Importance
Pulse oximetry guides triage and therapy decisions for COVID-19. Whether reported racial inaccuracies in oxygen saturation measured by pulse oximetry are present in patients with COVID-19 and associated with treatment decisions is unknown.


Objective
To determine whether there is differential inaccuracy of pulse oximetry by race or ethnicity among patients with COVID-19 and estimate the association of such inaccuracies with time to recognition of eligibility for oxygen threshold-specific COVID-19 therapies.


Design, Setting, and Participants
This retrospective cohort study of clinical data from 5 referral centers and community hospitals in the Johns Hopkins Health System included patients with COVID-19 who self-identified as Asian, Black, Hispanic, or White.


Exposures
Concurrent measurements (within 10 minutes) of oxygen saturation levels in arterial blood (SaO2) and by pulse oximetry (SpO2).


Main Outcomes and Measures
For patients with concurrent SpO2 and SaO2 measurements, the proportion with occult hypoxemia (SaO2<88% with concurrent SpO2 of 92%-96%) was compared by race and ethnicity, and a covariate-adjusted linear mixed-effects model was produced to estimate the association of race and ethnicity with SpO2 and SaO2 difference. This model was applied to identify a separate sample of patients with predicted SaO2 levels of 94% or less before an SpO2 level of 94% or less or oxygen treatment initiation. Cox proportional hazards models were used to estimate differences by race and ethnicity in time to recognition of eligibility for guideline-recommended COVID-19 therapies, defined as an SpO2 level of 94% or less or oxygen treatment initiation. The median delay among individuals who ultimately had recognition of eligibility was then compared.


Results
Of 7126 patients with COVID-19, 1216 patients (63 Asian [5.2%], 478 Black [39.3%], 215 Hispanic [17.7%], and 460 White [37.8%] individuals; 507 women [41.7%]) had 32 282 concurrently measured SpO2 and SaO2. Occult hypoxemia occurred in 19 Asian (30.2%), 136 Black (28.5%), and 64 non-Black Hispanic (29.8%) patients compared with 79 White patients (17.2%). Compared with White patients, SpO2 overestimated SaO2 by an average of 1.7% among Asian (95% CI, 0.5%-3.0%), 1.2% among Black (95% CI, 0.6%-1.9%), and 1.1% among non-Black Hispanic patients (95% CI, 0.3%-1.9%). Separately, among 1903 patients with predicted SaO2 levels of 94% or less before an SpO2 level of 94% or less or oxygen treatment initiation, compared with White patients, Black patients had a 29% lower hazard (hazard ratio, 0.71; 95% CI, 0.63-0.80), and non-Black Hispanic patients had a 23% lower hazard (hazard ratio, 0.77; 95% CI, 0.66-0.89) of treatment eligibility recognition. A total of 451 patients (23.7%) never had their treatment eligibility recognized, most of whom (247 [54.8%]) were Black. Among the remaining 1452 (76.3%) who had eventual recognition of treatment eligibility, Black patients had a median delay of 1.0 hour (95% CI, 0.23-1.9 hours; P = .01) longer than White patients. There was no significant median difference in delay between individuals of other racial and ethnic minority groups and White patients.


Conclusions and Relevance
The results of this cohort study suggest that racial and ethnic biases in pulse oximetry accuracy were associated with greater occult hypoxemia in Asian, Black, and non-Black Hispanic patients with COVID-19, which was associated with significantly delayed or unrecognized eligibility for COVID-19 therapies among Black and Hispanic patients. This disparity may contribute to worse outcomes among Black and Hispanic patients with COVID-19.

Racial and Ethnic Discrepancy in Pulse Oximetry and Delayed Identification of Treatment Eligibility Among Patients With COVID-19.

