A metagenomic DNA sequencing assay that is robust against environmental DNA contamination.

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present Sample-Intrinsic microbial DNA Found by Tagging and sequencing (SIFT-seq) a metagenomic sequencing assay that is robust against environmental DNA contamination introduced during sample preparation. The core idea of SIFT-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied SIFT-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of sepsis and inflammatory bowel disease in blood.

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.

OBJECTIVE: Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. MATERIALS AND METHODS: Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. RESULTS: Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. DISCUSSION: DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. CONCLUSION: DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

COVID-19 outcomes in patients waitlisted for kidney transplantation and kidney transplant recipients.

The COVID-19 pandemic has brought unprecedented challenges to the transplant community. The reduction in transplantation volume during this time is partly due to concerns over potentially increased susceptibility and worsened outcomes of COVID-19 in immunosuppressed recipients. The consequences of COVID-19 on patients waitlisted for kidney transplantation, however, have not previously been characterized. We studied 56 waitlisted patients and 80 kidney transplant recipients diagnosed with COVID-19 between March 13 and May 20, 2020. Despite similar demographics and burden of comorbidities between waitlisted and transplant patients, waitlisted patients were more likely to require hospitalization (82% vs. 65%, P = .03) and were at a higher risk of mortality (34% vs. 16%, P = .02). Intubation was required in one third of hospitalized patients in each group, and portended a very poor prognosis. The vast majority of patients who died were male (84% waitlist, 100% transplant). Multivariate analysis demonstrated waitlist status, age, and male sex were independently associated with mortality. COVID-19 has had a dramatic impact on waitlisted patients, decreasing their opportunities for transplantation and posing significant mortality risk. Understanding the impact of COVID-19 on waitlist patients in comparison to transplant recipients may aid centers in weighing the risks and benefits of transplantation in the setting of ongoing COVID-19.

Kidney allograft recipients, immunosuppression, and coronavirus disease-2019: a report of consecutive cases from a New York City transplant center.

BACKGROUND: Kidney graft recipients receiving immunosuppressive therapy may be at heightened risk for coronavirus disease 2019 (Covid-19) and adverse outcomes. It is therefore important to characterize the clinical course and outcome of Covid-19 in this population and identify safe therapeutic strategies. METHODS: We performed a retrospective chart review of 73 adult kidney graft recipients evaluated for Covid-19 from 13 March to 20 April 2020. Primary outcomes included recovery from symptoms, acute kidney injury, graft failure and case fatality rate. RESULTS: Of the 73 patients screened, 54 tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-39 with moderate to severe symptoms requiring hospital admission and 15 with mild symptoms managed in the ambulatory setting. Hospitalized patients were more likely to be male, of Hispanic ethnicity and to have cardiovascular disease. In the hospitalized group, tacrolimus dosage was reduced in 46% of patients and mycophenolate mofetil (MMF) therapy was stopped in 61% of patients. None of the ambulatory patients had tacrolimus reduction or discontinuation of MMF. Azithromycin or doxycycline was prescribed at a similar rate among hospitalized and ambulatory patients (38% versus 40%). Hydroxychloroquine was prescribed in 79% of hospitalized patients. Graft failure requiring hemodialysis occurred in 3 of 39 hospitalized patients (8%) and 7 patients died, resulting in a case fatality rate of 13% among Covid-19-positive patients and 18% among hospitalized Covid-19-positive patients. CONCLUSIONS: Data from our study suggest that a strategy of systematic triage to outpatient or inpatient care, early management of concurrent bacterial infections and judicious adjustment of immunosuppressive drugs rather than cessation is feasible in kidney transplant recipients with Covid-19.