7th joint conference "Joint Microbiology & Infection Conference 2024" of the DGHM and VAAM

Prof. Dr.

Chair

PD. Dr.

Healthcare-associated infections and pathogens: Prevention, surveillance, outbreaks und antibiotic stewardship 4

Prof.Dr.

Time-to event analyses for hospital stay: Nosocomial infections influence the length of stay for infections caused by respiratory viruses

Healthcare-associated infections and pathogens: Prevention, surveillance, outbreaks und antibiotic stewardship

Nosocomial transmission of respiratory viruses can contribute to increased morbidity and mortality and should therefore be prevented by preventive measures. In the present study, the extent to which nosocomial transmission influences the length of hospitalisation of patients was systematically investigatedMethods: In a retrospective monocentric analysis over 5 years, all hospitalised patients with evidence of influenza A, B, RSV and SARS-CoV-2 (original strain / Omicron) were included. Demographic data (age, sex), risk factors (COPD, diabetes, kidney desease, heart disease, cancer) and clinical course were recorded. Definitions were used to differentiate between nosocomial and community-acquired (CA) infections. A restricted mean survival time (RMST) (survival event is normal discharge) was used to calculate the influence of variables on the length of hospitalisation; the effects of each variable were compared by comparing Hazard Ratio (HR) and 95% CI. Deceased patients were excluded for the RMST / HR because of the bias problem on hospital stay.Results: A total of 2269 patients with specific virus detection were included, 263 of whom were defined as nosocomial. The hospitalisation times for patients with nosocomial infections compared to CA infections were significantly higher at 23.4 vs. 10.4 days (p &lt; 0.001) (differentiated by specific virus detection: Infl. A: 20.5 vs. 9.2 (p &lt; 0.001) / Infl. B 17.9. vs. 8.2 (&lt; 0.001) / RSV 24.6 vs. 10.7 (&lt; 0.001) / SARS-CoV-2 26.4 vs. 15.5 (0.497) / Omicron 25.5. vs. 9.64 (&lt; 0.001)). For patients with evidence of influenza A / B, RSV, SARS-CoV-2 and Omicron, the effect of a nosocomial infection on the length of stay was significant (HR = 0.52 (SARS-CoV-2) and HR &lt; 0.5). Other factors in individual virus detections with a significant influence on the length of hospitalisation were age, diabetes and a bacterial superinfection.Conclusion: Nosocomial infections have a significant impact on the length of stay and discharge of patients with specific viral infections. Preventive measures must therefore enforced in the management of patients with respiratory infections in order to prevent nosocomial infections.

Oral Presentation

Professor

Usage of oral contraceptives among healthcare workers: impact on COVID-19 booster vaccine immunogenicity

QuestionOral contraceptives profoundly impact various physiological functions, including the immune response. However, the influence of oral contraceptives (OC) on the immunogenicity of vaccinations in general, and in particular the COVID-19 vaccination, is largely unknown. This study explored the effects of OC on the immune response following COVID-19 booster vaccinations.MethodsFrom 18 November 2021 to 13 October 2023 the immunogenicity of the third COVID-19 vaccination as booster among 711 female healthcare workers (HCWs) between 18-50 years was assessed as part of the CoVacSer study. Individuals on long-term OC medication were compared to those not taking OCs. Blood serum samples, along with a comprehensive study questionnaire on sociodemographic data, were collected before (pre) and after (post) a third COVID-19 vaccination. Anti-SARS-CoV-2-Spike IgG levels were measured using the SERION ELISA agile SARS-CoV-2 IgG.Results19.7% (140/711) reported taking OC regularly. Univariate analysis comparing the groups with and without OC medication, found no differences in Anti-SARS-CoV-2-Spike IgG titres after the third COVID-19 vaccination (p=0.48). Yet, in the univariate analysis the relative increase of IgG levels after the third vaccination was significantly higher in cohort without OC (p=0.02). However, the linear regression model considering the factors smoking, age, BMI, COVID-19 booster vaccine, SARS-CoV-2 infection history and household size did not reveal a significant effect of OC medication on the IgG increase.Figure 1: Anti-SARS-CoV-2-Spike IgG levels related to the third COVID-19 vaccination separated by OCBAU/ml = Binding Antibody Units per millilitreConclusionsDespite the generally known interaction of OC intake and the immune response in women, our study was the first to investigate the influence of OC on the immunogenicity specifically for COVID-19 vaccination. Despite the large sample size of our study, we found no effect of OCs on antibody titres providing strong evidence that OCs have no influence on the immunogenicity of COVID-19 booster vaccinations.

