Peer-reviewed Publication: JCO Oncology Practice (February 15 2023)

Scott A. Soefje, PharmD, MBA, BCOP, FCCP, FHOPA; Corinne Carpenter, PhD; Katherine Carlson, PhD; Samir Awasthi, MD, PhD… more

PURPOSE: Median duration of daratumumab (DARA) administration for treatment of multiple myeloma is 3-7 hours for the intravenous formulation (DARA IV) and 3-5 minutes for the subcutaneous formulation (DARA SC). Here, we describe clinical administration characteristics of DARA using a novel method for data extraction from electronic health records. METHODS: Time-based measurements were extracted using a scheduling/pharmacy software program that tracked patient movement through appointments for patients initiating DARA in Mayo Clinic infusion centers from April 5, 2017, to October 14, 2021. Cohorts included patients who received DARA IV or DARA SC, or converted from DARA IV to DARA SC. The DARA SC cohort was further analyzed before (DARA SC initial) and after (DARA SC shortened) a reduction in the postadministration observation time mandated by the treatment plan. Events associated with administration-related reactions (ARRs) were also identified. RESULTS: Median total clinic times were 2.7-3.0 hours shorter for DARA SC versus DARA IV. Median clinic times were highest at dose 1 and decreased with subsequent doses. Median total chair times were 2.7-2.8 hours shorter for DARA SC versus DARA IV. Incidences of ARR-related events with DARA SC were low across doses. CONCLUSION: Reduced clinic times were observed with DARA SC, indicating that use of DARA SC as a treatment option results in time savings that may free clinic resources. Furthermore, novel methods of electronic health record data extraction can provide insights that may help inform clinic resource optimization.

Correspondence to: Akhilesh Pandey (pandey.akhilesh@mayo.edu)

Preprint: medRxiv (December 1 2022)

Pritha Ghosh,Michiel JM Niesen, Colin Pawlowski, Hari Bandi, Unice Yoo, Patrick J Lenehan, Praveen Kumar, Mihika Nadig… more

Abstract: Post-COVID-19 conditions, also known as long COVID, has significantly impacted the lives of many individuals, but the risk factors for this condition are poorly understood. In this study, we performed a retrospective EHR analysis of 89,843 individuals at a multi-state health system in the United States with PCR-confirmed COVID-19, including 1,086 patients diagnosed with long COVID and 1,086 matched controls not diagnosed with long COVID. For these two cohorts, we evaluated a wide range of clinical covariates, including laboratory tests, medication orders, phenotypes recorded in the clinical notes, and outcomes. We found that chronic pulmonary disease (CPD) was significantly more common as a pre-existing condition for the long COVID cohort than the control cohort (odds ratio: 1.9, 95% CI: [1.5, 2.6]). Additionally, long-COVID patients were more likely to have a history of migraine (odds ratio: 2.2, 95% CI: [1.6, 3.1]) and fibromyalgia (odds ratio: 2.3, 95% CI: [1.3, 3.8]). During the acute infection phase, the following lab measurements were abnormal in the long COVID cohort: high triglycerides (meanlongCOVID: 278.5 mg/dL vs. meancontrol: 141.4 mg/dL), low HDL cholesterol levels (meanlongCOVID: 38.4 mg/dL vs. meancontrol: 52.5 mg/dL), and high neutrophil-lymphocyte ratio (meanlongCOVID: 10.7 vs. meancontrol: 7.2). The hospitalization rate during the acute infection phase was also higher in the long COVID cohort compared to the control cohort (ratelongCOVID: 5% vs. ratecontrol: 1%). Overall, this study suggests that the severity of acute infection and a history of CPD, migraine, CFS, or fibromyalgia may be risk factors for long COVID symptoms. Our findings motivate clinical studies to evaluate whether suppressing acute disease severity proactively, especially in patients at high risk, can reduce incidence of long COVID.

