Interpretation Of Clinical Data Research Articles

Application of Natural Language Processing in Electronic Health Record Data Extraction for Navigating Prostate Cancer Care: A Narrative Review.

Introduction: Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. Materials and Methods: We conducted a literature search of PubMed and Google Scholar databases using the keywords: "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR" with variations of each. No language or time limits were imposed. All results were collected in a standardized manner, including country of origin, sample size, algorithm, objective of outcome, and model performance. The precision, recall, and the F1 score of studies were collected as a metric of model performance. Results: Of the 14 studies included in the review, 2 articles focused on documenting digital rectal examinations, 1 on identifying and quantifying pain secondary to PCa, 8 on extracting staging/grading information from clinical reports, with an emphasis on TNM-classification, risk stratification, and identifying metastasis, 2 articles focused on patient-centered post-treatment outcomes such as incontinence, erectile, and bowel dysfunction, and 1 on loneliness/social isolation following PCa diagnosis. All models showed moderate to high data annotation/extraction accuracy compared with the gold standard method of manual data extraction by chart review. Despite their potential, NLPs face challenges in handling ambiguous, institution-specific language and context nuances, leading to occasional inaccuracies in clinical data interpretation. Conclusion: NLP-based data extraction has effectively extracted various outcomes from PCa patients' EHRs. It holds the potential for automating outcome monitoring and data collection, resulting in time and labor savings.

On the problem of differential diagnosis in the detection of antineutrophil cytoplasmic antibodies

Vasculitides associated with antineutrophil cytoplasmic antibodies (ANCA) are a group of systemic autoimmune diseases characterized by necrotizing lesions of the walls of predominantly small vessels and the presence of ANCA against proteinase 3 or myeloperoxidase. However, an increase in ANCA levels can also be observed in other diseases, including autoimmune, malignant and infectious diseases, which complicates the interpretation of clinical and laboratory data and requires a differential diagnosis.

A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images

Machine intelligence has the potential to play a significant role in diagnosing, managing, and guiding the treatment of disease, which supports the rising demands on healthcare to provide rapid and accurate interpretation of clinical data. The global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus (SARSCoV-2) exposed a need for rapid clinical data interpretation in response to an unprecedented burden on the healthcare system. A new healthcare challenge has arisen – post-COVID syndrome or ‘long COVID’. Symptoms of the post-COVID syndrome can persist for months following infection with SARS-CoV-2, often characterised by fatigue, breathlessness, dizziness, and pain. Despite this additional healthcare burden, no tests can diagnose, monitor, or determine the efficacy of treatments/interventions to support recovery. In this paper, an array of machine-learning algorithms is trained to evaluate and detect COVID-19-associated changes to lung tissue from X-ray images. X-ray images are classified from open sources into three categories: COVID-19 patients, patients with pneumonia, and unaffected otherwise healthy individuals using existing Machine Learning (ML) and pre-trained deep learning models. Prioritising models with the fewest false positives and false negatives assessed the performance of different models in detecting COVID-19-associated lung tissue. In addition, image pre-processing, data augmentation, and hyperparameter tuning are used to achieve the best accuracy in the models. Different ML models, including K Nearest Neighbour (KNN), and decision trees (DT), as well as transfer learning models such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG-16, VGG-19), ResNet50, DenseNet201, Xception, and InceptionV3, were tested to evaluate the performance of these models for X-ray images classification. The comparative analysis indicates that VGG-19 with augmentation performed best among the ten algorithms with a training accuracy of 99%, testing accuracy of 98%, and precision of 90% for COVID-19, 90% for normal, and 100% for pneumonia. This higher accuracy for detecting COVID-19-associated lung changes on X-ray may be further developed to stratify patients suffering from post-COVID syndrome. This may enable future intervention studies to determine the efficacy of treatments or better track patients’ prognoses to be optimised.

