Thøger Gorm Jensen

Summary Purpose To identify predictors of hospital readmissions within 30 days after discharge following a bloodstream infection (BSI). Methods Population-based study of 30,843 patients surviving after their first BSI episode, 2007–2016. Using multivariate competing risk regression analyses, we estimated subhazard ratios (sHRs) and 95% confidence intervals (CIs) for all-cause, infection-related, and unplanned readmissions. Predictors included sex, age, comorbidities, causative microorganisms, length of stay (LOS), and prior hospital admissions. Results Within 30 days of discharge, 9655 patients (31.3%) were readmitted of which 3333 (34.5%) were infection-related and 7725 (80.0%) unplanned. Higher risks of all-cause readmissions were observed for males (sHR [95% CI]: 1.07 [1.03–1.12]), those aged 0–15 years (1.41 [1.26–1.58]), and patients with several comorbidities, especially malignancies and liver diseases (sHR 1.26–2.09). BSIs caused by most microorganisms other than Escherichia coli also predicted an increased readmission risk (sHR 1.11–1.39). LOS in the first, third, and fourth quartiles also predicted a higher readmission risk (sHR 1.26, 1.14, and 1.25, respectively). Patients with prior hospitalization also had a higher readmission risk, within 31–365 (sHR 1.22 [1.16–1.28]) and 1–30 days (1.70 [1.61–1.79]). Results were similar for unplanned readmissions. For infection-related readmissions, only LOS in the first quartile (1–5 days) was associated with a higher readmission risk (1.67 [1.52–1.85]). Over the study period, the median LOS decreased, whereas readmission rates increased. Conclusion Nearly one-third of patients discharged alive after a BSI were readmitted within 30 days. Further studies are needed to determine whether some of these readmissions may be avoidable.

Background. Whole-genome sequencing of bacterial isolates is increasingly becoming routine in clinical microbiology; however, subsequent analysis often needs to be started by a bioinformatician even for comprehensive pipelines. To increase the robustness of our workflow and free up bioinformatician work hours for development and advanced analysis, we aimed to produce a robust, customizable bioinformatic pipeline for bacterial genome assembly and routine analysis results that could be initiated by non-bioinformaticians. Results. We introduce the RSYD-BASIC pipeline for bacterial isolate sequence analysis and provide a demonstration of its functionality with two datasets composed of publicly available sequences, in which comparable results are obtained in most cases. In some instances, the pipeline provided additional information, corresponding to in vitro results where these could be obtained. In routine use at our department, the pipeline has already yielded clinically relevant results, allowing us to type a variety of bacterial pathogens isolated in our clinical laboratory. We also demonstrate how RSYD-BASIC results aided in disproving a potential outbreak. Conclusion. With the RSYD-BASIC pipeline, we present a configurable reads-to-results analysis pipeline operated by non-expert users that greatly eases the investigation of potential outbreaks by expert end users. Results obtained with publicly available sequences show comparable performance to the original methods, while underlining the importance of standardized methods.

Background: Little is known about the clinical characteristics and pathogens causing hospital-acquired bloodstream infections (HA-BSIs) in relation to an intensive care unit (ICU) stay. Methods: Population-based cohort study, comprising 35% of the Danish population, 2009–2016. We derived four patient groups with first-time HA-BSIs: no ICU stay during the admission (non-ICU patients) and HA-BSI acquired before, in, or after an ICU stay (before-ICU, in-ICU, and after-ICU patients). These groups were compared in relation to microbiological and clinical characteristics, including 28- and >28-day mortality. Results: Among 6888 HA-BSI patients, 4017, 792, 1388, and 691 were non-ICU, before-ICU, in-ICU, and after-ICU, respectively. The rates of several microorganisms differed between the patient groups, e.g., Enterococci (9.4% of non-ICU and 32.0% of in-ICU patients). The 28-day mortality was 26.3% in non-ICU, 45.0% in before-ICU, 35.6% in in-ICU, and 19.0% in after-ICU patients. The corresponding adjusted hazard ratios (95% confidence interval) were 2.10 (1.85–2.36), 1.67 (1.50–1.87), and 0.76 (0.63–0.91) (reference: non-ICU patients). There were few differences as regards >28-day mortality. Conclusions: We found large differences between common microorganisms and prognosis between the four patient groups. After-ICU patients had the lowest 28-day mortality despite age and comorbidity characteristics similar to the other three groups.

