Interim Analysis of Risk Factors for Severe Outcomes among a Cohort of Hospitalized Adults Identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)

48

As of May 15, 2020, the United States has reported the greatest number of coronavirus 49 disease 2019 (COVID-19) cases and deaths globally. 50

To describe risk factors for severe outcomes among adults hospitalized with COVID-19. 51 Design: Cohort study of patients identified through the Coronavirus Disease 2019-Associated 52 Hospitalization Surveillance Network. 53 Setting: 154 acute care hospitals in 74 counties in 13 states. 54 Patients: 2491 patients hospitalized with laboratory-confirmed COVID-19 during March 1–May 2, 2020. 55 Measurements: Age, sex, race/ethnicity, and underlying medical conditions. 56

Ninety-two percent of patients had ≥1 underlying condition; 32% required intensive care unit 57 (ICU) admission; 19% invasive mechanical ventilation; 15% vasopressors; and 17% died during 58 hospitalization. Independent factors associated with ICU admission included ages 50-64, 65-74, 75-84 59 and ≥85 years versus 18-39 years (adjusted risk ratio (aRR) 1.53, 1.65, 1.84 and 1.43, respectively); male 60 sex (aRR 1.34); obesity (aRR 1.31); immunosuppression (aRR 1.29); and diabetes (aRR 1.13). 61 Independent factors associated with in-hospital mortality included ages 50-64, 65-74, 75-84 and ≥85 62 years versus 18-39 years (aRR 3.11, 5.77, 7.67 and 10.98, respectively); male sex (aRR 1.30); 63 immunosuppression (aRR 1.39); renal disease (aRR 1.33); chronic lung disease (aRR 1.31); cardiovascular 64 disease (aRR 1.28); neurologic disorders (aRR 1.25); and diabetes (aRR 1.19). Race/ethnicity was not 65 associated with either ICU admission or death. 66

Data were limited to patients who were discharged or died in-hospital and had complete 67 chart abstractions; patients who were still hospitalized or did not have accessible medical records were 68 excluded. 69 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 3

In-hospital mortality for COVID-19 increased markedly with increasing age. These data help 70 to characterize persons at highest risk for severe COVID-19-associated outcomes and define target 71 groups for prevention and treatment strategies. 72 73 Funding Source: This work was supported by grant CK17-1701 from the Centers of Disease Control and 74 Prevention through an Emerging Infections Program cooperative agreement and by Cooperative 75 Agreement Number NU38OT000297-02-00 awarded to the Council of State and Territorial 76 Epidemiologists from the Centers for Disease Control and Prevention. 77 78 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 4

79 In December 2019, an outbreak of a novel coronavirus disease, termed coronavirus disease-80 2019 (COVID-19), was reported in China caused by a newly identified coronavirus, severe acute 81 respiratory syndrome coronavirus-2 (SARS-CoV-2). Since then, approximately 4.5 million cases of 82 COVID-19 have been reported globally (1). As of May 15, approximately 1.4 million cases, including 83 nearly 86,000 deaths, have been reported in the United States, and case counts continue to rise (1) with 84 evidence of widespread community transmission (2). 85 Previous reports from China, Italy, and New York City have demonstrated that hospitalized 86 patients are generally older and have underlying medical conditions, such as hypertension and diabetes 87 (3-5). These studies have also found that older patients and those with certain underlying medical 88 conditions like diabetes were at higher risk for severe outcomes (3, 6, 7). Among cases reported to the 89 U.S. Centers for Disease Control and Prevention (CDC) from local and state health departments, the 90 prevalence of underlying medical conditions increased as severity of infections increased (8, 9), although 91 findings were limited by missing or incomplete information. Questions remain about the independent 92 association of sex, race/ethnicity and specific underlying conditions with severe outcomes among 93 persons hospitalized with COVID-19, after adjusting for age and other important potential confounders. 94 Comprehensive data on U.S. patients with severe COVID-19 infections are needed to better 95 inform clinicians’ understanding of groups at risk for poor outcomes and to inform current prevention 96 efforts and future interventions. We rapidly implemented population-based surveillance for laboratory-97 confirmed COVID-19-associated hospitalizations, collecting clinical data from hospitalized patients in 154 98 hospitals in 13 states since March 1, 2020. In this interim analysis restricted to patients who were 99 discharged or died in-hospital and had completed medical chart abstractions, we describe the 100 characteristics of U.S. adults hospitalized with COVID-19 and assess risk factors for intensive care unit 101 (ICU) admission and in-hospital mortality. 102 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 5 103

