Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units

BACKGROUND

The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making.

METHODS

Thirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections.

RESULTS

A total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C-C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient.

CONCLUSION

This study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.

Development and validation of BLOOMY prediction scores for 14-day and 6-month mortality in hospitalised adults with bloodstream infections: a multicentre, prospective, cohort study

BACKGROUND

The burden of bloodstream infections remains high worldwide and cannot be confined to short-term in-hospital mortality. We aimed to develop scores to predict short-term and long-term mortality in patients with bloodstream infections.

METHODS

The Bloodstream Infection due to Multidrug-resistant Organisms: Multicenter Study on Risk Factors and Clinical Outcomes (BLOOMY) study is a prospective, multicentre cohort study at six German tertiary care university hospitals to develop and validate two scores assessing 14-day and 6-month mortality in patients with bloodstream infections. We excluded patients younger than 18 years or who were admitted to an ophthalmology or psychiatry ward. Microbiological, clinical, laboratory, treatment, and survival data were prospectively collected on day 0 and day 3 and then from day 7 onwards, weekly. Participants were followed up for 6 months. All patients in the derivation cohort who were alive on day 3 were included in the analysis. Predictive scores were developed using logistic regression and Cox proportional hazards models with a machine-learning approach. Validation was completed using the C statistic and predictive accuracy was assessed using sensitivity, specificity, and predictive values.

FINDINGS

Between Feb 1, 2017, and Jan 31, 2019, 2568 (61·5%) of 4179 eligible patients were recruited into the derivation cohort. The in-hospital mortality rate was 23·75% (95% CI 22·15-25·44; 610 of 2568 patients) and the 6-month mortality rate was 41·55% (39·54-43·59; 949 of 2284). The model predictors for 14-day mortality (C statistic 0·873, 95% CI 0·849-0·896) and 6-month mortality (0·807, 0·784-0·831) included age, body-mass index, platelet and leukocyte counts, C-reactive protein concentrations, malignancy (ie, comorbidity), in-hospital acquisition, and pathogen. Additional predictors were, for 14-day mortality, mental status, hypotension, and the need for mechanical ventilation on day 3 and, for 6-month mortality, focus of infection, in-hospital complications, and glomerular filtration rate at the end of treatment. The scores were validated in a cohort of 1023 patients with bloodstream infections, recruited between Oct 9, 2019, and Dec 31, 2020. The BLOOMY 14-day score showed a sensitivity of 61·32% (95% CI 51·81-70·04), a specificity of 86·36% (83·80-88·58), a positive predictive value (PPV) of 37·57% (30·70-44·99), and a negative predictive value (NPV) of 94·35% (92·42-95·80). The BLOOMY 6-month score showed a sensitivity of 69·93% (61·97-76·84), a specificity of 66·44% (61·86-70·73), a PPV of 40·82% (34·85-47·07), and a NPV of 86·97% (82·91-90·18).

INTERPRETATION

The BLOOMY scores showed good discrimination and predictive values and could support the development of protocols to manage bloodstream infections and also help to estimate the short-term and long-term burdens of bloodstream infections.

FUNDING

DZIF German Center for Infection Research.

TRANSLATION

For the German translation of the abstract see Supplementary Materials section.

SARS-CoV-2-specific antibody rearrangements in prepandemic immune repertoires of risk cohorts and patients with COVID-19

A considerable fraction of B cells recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with germline-encoded elements of their B cell receptor, resulting in the production of neutralizing and nonneutralizing antibodies. We found that antibody sequences from different discovery cohorts shared biochemical properties and could be retrieved across validation cohorts, confirming the stereotyped character of this naive response in coronavirus disease 2019 (COVID-19). While neutralizing antibody sequences were found independently of disease severity, in line with serological data, individual nonneutralizing antibody sequences were associated with fatal clinical courses, suggesting detrimental effects of these antibodies. We mined 200 immune repertoires from healthy individuals and 500 repertoires from patients with blood or solid cancers - all acquired prior to the pandemic - for SARS-CoV-2 antibody sequences. While the largely unmutated B cell rearrangements occurred in a substantial fraction of immune repertoires from young and healthy individuals, these sequences were less likely to be found in individuals over 60 years of age and in those with cancer. This reflects B cell repertoire restriction in aging and cancer, and may to a certain extent explain the different clinical courses of COVID-19 observed in these risk groups. Future studies will have to address if this stereotyped B cell response to SARS-CoV-2 emerging from unmutated antibody rearrangements will create long-lived memory.

