Researching COVID to enhance recovery (RECOVER) adult study protocol: Rationale, objectives and design (preprint)

Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged [≥]18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.

Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data (preprint)

RationalePrognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. ObjectivesThe study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. MethodsHierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of ARDS, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models predictions of risk. Measurements and Main ResultsTest performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In both development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, CRP, LDH, and D-dimer were often found to be important in the assignment of risk label. ConclusionsRisk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.

Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials (preprint)

ObjectivesTo investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials. DesignPhenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality. Setting and participantsData from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200). Outcome measure14-day mortality, with a secondary outcome of 28-day mortality. ResultsHydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes--an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%. ConclusionsThe assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.

Diagnostic utility of a Ferritin-to-Procalcitonin Ratio to differentiate patients with COVID-19 from those with Bacterial Pneumonia: A multicenter study (preprint)

ImportanceThere is a need to develop tools to differentiate COVID-19 from bacterial pneumonia at the time of clinical presentation before diagnostic testing is available. ObjectiveTo determine if the Ferritin-to-Procalcitonin ratio (F/P) can be used to differentiate COVID-19 from bacterial pneumonia. DesignThis case-control study compared patients with either COVID-19 or bacterial pneumonia, admitted between March 1 and May 31, 2020. Patients with COVID-19 and bacterial pneumonia co-infection were excluded. SettingA multicenter study conducted at three hospitals that included UCHealth and Phoebe Putney Memorial Hospital in the United States, and Yichang Central Peoples Hospital in China. ParticipantsA total of 242 cases with COVID-19 infection and 34 controls with bacterial pneumonia. Main Outcomes and MeasuresThe F/P in patients with COVID-19 or with bacterial pneumonia were compared. Receiver operating characteristic analysis determined the sensitivity and specificity of various cut-off F/P values for the diagnosis of COVID-19 versus bacterial pneumonia. ResultsPatients with COVID-19 pneumonia had a lower mean age (57.11 vs 64.4 years, p=0.02) and a higher BMI (30.74 vs 27.15 kg/m2, p=0.02) compared to patients with bacterial pneumonia. Cases and controls had a similar proportion of women (47% vs 53%, p=0.5) and COVID-19 patients had a higher prevalence of diabetes mellitus (32.6% vs 12%, p=0.01). The median F/P was significantly higher in patients with COVID-19 (4037.5) compared to the F/P in bacterial pneumonia (802, p<0.001). An F/P [≥] 877 used to diagnose COVID-19 resulted in a sensitivity of 85% and a specificity of 56%, with a positive predictive value of 93.2%, and a likelihood ratio of 1.92. In multivariable analyses, an F/P [≥] 877 was associated with greater odds of identifying a COVID-19 case (OR: 11.27, CI: 4-31.2, p<0.001). Conclusions and RelevanceAn F/P [≥] 877 increases the likelihood of COVID-19 pneumonia compared to bacterial pneumonia. Further research is needed to determine if obtaining ferritin and procalcitonin simultaneously at the time of clinical presentation has improved diagnostic value. Additional questions include whether an increased F/P and/or serial F/P associates with COVID-19 disease severity or outcomes.