced for macrolide and fluoroquinolone RAMs. Over half of the sequenced samples (55.2%) were resistant to macrolides. All sequenced samples from men who have sex with men (MSM) (24/24) were macrolide-resistant. Fluoroquinolone RAMs were found in 25.9% of the samples and occurrence did not differ between socio-demographic and sexual behaviour characteristics. Conclusion: Although limited in sample size, our data suggest no additional benefit of testing MG retrieved from MSM for macrolide resistance in Belgium, when making treatment decisions. The lower occurrence of macrolide resistance in other population groups, combined with emergence of fluoroquinolone RAMs support macrolide-resistance testing in these groups. Continued surveillance of resistance in MG in different population groups will be crucial to confirm our findings and to guide national testing and treatment strategies

Estimation of antimicrobial resistance of Mycoplasma genitalium, Belgium, 2022

Abstract: Background: Antimicrobial resistance (AMR) of Mycoplasma genitalium (MG) is a growing concern worldwide and surveillance is needed. In Belgium, samples are sent to the National Reference Centre of Sexually Transmitted Infections (NRC-STI) on a voluntary basis and representative or robust national AMR data are lacking. Aim: We aimed to estimate the occurrence of resistant MG in Belgium. Methods: Between July and November 2022, frozen remnants of MG-positive samples from 21 Belgian laboratories were analysed at the NRC-STI. Macrolide and fluoroquinolone resistance-associated mutations (RAMs) were assessed using Sanger sequencing of the 23SrRNA and parC gene. Differences in resistance patterns were correlated with surveillance methodology, sociodemographic and behavioural variables via Fisher\u2019s exact test and logistic regression analysis. Results: Of the 244 MG-positive samples received, 232 could be sequenced for macrolide and fluoroquinolone RAMs. Over half of the sequenced samples (55.2%) were resistant to macrolides. All sequenced samples from men who have sex with men (MSM) (24/24) were macrolide-resistant. Fluoroquinolone RAMs were found in 25.9% of the samples and occurrence did not differ between socio-demographic and sexual behaviour characteristics. Conclusion: Although limited in sample size, our data suggest no additional benefit of testing MG retrieved from MSM for macrolide resistance in Belgium, when making treatment decisions. The lower occurrence of macrolide resistance in other population groups, combined with emergence of fluoroquinolone RAMs support macrolide-resistance testing in these groups. Continued surveillance of resistance in MG in different population groups will be crucial to confirm our findings and to guide national testing and treatment strategies

Nationwide Harmonization Effort for Semi-Quantitative Reporting of SARS-CoV-2 PCR Test Results in Belgium.

From early 2020, a high demand for SARS-CoV-2 tests was driven by several testing indications, including asymptomatic cases, resulting in the massive roll-out of PCR assays to combat the pandemic. Considering the dynamic of viral shedding during the course of infection, the demand to report cycle threshold (Ct) values rapidly emerged. As Ct values can be affected by a number of factors, we considered that harmonization of semi-quantitative PCR results across laboratories would avoid potential divergent interpretations, particularly in the absence of clinical or serological information. A proposal to harmonize reporting of test results was drafted by the National Reference Centre (NRC) UZ/KU Leuven, distinguishing four categories of positivity based on RNA copies/mL. Pre-quantified control material was shipped to 124 laboratories with instructions to setup a standard curve to define thresholds per assay. For each assay, the mean Ct value and corresponding standard deviation was calculated per target gene, for the three concentrations (10, 10 and 10 copies/mL) that determine the classification. The results of 17 assays are summarized. This harmonization effort allowed to ensure that all Belgian laboratories would report positive PCR results in the same semi-quantitative manner to clinicians and to the national database which feeds contact tracing interventions