Correspondence to: Ryan Hurt (Hurt.Ryan@mayo.edu) and Venky Soundararajan (venky@nference.net)

Peer-reviewed Publication: MDPI Vaccines (September 9 2022)

Preprint: OSF Preprints (December 2 2021)

Featured in: Washington Post · Reuters · USA Today · New York Times · WSJ · The Scientist · Medscape · Bloomberg · Daily Mail · CNN · The Hill · Fortune · Euronews · Economic Times · Financial Times

AJ Venkatakrishnan Praveen Anand Patrick J Lenehan Rohit Suratekar Bharathwaj Raghunathan Michiel J.M. Niesen Venky Soun… more

The emergence of a heavily mutated SARS-CoV-2 variant (Omicron; Pango lineage B.1.1.529 and BA sublineages) and its rapid spread to over 75 countries raised a global public health alarm. Characterizing the mutational profile of Omicron is necessary to interpret its clinical phenotypes which are shared with or distinctive from those of other SARS-CoV-2 variants. We compared the mutations of the initially circulating Omicron variant (now known as BA.1) with prior variants of concern (Alpha, Beta, Gamma, and Delta), variants of interest (Lambda, Mu, Eta, Iota, and Kappa), and ~1500 SARS-CoV-2 lineages constituting ~5.8 million SARS-CoV-2 genomes. Omicron’s Spike protein harbors 26 amino acid mutations (23 substitutions, 2 deletions, and 1 insertion) that are distinct compared to other variants of concern. While the substitution and deletion mutations appeared in previous SARS-CoV-2 lineages, the insertion mutation (ins214EPE) was not previously observed in any other SARS-CoV-2 lineage. Here, we consider and discuss various mechanisms through which the nucleotide sequence encoding for ins214EPE could have been acquired, including local duplication, polymerase slippage, and template switching. Although we are not able to definitively determine the mechanism, we highlight the plausibility of template switching. Analysis of the homology of the inserted nucleotide sequence and flanking regions suggests that this template-switching event could have involved the genomes of SARS-CoV-2 variants (e.g., the B.1.1 strain), other human coronaviruses that infect the same host cells as SARS-CoV-2 (e.g., HCoV-OC43 or HCoV-229E), or a human transcript expressed in a host cell that was infected by the Omicron precursor.

Correspondence to: Venky Soundararajan (venky@nference.net)

Preprint: medRxiv (August 15 2022)

Featured in: News Medical Life Sciences

Mihika Nadig, Michiel Niesen, Patrick J Lenehan, Vineet Agarwal, Jason Ross, Sankar Ardhanari, Aiveliagaram J Venkatakri… more

Omicron sub-lineages such as BA2.12.1 and BA5 have breached prior infection-induced immunity and vaccine-induced immunity. This capacity of Omicron to reinfect patients calls for a characterization of vaccination, infection, and reinfection patterns. We analyzed de-identified longitudinal electronic health records for 389,746 individuals (88,679 fully-vaccinated, 184,205 boosted, 73,184 with prior infection) across a multi-state health system. Compared to individuals with only full vaccination, the rates of SARS-CoV-2 infections in the Omicron era were reduced for individuals with additional prior infection (1.4 to 1.8-fold reduced, depending on vaccine status) or booster vaccination (1.3 to 2.0-fold reduced). Although prior infection was associated with lower incidence of SARS-CoV-2 infection, we found that the relative risk (RR) of infections for individuals with prior infection has increased during Omicron. During October, 2021, RR was 0.11 [0.10-0.13, 95% CI] while during May, 2022, it increased to 0.57 [0.46-0.68, 95% CI], suggesting an increase in reinfections with Omicron. Furthermore, we found that time since prior infection is associated with risk of reinfection, providing evidence of waning immunity. Prior infections before June, 2021, were associated with marginal reduction in risk of infection (eg., RR = 0.80 [0.68-0.90] for prior infection during January, 2021), while recent prior infections were associated with significant reduction in risk (eg., RR = 0.24 [0.20-0.29, 95% CI] for prior infection during November, 2021). Despite an observed increase in reinfections and vaccine breakthrough infections, our findings emphasize the protective effect of natural and vaccine immunity, with prior infection providing ~6 months of protection from reinfection.

Correspondence to: Venky Soundararajan (venky@nference.net)

Peer-reviewed Publication: Lancet: Digital Health (July 11 2022)

Seul Kee Byeon, Anil K Madugundu, Kishore Garapati, Madan Gopal Ramarajan, Mayank Saraswat, Praveen Kumar-M, Travis Hugh… more

COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.

Correspondence to: Akhilesh Pandey (pandey.akhilesh@mayo.edu)