Elisa Gentilotti

Motivation: Causal effect estimates can often be biased in clinical and epidemiological studies as patient cohorts frequently exhibit substantial covariate imbalances between treated and control groups, often amplified in multicentre studies due to heterogeneous recruitment, clinical practice, and case mix. Covariate balancing methods are therefore essential for valid causal inference. However, their application becomes challenging when data are distributed across cohorts and cannot be pooled because of privacy, legal, or institutional constraints, leaving a gap in practical methods for causal effect estimation in federated and imbalanced clinical data settings. Results: We develop a privacy-preserving framework for covariate balancing and causal effect estimation across distributed data providers, combining federated aggregation with differential privacy to enable propensity score subclassification and matching without sharing individual-level records. Matching relies on non-disclosive quantities and differentially private distance evaluation, and the resulting matched subsets remain local to each server. Balance can be assessed through federated diagnostics and privacy-preserving visualisations, and we provide secure estimators for average treatment effects with associated uncertainty quantification. We implement this framework in the DataSHIELD federated analysis platform via 2 R packages. In simulations, we demonstrate agreement between federated and centralised analyses in the absence of privacy noise and quantify the bias–variance trade-offs induced by differential privacy. We illustrate applicability in two multinational settings—a Long COVID cohort and very preterm birth cohorts—showing that the approach enables practical causal analyses under real-world data protection constraints. Availability and Implementation: The DataSHIELD packages are available on Github ( https://github.com/roygusinow/dsMatchingClient , https://github.com/roygusinow/dsMatching ). Additional methodological details are provided in the Supplementary Material. Contact: Jan Hasenauer: jan.hasenauer@uni-bonn.de

Background: Persistent low-grade inflammation has been proposed as part of the biological mechanisms underlying post-COVID condition (PCC), which can result in laboratory tests abnormalities. However, the accuracy of routine laboratory tests for the diagnosis and follow-up of PCC is still under discussion. Methods: Patients with SARS-CoV-2 infection enrolled in the prospective, multinational ORCHESTRA cohort study, which included both European and non-European countries, were followed up for 18 months after acute infection. Blood test results were collected at acute infection and at 6, 12, and 18 months. A multivariable analysis was performed to estimate the relationship between the alterations of biochemical markers and the presence of four distinct PCC phenotypes, identified previously through a principal component analysis—respiratory (RESc), chronic pain (CPc), chronic fatigue (CFc), and neurosensorial (NSc)—during follow-up. Furthermore, this study investigated the correlation between biochemical parameters measured during the acute phase and the subsequent development of PCC. Finally, the relationship between the severity of the acute infection and biochemical abnormalities observed during follow-up was assessed. Results: The cohort included 4587 patients, 58% male, with a mean age of 58.7 (±15.5) years. A robust multivariable analysis demonstrated that, compared to controls, patients with PCC, and in particular those in the RESc cluster, presented higher mean C-reactive protein (CRP) levels at the 12- and 18-month follow-up (p-value = 0.01). In each follow-up, CRP values in patients with PCC and RESc were above 3 mg/L, corresponding to those observed in low-grade inflammation (3–10 mg/L). The severity of COVID-19 acute infection was associated with increased levels of CRP, ferritin and LDH during follow-up (p < 0.001). Biochemistry abnormalities detected during the early stages of acute COVID-19 did not correlate with an increased risk of developing PCC and its phenotypes. Conclusions: In patients with the RESc PCC phenotype, identified through a principal component analysis, blood test abnormalities consistent with prolonged and sustained low-grade inflammation can be detected up to 18 months after acute infection, supporting its role in the pathogenesis of PCC. Based on these results, trials on anti-inflammatory drugs, together with symptom-tailored interventions for patients with RESc, should be planned to prove their effectiveness in managing PCC and improving patient outcomes.

