IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

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Static orientational factor for two-dimensional system with moments randomly distributed on cone with * = 58.4°.~15% in the deconvolutions. Moreover, the percent discrepancies are nearly constant, and the linearity of a plot similar to that in Figure 5 is hardly affected. Hence, our conclusion that the two-dimensional two-particle theory for (?(r) works well for all of the densities shown in Figure 5 is not sensitive to the model assumed for the orientational distribution of transition moments.Polarized fluorescence profiles obtained at higher chromophore densities than those shown in Table I show an interesting anomaly in that the fluorescence components /E, I_ do not converge together at long times. At 1511 X 10"6 chromophores/Á2, the two profiles intersect after ~1 ns, and I± decays more slowly than It at long times. At 2932 X 10"6 chromophores/Á2,1± remains below /E at all times, and the phenomenological decay times are markedly reduced. These ODRB densities are extremely large (the latter density corresponds to packing and average of ~16 chromophores per circle of radius R0 = 44.7 Á), and these polarization effects may result from excimer formation.We showed earlier8 that the three-dimensional two-particle theory of Huber et al.1 furnishes an accurate description of transport in solution for reduced concentrations up to ~3 in the absence of orientational correlation. Figure 5 indicates that the two-dimensional analogue (eq 3) of the Huber theory is valid for reduced ODRB densities CD up to ~5. This result is of particular interest, because density expansions of (?(Z) converge more slowly in systems of lower dimensionality. Hence, one may expect the demonstrated validity of the two-particle theory to be exhibited a fortieri in random three-dimensional systems; the system dimensionality does not pose fundamental problems in our understanding of singlet excitation transport.

Diffraction techniques have shown that the crystal structure of naphthalene has a unit cell with Ci symmetry. These studies were unable, however, to resolve any departure of the molecular structure from the D2h symmetry observed in the gaseous state. We found recently that the solid-state 13C-nuclear magnetic resonance (NMR) chemical shifts for naphthalene exhibit the Ci symmetry of the unit cell. If these chemical-shift data reflect structural distortions of the molecule, rather than simply intermolecular effects on the shifts owing to the Ci symmetry of the environment of each molecule, one could assert that the NMR data are able to reveal structural information beyond the limits of the diffraction methods. Here we show that this is the case by performing ab initio quantum-mechanical calculations of the 13C chemical shifts for naphthalene, and their derivatives, with respect to structural parameters. We find that intermolecular shift terms (which of necessity exhibit Ci symmetry) can account for only about 30% of the maximum deviations from D2h symmetry; the remainder must therefore result from structural distorations of the molecules below D2h symmetry. This sensitivity of NMR chemical shifts to very small changes in molecular structure opens up the possibility of using solid-state NMR along with quantum-chemical methods to refine structural parameters obtained from diffraction methods.