Safety of subcutaneous versus intravenous ceftriaxone administration in older patients: A retrospective study.

BACKGROUND: Antibiotics play a central role in infection management. In older patients, antibiotics are frequently administered subcutaneously. Ceftriaxone pharmacokinetics after subcutaneous administration is well documented, but little data are available on its safety. METHODS: We compared the occurrence of adverse events associated with ceftriaxone administered subcutaneously versus intravenously in ≥75-year-old patients. We used data from a single-center, retrospective, clinical-administrative database to compare the occurrence of adverse events at day 14 and outcome at day 21 in older patients who received ceftriaxone via the subcutaneous route or the intravenous route at Rennes University Hospital, France, from May 2020 to February 2023. RESULTS: The subcutaneous and intravenous groups included 402 and 3387 patients, respectively. Patients in the subcutaneous group were older and more likely to receive palliative care. At least one adverse event was reported for 18% and 40% of patients in the subcutaneous and intravenous group, respectively (RR = 2.21). Mortality at day 21 was higher in the subcutaneous route group, which could be linked to between-group differences in clinical and demographic features. CONCLUSIONS: In ≥75-year-old patients, ceftriaxone administered by the subcutaneous route is associated with less-adverse events than by the intravenous route. The subcutaneous route, which is easier to use, has a place in infection management in geriatric settings.

Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography.

Aims: Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning. Methods and results: We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization). Conclusion: This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

Prognostic Value of Baseline Tumor Burden and Tumor Dissemination Extracted From 18 F-FDG PET/CT in a Cohort of Adult Patients With Early or Advanced Hodgkin Lymphoma.

PURPOSE: We aimed to assess the prognostic value of baseline tumor burden and dissemination parameters extracted from 18 F-FDG PET/CT in patients with early or advanced Hodgkin lymphoma (HL) treated with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or escalated BEACOPP (increased bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone). PATIENTS AND METHODS: Patients aged ≥18 years with classical Hodgkin lymphoma were retrospectively included. Progression-free survival (PFS) analysis of dichotomized clinicobiological and PET/CT parameters (SUV max , TMTV, TLG, D max , and D bulk ) was performed. Optimal cutoff values for quantitative metrics were defined as the values maximizing the Youden index from receiver operating characteristic analysis. PFS rates were estimated with Kaplan-Meier curves, and the log-rank test was used to assess statistical significance. Hazard ratios were calculated using Cox proportional hazards models. RESULTS: With a median age of 32 years, 166 patients were enrolled. A total of 111 patients had ABVD or ABVD-like treatment with or without radiotherapy and 55 patients with escalated BEACOPP treatment. The median follow-up was 55 months. Only International Prognostic Score (IPS >1), TMTV >107 cm 3 , and TLG >1628 were found to be significant prognostic factors for PFS on univariate analysis. Multivariate analysis revealed that IPS and TLG were independently prognostic and, combined, identified 4 risk groups ( P < 0.001): low (low TLG and low IPS; 4-year PFS, 95%), intermediate-low (high IPS and low TLG; 4-year PFS, 79%), intermediate-high (low IPS and high TLG; 4-year PFS, 78%), and high (high TLG and high IPS; 4-year PFS, 71%). CONCLUSIONS: Combining baseline TLG with IPS could improve PFS prediction.

Leveraging Clinical Data Warehouses to Measure Impact of Update Prescription Guidelines of Polyvalent Immunoglobulins in 2018 in France.

In France and in other countries, we observed a significant growth in human polyvalent immunoglobulins (PvIg) usage. PvIg is manufactured from plasma collected from numeral donors, and its production is complex. Supply tensions have been observed for several years, and it is necessary to limit their consumption. Therefore, French Health Authority (FHA) provided guidelines in June 2018 to restrict their usage. This research aims to assess the guidelines' impact of the FHA on the use of PvIg. We analyzed data from Rennes University Hospital, where all PvIg prescriptions are reported electronically with quantity, rhythm, and indication. From the clinical data warehouses of RUH, we extracted comorbidities and lab results to evaluate the more complex guidelines. We globally noticed a reduction in the consumption of PvIg after the guidelines. Compliance with the recommended quantities and rhythms have also been observed. By combining two sources of data, we have been able to show an impact of FHA's guidelines on the consumption of PvIg.

Dissemination patterns of Hodgkin lymphoma using a probability network model based on [18F]-FDG PET/CT.