Risk factors for SARS-CoV-2 acquisition among vaccinated individuals in nosocomial outbreaks - a case-control study

Introduction: Despite the widespread deployment of SARS-CoV-2 vaccines, breakthrough infections continue to pose challenges, particularly in healthcare settings. Nosocomial outbreaks remain a significant concern, warranting an in-depth investigation into the risk factors associated with SARS-CoV-2 acquisition among vaccinated individuals.Goals: To identify risk factors for SARS-CoV-2 acquisition among vaccinated patients and healthcare providers in nosocomial outbreaks at Charité hospitals in Berlin, Germany, in 2022.Materials &amp; Methods: In this retrospective case-control study, we investigated SARS-CoV-2 acquisition in 114 nosocomial SARS-CoV-2 outbreaks notified to the healthcare authorities in 2022. Cases included 587 patients and 385 staff members who tested positive for SARS-CoV-2 during the outbreaks. Controls (not tested positive for SARS-CoV-2), were matched (1:1) to cases based on their patient/staff status and the time shared with a case on the ward 14 days before the case tested positive. All participants were fully vaccinated and completed a questionnaire regarding potential risk or protective factors for SARS-CoV-2 acquisition. Univariable analysis and multivariable logistic regression models were used.Results: In total, 271 cases and 271 controls participated in the study. Our findings indicate that cases were more likely to be immunized &gt; 3 months ago compared with controls (77.5% versus 68.6%, p= 0.02). Cases more often suffered from diabetes (15.2% versus 9.0%, p = 0.03) and less often had a previous SARS-CoV-2 infection (6.7 versus 15.7%, p &lt; 0.01). Multivariable logistic models identified previous SARS-CoV-2 infection as an independent protective factor (OR=0.25, 95% CI: 0.11- 0.53). Independent risk factors were immunization &gt; 3 months ago (OR=1.6, 95% CI: 1.05 – 2.7) and diabetes (OR =2.26, 95% CI: 1.13 – 4.72).Summary: Our analyses suggest that certain primary diseases and an immunization older than 3 months were associated with increased risks of breakthrough infections in healthcare settings. However, a previous SARS-CoV-2 infection may have a protective effect. These findings represent preliminary results and warrant further validation.

Influence of vaccine administrations on dominance intervals of SARS-CoV-2 variants – A modelling observational study of regional German data 2021 - 2023

IntroductionThe emergence of omicron SARS-CoV-2 resulted in the displacement of delta and later displacement events between omicron variants. However, patterns of dominance intervals varied geographically within and between countries.GoalsStarting from the hypothesis that any single vaccination influences pandemic developments, we investigated whether vaccination densities influenced dominance intervals of the most prevalent variants in 16 German federal states (BL).Materials &amp; MethodsOur weekly data (01/2021–01/2023) consisted of &gt;1.1M SARS-CoV-2 sequences from DESH, IMS-SC2 and GISAID. Duplicates were removed as were sequences with a N content of ≥95% and weeks with n&lt;10 reported sequences. Displacement occurred when a given cluster made ≥50% of all sequences (tp). We calculated the time-to-displacement interval (TTD) for omicron (1.1.529* variants) displacing delta (1.617.2*), BA.2 (1.1.529.2*) displacing BA.1 (1.1.529.1*) and BA.5 (1.1.529.5*) displacing BA.2. TTD was the interval [weeks] between the onset (first report) and tp. The displaced variant cluster was dubbed "predecessor", and the new dominant cluster "successor". We used Cox Regression to model TTD in 48 independent displacement events (3x16 BL), controlling for selected covariates (Fig. 1).ResultsOur model proposed a 12% [4; 21%] CI-95% increased risk of displacement for every outdated vaccine administration per 100 population. In contrast, every original vaccine administration per 100 population led to a 9% [3; 14%] decreased displacement risk (Fig. 1). The effect of low outdated vaccine density was most apparent in the delta-omicron event (mean ∆TTD 2.5 weeks [-0.6; 5.6] 95%-CI, log-rank test p=0.05), in contrast to BA.1-BA.2 (0.3, p=0.75) and BA.2-BA.5 (0.8, p=0.35).SummaryVaccine densities at BL-level influenced TTD in two ways. Vaccines administered before omicron onset helped delaying the displacement of delta. When the same vaccines became outdated due to omicron onset (immune evasive), they helped to accelerate delta displacement by omicron. The effect size dropped in following intra-omicron displacement events. Further research is needed to investigate causality.