Biomarkers of human gut microbiota diversity and dysbiosis

The association of gut microbiota dysbiosis with various human diseases is being substantiated with increasing evidence. Metabolites derived from both, microbiota and the human host play a central role in disease susceptibility and disease progression by extensively modulating host physiology and metabolism. Several of these metabolites have the potential to serve as diagnostic biomarkers for monitoring disease states in conjunction with intestinal microbiota dysbiosis. In this narrative review we evaluate the potential of trimethylamine-N-oxide, short-chain fatty acids, 3-indoxyl sulfate, p-cresyl sulfate, secondary bile acids, hippurate, human β-defensin-2, chromogranin A, secreted immunoglobulins and zonulin to serve as biomarkers for metabolite profiling and diagnostic suitability for dysbiosis and disease.

The impact of technical and clinical factors on fecal microbiota transfer outcomes for the treatment of recurrent Clostridioides difficile infections in Germany

Introduction

Fecal microbiota transfer (FMT) is highly effective in the treatment and prevention of recurrent Clostridioides difficile infection (rCDI) with cure rates of about 80% after a single treatment. Nevertheless, the reasons for failure in the remaining 20% remain largely elusive. The aim of the present study was to investigate different potential clinical predictors of response to FMT in Germany.

Methods

Information was extracted from the MicroTrans Registry (NCT02681068), a retrospective observational multicenter study, collecting data from patients undergoing FMT for recurrent or refractory CDI in Germany. We performed binary logistic regression with the following covariates: age, gender, ribotype 027, Eastern Co-operative Oncology Group score, immunosuppression, preparation for FMT by use of proton pump inhibitor, antimotility agents and bowel lavage, previous recurrences, severity of CDI, antibiotic induction treatment, fresh or frozen FMT preparation, and route of application.

Results

Treatment response was achieved in 191/240 evaluable cases (79.6%) at day 30 (D30) post FMT and 78.1% at day 90 (D90) post FMT. Assessment of clinical predictors for FMT failure by forward and confirmatory backward-stepwise regression analysis yielded higher age as an independent predictor of FMT failure (p = 0.001; OR 1.060; 95%CI 1.025-1.097).

Conclusion

FMT in Germany is associated with high cure rates at D30 and D90. No specific pre-treatment, preparation or application strategy had an impact on FMT success. Only higher age was identified as an independent risk factor for treatment failure. Based on these and external findings, future studies should focus on the assessment of microbiota and microbiota-associated metabolites as factors determining FMT success.

Azole-resistant invasive aspergillosis in a patient with acute myeloid leukaemia in Germany

We report the first culture-proven case of invasive aspergillosis (IA) caused by azole-resistant Aspergillus fumigatus in a patient with acute myeloid leukaemia in Germany. IA presented as breakthrough infection under posaconazole prophylaxis. Analysis of the resistance mechanism revealed the TR/L98H mutation in the cyp51A gene, which indicates an environmental origin of the strain. This case underscores the need for monitoring azole resistance in Aspergillus spp. and for routine susceptibility testing of moulds.

Azole-resistant invasive aspergillosis in a patient with acute myeloid leukaemia in Germany

We report the first culture-proven case of invasive aspergillosis (IA) caused by azole-resistant Aspergillus fumigatus in a patient with acute myeloid leukaemia in Germany. IA presented as breakthrough infection under posaconazole prophylaxis. Analysis of the resistance mechanism revealed the TR/L98H mutation in the cyp51A gene, which indicates an environmental origin of the strain. This case underscores the need for monitoring azole resistance in Aspergillus spp. and for routine susceptibility testing of moulds.