104 Surveillance Overview 105 The Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET) has 106 been previously described (10). Eligible COVID-19-associated hospitalizations occurred among persons 107 who (1) resided in a pre-defined surveillance catchment area; and (2) had a positive SARS-CoV-2 test 108 within 14 days prior to or during hospitalization. Hospitalization was defined as admission to an 109 inpatient ward for any length of time, an observation unit stay for ≥24 hours, or a combined stay in an 110 emergency department and observation unit for ≥24 hours. 111 COVID-NET surveillance occurs in acute care hospitals within 99 counties located in 14 states 112 (California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan, Minnesota, New Mexico, New 113 York, Ohio, Oregon, Tennessee, and Utah), covering a catchment population of approximately 32 million 114 persons (~10% of the U.S. population). Although COVID-NET includes all age groups, for this analysis, we 115 excluded children <18 years of age due to small counts (n=101) and one surveillance site (Iowa) for 116 which medical chart abstractions were not conducted on identified cases. We also excluded patients 117 who were still hospitalized at the time of this analysis and all patients for whom medical chart 118 abstractions had not yet been completed. Because the COVID-19 pandemic limited the ability of 119 surveillance officers to access medical records at facilities, patients were more likely to be included in 120 this analysis if they were hospitalized at facilities that provided surveillance officers remote chart access, 121 participated in Health Information Exchanges, or were able to mail or fax records. COVID-NET 122 surveillance was initiated for cases with hospital admission on or after March 1, 2020. 123 Laboratory-confirmed COVID-19-associated hospitalizations were identified using laboratory 124 and reportable condition databases, hospital infection control databases, electronic medical records, 125 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 6 and/or review of hospital discharge records. Laboratory tests were ordered at the discretion of the 126 treating healthcare provider. 127 Medical chart reviews for demographic and clinical data were conducted by trained surveillance 128 officers using a standard case report form. Underlying medical conditions were categorized into major 129 groups (Appendix Table 1). Obesity and severe obesity were defined as a calculated body mass index 130 (BMI) ≥30 kg/m2 and BMI ≥40 kg/m2, respectively. Chest radiograph results were obtained from the 131 radiology reports and not from review of the original radiograph. We defined severe outcomes as either 132 ICU admission or in-hospital mortality. We hypothesized that increasing age and underlying medical 133 conditions would be associated with an increased risk of ICU admission and in-hospital mortality. 134 135 Statistical Analysis 136 After the exclusions noted above, we included adults hospitalized within 154 acute care 137 hospitals in 74 counties in 13 states with an admission date during March 1–May 2, 2020 who had either 138 been discharged from the hospital or died during hospitalization and had complete medical chart 139 abstractions. We calculated proportions using the number of patients with data available on each 140 characteristic as the denominator. 141 To construct multivariable models for ICU admission and in-hospital mortality, we first assessed 142 collinearity among underlying medical condition categories and outpatient use of ACE-inhibitors and 143 ARBs. We examined the association of demographic factors, underlying medical conditions, and 144 outpatient use of ACE-inhibitors and ARBs with ICU admission and in-hospital death using chi square 145 tests. Variables considered for inclusion in the final models included current or former smoker, 146 hypertension, obesity, diabetes, chronic lung disease (CLD), cardiovascular disease (CVD) (excluding 147 hypertension), neurologic disorders, renal disease, immunosuppression, gastrointestinal/liver disease, 148 hematologic conditions, rheumatologic/autoimmune conditions, and outpatient use of ACE-inhibitors or 149 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 7 ARBs. All multivariable models included age categorized into the following groups (18–39, 40–49, 50–150 64, 65–74, 75–84, and ≥85 years), sex, and race/ethnicity. Other variables with p-values <0.10 in 151 bivariate analyses were included in the multivariable analyses. Log-linked Poisson generalized 152 estimating equations regression with an exchangeable correlation matrix (11, 12), clustered by site, was 153 used to generate adjusted risk ratios (aRR), 95% confidence intervals (CI), and two-sided p-values for the 154 risk of ICU admission and in-hospital death. We also constructed separate multivariable models to 155 examine the association between the number of underlying medical conditions and ICU admission or in-156 hospital death. Two-sided p-values <0.05 were considered statistically significant. All analyses were 157 performed using the SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). 