ABSTRACT
The Bronx was an early epicenter of the COVID-19 pandemic in the USA. We conducted temporal genomic surveillance of 104 SARS-CoV-2 genomes across the Bronx from March October 2020. Although the local structure of SARS-CoV-2 lineages mirrored those of New York City and New York State, temporal sampling revealed a dynamic and changing landscape of SARS-CoV-2 genomic diversity. Mapping the trajectories of mutations, we found that while some became 'endemic' to the Bronx, other, novel mutations rose in prevalence in the late summer/early fall. Geographically resolved genomes enabled us to distinguish between cases of reinfection and persistent infection in two pediatric patients. We propose that limited, targeted, temporal genomic surveillance has clinical and epidemiological utility in managing the ongoing COVID pandemic.
ABSTRACT
OBJECTIVE: To determine how cohorting patients based on presenting complaints affects risk of nosocomial infection in crowded Emergency Departments (EDs) under conditions of high and low prevalence of COVID-19. METHODS: This was a retrospective analysis of presenting complaints and PCR tests collected during the COVID-19 epidemic from 4 EDs from a large hospital system in Bronx County, NY, from May 1, 2020 to April 30, 2021. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were calculated for a symptom screen based on the CDC list of COVID-19 symptoms: fever/chills, shortness of breath/dyspnea, cough, muscle or body ache, fatigue, headache, loss of taste or smell, sore throat, nasal congestion/runny nose, nausea, vomiting, and diarrhea. PPV was calculated for varying values of prevalence. RESULTS: There were 80,078 visits with PCR tests. The sensitivity of the symptom screen was 64.7% (95% CI: 63.6, 65.8), specificity 65.4% (65.1, 65.8). PPV was 16.8% (16.5, 17.0) and NPV was 94.5% (94.4, 94.7) when the observed prevalence of COVID-19 in the ED over the year was 9.7%. The PPV of fever/chills, cough, body and muscle aches and nasal congestion/runny nose were each approximately 25% across the year, while diarrhea, nausea, vomiting and headache were less predictive, (PPV 4.7%-9.6%) The combinations of fever/chills, cough, muscle/body aches, and shortness of breath had PPVs of 40-50%. The PPV of the screen varied from 3.7% (3.6, 3.8) at 2% prevalence of COVID-19 to 44.3% (44.0, 44.7) at 30% prevalence. CONCLUSION: The proportion of patients with a chief complaint of COVID-19 symptoms and confirmed COVID-19 infection was exceeded by the proportion without actual infection. This was true when prevalence in the ED was as high as 30%. Cohorting of patients based on the CDC's list of COVID-19 symptoms will expose many patients who do not have COVID-19 to risk of nosocomially acquired COVID-19. EDs should not use the CDC list of COVID-19 symptoms as the only strategy to minimize exposure.
Subject(s)
COVID-19 , COVID-19/diagnosis , COVID-19/epidemiology , Cough , Emergency Service, Hospital , Humans , Retrospective Studies , SARS-CoV-2ABSTRACT
BACKGROUND: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. OBJECTIVE: In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. METHODS: Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. RESULTS: Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). CONCLUSIONS: We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning-based clinical decision support tools.
Subject(s)
COVID-19/mortality , Machine Learning , COVID-19/virology , Female , Humans , Male , Middle Aged , Models, Statistical , Pandemics , Retrospective Studies , SARS-CoV-2/isolation & purification , Survival AnalysisABSTRACT
With the global outbreak of COVID-19, the demand for testing rapidly increased and quickly exceeded the testing capacities of many laboratories. Clinical tests which receive CE (Conformité Européenne) and Food and Drug Administration (FDA) authorisations cannot always be tested thoroughly in a real-world environment. Here we demonstrate the long-term stability of nasopharyngeal swab specimens for SARS-CoV-2 molecular testing across three assays recently approved by the US FDA under Emergency Use Authorization. This study demonstrates that nasopharyngeal swab specimens can be stored under refrigeration or even ambient conditions for 21 days without clinically impacting the results of the real-time reverse transcriptase-PCR testing.
Subject(s)
COVID-19/diagnosis , SARS-CoV-2/isolation & purification , Specimen Handling/methods , COVID-19/virology , COVID-19 Nucleic Acid Testing , Humans , Laboratories, Hospital , Nasopharynx/virology , Refrigeration , SARS-CoV-2/genetics , Time FactorsABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) claimed over 4 million lives by July 2021 and continues to pose a serious public health threat. OBJECTIVES: Our retrospective study utilized respiratory pathogen panel (RPP) results in patients with SARS-CoV-2 to determine if coinfection (i.e. SARS-CoV-2 positivity with an additional respiratory virus) was associated with more severe presentation and outcomes. METHODS: All patients with negative influenza/respiratory syncytial virus testing who underwent RPP testing within 7 days of a positive SARS-CoV-2 test at a large, academic medical centre in New York were examined. Patients positive for SARS-CoV-2 with a negative RPP were compared with patients positive for SARS-CoV-2 and positive for a virus by RPP in terms of biomarkers, oxygen requirements and severe COVID-19 outcome, as defined by mechanical ventilation or death within 30 days. RESULTS: Of the 306 SARS-CoV-2-positive patients with RPP testing, 14 (4.6%) were positive for a non-influenza virus (coinfected). Compared with the coinfected group, patients positive for SARS-CoV-2 with a negative RPP had higher inflammatory markers and were significantly more likely to be admitted (Pâ=â0.01). Severe COVID-19 outcome occurred in 111 (36.3%) patients in the SARS-CoV-2-only group and 3 (21.4%) patients in the coinfected group (Pâ=â0.24). CONCLUSIONS: Patients infected with SARS-CoV-2 along with a non-influenza respiratory virus had less severe disease on presentation and were more likely to be admitted-but did not have more severe outcomes-than those infected with SARS-CoV-2 alone.