Empowering Medical Monitors: AI-Enabled Semantic Parsing for Enhanced Clinical Data Interpretation

Empowering Medical Monitors: AI-Enabled Semantic Parsing for Enhanced Clinical Data Interpretation

Abstract 1771: NYU Langone Genome PACT - Genome Profiling of Actionable Cancer Targets (LG-PACT) for clinical patient molecular diagnostics and treatment

Abstract Next-generation sequencing (NGS) for the detection of somatic variants has become the tool of choice in a variety of molecular oncology fields and in the clinic. Its use ranges from sequencing entire tumor genomes and transcriptomes to targeted clinical diagnostic gene panels. The NYU Langone Genome PACT assay is a qualitative in vitro diagnostic test that uses targeted next generation sequencing of formalin-fixed paraffin-embedded (FFPE) tumor tissue matched with normal specimens from patients to detect gene alterations in a 607-gene panel. Indications for testing are cancer (solid tumors or hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, or treatment options including targeted therapies and eligibility for clinical trials. The test is intended to provide information on somatic mutations including point mutations, copy number aberrations, and small insertions/deletions (indels) for diagnostic and treatment decisions. LG-PACT is a United States Food and Drug Administration (FDA) approved diagnostic test. The clinical interpretation of sequencing data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. The final mostly manual review of data is intensive, involving highly trained scientists, encompassing literature review, interpretation and tier classification by pathologists, who then provide a complete molecular diagnostic report to the treating oncologists. Since January 2022 to present (October 2023) we have provided clinical genomic reports for 1029 oncological cases from 124 different cancers and their subtypes, including Brain (489 cases incl. meningioma, glioma and glioblastoma), Gastrointestinal (123), Lung (114), among others. We first present the technical challenges of validating an NGS oncological diagnostic targeted assay for appropriate clinical grade accuracy and sensitivity acceptable for patient care. We show how copy number alterations provide a more comprehensive description of the tumors genomic profile. We then outline the actionability of targeted panel sequencing for our current patient cohort. Where analysis of variant detection has led to 47% (490) of our clinical tumor samples containing known tier 1 therapeutic variants. We conclude with presenting case studies that identify both the clinical utility and informatic challenges of variant calling specific to gene panel sequencing e.g., i) the capture of potential targetable rare and novel indels and multivariant mutations in exons 19 and 20 of EGFR, and ii) a unique KIT tandem duplication event in a gastrointestinal stromal tumor (GIST). We demonstrate the value in precision oncology for multiple cancer types and how the capture of unique variants can provide better targeted treatment options to cancer patients. Citation Format: Varshini Vasudevaraja, Yiying Yang, Jonathan Serrano, Kazimierz Wrzeszczynski, Matija Snuderl. NYU Langone Genome PACT - Genome Profiling of Actionable Cancer Targets (LG-PACT) for clinical patient molecular diagnostics and treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1771.

Primary Tuberculosis in Adults: Diagnostic Errors

Primary Tuberculosis in Adults: Diagnostic Errors

Distribution of the C9orf72 hexanucleotide repeat expansion in healthy subjects: a multicenter study promoted by the Italian IRCCS network of neuroscience and neurorehabilitation.

High repeat expansion (HRE) alleles in C9orf72 have been linked to both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD); ranges for intermediate allelic expansions have not been defined yet, and clinical interpretation of molecular data lacks a defined genotype-phenotype association. In this study, we provide results from a large multicenter epidemiological study reporting the distribution of C9orf72 repeats in healthy elderly from the Italian population. A total of 967 samples were collected from neurologically evaluated healthy individuals over 70 years of age in the 13 institutes participating in the RIN (IRCCS Network of Neuroscience and Neurorehabilitation) based in Italy. All samples were genotyped using the AmplideXPCR/CE C9orf72 Kit (Asuragen, Inc.), using standardized protocols that have been validated through blind proficiency testing. All samples carried hexanucleotide G4C2 expansion alleles in the normal range. All samples were characterized by alleles with less than 25 repeats. In particular, 93.7% of samples showed a number of repeats ≤10, 99.9% ≤20 repeats, and 100% ≤25 repeats. This study describes the distribution of hexanucleotide G4C2 expansion alleles in an Italian healthy population, providing a definition of alleles associated with the neurological healthy phenotype. Moreover, this study provides an effective model of federation between institutes, highlighting the importance of sharing genomic data and standardizing analysis techniques, promoting translational research. Data derived from the study may improve genetic counseling and future studies on ALS/FTD.

Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis

Feline infectious peritonitis (FIP) is a severe feline coronavirus-associated syndrome in cats, which is invariably fatal without anti-viral treatment. In the majority of non-effusive FIP cases encountered in practice, confirmatory diagnostic testing is not undertaken and reliance is given to the interpretation of valuable, but essentially non-specific, clinical signs and laboratory markers. We hypothesised that it may be feasible to develop a machine learning (ML) approach which may be applied to the analysis of clinical data to aid in the diagnosis of disease. A dataset encompassing 1939 suspected FIP cases was scored for clinical suspicion of FIP on the basis of history, signalment, clinical signs and laboratory results, using published guidelines, comprising 683 FIP (35.2%), and 1256 non-FIP (64.8%) cases. This dataset was used to train, validate and evaluate two diagnostic machine learning ensemble models. These models, which analysed signalment and laboratory data alone, allowed the accurate discrimination of FIP and non-FIP cases in line with expert opinion. To evaluate whether these models may have value as a diagnostic tool, they were applied to a collection of 80 cases for which the FIP status had been confirmed (FIP: n = 58 (72.5%), non–FIP: n = 22 (27.5%)). Both ensemble models detected FIP with an accuracy of 97.5%, an area under the curve (AUC) of 0.969, sensitivity of 95.45% and specificity of 98.28%. This work demonstrates that, in principle, ML can be usefully applied to the diagnosis of non-effusive FIP. Further work is required before ML may be deployed in the laboratory as a diagnostic tool, such as training models on datasets of confirmed cases and accounting for inter-laboratory variation. Nevertheless, these results illustrate the potential benefit of applying ML to standardising and accelerating the interpretation of clinical pathology data, thereby improving the diagnostic utility of existing laboratory tests.

A Methodological Framework for Meta-analysis and Clinical Interpretation of Subgroup Data: The Case of Major Adverse Cardiovascular Events With GLP-1 Receptor Agonists and SGLT2 Inhibitors in Type 2 Diabetes.

We present a methodological framework for conducting and interpreting subgroup meta-analyses. Methodological steps comprised evaluation of clinical heterogeneity regarding the definition of subpopulations, credibility assessment of subgroup meta-analysis, and translation of relative into absolute treatment effects. We used subgroup data from type 2 diabetes cardiovascular outcomes trials (CVOTs) with glucagon-like peptide 1 (GLP-1) receptor agonists and sodium-glucose cotransporter 2 (SGLT2) inhibitors for patients with established cardiovascular disease and those at high cardiovascular risk without manifest cardiovascular disease. First, we evaluated the variability in definitions of the subpopulations across CVOTs using major adverse cardiovascular events (MACE) incidence in the placebo arm as a proxy for baseline cardiovascular risk. As baseline risk did not differ considerably across CVOTs, we conducted subgroup meta-analyses of hazard ratios (HRs) for MACE and assessed the credibility of a potential effect modification. Results suggested using the same overall relative effect for each of the two subpopulations (HR 0.85, 95% CI 0.80-0.90, for GLP-1 receptor agonists and HR 0.91, 95% CI 0.85-0.97, for SGLT2 inhibitors). Finally, we calculated 5-year absolute treatment effects (number of fewer patients with event per 1,000 patients). Treatment with GLP-1 receptor agonists resulted in 30 fewer patients with event in the subpopulation with established cardiovascular disease and 14 fewer patients with event in patients without manifest cardiovascular disease. For SGLT2 inhibitors, the respective absolute effects were 18 and 8 fewer patients with event per 1,000 patients. This framework can be applied to subgroup meta-analyses regardless of outcomes or modification variables.

Modernizing AD clinical trials: why are we designing studies for disease modifying treatments using the symptomatic treatment paradigm?