158 These data were collected as part of routine public health surveillance and determined to be 159 non-research by CDC. Participating sites obtained approval for the COVID-NET surveillance protocol 160 from their respective state and local IRBs, as required. 161 162 Role of the Funding Source 163 This work was supported by grant CK17-1701 from the CDC through an Emerging Infections 164 Program cooperative agreement and by cooperative agreement NU38OT000297-02-00 awarded to the 165 Council of State and Territorial Epidemiologists from the CDC. 166 167

168 A total of 16,318 hospitalized COVID-19 patients were reported to COVID-NET with an admission 169 date during March 1–May 2, 2020. After excluding 101 pediatric patients, 74 patients from Iowa where 170 detailed medical chart abstractions are not conducted, and 13,652 patients who were either still 171 hospitalized or did not yet have completed medical chart abstractions, 2,491 (15%) COVID-19-associated 172 hospitalized adults from 13 surveillance sites were included in this analysis (Figure 1). Patients came 173 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 8 from 43% (154/357) of the acute care hospitals included in COVID-NET surveillance across the 13 174 surveillance sites (Appendix Table 2). The percentage of facilities contributing data out of the total 175 number of facilities by site ranged from 19% to 100%. The median age of included and excluded 176 patients (62 vs. 63 years, respectively) was similar (Appendix Table 3). The highest proportion of 177 patients included in this analysis were from Minnesota (20%), Tennessee (20%), New York (12%), and 178 Maryland (10%), and Connecticut (9%) (Appendix Table 3). 179 180 Characteristics of hospitalized patients with COVID-19 181 Among the 2,491 hospitalized adults, median age was 62 years (interquartile range (IQR), 50–182 75), and almost 75% were ≥50 years (Table 1). Forty-seven percent of patients (n=1178/2490) were 183 non-Hispanic whites, 30% non-Hispanic blacks (n=755/2490), and 12% Hispanics (n=306/2490). Nearly 184 one-third of patients were current or former smokers. 185 Almost all patients (n=2278/2489, 92%) had ≥1 underlying medical condition, with hypertension 186 (n=1428/2488, 57%), obesity (n=1154/2332, 50%), and chronic metabolic disease (n=1024/2486, 187 41%) most frequently documented. Among patients with chronic metabolic disease, 80% (n=819/1024) 188 had diabetes mellitus; hypertension alone was documented in only 4% (n=91/2490) of patients. 189 Seventeen percent (n=316/1892) took ACE-inhibitors, and 14% (n=257/1895) took ARBs prior to 190 hospitalization. The proportion of patients with any documented underlying medical condition 191 increased with age (p<0.05)(Figure 2A). Prevalence of CLD, neurologic conditions, obesity, and renal 192 disease varied between males and females (p<0.05, Figure 2B). CVD, CLD, and neurologic conditions 193 were more prevalent among non-Hispanic whites, while diabetes, hypertension, obesity and renal 194 disease were more common among non-Hispanic blacks (p<0.05, Figure 2C). 195 Cough (75%), fever or chills (74%), and shortness of breath (70%) were commonly 196 documented symptoms at admission (Table 2 and Appendix Table 4). Gastrointestinal symptoms, 197 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 9 including nausea, vomiting, and diarrhea, were documented in almost 30% of patients. Median length 198 of hospitalization was 6 days (IQR, 3–11), and median days from symptom onset to hospital admission 199 was 6 days (IQR, 3–8). Median values of initial vital signs were within normal range, except for elevated 200 blood pressure (Table 2). Thirty-three individuals had a pathogen detected from positive blood cultures 201 (Appendix Table 5). Viral co-detections from respiratory specimens were rare among those who were 202 tested (n=38/1549, 2.5%) (Appendix Table 6). Among 1932 patients with chest radiograph 203 performed, 92% (n=1769) were documented as abnormal with infiltrate or consolidation (n=1574/1932, 204 81%) documented most frequently (Appendix Table 7). Ninety-five percent (n=540/566) of patients with 205 chest computerized tomography (CT) had abnormal findings, and ground glass opacity 206 was documented in 62% (n=350/566) (Appendix Table 7). 207 Forty-five percent (n=1125/2482) of patients received investigational medication regimens for 208 COVID-19 during hospitalization (Table 2). The most common regimens included hydroxychloroquine 209 (n=1065/2479, 43%) and the combination of azithromycin and ≥1 COVID-19 treatment (n=725/2479, 210 29%) (non-mutually exclusive categories). The most frequent discharge diagnoses recorded in hospital 211 discharge summaries were pneumonia (n=1395/2485, 56%), acute respiratory failure (n=999/2487, 212 40%), acute renal failure (n=456/2,485, 18%), and sepsis (n=443/2,479, 18%). 