Comparison of SARS-CoV-2 detection in Saliva by real-time RT-PCR and RT-PCR/MALDI-TOF Methods

The coronavirus disease 2019 (COVID-19) pandemic has accelerated the need for rapid implementation of diagnostic assays for detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in respiratory specimens. While multiple molecular methods utilize nasopharyngeal specimens, supply chain constraints and need for easier and safer specimen collection warrant alternative specimen types, particularly saliva. Although saliva has been found to be a comparable clinical matrix for detection of SARS-CoV-2, evaluations of diagnostic and analytic performance across platforms for this specimen type are limited. Here, we compared two methods for SARS-CoV-2 detection in saliva: the Roche cobas(R) 6800/8800 SARS-CoV-2 real-time RT-PCR Test and the Agena Biosciences MassARRAY(R) SARS-CoV-2 Panel/MassARRAY(R) System. Overall, both systems had high agreement with one another, and both demonstrated high diagnostic sensitivity and specificity when compared to matched patient upper respiratory specimens. We also evaluated the analytical sensitivity of each platform and determined the limit of detection of the Roche assay was four times lower than that of Agena for saliva specimens (390.6 v. 1,562.5 copies/mL). Furthermore, across individual target components of each assay, T2 and N2 targets had the lowest limits of detection for each platform, respectively. Together, we demonstrate that saliva represents an appropriate specimen for SARS-CoV-2 detection in two technologies that have high agreement and differ in analytical sensitivities overall and across individual component targets. The addition of saliva as an acceptable specimen and understanding the sensitivity for testing on these platforms can further inform public health measures for screening and detection to combat the COVID-19 pandemic.

Convalescent plasma treatment of severe COVID-19: A matched control study

BackgroundSince December 2019, Coronavirus Disease 2019 (COVID-19) has become a global pandemic, causing mass morbidity and mortality. Prior studies in other respiratory infections suggest that convalescent plasma transfusion may offer benefit to some patients. Here, the outcomes of thirty-nine hospitalized patients with severe to life-threatening COVID-19 who received convalescent plasma transfusion were compared against a cohort of retrospectively matched controls. MethodsPlasma recipients were selected based on supplemental oxygen needs at the time of enrollment and the time elapsed since the onset of symptoms. Recipients were transfused with convalescent plasma from donors with a SARS-CoV-2 (severe acute respiratory disease coronavirus 2) anti-spike antibody titer of 31:320 dilution. Matched control patients were retrospectively identified within the electronic health record database. Supplemental oxygen requirements and survival were compared between plasma recipients and controls. ResultsConvalescent plasma recipients were more likely than control patients to remain the same or have improvements in their supplemental oxygen requirements by post-transfusion day 14, with an odds ratio of 0.86 (95% CI: 0.75[~]0.98; p = 0.028). Plasma recipients also demonstrated improved survival, compared to control patients (log-rank test: p = 0.039). In a covariates-adjusted Cox model, convalescent plasma transfusion improved survival for non-intubated patients (hazard ratio 0.19 (95% CI: 0.05 [~]0.72); p = 0.015), but not for intubated patients (1.24 (0.33[~]4.67); p = 0.752). ConclusionsConvalescent plasma transfusion is a potentially efficacious treatment option for patients hospitalized with COVID-19; however, these data suggest that non-intubated patients may benefit more than those requiring mechanical ventilation.

Acute Kidney Injury in Hospitalized Patients with COVID-19

ImportancePreliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. ObjectiveTo provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. DesignObservational, retrospective study. SettingAdmitted to hospital between February 27 and April 15, 2020. ParticipantsPatients aged [≥]18 years with laboratory confirmed COVID-19 ExposuresAKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and MeasuresFrequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. ResultsA total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. Conclusions and RelevanceAKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19? FindingsIn this observational study of 3,235 hospitalized patients with COVID-19 in New York City, AKI occurred in 46% of patients and 20% of those patients required dialysis. AKI was associated with increased mortality. 44% of patients discharged alive had residual acute kidney disease. A machine learned predictive model using baseline features for dialysis requirement had an AUC Of 0.79. MeaningAKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge.