AbstractBackgroundUntil the accelerated approvals of aducanumab and lecanemab, the only approved drugs for Alzheimer’s disease (AD) were symptomatic treatments, which showed relatively large improvements but did not alter disease progression. Multiple decades of designing and analyzing trials for symptomatic treatments have optimized the process for acetyl cholinesterase inhibitors and NMDA receptor antagonists, but this paradigm is not optimal for disease‐modifying therapies. As the AD research community continues to transition towards treatments with the potential to alter disease progression, significant course corrections in the design and analysis of clinical trials are required to ensure that treatments with the largest potential for impact do not fail because they’re being evaluated out of context.MethodSeveral design aspects from AD clinical trials are evaluated in the context of disease‐modifying treatments, including the expected effect size, patient population, outcomes, multiplicity adjustments, trial duration, use of biomarkers, and timing of visits. We use publicly available trial results to demonstrate that both the clinical trial process and public interpretation of data will result in costly losses unless significant improvements are accepted and implementedResultIn the context of donepezil’s expected 3‐point change on ADAS‐cog over 6 months, the results for lecanemab (1.5 points over 18 months) seem small, yet lecanemab improvements can be reasonably expected to match donepezil after three years of treatment and continue to increase. Symptomatic treatments were required to demonstrate significant improvement on multiple outcomes, but disease‐modifying treatments should address the underlying disease process. Trials of aducanumab, donanemab, and lecanemab show consistent disease modification of 5 to 6 months less progression over 18 months when outcomes are converted to units of time and combined. This time‐converted composite (which may include biomarkers) can show consistent results earlier in the disease course, allowing prodromal and early AD patients to be studied with greater success.ConclusionThe current message sent by recent approvals is that only sponsors with resources to field clinical trials with thousands of patients have a chance of gaining regulatory approval. By modernizing clinical trials, more sponsors will have a better chance of success, meaning better treatments available to more patients sooner.

Preferences for Monitoring Comprehensive Heart Failure Care: A Latent Class Analysis.

To measure preference heterogeneity for monitoring systems among patients with a chronic heart failure. A best-worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred. Data from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute "monitoring," with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization. Using LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

DNMT3A R882H Exhibits Greater Inflammatory Potential Than R882C in Primary Hematopoietic Stem and Progenitor Cell Knock-in Model and Population Data

Clonal hematopoiesis (CH) is an age-associated phenomenon which is known to increase the risk for hematologic malignancy and cardiovascular disease. The most commonly mutated gene in CH is de novo DNA methyltransferase DNMT3A. R882H and R882C are the most common hotspot mutations in this gene, and these mutations are commonly grouped together in interpretation of clinical data even though in vitro experiments suggest divergence in their biochemical consequences. Here, we used CRISPR knock-in of primary hematopoietic stem and progenitor cells (HSPCs) along with population data from the UK Biobank (UKB) to evaluate the extent to which R882H and R882C differentially predispose to inflammatory phenotypes. Using two homology donor (HD) templates with distinct self-cleaving fluorescent tags, we used FACS to isolate homozygous R882H- or R882C-mutant cells from six unique samples of cord blood HSPCs. We subsequently cultured these isogeneic mutant cells and their mock-electroporated wild-type (WT) controls in monocyte differentiation media and evaluated their transcriptomes via bulk RNA sequencing (RNAseq) and production of secreted cytokines in a Luminex-based assay. Analysis of transcripts using differential expression for repeated measures (Dream) revealed that, when compared to WT, the mutants demonstrated many shared differentially expressed genes (DEGs) but also exhibited many DEGs unique to each variant. Furthermore, gene set enrichment analysis (GSEA) of Hallmark pathways revealed common enrichment of cell cycle related pathways in both variants, but found that R882H displayed greater enrichment of inflammation-related pathways than either WT or R882C (Figure 1A). Rank-order GO and KEGG pathway enrichment comparison of R882H vs R882C showed further evidence of bias in inflammation-related pathways among the most upregulated gene sets (e.g., GO:0019882, Antigen Processing & Presentation and KEGG:hsa04145, Phagosome). IL-6 is significantly regulated by R882 genotype in our model; levels of IL-6 for R882H (13,065 ± 2,446; mean ± s.e. in pg/mL) but not R882C (10,340 ± 2,446) were significantly different than WT (6,281 ± 1,730; p = 0.014). We next sought to better understand if this biased inflammatory priming would also be observed for heterozygous mutations and across different cell types, so using mutant and WT HD templates we performed single cell RNAseq on CRISPR'd heterozygous mutants and knock-in WT controls. Comparing R882H to R882C in this context demonstrated upregulation of numerous inflammation-related genes, including CXCL8 (IL-8), S100A8, and S100A9. GSEA of Hallmark pathways revealed enrichment of inflammatory pathways (e.g., Inflammatory Response & TNFA Signaling via NFKB) in monocytic cells but also in progenitor cells. We then investigated whether these transcriptional differences between R882H and R882C might be due to divergent patterns of DNA (hypo)methylation. Employing 5-letter next-generation sequencing (A, C, G, T, methyl-C), we compared the methylation landscape of three isogeneic donors with homozygous R882H, R882C, or mock-electroporated WT genotypes. As expected, comparison of either mutant to WT showed a global hypomethylation. Comparison of R882H to R882C revealed no systematic bias in methylation of gene promoters but a pervasive hypomethylation of gene bodies. A gene set of the 50 most upregulated genes from our single cell RNAseq experiment was significantly enriched in rank order analysis of R882H vs R882C hypomethylation of gene bodies (NES = 1.47, p = 0.036) but not promoters (NES = -0.86, p = 0.71). Finally, we asked if there were differences in patient phenotypes for those with R882H or R882C clonal hematopoiesis. Here, we used our previously published UKB clonal hematopoiesis cohort (Vlasschaert et al. 2023, Blood). In Cox proportional hazards models controlling for basic demographics (N = 451K) or demographics plus cardiovascular-relevant covariates (N = 390K), we found that R882H but not R882C was associated with significantly greater incidence of heart failure and of a composite measure consisting of death or coronary artery disease (Figure 1B). In conclusion, our study found evidence in experimental models and patient data that DNMT3A R882H may pose a greater risk for inflammatory phenotypes than R882C.

