Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

Background New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

System-wide transcriptome damage and tissue identity loss in COVID-19 patients.

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections.

Early introductions and transmission of SARS-CoV-2 variant B.1.1.7 in the United States.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

Rapid Implementation of Severe Acute Respiratory Syndrome Coronavirus 2 Emergency Use Authorization RT-PCR Testing and Experience at an Academic Medical Institution.

An epidemic caused by an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019 has since rapidly spread internationally, requiring urgent response from the clinical diagnostics community. We present a detailed overview of the clinical validation and implementation of the first laboratory-developed real-time RT-PCR test offered in the NewYork-Presbyterian Hospital system following the Emergency Use Authorization issued by the US Food and Drug Administration. Nasopharyngeal and sputum specimens (n = 174) were validated using newly designed dual-target real-time RT-PCR (altona RealStar SARS-CoV-2 Reagent) for detecting SARS-CoV-2 in upper respiratory tract and lower respiratory tract specimens. Accuracy testing demonstrated excellent assay agreement between expected and observed values and comparable diagnostic performance to reference tests. The limit of detection was 2.7 and 23.0 gene copies per reaction for nasopharyngeal and sputum specimens, respectively. Retrospective analysis of 1694 upper respiratory tract specimens from 1571 patients revealed increased positivity in older patients and males compared with females, and an increasing positivity rate from approximately 20% at the start of testing to 50% at the end of testing 3 weeks later. Herein, we demonstrate that the assay accurately and sensitively identifies SARS-CoV-2 in multiple specimen types in the clinical setting and summarize clinical data from early in the epidemic in New York City.

Clinical Performance of SARS-CoV-2 Molecular Tests.

Molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the gold standard for diagnosis of coronavirus disease 2019 (COVID-19), but the clinical performance of these tests is still poorly understood, particularly with regard to disease course, patient-specific factors, and viral shedding. From 10 March to 1 May 2020, NewYork-Presbyterian laboratories performed 27,377 SARS-CoV-2 molecular assays from 22,338 patients. Repeat testing was performed for 3,432 patients, of which 2,413 had initial negative and 802 had initial positive results. Repeat-tested patients were more likely to have severe disease and low viral loads. The negative predictive value of the first-day result among repeat-tested patients was 81.3% The clinical sensitivity of SARS-CoV-2 molecular assays was estimated between 58% and 96%, depending on the unknown number of false-negative results in single-tested patients. Conversion to negative was unlikely to occur before 15 to 20 days after initial testing or 20 to 30 days after the onset of symptoms, with 50% conversion occurring at 28 days after initial testing. Conversion from first-day negative to positive results increased linearly with each day of testing, reaching 25% probability in 20 days. Sixty patients fluctuated between positive and negative results over several weeks, suggesting that caution is needed when single-test results are acted upon. In summary, our study provides estimates of the clinical performance of SARS-CoV-2 molecular assays and suggests time frames for appropriate repeat testing, namely, 15 to 20 days after a positive test and the same day or next 2 days after a negative test for patients with high suspicion for COVID-19.

Comparison of Two High-Throughput Reverse Transcription-PCR Systems for the Detection of Severe Acute Respiratory Syndrome Coronavirus 2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as the cause of a worldwide pandemic. Many commercial SARS-CoV-2 reverse transcription-PCR (RT-PCR) assays have received Emergency Use Authorization from the U.S. Food and Drug Administration. However, there are limited data describing their performance, in particular the performance of high-throughput SARS-CoV-2 RT-PCR systems. We analyzed the diagnostic performance of two high-throughput systems: cobas 6800 and Panther Fusion, and their associated RT-PCR assays, with a collection of 389 nasopharyngeal specimens. The overall agreement between the platforms was 96.4% (375/389). Cohen's kappa analysis rated the strength of agreement between the two platforms as "almost perfect" (κ = 0.922; standard error, 0.051). Furthermore, there was no significant difference between corresponding cycle threshold values generated on the two systems (P value = 0.88; Student's t test). Taken together, these data imply that the two platforms can be considered comparable in terms of their clinical performance. We believe that this information will be useful for those who have already adopted these platforms or are seeking to implement high-throughput RT-PCR testing to stem the SARS-CoV-2 pandemic.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.