Prof.Dr.Med.

PD Dr. med. habil.

Leveraging mobility data during a pandemic to implement a targeted sampling strategy for surveillance

QuestionEfficient surveillance strategies are essential to keep track of the rapid developments during a pandemic. We investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the SARS-CoV-2 pandemic in the federal state of Thuringia, Germany.MethodsWe received anonymized and aggregated mobile service data from T-systems, containing circa 200 Mio data points from 2020 to 2021. Additionally, we sequenced over 6,500 SARS-CoV-2 Alpha genomes (B.1.1.7) across seven months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes. The dataset was screened for mutational clusters, and by combining the samples' time and location data with the mobile service data, the spread of identified clusters in Thuringia was analyzed.ResultsWe could track the existence and spread of nine persistent mutation variants within the Alpha lineage. Seven formed separate phylogenetic clusters with different spreading patterns in Thuringia, of which two contained an additional sub-cluster, totaling nine Thuringian clusters. Our data suggests that mobile service data can indicate these clusters' spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept proved successful as we introduced a mobility-guided sampling strategy to track the low-prevalence Omicron sublineage BQ.1.1.ConclusionsThe combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.

Prof. Dr.med.

Dr.med.

Evaluating multiple machine-learning algorithms as surveillance-related tools based on the example of the hospitalization rate 2.0 during the SARS-CoV-2 Pandemic

Introduction: While machine learning algorithms are gaining popularity in clinical research, their feasibility as surveillance-related tools is still a work-in-progress.Goals: To address this, we conducted a comprehensive evaluation of different machine learning models as survey-related tools for their performance on the example of the so-called Hospitalization Rate 2.0, which aims to differentiate cases admitted due to COVID-19 from incidental SARS-CoV-2 positive cases.Methods: Single-center characterization of 345 patients hospitalized with a positive SARS-CoV-2 PCR test were included in the model development (Jan. 2022- June 2022). Outcomes were defined as: Primary case (admitted due to COVID-19) and incidental case (admitted for another reason). A total of 6 models were applied including 3 linear models (Logistic Regression (LR)), Linear Discriminant Analysis (LDA), Support Vector Classification (SVC)) and 3 non-linear models (K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGboost)). Models were then evaluated according to the &quot;Area Under the Curve&quot; of the &quot;Receiver Operating Characteristic&quot; (AUC-ROC), accuracy, precision, recall, F1-score, sensitivity, and specificity.Results: Among the linear classifiers, LR showed the best performance over all metrics (accuracy (0.75), precision (0.74), recall (0.75), F1-score (0.74) and sensitivity (0.75)), followed by LDA and SVC which had better specificity values (0.83 and 0.86 respectively). From the non-linear classifiers, RF was superior to KNN in terms of accuracy, precision, recall, F1 score and sensitivity, while KNN had higher specificity. Of all the applied classifiers, the XGBoost model had the strongest discriminative ability with the highest accuracy (0.80), sensitivity (0.92), precision (0.82), recall (0.78) and F1 score (0.79).[sg1]Summary: Our results indicated that the XGBoost model has better capability and accuracy than the other models applied for identifying primary cases. Implementation of such models as surveillance-related tools could be the very first step to count primary cases, and furthermore optimize resource allocation and hospital bed occupancy.

Oral Session
Prevention of Respiratory Virus Infections

Appointment

Chair

Topics

Description

Programme

Oral SessionPrevention of Respiratory Virus Infections

Appointment

Chair

Topics

Description

Programme

Oral Session
Prevention of Respiratory Virus Infections