Analysis of hospitalized COVID-19 patients in the Mount Sinai Health System using electronic medical records (EMR) reveals important prognostic factors for improved clinical outcomes

ImportanceThere is an urgent need to understand patient characteristics of having COVID-19 disease and evaluate markers of critical illness and mortality. ObjectiveTo assess association of clinical features on patient outcomes. Design, Setting, and ParticipantsIn this observational case series, patient-level data were extracted from electronic medical records for 28,336 patients tested for SARS-CoV-2 at the Mount Sinai Health System from 2/24/ to 4/15/2020, including 6,158 laboratory-confirmed cases. ExposuresConfirmed COVID-19 diagnosis by RT-PCR assay from nasal swabs. Main Outcomes and MeasuresEffects of race on positive test rates and mortality were assessed. Among positive cases admitted to the hospital (N = 3,273), effects of patient demographics, hospital site and unit, social behavior, vital signs, lab results, and disease comorbidities on discharge and death were estimated. ResultsHispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to population base rates (p<2e-16); however, no differences in mortality rates were observed in the hospital. Outcome differed significantly between hospitals (Grays T=248.9; p<2e-16), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR=1.05 [95% CI, 1.04-1.06]; p=1.15e-32), oxygen saturation (HR=0.985 [95% CI, 0.982-0.988]; p=1.57e-17), care in ICU areas (HR=1.58 [95% CI, 1.29-1.92]; p=7.81e-6), and elevated creatinine (HR=1.75 [95% CI, 1.47-2.10]; p=7.48e-10), alanine aminotransferase (ALT) (HR=1.002, [95% CI 1.001-1.003]; p=8.86e-5) white blood cell (WBC) (HR=1.02, [95% CI 1.01-1.04]; p=8.4e-3) and body-mass index (BMI) (HR=1.02, [95% CI 1.00-1.03]; p=1.09e-2). Asthma (HR=0.78 [95% CI, 0.62-0.98]; p=0.031) was significantly associated with increased length of hospital stay, but not mortality. Deceased patients were more likely to have elevated markers of inflammation. Baseline age, BMI, oxygen saturation, respiratory rate, WBC count, creatinine, and ALT were significant prognostic indicators of mortality. Conclusions and RelevanceWhile race was associated with higher risk of infection, we did not find a racial disparity in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. We identified clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk and evaluate the impact on survival.

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions. One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.

Clinical Characteristics of Hospitalized Covid-19 Patients in New York City

BackgroundThe coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. MethodsDemographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. ResultsA total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (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 ug/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 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. ConclusionsThis is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

Differential ventilation using flow control valves as a potential bridge to full ventilatory support during the COVID-19 crisis

BackgroundIt has been projected that there will be too few ventilators to meet demand during the COVID-19 (SARS CoV-2) pandemic. Ventilator sharing has been suggested as a crisis standard of care strategy to increase availability of mechanical ventilation. The safety and practicality of shared ventilation in patients is unknown. We designed and evaluated a system whereby one mechanical ventilator can be used to simultaneously ventilate two patients who have different lung compliances using a custom manufactured flow control valve to allow for individual adjustment of tidal volume and airway pressure for each patient. MethodsThe system was first evaluated in a simulation lab using two human patient simulators under expected clinical conditions. It was then tested in an observational study of four patients with acute respiratory failure due to COVID-19. Two separately ventilated COVID-19 patients were connected to a single ventilator for one hour. This intervention was repeated in a second pair of patients. Ventilatory parameters (tidal volume, peak airway pressures, compliance) were recorded at five minute intervals during both studys. Arterial blood gases were taken at zero, thirty, and sixty minutes. The primary outcome was maintenance of stable acid-base status and oxygenation during shared ventilation. ResultsTwo male and two female patients, age range 32-56 yrs, participated. Ideal body weight and driving pressure were markedly different among patients. All patients demonstrated stable physiology and ventilation for the duration of shared ventilation. In one patient tidal volume was increased after 30 minutes to correct a respiratory acidosis. ConclusionsDifferential ventilation using a single ventilator and a split breathing circuit with flow control valves is possible. A single ventilator could feasibly be used to safely ventilate two COVID-19 patients simultaneously as a bridge to full ventilatory support. Summary StatementNot applicable.