213 Thirty-two percent (n=798/2490) of patients required ICU admission, with a median length of 214 ICU stay of 6 days (range, 1-41; IQR, 2–11) (Table 2). Median days from symptom onset to ICU 215 admission was 7 days (range, 0–25; IQR, 4–10), and median days from hospital admission to ICU 216 admission was 1 day (range, 0–19; IQR, 0–2). Among 2,489 hospitalized patients, the highest respiratory 217 support received was invasive mechanical ventilation in 19% (n=462), bilevel positive airway pressure 218 (BIPAP) or continuous positive airway pressure (CPAP) in 3% (n=82), and high flow nasal cannula (HFNC) 219 in 7% (n=170). Fifty-three percent (n=246/462) of patients that received invasive mechanical ventilation 220 died in-hospital (median age, 71 years; IQR, 62–79); the proportion of patients receiving mechanical 221 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 10 ventilation who died increased with age (p<0.0001). Vasopressors were used in 15% (n=373/2486) of 222 patients, while renal replacement therapy was used in 5% (n=115/2487). As age increased, so did 223 the proportion of patients who required ICU admission, invasive mechanical ventilation, and 224 vasopressors (p<0.05, Figure 3A). Males were admitted to the ICU and treated with invasive mechanical 225 ventilation, HFNC, or vasopressors more frequently than females (p<0.05) (Figure 3B). Non-Hispanic 226 whites more frequently received BIPAP, CPAP or HFNC (p<0.05, Figure 3C). 227 Overall, seventeen percent (n=420/2490) of patients died during hospitalization (Table 2). The 228 proportion of patients who died increased with increasing age groups, ranging from 3% among 18–49 229 years to 10% among 50–64 years to 29% among ≥65 years (Figure 3A). Males died more frequently 230 compared to females (p<0.05) (Figure 3B), as did non-Hispanic whites compared to other 231 race/ethnicities (p<0.05, Figure 3C). 232 Among 420 patients who died, median age was 76 years (range, 24–97; IQR, 66–85); 58% 233 (n=244) were male; 71% (n=299) were admitted to the ICU; and 59% (n=246) received invasive 234 mechanical ventilation. The median length of hospitalization among patients who died was 7 days 235 (range, 0–40; IQR, 4–12). 236 237 Risk factors for ICU admission and death 238 Factors independently associated with ICU admission included age 50–64 years (adjusted risk 239 ratio (aRR) = 1.53; 95% confidence interval (CI), 1.28 to 1.83); 65–74 years (aRR = 1.65; CI, 1.34 to 2.03); 240 75–84 years (aRR = 1.84; CI, 1.60 to 2.11); ≥85 years (aRR = 1.43; CI, 1.00 to 2.04); male sex (aRR = 1.34; 241 CI, 1.20 to 1.50); obesity (aRR = 1.31; CI, 1.16 to 1.47); diabetes (aRR = 1.13; CI, 1.03 to 1.24); and 242 immunosuppression (aRR = 1.29; CI, 1.13 to 1.47) (Table 3A). 243 Independent factors associated with in-hospital mortality included age 50–64 years (aRR = 3.11; 244 CI 1.50 to 6.46); age 65–74 years (aRR = 5.77; CI, 2.64 to 12.64); age 75–84 years (aRR = 7.67; CI, 3.35 to 245 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 11 17.59); age ≥85 years (aRR = 10.98; CI, 5.09 to 23.69); male sex (aRR = 1.30; CI, 1.14 to 1.49); diabetes 246 (aRR = 1.19; CI, 1.01 to 1.40); CLD (aRR = 1.31; CI, 1.13 to 1.52); CVD (aRR = 1.28; CI, 1.03 to 1.58); 247 neurologic disorders (aRR = 1.25; CI, 1.04 to 1.50); renal disease (aRR = 1.33; CI, 1.10 to 1.61); and 248 immunosuppression (aRR = 1.39; CI, 1.13 to 1.70) (Table 3B). 249 Having ≥3 underlying medical conditions was significantly associated with higher risk of 250 ICU admission and death after adjusting for age group, sex, and race/ethnicity (Appendix Table 8). 251 252

253 Using a geographically diverse, multi-site, population-based U.S. surveillance system, we found 254 that among adults hospitalized with laboratory-confirmed COVID-19, almost one-third required ICU 255 admission, 19% received invasive mechanical ventilation, and 17% died during hospitalization. About 256 75% of patients were ≥50 years, and >90% had underlying medical conditions. Older age, being male, 257 and the presence of certain underlying medical conditions were associated with a higher risk of ICU 258 admission and in-hospital mortality. Race/ethnicity was not independently associated with either 259 outcome among hospitalized patients. This information can alert healthcare providers to patients at 260 greatest risk of severe outcomes and help target prevention strategies and future interventions. 