Faculty Bibliography

Although most individuals recover from acute SARS-CoV-2 infection, a significant number continue to suffer from Post-Acute Sequelae of SARS-CoV-2 (PASC), including the unexplained symptoms that are frequently referred to as long COVID, which could last for weeks, months, or even years after the acute phase of illness. The National Institutes of Health is currently funding large multi-center research programs as part of its Researching COVID to Enhance Recover (RECOVER) initiative to understand why some individuals do not recover fully from COVID-19. Several ongoing pathobiology studies have provided clues to potential mechanisms contributing to this condition. These include persistence of SARS-CoV-2 antigen and/or genetic material, immune dysregulation, reactivation of other latent viral infections, microvascular dysfunction, and gut dysbiosis, among others. Although our understanding of the causes of long COVID remains incomplete, these early pathophysiologic studies suggest biological pathways that could be targeted in therapeutic trials that aim to ameliorate symptoms. Repurposed medicines and novel therapeutics deserve formal testing in clinical trial settings prior to adoption. While we endorse clinical trials, especially those that prioritize inclusion of the diverse populations most affected by COVID-19 and long COVID, we discourage off-label experimentation in uncontrolled and/or unsupervised settings. Here, we review ongoing, planned, and potential future therapeutic interventions for long COVID based on the current understanding of the pathobiological processes underlying this condition. We focus on clinical, pharmacological, and feasibility data, with the goal of informing future interventional research studies.

OBJECTIVES:The aim of this study was to understand how demographic and treatment-related factors impact responses to fostemsavir-based regimens. DESIGN:BRIGHTE is an ongoing phase 3 study evaluating twice-daily fostemsavir 600 mg and optimized background therapy (OBT) in heavily treatment-experienced individuals failing antiretroviral therapy with limited treatment options (Randomized Cohort 1-2 and Nonrandomized Cohort 0 fully active antiretroviral classes). METHODS:Virologic response rates (HIV-1 RNA <40 copies/ml, Snapshot analysis) and CD4+ T-cell count increases in the Randomized Cohort were analysed by prespecified baseline characteristics (age, race, sex, region, HIV-1 RNA, CD4+ T-cell count) and viral susceptibility to OBT. Safety results were analysed by baseline characteristics for combined cohorts (post hoc). RESULTS:In the Randomized Cohort, virologic response rates increased between Weeks 24 and 96 across most subgroups. Virologic response rates over time were most clearly associated with overall susceptibility scores for new OBT agents (OSS-new). CD4+ T-cell count increases were comparable across subgroups. Participants with baseline CD4+ T-cell counts less than 20 cells/μl had a mean increase of 240 cells/μl. In the safety population, more participants with baseline CD4+ T-cell counts less than 20 vs. at least 200 cells/μl had grade 3/4 adverse events [53/107 (50%) vs. 24/96 (25%)], serious adverse events [58/107 (54%) vs. 25/96 (26%)] and deaths [16/107 (15%) vs. 2/96 (2%)]. There were no safety differences by other subgroups. CONCLUSION:Week 96 results for BRIGHTE demonstrate comparable rates of virologic and immunologic response (Randomized Cohort) and safety (combined cohorts) across subgroups. OSS-new is an important consideration when constructing optimized antiretroviral regimens for heavily treatment-experienced individuals with limited remaining treatment options.

BACKGROUND:Convalescent plasma (CP) for treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown preliminary signs of effectiveness in moderate to severely ill patients in reducing mortality. While studies have demonstrated a low risk of serious adverse events, the comprehensive incidence and nature of the spectrum of transfusion reactions to CP is unknown. We retrospectively examined 427 adult inpatient CP transfusions to determine incidence and types of reactions, as well as clinical parameters and risk factors associated with transfusion reactions. STUDY DESIGN AND METHODS:Retrospective analysis was performed for 427 transfusions to 215 adult patients with coronavirus 2019 (COVID-19) within the Mount Sinai Health System, through the US Food and Drug Administration emergency investigational new drug and the Mayo Clinic Expanded Access Protocol to Convalescent Plasma approval pathways. Transfusions were blindly evaluated by two reviewers and adjudicated by a third reviewer in discordant cases. Patient demographics and clinical and laboratory parameters were compared and analyzed. RESULTS:Fifty-five reactions from 427 transfusions were identified (12.9% incidence), and 13 were attributed to transfusion (3.1% incidence). Reactions were classified as underlying COVID-19 (76%), febrile nonhemolytic (10.9%), transfusion-associated circulatory overload (9.1%), and allergic (1.8%) and hypotensive (1.8%) reactions. Statistical analysis identified increased transfusion reaction risk for ABO blood group B or Sequential Organ Failure Assessment scores of 12 to 13, and decreased risk within the age group of 80 to 89 years. CONCLUSION:Our findings support the use of CP as a safe, therapeutic option from a transfusion reaction perspective, in the setting of COVID-19. Further studies are needed to confirm the clinical significance of ABO group B, age, and predisposing disease severity in the incidence of transfusion reaction events.