Systematic Literature Review to Identify Influencing Factors of Efficacy and Safety Outcomes of Chimeric Antigen Receptor T-Cell Therapies in Large B-Cell Lymphoma

Background: The interpretation of clinical data involving chimeric antigen receptor T-cell therapies (CAR-T's) in Large B-Cell Lymphoma (LBCL) requires a thorough understanding of factors that influence efficacy and safety outcomes. Materials & Methods: To identify relevant influencing factors of efficacy and safety outcomes, we conducted a systematic literature review for CAR-T cell therapies. Articles were searched on PubMed from 06.2018 till 06.2023. Data for progression free survival (PFS), overall survival (OS), complete response (CR), objective response rate (ORR), duration of response (DOR), and safety (i.e. Immune Effector Cell-Associated Neurotoxicity Syndrome [ICANS], and Cytokine Release Syndrome [CRS]) were extracted in accordance with the PRISMA guidelines. Results: A total of 236 articles were identified and narrowed to 44 publications for full text screening. 13 articles were included in the final analysis. The main reasons for exclusion were other indications, missing data, and lack of significance. Factors affecting OS that were identified in more than one publication were Eastern Cooperative Oncology Group Performance Status Scale (ECOG PS) ≥ 2, presence of bulky disease, and high lactate dehydrogenase (LDH) values. For PFS it was presence of bridging therapy before CAR-T infusion, and LDH values as well. Table 1 presents the full list of identified variables. For DOR no influencing factor was observed. Influencing factors for safety outcomes are summarized in table 2. Evidence is less compelling here because all identified variables were supported by one publication only. Conclusion: This review provides a set of factors that influences efficacy and safety outcomes of CAR-T therapies in LBCL. It can be used to assess comparability of different treatment arms in randomized controlled studies or to make careful adjustments when performing indirect treatment comparisons in order to better understand the true value of innovative treatments.

Abstract 12374: Machine Learning-Based Evaluation of Elevated Left Ventricular End-Diastolic Pressure

Background: Left ventricular end-diastolic pressure (LVEDP) is clinically relevant due to its association with diastolic dysfunction and other outcomes. The gold-standard method of measuring LVEDP is invasive intra-cardiac catheterization. Echocardiography is routinely used for non-invasive estimation of left ventricular (LV) filling pressures; however, correlation with invasive LVEDP is variable. Goal: We sought to use machine learning (ML) algorithms to predict elevated LVEDP (> 20 mmHg) using clinical, echocardiographic, and biochemical parameters. Methods: We used a cohort of 460 consecutive patients without atrial fibrillation or significant mitral valve disease who underwent transthoracic echocardiography within 24 hours of elective heart catheterization. We included patients’ clinical (e.g. age), echocardiographic (e.g. left ventricular ejection fraction), and biochemical (e.g. N-terminal brain natriuretic peptide) data. We fit logistic regression, random forest, gradient boosting, and support vector machine algorithms in a 20-iteration train-validate-test workflow and measured performance using average area under the receiver operating characteristic curve (AUROC). We also performed multi-class classification of the patients’ cardiac conditions and predicted elevated tau (> 45), the gold-standard parameter for LV diastolic dysfunction. For each outcome, logistic regression weights were used to identify clinically relevant variables. Results: ML algorithms predicted LVEDP > 20 mmHg with good performance (AUROC = 0.779). ML models showed excellent performance predicting elevated tau (AUROC = 0.830) and classifying a variety of cardiac conditions (AUROC = 0.837 - 0.973). We identified several clinical variables (e.g. BMI, heart rate) relevant to LVEDP prediction. Conclusion: Our study shows ML approaches can robustly predict elevated LVEDP and tau. ML may assist in the clinical interpretation of echocardiographic data.