261 In a published COVID-NET analysis, we found that when comparing the racial/ethnic distribution 262 of residents of the surveillance catchment areas to the racial/ethnic distribution of COVID-19-associated 263 hospitalizations, non-Hispanic blacks were disproportionately hospitalized with COVID-19 compared to 264 non-Hispanic whites (10). In this analysis, however, we found that once hospitalized, non-Hispanic 265 blacks did not have increased risk of poorer outcomes compared to other race/ethnicities after adjusting 266 for age and underlying conditions. In a preprinted article of U.S. Veterans seeking care at VA Hospitals, 267 Rentsch et al. found no association between black race and ICU admission (13). Similarly, a large study 268 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 12 of patients hospitalized in New York City did not find race/ethnicity to be associated with ICU admission 269 or death (4). 270 COVID-19-associated hospitalizations, ICU admissions, and deaths have been shown to occur 271 more frequently with increasing age (6, 9, 14). In our study, age ≥65 years was the strongest 272 independent predictor of ICU admission and in-hospital mortality. Persons aged 75–84 years had the 273 highest the risk of ICU admission compared to 18-49 years old, and those ≥85 years experienced 11 274 times the risk of death. These findings are similar to other studies from China, Europe, and the United 275 States (4, 9, 14-17). Our data provide support that older persons are particularly vulnerable to severe 276 COVID-19 disease and should be targeted for aggressive preventive measures (8). 277 Being male was associated with a higher risk of ICU admission and death after adjusting for age, 278 race/ethnicity and underlying conditions. Other studies have similarly shown male sex to be associated 279 with COVID-19-associated hospitalizations (4, 18), ICU admissions (19), and need for mechanical 280 ventilation (20). 281 Similar to other U.S. studies, we found that nearly all hospitalized patients with COVID-19 had at 282 least one underlying medical condition (4). In contrast, underlying medical conditions were documented 283 in only 25–50% of hospitalized cases from China (3, 21). Our analysis further demonstrated that a higher 284 number of underlying medical conditions increased the risk of ICU admission (1.3 times the risk in 285 persons with ≥3 vs. no underlying condition) and in-hospital mortality (1.8 times the risk in persons with 286 ≥3 vs. no underlying condition). 287 In a retrospective case study among 1590 laboratory-confirmed hospitalized COVID-19 cases in 288 575 Chinese hospitals, Guan et al. found that after adjusting for age and smoking status, chronic 289 obstructive pulmonary disease, diabetes, hypertension and malignancy were risk factors for a composite 290 endpoint of ICU admission, invasive mechanical ventilation, and death (3). Similarly, we found an 291 association between underlying medical conditions and severe outcomes, with diabetes, CLD, CVD, 292 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 13 neurologic disease, renal disease and immunosuppression associated with in-hospital death, and 293 diabetes, obesity, and immunosuppression associated with ICU admission. While hypertension was 294 highly prevalent in our patient population, it was not associated with ICU admission or death. Additional 295 studies are needed to determine whether hypertension, which is also highly prevalent in the U.S. 296 population, increases the risk of COVID-19-associated hospitalizations and whether the duration of 297 hypertension and the degree to which it is controlled impact the risk for severe COVID-19 disease. 298 Similarly, the associations between the duration and degree of glycemic control in diabetes and severity 299 of COVID-19 disease require further investigation. Obesity, which was also highly prevalent in this 300 cohort, imparted increased risk for ICU admission, but not death. This finding may, in part, be explained 301 by a trend of decreasing obesity prevalence with increasing age, which was a strong risk factor for 302 mortality. Healthcare providers should be aware of these findings to appropriately triage and manage 303 patients with high-risk conditions that may either increase risk for hospitalization or poorer outcomes 304 once hospitalized (22, 23). 305 We collected data on initial symptoms, vital signs and laboratory values to characterize disease 306 severity at admission. While approximately 70% of patients had shortness of breath at admission, the 307 median oxygen saturation at admission was 94% on room air. Other admission vital signs and laboratory 308 values were also largely within normal ranges. Because we did not collect data on vital signs or 309 laboratory values during the hospital course, we may not have fully captured the onset of clinical 310 deterioration that has been reported during the second week after illness onset (24). We limited our 311 analysis to patients that had either been discharged or died in-hospital and found that 15% of patients 312 received vasopressor support, and 19% received invasive mechanical ventilation. Other U.S. studies 313 have found that up to 32% of hospitalized patients have received vasopressors and 29–33% have 