Background: Little is known about the clinical characteristics and pathogens causing hospital-acquired bloodstream infections (HA-BSIs) in relation to an intensive care unit (ICU) stay. Methods: Population-based cohort study, comprising 35% of the Danish population, 2009-2016. We derived four patient groups with first-time HA-BSIs: No ICU stay during the admission (non-ICU patients), HA-BSI acquired before, in, or after an ICU stay (before-ICU, in-ICU, and after-ICU patients). These groups were compared in relation to microbiological and clinical characteristics, including 28- and >28-day mortality. Results: Among 6888 HA-BSI patients, 4017, 792, 1388, and 691 were non-ICU, before-ICU, in-ICU, and after-ICU, respectively. The rates of several microorganisms differed between the patient groups, e.g. Enterococci (9.4% of non-ICU and 32.0% of in-ICU patients). The 28-day mortality was 26.3% in non-ICU, 45.0% in before-ICU, 35.6% in in-ICU, and 19.0% in after-ICU patients. The corresponding adjusted hazard ratios (95% confidence interval) were 2.10 (1.85-2.36), 1.67 (1.50-1.87), and 0.76 (0.63-0.91) (reference: non-ICU patients). There were few differences as regards >28-day mortality. Conclusions: We found large differences between common microorganisms and prognosis between the four patient groups. After-ICU patients had the lowest 28-day mortality despite age and comorbidity characteristics similar to the other three groups.

We developed four algorithms for the automatic capture of C-reactive protein (CRP) peaks in 296 adult patients with acute myeloid leukemia who had bloodstream infection (BSI) episodes, negative blo...

A highly virulent sub-lineage of the Streptococcus pyogenes M1 clone has been rapidly expanding throughout Denmark since late 2022 and now accounts for 30% of the new invasive group A streptococcal infections. We aimed to investigate whether a shift in variant composition can account for the high incidence rates observed over winter 2022/23, or if these are better explained by the impact of COVID-19-related restrictions on population immunity and carriage of group A Streptococcus.

Objectives and study design: In this population-based study of 602 patients, we amended C-reactive protein (CRP) and plasma albumin (PA) levels around the diagnosis of diffuse large B-cell lymphoma (DLBCL) to the International Prognostic Index (IPI) and assessed 0–90, 91–365, and +365-day survival. Results: The CRP did not contribute to the IPI's prognostic or discriminatory ability, regardless of time period, particularly not in models with PA. In contrast, the PA was an important contributor, especially in the 0–90 day period, but also up to one year after the diagnosis. For day 0–90, the model with the IPI only had an Area Under the Receiver Operating Characteristics (AUROC) of 0.742, whereas the IPI with PA as a continuous variable rendered an AUROC of 0.841. Especially the lower PA quartile (18–32 g/L) contributed to the worse prognosis. Conclusions: The amendment of PA to the IPI may significantly improve the short-term prognostic and discriminative ability.

Serological SARS-CoV-2 assays are needed to support clinical diagnosis and epidemiological investigations. Recently, assays for the large-volume detection of total antibodies (Ab) and immunoglobulin (Ig) G and M against SARS-CoV-2 antigens have been developed, but there are limited data on the diagnostic accuracy of these assays. This study was organized as a Danish national collaboration and included fifteencommercial and one in-house anti-SARS-CoV-2 assays in sixteen laboratories. Sensitivity was evaluated using 150 serum samples from individuals diagnosed with asymptomatic,mild or moderate nonhospitalized (n=129) or hospitalized (n=31) COVID-19, confirmed bynucleic acid amplification tests, collected 13-73 days from symptom onset. Specificity and cross-reactivity were evaluated in samples collected prior to the SARS-CoV-2 epidemic from > 586 blood donors and patients with autoimmune diseases or CMV or EBV infections. Predefined specificity criteria of ≥ 99% were met by all total-Ab and IgG assays except one (Diasorin/LiaisonXL-IgG 97.2%). The sensitivities in descending order were: Wantai/ELISA total-Ab (96.7%), CUH/NOVO in-house ELISA total-Ab (96.0%), Ortho/Vitros total-Ab (95.3%), YHLO/iFlash-IgG (94.0%), Ortho/Vitros-IgG (93.3%), Siemens/Atellica total-Ab (93.2%), Roche-Elecsys total-Ab (92.7%), Abbott-Architect-IgG (90.0%), Abbott/Alinity-IgG (median 88.0%), Diasorin/LiaisonXL-IgG (84.6%),Siemens/Vista total-Ab (81.0%), Euroimmun/ELISA-IgG (78.0%), and Snibe/Maglumi-IgG (median 78.0%). The IgM results were variable, but one assay (Wantai/ELISA-IgM) hadboth high sensitivity (82.7%) and specificity (99%). The rate of seropositivity increased with time from symptom onset and symptom severity. In conclusion, predefined sensitivity and specificity acceptance criteria of 90%/99%, respectively, for diagnostic use were met in five of six total-Ab and three of seven IgG assays.