13 Investigating the Influence of Educational Attainment and Crystalized Verbal Skills on Verbal Fluency Performance Among Patients from a Community-Based Outpatient Neurology Clinic

Objective:Neuropsychological measures of verbal fluency help detect cognitive decline and neuropathology. The discrepancy between semantic verbal fluency and phonemic verbal fluency is commonly utilized to differentiate between cortical and subcortical processes. Understanding how other factors influence a patient’s verbal fluency scores is vital in informing clinical interpretation of neuropsychological test data. This study aimed to investigate how educational attainment and crystalized verbal skills (i.e., word reading and vocabulary) influence verbal fluency performance among a clinical sample of patients seen for neuropsychological evaluation services at a community-based outpatient neurology clinic.Participants and Methods:We utilized data from N=26 patients [mean age = 50.5 (SD = 22.0), 31% female, mean education = 13.5 (SD = 2.3)] who completed neuropsychological evaluations as part of their clinical care at an outpatient neurology clinic. Participants were included in this study if they had complete data for all variables of interest. We used Pearson correlation analyses to investigate associations between each predictor variable of interest (years of education, WRAT-5 Reading, WASI-2 Vocabulary) and age-norm corrected D-KEFS Verbal Fluency scores. Prior to analysis, all variables were converted to z-scores.Results:We found that years of education (r = 0.49, p = 0.01) and vocabulary (r = 0.41, p = 0.04) were significantly positively correlated with category fluency performance. Reading was also positively correlated with category fluency at trend level, but this association was not statistically significant (r = 0.36, p = 0.07). We found that vocabulary (r = 0.47, p = 0.02) and reading (r = 0.51, p = 0.007) were significantly positively correlated with phonemic fluency performance, while the association between education and phonemic verbal fluency performance was not significant (r = 0.27, p = 0.18).Conclusions:Our results suggest that educational attainment and crystalized verbal skills are positively associated with verbal fluency performance, though the degree of influence of these individual factors may differ for category fluency vs phonemic fluency performance. Our results have implications for the clinical practice of neuropsychology. Namely, appreciating a patient’s educational attainment and crystalized verbal skills can help guide clinical interpretation of whether or not a patient’s verbal fluency test scores reflect a decline from their baseline. This may be particularly important to consider among patients with high educational attainment or high premorbid verbal skills, as a subtle decline in their verbal fluency abilities may not be appreciated if relying only on age-based norms for interpretation. This is clinically relevant including when assessing for the early stages of neurodegenerative disorders (e.g., Alzheimer’s disease, Primary Progressive Aphasia) or for subtle changes associated with stroke or brain injury.

Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss.

Circumpapillary retinal nerve fiber layer thickness (RNFLT) measurement aids in the clinical diagnosis of glaucoma. Spectral domain optical coherence tomography (SD-OCT) machines measure RNFLT and provide normative color-coded plots. In this retrospective study, we investigate whether normative percentiles of RNFLT (pRNFLT) from Spectralis SD-OCT improve prediction of glaucomatous visual field loss over raw RNFLT. A longitudinal database containing OCT scans and visual fields from Massachusetts Eye & Ear glaucoma clinic patients was generated. Reliable OCT-visual field pairs were selected. Spectralis OCT normative distributions were extracted from machine printouts. Supervised machine learning models compared predictive performance between pRNFLT and raw RNFLT inputs. Regional structure-function associations were assessed with univariate regression to predict mean deviation (MD). Multivariable classification predicted MD, pattern standard deviation, MD change per year, and glaucoma hemifield test. There were 3016 OCT-visual field pairs that met the reliability criteria. Spectralis norms were found to be independent of age, sex, and ocular magnification. Regional analysis showed significant decrease in R2 from pRNFLT models compared to raw RNFLT models in inferotemporal sectors, across multiple regressors. In multivariable classification, there were no significant improvements in area under the curve of receiver operating characteristic curve (ROC-AUC) score with pRNFLT models compared to raw RNFLT models. Our results challenge the assumption that normative percentiles from OCT machines improve prediction of glaucomatous visual field loss. Raw RNFLT alone shows strong prediction, with no models presenting improvement by the manufacturer norms. This may result from insufficient patient stratification in tested norms. Understanding correlation of normative databases to visual function may improve clinical interpretation of OCT data.