314 received invasive mechanical ventilation (19, 20), though some of these studies included patients who 315 were still hospitalized at the time of analysis. In our study, 53% of patients requiring mechanical 316 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 14 ventilation died, which is higher than the 36% reported in a recent study from New York City (4). In 317 general, the proportion of patients with severe outcomes was higher in the United States than reported 318 from China (6). Differences between patients’ outcomes in the United States and China may reflect 319 differences in clinical practices or varying thresholds for hospitalization (18). These proportions of 320 severe outcomes among U.S. patients are also generally higher than those found in U.S. adults 321 hospitalized with seasonal influenza (25, 26). Our findings may help to inform resource planning and 322 allocation in healthcare facilities during the COVID-19 pandemic. 323 There are several limitations to our analysis. First, it is likely that not all COVID-19-associated 324 hospitalizations were captured because of the lack of widespread testing capability during the study 325 period and because identification of COVID-19 patients was largely reliant on clinician-directed testing. 326 Second, clinical practices and availability of specific interventions may differ across hospitals, which 327 might have influenced findings. Third, COVID-NET is an ongoing surveillance system, and only 15% of 328 the 16,318 COVID-19 hospitalized patients were included, representing those who were discharged or 329 died in-hospital during March 1–May 2, 2020 and for whom medical records were available and chart 330 abstractions were completed. These restrictions may have resulted in selection bias. However, there 331 was no difference in the age and sex distribution between cases included and excluded from the 332 analysis. The geographic distribution of cases included versus excluded from this analysis differed, 333 which may have impacted the racial and ethnic distribution of cases included in this analysis as 334 compared to the racial and ethnic distribution of the surveillance catchment population; however, as we 335 do not yet have complete data on race/ethnicity for all identified cases, we were not able to assess this 336 further. Nevertheless, COVID-NET encompasses a large geographic area with multiple hospitals and 337 likely offers a more racially and ethnically diverse patient population compared to other single-center or 338 state-based studies. Lastly, small counts limited our ability to determine risk factors for severe 339 outcomes among all racial and ethnic groups. COVID-NET data will become more robust as additional 340 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 15 medical chart reviews are completed and may allow further investigation within these racial and ethnic 341 groups over time. 342 Based on preliminary findings from this multi-site, geographically diverse study, a high 343 proportion of patients hospitalized with COVID-19 received aggressive interventions and had poor 344 outcomes. Increasing age was the strongest predictor of in-hospital mortality. Prevention strategies, 345 such as social distancing and rigorous hand hygiene, are key to minimizing the risk of infection in high-346 risk patients. These data help to characterize persons at highest risk for severe COVID-19-associated 347 disease in the United States and to define target groups for future prevention and treatment strategies 348 as they become available. 349 350 ACKNOWLEDGMENTS 351 Ashley Coates, Pam Daily Kirley, Gretchen Rothrock, California Emerging Infections Program; Nisha 352 Alden, Rachel Herlihy, Breanna Kawasaki, Colorado Department of Public Health and Environment; Paula 353 Clogher, Hazal Kayalioglu, Amber Maslar, Adam Misiorski, Danyel Olson, Christina Parisi, Connecticut 354 Emerging Infections Program; Kyle Openo, Emily Fawcett, Jeremiah Williams, Katelyn Lengacher, Georgia 355 Emerging Infections Program; Andrew Weigel, Iowa Department of Health; Brian Bachaus, Timothy 356 Blood, David Blythe, Alicia Brooks, Judie Hyun, Elisabeth Vaeth, Cindy Zerrlaut, Maryland Department of 357 Health; Jim Collins, Kimberly Fox, Sam Hawkins, Justin Henderson, Shannon Johnson, Libby Reeg, 358 Michigan Department of Health and Human Services; Austin Bell, Kayla Bilski, Erica Bye, Emma 359 Contestabile, Richard Danila, Kristen Ehresmann, Hannah Friedlander, Claire Henrichsen, Emily 360 Holodnick, Ruth Lynfield, Katherine Schliess, Samantha Siebman, Kirk Smith, Maureen Sullivan, 361 Minnesota Department of Health; Cory Cline, New Mexico Department of Health; Kathy Angeles, Lisa 362 Butler, Emily Hancock, Sarah Khanlian, Meaghan Novi, Sarah Shrum, New Mexico Emerging Infections 363 Program Albuquerque; Nancy Spina, Grant Barney, Suzanne McGuire, New York State Health 364 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 