PURPOSE: The preferred hypothesis for the dissemination patterns of Hodgkin lymphoma (HL) is the contiguity hypothesis. However, this hypothesis is based on studies performed before the advent of [18F]-FDG PET/CT which is now the established reference for HL staging. This study aims to extract the dissemination patterns of HL using [18F]-FDG PET/CT and a probability network model. METHODS: We retrospectively analyzed [18F]-FDG PET/CT performed for initial staging of patients with classical HL. The HL involvement status (presence of absence) was reported for 19 supra- and infra-diaphragmatic lymph node regions and 4 extranodal regions (lung, spleen, liver, and osteo- medullary). The analysis of HL dissemination was carried out using HL involvement status for all regions through 3 distinct methods: comparison of nearby lymph node regions, correlation assessment between all regions and relationship strength between all regions using Ising network model. RESULTS: A total of 196 patients were included. Our results showed strong relationships between nearby involved lymph node regions (for example between the left pelvic and the abdominal lymph node regions (relationship strength = 0.980)) and between more distant regions (for example between right and left axillary lymph node regions (strength = 0.714)). Furthermore, involvement of the infra-diaphragmatic lymph node regions was significantly correlated with Ann Arbor stage IV (phi = 0.56, p < 0.001). CONCLUSION: This study confirms the hypothesis of lymphatic dissemination of HL in a contiguous mode, with additional links between more distant regions. These predictable dissemination patterns could be useful for the initial staging assessment of patients with HL using [18F]-FDG PET/CT.

Linking Biomedical Data Warehouse Records With the National Mortality Database in France: Large-scale Matching Algorithm.

BACKGROUND: Often missing from or uncertain in a biomedical data warehouse (BDW), vital status after discharge is central to the value of a BDW in medical research. The French National Mortality Database (FNMD) offers open-source nominative records of every death. Matching large-scale BDWs records with the FNMD combines multiple challenges: absence of unique common identifiers between the 2 databases, names changing over life, clerical errors, and the exponential growth of the number of comparisons to compute. OBJECTIVE: We aimed to develop a new algorithm for matching BDW records to the FNMD and evaluated its performance. METHODS: We developed a deterministic algorithm based on advanced data cleaning and knowledge of the naming system and the Damerau-Levenshtein distance (DLD). The algorithm's performance was independently assessed using BDW data of 3 university hospitals: Lille, Nantes, and Rennes. Specificity was evaluated with living patients on January 1, 2016 (ie, patients with at least 1 hospital encounter before and after this date). Sensitivity was evaluated with patients recorded as deceased between January 1, 2001, and December 31, 2020. The DLD-based algorithm was compared to a direct matching algorithm with minimal data cleaning as a reference. RESULTS: All centers combined, sensitivity was 11% higher for the DLD-based algorithm (93.3%, 95% CI 92.8-93.9) than for the direct algorithm (82.7%, 95% CI 81.8-83.6; P<.001). Sensitivity was superior for men at 2 centers (Nantes: 87%, 95% CI 85.1-89 vs 83.6%, 95% CI 81.4-85.8; P=.006; Rennes: 98.6%, 95% CI 98.1-99.2 vs 96%, 95% CI 94.9-97.1; P<.001) and for patients born in France at all centers (Nantes: 85.8%, 95% CI 84.3-87.3 vs 74.9%, 95% CI 72.8-77.0; P<.001). The DLD-based algorithm revealed significant differences in sensitivity among centers (Nantes, 85.3% vs Lille and Rennes, 97.3%, P<.001). Specificity was >98% in all subgroups. Our algorithm matched tens of millions of death records from BDWs, with parallel computing capabilities and low RAM requirements. We used the Inseehop open-source R script for this measurement. CONCLUSIONS: Overall, sensitivity/recall was 11% higher using the DLD-based algorithm than that using the direct algorithm. This shows the importance of advanced data cleaning and knowledge of a naming system through DLD use. Statistically significant differences in sensitivity between groups could be found and must be considered when performing an analysis to avoid differential biases. Our algorithm, originally conceived for linking a BDW with the FNMD, can be used to match any large-scale databases. While matching operations using names are considered sensitive computational operations, the Inseehop package released here is easy to run on premises, thereby facilitating compliance with cybersecurity local framework. The use of an advanced deterministic matching algorithm such as the DLD-based algorithm is an insightful example of combining open-source external data to improve the usage value of BDWs.

Phenotyping of Heart Failure with Preserved Ejection Faction Using Health Electronic Records and Echocardiography.

Patients suffering from heart failure (HF) symptoms and a normal left ventricular ejection fraction (LVEF 50%) present very different clinical phenotypes that could influence their survival. This study aims to identify phenotypes of this type of HF by using the medical information database from Rennes University Hospital Center. We present a preliminary work, where we explore the use of clinical variables from health electronic records (HER) in addition to echocardiography to identify several phenotypes of patients suffering from heart failure with preserved ejection fraction. The proposed methodology identifies 4 clusters with various characteristics (both clinical and echocardiographic) that are linked to survival (death, surgery, hospitalization). In the future, this work could be deployed as a tool for the physician to assess risks and contribute to support better care for patients.

Methylated ccfDNA from plasma biomarkers of Alzheimer's disease using targeted bisulfite sequencing.

Aim: Noninvasive biomarkers such as methylated ccfDNA from plasma could help to support the diagnosis of Alzheimer's disease (AD). Methods: A targeted sequencing protocol was developed to identify candidate biomarkers of AD in methylated ccfDNA extracted from plasma. Results: The authors identified differentially methylated CpGs, regions of which were the same as those identified in previous AD studies. Specifically, a differentially methylated CpG of the LHX2 gene previously identified in a plasma study of AD was replicated in the study. The MBP and DUSP22 regions have been identified in other brain studies of AD and in the authors' study. Conclusion: Although these biomarkers must be validated in other cohorts, methylated ccfDNA could be a relevant noninvasive biomarker in AD.

