Overt and occult hypoxemia in patients hospitalized with novel coronavirus disease 2019

Abstract

Background: Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously, but occult hypoxemia can occur in Black patients because the technique is affected by skin color. Oxygen dissociation curves allow non-invasive estimation of P/F ratios (ePFR) but this approach remains unproven. Research Question: Can ePFRs measure overt and occult hypoxemia? Study Design and methods: We retrospectively studied COVID-19 hospital encounters (n=5319) at two academic centers (University of Virginia [UVA] and Emory University). We measured primary outcomes (death or ICU transfer within 24 hours), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score (NEWS) and Sepsis-3). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AOR) and area under receiver operating characteristics curves (AUROC). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test. Results: Overt hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; p<0.01) with better discrimination (AUROC: 0.76 [UVA]; 0.71 [Emory]) than NEWS (AUROC: 0.70 [UVA]; 0.70 [Emory]) or Sepsis-3 (AUROC: 0.68 [UVA]; 0.65 [Emory]). We found racial differences consistent with occult hypoxemia. Black patients had better apparent oxygenation (K-S distance: 0.17 [both sites]; p<0.01) but, for comparable ePFRs, worse outcomes than other patients (AOR: 2.2 [UVA]; 1.2 [Emory], p<0.01). Interpretation: The ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models like NEWS and Sepsis-3. By accounting for biased oximetry as well as clinicians' real-time responses to it (supplemental oxygen adjustment), ePFRs may enable statistical modelling of racial disparities in outcomes attributable to occult hypoxemia.

Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study

“…Additionally, we extracted fraction of inspired oxygen and supplemental oxygen flowrate and combined it with peripheral oxygen saturation to estimate the PO 2 to FiO 2 ratio (ePFR) following the methods of Gadrey, et al . ( 19 , 20 ). Eleven frequently measured laboratory measurements (serum sodium, potassium, chloride, bicarbonate, blood urea nitrogen (BUN), creatinine, glucose, calcium, white blood cell count, hematocrit, platelet count) were extracted from the electronic data warehouse.…”

“…We calculated, in 30-minute windows with 50% overlap, the following 18 measures: the mean and standard deviation of heart rate (HR), respiratory rate (RR), pulse oximetry (SO2); mean non-invasive systolic and diastolic blood pressure, or invasive blood pressure in its absence (SBP and DBP); the three pairwise cross-correlations between HR, RR, and SO2; standard deviation of heart inter-beat intervals (sRRI); local dynamics score (LDs) and local dynamics density (LDd) of heart inter-beat intervals (12); coefficient of sample entropy (COSEn) (13); the slope of log variance vs. log scale between scales 4 and 12 for detrended fluctuation analysis of heart Vital signs (heart rate, respiratory rate, oxygen saturation, blood pressure, and temperature) were extracted from flowsheets. Additionally, we extracted fraction of inspired oxygen and supplemental oxygen flowrate and combined it with peripheral oxygen saturation to estimate the PO 2 to FiO 2 ratio (ePFR) following the methods of Gadrey,et al (19,20). Eleven frequently measured laboratory measurements (serum sodium, potassium, chloride, bicarbonate, blood urea nitrogen (BUN), creatinine, glucose, calcium, white blood cell count, hematocrit, platelet count) were extracted from the electronic data warehouse.…”