Fetal brain volumes and neurodevelopmental outcome of intrauterine growth restricted fetuses

ObjectiveThis study aims to investigate the correlation of MRI measured fetal brain volumetrics with long-term neurodevelopmental outcome, among intrauterine growth restricted (IUGR) and apparently healthy fetuses. Study designA historical cohort study at a single tertiary referral medical center during 6 years period, of fetuses diagnosed with IUGR due to placental insufficiency, and apparently healthy fetuses, who had fetal brain MRI scan. The volumes of the supratentorial brain region, both hemispheres and the cerebellum were measured by 3D MRI semi-automated volume measurements. The cerebellar to supratentorial ratio (CER/ST) was calculated. Volumes were plotted on normal growth curves. 17 IUGR fetuses, and 53 apparently healthy fetuses adhered to participate in the VABS-II questionnaire, evaluating neurodevelopmental outcome. Results70 patients (mean age at conducting VABS-II questionnaire 4.4 ± 2.1 years, 38 males) were evaluated. Among fetuses born in a gestational age of 36 weeks or later, IUGR fetuses demonstrated a significantly larger number of abnormal results in the VABS-II communication domain (p =.049). No significant differences were found in other domains or in overall neurodevelopmental outcome. The CER/ST ratio correlated with the overall neurodevelopmental outcome of the total study population (r = 0.40, p <.001), and of the IUGR group separately (ρ = 0.58, p =.02). ConclusionCER/ST ratio measured using fetal brain MRI was found to be correlated with long term neurodevelopmental outcome. This result may aid in clinical interpretation of biometric data obtained by fetal brain MRI, linking it with long term neurodevelopmental outcome.

Challenges in clinical interpretation of next-generation sequencing data: Advantages and Pitfalls

Next-generation sequencing (NGS) technologies have completely changed the field of genomics by making it possible to rapidly and inexpensively produce genome-scale sequence data with higher precision and resolution. In recent years, rapid technological advancements driven by companies and academic institutions have expanded the range of NGS applications, from research to the clinic. Latest scientific developments have highlighted the effects of NGS technology on complex and Mendelian diseases, particularly cancer. However, the rapid adoption of NGS poses significant challenges in data processing, storage, management, and interpretation, as well as sequencing quality control, making it challenging to integrate the sequence data into clinical practice. Here, we first outline the technical features and performance of the most recent NGS platforms. Furthermore, we summarize the applications of NGS technologies in clinical diagnostics, data interpretation, and valuable bioinformatic tools and databases for analysis. Common issues in NGS processes are also discussed.

Alternative promoters and splicing create multiple functionally distinct isoforms of oestrogen receptor alpha in breast cancer and healthy tissues.

Oestrogen receptor alpha (ER) is involved in cell growth and proliferation and functions as a transcription factor, a transcriptional coregulator, and in cytoplasmic signalling. It affects, for example, bone, endometrium, ovaries and mammary epithelium. It is a key biomarker in clinical management of breast cancer, where it is used as a prognostic and treatment-predictive factor, and a therapeutical target. Several ER isoforms have been described, but transcript annotation in public databases is incomplete and inconsistent, and functional differences are not well understood. We have analysed short- and long-read RNA sequencing data from breast tumours, breast cancer cell lines, and normal tissues to create a comprehensive annotation of ER transcripts and combined it with experimental studies of full-length protein and six alternative isoforms. The isoforms have varying transcription factor activity, subcellular localisation, and response to the ER-targeting drugs tamoxifen and fulvestrant. Antibodies differ in ability to detect alternative isoforms, which raises concerns for the interpretation of ER-status in routine pathology. Future work should investigate the effects of alternative isoforms on patient survival and therapy response. An accurate annotation of ER isoforms will aid in interpretation of clinical data and inform functional studies to improve our understanding of the ER in health and disease.

Neuromuscular disease genetics in under-represented populations: increasing data diversity.

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.