Subject(s)
COVID-19 , Coinfection , Coinfection/epidemiology , Humans , Pandemics , Retrospective Studies , SARS-CoV-2ABSTRACT
Patients with cancer have been identified in several studies to be at high risk of developing severe COVID-19; however, rates of SARS-CoV-2 IgG seroconversion and its association with cancer types and anti-cancer therapy remain obscure. We conducted a retrospective cohort study in patients with cancer that underwent SARS-CoV-2 IgG testing. Two hundred and sixty-one patients with a cancer diagnosis underwent SARS-CoV-2 IgG testing and demonstrated a high rate of seroconversion (92%). However, significantly lower seroconversion was observed in patients with hematologic malignancies (82%), patients that received anti-CD-20 antibody therapy (59%) and stem cell transplant (60%). Interestingly, all 17 patients that received immunotherapy, including 16 that received anti-PD-1/PD-L1 monoclonal antibodies, developed SARS-Cov-2 IgG antibodies (100% seroconversion). These data show differential rates of seroconversion in specific patient groups and bear importance for clinical monitoring and vaccination strategies that are being developed to mitigate the COVID-19 pandemic.
Subject(s)
Antibodies, Viral/blood , Immunoglobulin G/blood , Neoplasms/immunology , SARS-CoV-2/immunology , Seroconversion , Adult , Aged , Aged, 80 and over , COVID-19 Nucleic Acid Testing , Female , Humans , Male , Middle Aged , Neoplasms/drug therapy , Retrospective Studies , Young AdultABSTRACT
CONTEXT.: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) testing is used for serosurveillance and will be important to evaluate vaccination status. Given the urgency to release coronavirus disease 2019 (COVID-19) serology tests, most manufacturers have developed qualitative tests. OBJECTIVE.: To evaluate clinical performance of 6 different SARS-CoV-2 IgG assays and their quantitative results to better elucidate the clinical role of serology testing in COVID-19. DESIGN.: Six SARS-CoV-2 IgG assays were tested using remnant specimens from 190 patients. Sensitivity and specificity were evaluated for each assay with the current manufacturer's cutoff and a lower cutoff. A numeric result analysis and discrepancy analysis were performed. RESULTS.: Specificity was higher than 93% for all assays, and sensitivity was higher than 80% for all assays (≥7 days post-polymerase chain reaction testing). Inpatients with more severe disease had higher numeric values compared with health care workers with mild or moderate disease. Several discrepant serology results were those just below the manufacturers' cutoff. CONCLUSIONS.: Severe acute respiratory syndrome coronavirus 2 IgG antibody testing can aid in the diagnosis of COVID-19, especially with negative polymerase chain reaction. Quantitative COVID-19 IgG results are important to better understand the immunologic response and disease course of this novel virus and to assess immunity as part of future vaccination programs.
Subject(s)
Antibodies, Viral/blood , COVID-19 Serological Testing/methods , COVID-19/immunology , Immunoglobulin G/blood , SARS-CoV-2/immunology , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19 Serological Testing/statistics & numerical data , Cohort Studies , Humans , New York City/epidemiology , Pandemics , Sensitivity and Specificity , Severity of Illness IndexABSTRACT
In February of 2020, New York City was unprepared for the COVID-19 pandemic. Cases of SARS-CoV-2 infection appeared and spread rapidly. Hospitals had to repurpose staff and establish diagnostic testing for this new viral infection. In the background of the usual respiratory pathogen testing performed in the clinical laboratory, SARS-CoV-2 testing at the Montefiore Medical System grew exponentially, from none to hundreds per day within the first week of testing. The job of appropriately routing SARS-CoV-2 viral specimens became overwhelming. Additional staff was required to triage these specimens to multiple in-house testing platforms as well as external reference laboratories. Since medical school classes and many research laboratories shut down at the Albert Einstein College of Medicine and students were eager to help fight the pandemic, we seized the opportunity to engage and train senior MD-PhD students to assist in triaging specimens. This volunteer force enabled us to establish the "Pathology Command Center," staffed by these students as well as residents and furloughed dental associates. The Pathology Command Center staff were tasked with the accessioning and routing of specimens, answering questions from clinical teams, and updating ever evolving protocols developed in collaboration with a team of Infectious Disease clinicians. Many lessons were learned during this process, including how best to restructure an accessioning department and how to properly onboard students and repurpose staff while establishing safeguards for their well-being during these unprecedented times. In this article, we share some of our challenges, successes, and what we ultimately learned as an organization.