16 Department; Sophrena Bushey, Christina Felsen, Maria Gaitan, Anita Gellert, RaeAnne Kurtz, Christine 365 Long, Shantel Peters, Marissa Tracy, University of Rochester School of Medicine and Dentistry; Laurie 366 Billing, Maya Scullin, Jessica Shiltz, Ohio Department of Health; Nicole West, Oregon Health Authority; 367 Kathy Billings, Katie Dyer, Anise Elie, Karen Leib, Tiffanie Markus, Terri McMinn, Danielle Ndi, 368 Manideepthi Pemmaraju, Vanderbilt University Medical Center; Keegan McCaffrey, Utah Department of 369 Health; Clarissa Aquino, Ryan Chatelain, Andrea George, Jacob Ortega, Andrea Price, Ilene Risk, Melanie 370 Spencer, Ashley Swain, Salt Lake County Health Department; Mimi Huynh, Monica Schroeder, Council of 371 State and Territorial Epidemiologists; Shua J. Chai, Field Services Branch, Division of State and Local 372 Readiness, Center for Preparedness and Response, Centers for Disease Control and Prevention; Sharad 373 Aggarwal, Lanson Broecker, Aaron Curns, Rebecca M. Dahl, Alexandra Ganim, Rainy Henry, Sang Kang, 374 Sonja Nti-Berko, Robert Pinner, Mila Prill, Scott Santibanez, Alvin Shultz, Sheng-Te Tsai, Henry Walke, 375 Venkata Akesh R. Vundi, CDC. 376 377

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It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 1 Figure 1. Flow diagram of analytic sample. 16,318 hospitalized COVID-NET patients reported with admission date during March 1–May 2, 2020 16,217 hospitalized adults 2,491 (15.3%) included in analysis Excluded 1 site’s cases (no clinical data available) n=74 Excluded cases due to incomplete medical chart reviews n=13,652 Pediatric cases N=101 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 2 Table 1. Demographic and clinical characteristics of adults hospitalized with COVID-19 — COVID-NET, 13 sites (N=2,491) n, % Age in years (median, IQR) 62, 50–75 Age category 18–39 302 (12.1) 40–49 319 (12.8) 50–64 744 (29.9) 65–74 478 (19.2) 75–84 397 (15.9) 85+ 251 (10.1) Male 1,326 (53.2) Race/ethnicity (n=2,490) Non-Hispanic White 1,178 (47.3) Non-Hispanic Black 755 (30.3) Hispanic 306 (12.3) Non-Hispanic Other* 158 (6.3) Unknown 93 (3.7) Residence at time of hospitalization (n=2,482) Private residence 1,899 (76.5) Facility† 495 (19.9) Homeless/Shelter 40 (1.6) Other‡ 45 (1.8) Unknown 3 (0.1) Smoker (n=2,489) Current 150 (6.0) Former 642(25.8) No or Unknown 1,697 (68.2) Any underlying condition§ (n=2,489) 2,278 (91.5) Hypertension (n=2,488) 1,428 (57.4) Obesity‖ (n=2,332) 1154 (49.7) Severe obesity‖ (n=2,332) 325 (14.0) Chronic metabolic disease (n=2,486) 1,024 (41.2) Diabetes mellitus (n=2,486) 819 (32.9) Chronic lung disease (n=2,484) 747 (30.1) Asthma (n=2,484) 314 (12.6) Chronic Obstructive Pulmonary Disease (n=2,484) 266 (10.7) Cardiovascular disease (n=2,486) 859 (34.6) Coronary artery disease (n=2,486) ¶ 352 (14.2) Congestive heart failure (n=2,486) 284 (11.4) Neurologic disease (n=2,484) 548 (22.1) Renal disease (n=2,488) 386 (15.5) Immunosuppressive condition (n=2,487) 263 (10.6) Gastrointestinal or Liver disease (n=2,486) 118 (4.7) Hematologic condition (n=2,483) 80 (3.2) Rheumatologic or autoimmune disease (n=2,486) 77 (3.1) for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 3 Pregnancy (n=279)** 36 (12.9) Number of underlying medical conditions (by major category) (n=2,490) 0 212 (8.5) 1 480 (19.3) 2 510 (20.5) 3+ 1,288 (51.7) Outpatient use of medications ACE-inhibitor (n=1,892) 316 (16.7) Angiotensin receptor blockers (ARBs) (n=1,895) 257 (13.6) IQR = interquartile range; ACE = angiotensin-converting enzyme inhibitors *Non-Hispanic Other includes: Non-Hispanic American Indian or Alaskan Native (n=24), Non-Hispanic Asian (n=128), and Non- Hispanic multiracial (n=6) †Facility includes rehabilitation facilities, assisted living/residential care, group homes, nursing homes, skilled nursing facilities, long-term care facilities, long-term acute care hospitals, alcohol/drug treatment centers, and psychiatric facilities. ‡Other includes home with services (n=43), correctional facility (n=1), and hospice (n=1). §See Supplementary Table 1 for definitions of underlying medical conditions. ‖Obesity is defined as body mass index (BMI) ≥30 kg/m2, and severe obesity is defined as BMI ≥40 kg/m2. ¶Includes coronary artery disease, history of coronary artery bypass grafting, and history of myocardial infarction **Denominator includes women aged 15–49 years. for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 4 Figure 2. Select underlying medical conditions* of adults hospitalized with COVID-19, by age, sex, and race/ethnicity — COVID-NET, 13 sites (N=2,491) A. Age (n=2,489) *p-value <0.05 CVD = Cardiovascular disease (excluding hypertension); HTN = hypertension; CLD = chronic lung disease. B. Sex (n=2,489) *p-value <0.05 CVD = Cardiovascular disease (excluding hypertension); HTN = hypertension; CLD = chronic lung disease. 