Background: Strength and muscle mass are predictors of relevant clinical outcomes in critically ill patients, but in hospitalized patients with COVID-19, it remains to be determined. In this prospective observational study, we investigated whether muscle strength or muscle mass are predictive of hospital length of stay (LOS) in patients with moderate to severe COVID-19 patients. Methods: We evaluated prospectively 196 patients at hospital admission for muscle mass and strength. Ten patients did not test positive for SARS-CoV-2 during hospitalization and were excluded from the analyses. Results: The sample comprised patients of both sexes (50% male) with a mean age (SD) of 59 (±15) years, body mass index of 29.5 (±6.9) kg/m2. The prevalence of current smoking patients was 24.7%, and more prevalent coexisting conditions were hypertension (67.7%), obesity (40.9%), and type 2 diabetes (36.0%). Mean (SD) LOS was 8.6 days (7.7); 17.0% of the patients required intensive care; 3.8% used invasive mechanical ventilation; and 6.6% died during the hospitalization period. The crude hazard ratio (HR) for LOS was greatest for handgrip strength comparing the strongest versus other patients (1.47 [95% CI: 1.07–2.03; P = 0.019]). Evidence of an association between increased handgrip strength and shorter hospital stay was also identified when handgrip strength was standardized according to the sex-specific mean and standard deviation (1.23 [95% CI: 1.06–1.43; P = 0.007]). Mean LOS was shorter for the strongest patients (7.5 ± 6.1 days) versus others (9.2 ± 8.4 days). Evidence of associations were also present for vastus lateralis cross-sectional area. The crude HR identified shorter hospital stay for patients with greater sex-specific standardized values (1.20 [95% CI: 1.03–1.39; P = 0.016]). Evidence was also obtained associating longer hospital stays for patients with the lowest values for vastus lateralis cross-sectional area (0.63 [95% CI: 0.46–0.88; P = 0.006). Mean LOS for the patients with the lowest muscle cross-sectional area was longer (10.8 ± 8.8 days) versus others (7.7 ± 7.2 days). The magnitude of associations for handgrip strength and vastus lateralis cross-sectional area remained consistent and statistically significant after adjusting for other covariates. Conclusions: Muscle strength and mass assessed upon hospital admission are predictors of LOS in patients with moderate to severe COVID-19, which stresses the value of muscle health in prognosis of this disease.

Muscle strength and muscle mass as predictors of hospital length of stay in patients with moderate to severe COVID-19: a prospective observational study.

During the COVID-19 2020 outbreak, a large body of data has been provided on general management and outcomes of hospitalized COVID-19 patients. Yet, relatively little is known on characteristics and outcome of patients managed in Internal Medicine Units (IMU). To address this gap, the Italian Society of Internal Medicine has conducted a nationwide cohort multicentre study on death outcome in adult COVID-19 patients admitted and managed in IMU. This study assessed 3044 COVID-19 patients at 41 referral hospitals across Italy from February 3rd to May 8th 2020. Demographics, comorbidities, organ dysfunction, treatment, and outcomes including death were assessed. During the study period, 697 patients (22.9%) were transferred to intensive care units, and 351 died in IMU (death rate 14.9%). At admission, factors independently associated with in-hospital mortality were age (OR 2.46, p = 0.000), productive cough (OR 2.04, p = 0.000), pre-existing chronic heart failure (OR 1.58, p = 0.017) and chronic obstructive pulmonary disease (OR 1.17, p = 0.048), the number of comorbidities (OR 1.34, p = 0.000) and polypharmacy (OR 1.20, p = 0.000). Of note, up to 40% of elderly patients did not report fever at admission. Decreasing PaO2/FiO2 ratio at admission was strongly inversely associated with survival. The use of conventional oxygen supplementation increased with the number of pre-existing comorbidities, but it did not associate with better survival in patients with PaO2/FiO2 ratio < 100. The latter, significantly benefited by the early use of non-invasive mechanical ventilation. Our study identified PaO2/FiO2 ratio at admission and comorbidity as the main alert signs to inform clinical decisions and resource allocation in non-critically ill COVID-19 patients admitted to IMU.

/pdf/clinical-factors-associated-with-death-in-3044-covid-19-5emuzsfb8w.pdf

Clinical factors associated with death in 3044 COVID-19 patients managed in internal medicine wards in Italy: results from the SIMI-COVID-19 study of the Italian Society of Internal Medicine (SIMI).

Throughout 2020, the coronavirus disease 2019 (COVID‐19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID‐19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID‐19 is still needed because of the limited healthcare resources available.

Prediction models for severe manifestations and mortality due to COVID‐19: A systematic review

PurposeThe purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes.PatientsPatients from this study are f...

https://journals.sagepub.com/doi/pdf/10.1177/21501327211018559

Body Mass Index, Multi-Morbidity, and COVID-19 Risk Factors as Predictors of Severe COVID-19 Outcomes.

Background Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. Objective To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. Design Retrospective observational cohort study. Settings Five hospitals in Maryland and Washington, D.C. Patients Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. Measurements A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. Results Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. Limitation The SCARP tool was developed by using data from a single health system. Conclusion Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. Primary funding source Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

Jiyang Wen

Papers

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.