Several studies reported alterations of the human gut microbiota (GM) during COVID-19. To evaluate the potential role of the GM as an early predictor of COVID-19 at disease onset, we analyzed gut microbial samples of 315 COVID-19 patients that differed in disease severity. We observed significant variations in microbial diversity and composition associated with increasing disease severity, as the reduction of short-chain fatty acid producers such as Faecalibacterium and Ruminococcus , and the growth of pathobionts as Anaerococcus and Campylobacter . Notably, we developed a multi-class machine-learning classifier, specifically a convolutional neural network, which achieved an 81.5% accuracy rate in predicting COVID-19 severity based on GM composition at disease onset. This achievement highlights its potential as a valuable early biomarker during the first week of infection. These findings offer promising insights into the intricate relationship between GM and COVID-19, providing a potential tool for optimizing patient triage and streamlining healthcare during the pandemic. IMPORTANCE: Efficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial diversity and disease severity were observed. Notably, a convolutional neural network classifier was developed, achieving an 81.5% accuracy in predicting disease severity based on GM composition at disease onset. These findings suggest that GM profiling could enhance early triage processes, offering a novel approach to optimizing patient management during the pandemic.

COVID-19 has been associated with having a negative impact on patients’ gut microbiome during both active disease and in the post-acute phase. In acute COVID-19, rapid alteration of the gut microbiome composition was observed, showing on one side a reduction in beneficial symbionts (e.g., Roseburia, Lachnospiraceae) and on the other side an increase in opportunistic pathogens such as Enterococcus and Proteobacteria. Alpha diversity tends to decrease, especially initially with symptom onset and hospital admission. Although clinical recovery appears to align with improved gut homeostasis, this process could take several weeks, even in mild infections. Moreover, patients with COVID-19 post-acute syndrome showed changes in gut microbiome composition, with specific signatures associated with decreased respiratory function up to 12 months following acute disease. Potential treatments, especially probiotic-based therapy, are under investigation. Open questions remain on the possibility to use gut microbiome data to predict disease progression and on potential confounders that may impair result interpretation (e.g., concomitant therapies in the acute phase; reinfection, vaccines, and occurrence of novel conditions or diseases in the post-acute syndrome). Understanding the relationships between gut microbiome dynamics and disease progression may contribute to better understanding post-COVID syndrome pathogenesis or inform personalized treatment that can affect specific targets or microbiome markers.

The clinical impact of anti-spike monoclonal antibodies (mAb) in Coronavirus Disease 2019 (COVID-19) breakthrough infections is unclear. We present the results of an observational prospective cohort study assessing and comparing COVID-19 progression in high-risk outpatients receiving mAb according to primary or breakthrough infection. Clinical, serological and virological predictors associated with 28-day COVID-19-related hospitalization were identified using multivariate logistic regression and summarized with odds ratio (aOR) and 95% confidence interval (CI). A total of 847 COVID-19 outpatients were included: 414 with primary and 433 with breakthrough infection. Hospitalization was observed in 42/414 (10.1%) patients with primary and 8/433 (1.8%) patients with breakthrough infection (p < 0.001). aOR for hospitalization was significantly lower for breakthrough infection (aOR 0.12, 95%CI: 0.05–0.27, p < 0.001) and higher for immunocompromised status (aOR:2.35, 95%CI:1.08–5.08, p = 0.003), advanced age (aOR:1.06, 95%CI: 1.03–1.08, p < 0.001), and male gender (aOR:1.97, 95%CI: 1.04–3.73, p = 0.037). Among the breakthrough infection group, the median SARS-CoV-2 anti-spike IgGs was lower (p < 0.001) in immunocompromised and elderly patients >75 years compared with that in the immunocompetent patients. Our findings suggest that, among mAb patients, those with breakthrough infection have significantly lower hospitalization risk compared with patients with primary infection. Prognostic algorithms combining clinical and immune-virological characteristics are needed to ensure appropriate and up-to-date clinical protocols targeting high-risk categories.