Mobile health (mHealth) interventions that are integrated in HIV clinical settings to facilitate ongoing patient-provider communication between primary care visits are garnering evidence for their potential in improving HIV outcomes. Rango is an mHealth intervention to support engagement in HIV care and treatment adherence. This study used a single-arm prospective design with baseline and 6-month assessments for pre-post comparisons, as well as a matched patient sample for between-group comparisons to test Rango's preliminary efficacy in increasing viral suppression. The Rango sample (n = 406) was predominantly 50 years of age or older (63%; M = 50.67; SD = 10.97, 23-82), Black/African-American (44%) or Hispanic/Latinx (38%), and male (59%). At baseline, 18% reported missing at least one dose of ART in the prior three days and chart reviews of recent VL showed that nearly 82% of participants were virally suppressed. Overall 95% of the patients enrolled in Rango returned for a medical follow-up visit. Of the 65 unsuppressed patients at baseline who returned for a medical visit, 38 (59%) achieved viral suppression and only 5% of the suppressed group at baseline had an increase in viral load at 6 months despite being at risk for ART non-adherence. While viral suppression was similar between Rango participants and patients receiving treatment as usual over the same time period, it is unknown whether those patients were similarly at risk for non-adherence. Our findings support efforts to formally test this innovative approach in addressing ART non-adherence and viral suppression particularly to reach HIV treatment goals.

OBJECTIVE:The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. DESIGN:Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. SETTING:All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. PARTICIPANTS:Participants over the age of 18 years were included. PRIMARY OUTCOMES:We investigated in-hospital mortality during the study period. RESULTS:A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. CONCLUSIONS:In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

BACKGROUND:COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE:The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS:We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS:Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS:We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a new human disease with few effective treatments¹. Convalescent plasma, donated by persons who have recovered from COVID-19, is the acellular component of blood that contains antibodies, including those that specifically recognize SARS-CoV-2. These antibodies, when transfused into patients infected with SARS-CoV-2, are thought to exert an antiviral effect, suppressing virus replication before patients have mounted their own humoral immune responses^2,3. Virus-specific antibodies from recovered persons are often the first available therapy for an emerging infectious disease, a stopgap treatment while new antivirals and vaccines are being developed^1,2. This retrospective, propensity score-matched case-control study assessed the effectiveness of convalescent plasma therapy in 39 patients with severe or life-threatening COVID-19 at The Mount Sinai Hospital in New York City. Oxygen requirements on day 14 after transfusion worsened in 17.9% of plasma recipients versus 28.2% of propensity score-matched controls who were hospitalized with COVID-19 (adjusted odds ratio (OR), 0.86; 95% confidence interval (CI), 0.75-0.98; chi-square test P value = 0.025). Survival also improved in plasma recipients (adjusted hazard ratio (HR), 0.34; 95% CI, 0.13-0.89; chi-square test P = 0.027). Convalescent plasma is potentially effective against COVID-19, but adequately powered, randomized controlled trials are needed.

BACKGROUND:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. The proportion of infected individuals who seroconvert is still an open question. In addition, it has been shown in some individuals that viral genome can be detected up to 3 months after symptom resolution. We investigated both seroconversion and PCR positivity in a large cohort of convalescent serum donors in the New York City (NY, USA) region. METHODS:In this observational study, we ran an outreach programme in the New York City area. We recruited participants via the REDCap (Vanderbilt University, Nashville, TN, USA) online survey response. Individuals with confirmed or suspected SARS-CoV-2 infection were screened via PCR for presence of viral genome and via ELISA for presence of anti-SARS-CoV-2 spike antibodies. One-way ANOVA and Fisher's exact test were used to measure the association of age, gender, symptom duration, and days from symptom onset and resolution with positive antibody results. FINDINGS/RESULTS:Between March 26 and April 10, 2020, we measured SARS-CoV-2 antibody titres in 1343 people. Of the 624 participants with confirmed SARS-CoV-2 infection who had serologies done after 4 weeks, all but three seroconverted to the SARS-CoV-2 spike protein, whereas 269 (37%) of 719 participants with suspected SARS-CoV-2 infection seroconverted. PCR positivity was detected up to 28 days from symptom resolution. INTERPRETATION/CONCLUSIONS:Most patients with confirmed COVID-19 seroconvert, potentially providing immunity to reinfection. We also report that in a large proportion of individuals, viral genome can be detected via PCR in the upper respiratory tract for weeks after symptom resolution, but it is unclear whether this signal represents infectious virus. Analysis of our large cohort suggests that most patients with mild COVID-19 seroconvert 4 weeks after illness, and raises questions about the use of PCR to clear positive individuals. FUNDING/BACKGROUND:None.

Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1β in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.