Currently, the diagnosis of Alzheimer's disease (AD) is based on symptoms and medical imaging, and definitive clinical diagnosis is only possible postmortem. The identification of noninvasive biomarkers such as methylated ccfDNA is crucial for the diagnosis, prognosis and monitoring of AD. However, the analysis of ccfDNA from plasma is a challenge because it is highly fragmented and present in low amounts and originates from various tissues. The authors developed a targeted sequencing protocol using genes previously reported in AD literature (brain, blood and plasma) to identify potential noninvasive biomarkers in plasma. The authors identified positions identical to those in the literature as well as potential novel sites located in the promoter, exon and intron regions of these genes. Although these results must be validated in a large cohort, methylated ccfDNA could be a useful noninvasive biomarker for AD.

PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data.

MOTIVATION: It is more and more common to perform multi-omics analyses to explore the genome at diverse levels and not only at a single level. Through integrative statistical methods, multi-omics data have the power to reveal new biological processes, potential biomarkers and subgroups in a cohort. Matrix factorization (MF) is an unsupervised statistical method that allows a clustering of individuals, but also reveals relevant omics variables from the various blocks. RESULTS: Here, we present PIntMF (Penalized Integrative Matrix Factorization), an MF model with sparsity, positivity and equality constraints. To induce sparsity in the model, we used a classical Lasso penalization on variable and individual matrices. For the matrix of samples, sparsity helps in the clustering, while normalization (matching an equality constraint) of inferred coefficients is added to improve interpretation. Moreover, we added an automatic tuning of the sparsity parameters using the famous glmnet package. We also proposed three criteria to help the user to choose the number of latent variables. PIntMF was compared with other state-of-the-art integrative methods including feature selection techniques in both synthetic and real data. PIntMF succeeds in finding relevant clusters as well as variables in two types of simulated data (correlated and uncorrelated). Next, PIntMF was applied to two real datasets (Diet and cancer), and it revealed interpretable clusters linked to available clinical data. Our method outperforms the existing ones on two criteria (clustering and variable selection). We show that PIntMF is an easy, fast and powerful tool to extract patterns and cluster samples from multi-omics data. AVAILABILITY AND IMPLEMENTATION: An R package is available at https://github.com/mpierrejean/pintmf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects.

Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome-wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a single block, while the last approach considers each haplotype independently. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.

Exploring the Link Between Additive Heritability and Prediction Accuracy From a Ridge Regression Perspective.

Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts for the many small contributions of SNPs in the explanation of a phenotype. This paper approaches heritability from a machine learning perspective, and examines the close link between mixed models and ridge regression. Our contribution is two-fold. First, we propose estimating genomic heritability using a predictive approach via ridge regression and Generalized Cross Validation (GCV). We show that this is consistent with classical mixed model based estimation. Second, we derive simple formulae that express prediction accuracy as a function of the ratio n p , where n is the population size and p the total number of SNPs. These formulae clearly show that a high heritability does not imply an accurate prediction when p > n. Both the estimation of heritability via GCV and the prediction accuracy formulae are validated using simulated data and real data from UK Biobank.

Comparison of commercially available whole-genome sequencing kits for variant detection in circulating cell-free DNA.

Circulating cell-free DNA (ccfDNA) has great potential for non-invasive diagnosis, prognosis and monitoring treatment of disease. However, a sensitive and specific whole-genome sequencing (WGS) method is required to identify novel genetic variations (i.e., SNVs, CNVs and INDELS) on ccfDNA that can be used as clinical biomarkers. In this article, five WGS methods were compared: ThruPLEX Plasma-seq, QIAseq cfDNA All-in-One, NEXTFLEX Cell Free DNA-seq, Accel-NGS 2 S PCR FREE DNA and Accel-NGS 2 S PLUS DNA. The Accel PCR-free kit did not produce enough material for sequencing. The other kits had significant common number of SNVs, INDELs and CNVs and showed similar results for SNVs and CNVs. The detection of variants and genomic signatures depends more upon the type of plasma sample rather than the WGS method used. Accel detected several variants not observed by the other kits. ThruPLEX seemed to identify more low-abundant SNVs and SNV signatures were similar to signatures observed with the QIAseq kit. Accel and NEXTFLEX had similar CNV and SNV signatures. These results demonstrate the importance of establishing a standardized workflow for identifying non-invasive candidate biomarkers. Moreover, the combination of variants discovered in ccfDNA using WGS has the potential to identify enrichment pathways, while the analysis of signatures could identify new subgroups of patients.

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration.

Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.

Performance evaluation of DNA copy number segmentation methods.

A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. To make an objective and reproducible performance assessment, we have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling publicly available SNP microarray data from genomic regions with known copy-number state. The original data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. This article describes this framework and its application to a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons of the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. This comparison study may be reproduced using the open source and cross-platform R package jointseg, which implements the proposed data generation and evaluation framework: http://r-forge.r-project.org/R/?group_id=1562.