Background: Surgical site infections are a leading cause of morbidity and mortality after caesarean section, especially in Low and Middle Income Countries. We hypothesized that a combined infection prevention and control with antimicrobial stewardship joint program would decrease the rate of post- caesarean section surgical site infections at the Obstetrics & Gynaecology Department of a Tanzanian tertiary hospital. Methods: The intervention included: 1. formal and on-job trainings on infection prevention and control; 2. evidence-based education on antimicrobial resistance and good antimicrobial prescribing practice. A second survey was performed to determine the impact of the intervention. The primary outcome of the study was post-caesarean section surgical site infections prevalence and secondary outcome the determinant factors of surgical site infections before/after the intervention and overall. The microbiological characteristics and patterns of antimicrobial resistance were ascertained. Results: Total 464 and 573 women were surveyed before and after the intervention, respectively. After the intervention, the antibiotic prophylaxis was administered to a significantly higher number of patients (98% vs 2%, p < 0.001), caesarean sections were performed by more qualified operators (40% vs 28%, p = 0.001), with higher rates of Pfannenstiel skin incisions (29% vs 18%, p < 0.001) and of absorbable continuous intradermic sutures (30% vs 19%, p < 0.001). The total number of post-caesarean section surgical site infections was 225 (48%) in the pre-intervention and 95 (17%) in the post intervention group (p < 0.001). A low prevalence of gram-positive isolates and of methicillin-resistant Staphylococus aureus was detected in the post-intervention survey. Conclusions: Further researches are needed to better understand the potential of a hospital-based multidisciplinary approach to surgical site infections and antimicrobial resistance prevention in resource-constrained settings.

Background. Surgical site infections are a leading cause of morbidity and mortality after caesarean section, especially in Low and Middle Income Countries. We hypothesized that a combined infection prevention and control with antimicrobial stewardship joint program would decrease the rate of post- caesarean section surgical site infections at the Obstetrics & Gynaecology Department of a Tanzanian tertiary hospital. Methods. The intervention included: 1. formal and on-job trainings on infection prevention and control; 2. evidence-based education on antimicrobial resistance and good antimicrobial prescribing practice. A second survey was performed to determine the impact of the intervention. The primary outcome of the study was post-caesarean section surgical site infections prevalence and secondary outcome the determinant factors of surgical site infections before/after the intervention and overall. The microbiological characteristics and patterns of antimicrobial resistance were ascertained.Results. Total 464 and 573 women were surveyed before and after the intervention, respectively. After the intervention, the antibiotic prophylaxis was administered to a significantly higher number of patients (98% vs 2%, pvs 28%, p=0.001), with higher rates of Pfannenstiel skin incisions (29% vs 18%, pvs 19%, ppStaphylococus aureus was detected in the post-intervention survey. Conclusions. Further researches are needed to better understand the potential of a hospital-based multidisciplinary approach to surgical site infections and antimicrobial resistance prevention in resource-constrained settings.

Background Surgical site infections after caesarean section are a leading cause of morbidity and mortality, especially in Low and Middle Income Countries. We hypothesized that combining infection prevention and control with an antimicrobial stewardship programme would decrease the rate of post- caesarean section surgical site infections at the Obstetrics & Gynaecology Department of a Tanzanian tertiary hospital. Methods A first survey was conducted to assess the burden of post-CS SSI. Thereafter, an intervention was introduced including: 1. formal on-the-job training on infection prevention and control; 2. evidence-based education on antimicrobial resistance and good antimicrobial prescribing practice. A second survey was performed to determine the impact of the intervention. The primary outcome of the study was information on the prevalence of post-caesarean section surgical site infections whereas the secondary outcome was the identification of the determinant factors of surgical site infections before/after the intervention and overall. The microbiological characteristics and patterns of antimicrobial resistance were also established. Results A total of 464 and 573 women were surveyed before and after the intervention, respectively. After the intervention, the antibiotic prophylaxis was administered to a significantly higher number of patients (98% vs 2%, p <0.001), caesarean sections were performed by more qualified operators (40% vs 28%, p =0.001), with higher rates of Pfannenstiel skin incisions (29% vs 18%, p <0.001) and of absorbable continuous intradermic sutures (30% vs 19%, p <0.001). The total number of post-caesarean section surgical site infections was 225 (48%) in the pre-intervention group and 95 (17%) in the post intervention group ( p <0.001). A low prevalence of gram-positive isolates and of methicillin-resistant Staphylococus aureus was detected in the post-intervention survey. Conclusions Further research is needed to better understand the potential of a hospital-based multidisciplinary approach to surgical site infections and antimicrobial resistance prevention in resource-constrained settings.