83 8 23 22 27 7 8 66 4 91 23 31 37 57 11 16 57 10 97 57 33 37 74 12 34 36 25 0 10 20 30 40 50 60 70 80 90 100Percent (%) Underlying Medical Condition 18-49 years 50-64 years 65+ years 89 36 28 34 58 11 20 44 17 94 33 33 32 57 10 25 56 14 0 10 20 30 40 50 60 70 80 90 100Percent (%) Underlying Medical Condition Male Female for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 5 C. Race/Ethnicity (n=2,488) *p-value <0.05 CVD = Cardiovascular disease (excluding hypertension); HTN = hypertension; CLD = chronic lung disease. *The underlying medical condition categories are not mutually exclusive. Patients can have more than once underlying medical condition. 94 42 35 30 60 12 29 45 16 95 34 32 40 63 12 19 58 20 82 13 13 30 40 7 8 54 6 0 10 20 30 40 50 60 70 80 90 100Percent (%) Underlying Medical Condition Non-Hispanic White Non-Hispanic Black Hispanic for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 6 Table 2. Clinical course, interventions, and outcomes of adults hospitalized with COVID-19 — COVID- NET, 13 sites (N=2,491) % Symptoms on admission* (n=2,482) Cough 1,855 (74.7) Fever or chills 1,835 (73.9) Shortness of breath 1,740 (70.1) Muscle aches/myalgias 722 (29.1) Diarrhea 676 (27.2) Nausea or vomiting 621 (25.0) Hospitalization length of stay in days, median (IQR) (n=2,487) 6 (3–11) Days from symptom onset to hospitalization (median, IQR) (n=1,937) 6 (3–8) Initial vital signs Temperature (◦Celsius, median, IQR) (n=2,469) 37.4 (36.9–38.1) Heart rate (median, IQR) (n=2,479) 95 (83–108) Systolic blood pressure (median, IQR) (n=2,483) 132 (118–147) Respiratory rate (median, IQR) (n=2,461) 20 (18–23) Oxygen saturation (among those on room air) (median, IQR) (n=1,969) 94 (92–97) Initial laboratory values White blood cell count (median, IQR) – per mm3 (n=2,458) 6.3 (4.7–8.5) Hematocrit (median, IQR) - % (n=2,461) 40.6 (36.9–43.9) Platelet count (median, IQR) – per mm3 (n=2,461) 195.0 (156.0–249.0) Sodium (median, IQR) – mmol/L (n=2,460) 137.0 (134.0–139.0) Blood Urea Nitrogen (median, IQR) – mg/dl (n=2,443) 16.0 (11.0–25.0) Creatinine (median, IQR) – mg/dl (n=2,462) 1.0 (0.8–1.4) Glucose (median, IQR) – mg/dl (n=2,459) 117.0 (102.0–149.0) Aspartate transaminase (median, IQR) – U/L (n=2,149) 40.0 (28.0–61.0) Alanine aminotransferase (median, IQR) – U/L (n=2,164) 31.0 (20.0–50.0) Arterial pH (median, IQR) (n=487) 7.35 (7.40–7.45) Abnormal chest X-ray during hospitalization (n=1,932) 1,769 (91.6) Abnormal chest CT during hospitalization (n=566) 540 (95.4) Ground glass opacities (n=566) 350 (61.8) Investigational medication regimens for COVID-19† (n=2,482) 1,125 (45.3) Hydroxychloroquine‡ (n=2,479) 1,065 (43.0) Azithromycin + ≥ 1 other COVID-19 treatment (n=2,479) 725 (29.2) Tocilizumab (n=2,479) 103 (4.2) Atazanavir (n=2,479)§ 94 (3.8) Remdesivir (n=2,479)‡ 53 (2.1) Lopinavir/ritonavir (n=2,479)§ 27 (1.1) Convalescent plasma (n=2,479) 9 (0.4) Chloroquine (n=2,479) 7 (0.3) Sarilumab (n=2,479)‡ 6 (0.2) Investigational drug (not specified) RCT (n=2,479) 1 (0.0) for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 7 ICU Admission (n=2,490) 798 (32.0) ICU length of stay (days, median, IQR) (n=771) 6 (2–11) Highest level of respiratory support required (n=2,489) Invasive mechanical ventilation 462 (18.6) BIPAP or CPAP 82 (3.3) High flow nasal cannula 170 (6.8) ECMO (n=2,487) 9 (0.4) Vasopressor use (n=2,486) 373 (15.0) Systemic steroids (n=2,489) 321 (12.9) Renal replacement therapy (n=2,487) 115 (4.6) Discharge Diagnoses‖ Pneumonia (n=2,485) 1,395 (56.1) Acute respiratory failure (n=2,487) 999 (40.2) Acute renal failure (n=2,485) 456 (18.4) Sepsis (n=2,479) 443 (17.9) Acute respiratory distress syndrome (n=2,485) 255 (10.3) Encephalitis (n=2,482) 151 (6.1) Congestive heart failure (n=2,485) 51 (2.1) Asthma exacerbation (n=2,486) 43 (1.7) COPD exacerbation (n=2,486) 39 (1.6) Acute myocardial infarction (n=2,485) 38 (1.5) In-hospital death (n=2,490) 420 (16.9) IQR = interquartile range; CT = computed tomography; ICU = intensive care unit; BIPAP = bilevel positive airway pressure; CPAP = continuous positive airway pressure; ECMO = extracorporeal membrane oxygenation; COPD = chronic obstructive pulmonary disease *See Supplementary Table 2 for additional symptom data. †Not mutually exclusive categories ‡Includes randomized controlled trials where it cannot be determined whether the case received treatment vs. placebo (remdesivir, 24; hydroxychloroquine 15; and sarilumab 5). §Persons with HIV/AIDS were excluded. ‖Discharge diagnoses recorded from the hospital discharge summary and not based on ICD-10 discharge codes. for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 8 Figure 3. Interventions and outcomes of adults hospitalized with COVID-19, by age, sex, and race/ethnicity — COVID-NET, 13 sites (N=2,491) A. Age (n=2,490) *p-value <0.05 ICU = intensive care unit; BIPAP = bilevel positive airway pressure; CPAP = continuous positive airway pressure; HFNC = high flow nasal cannula; RRT = renal replacement therapy B. Sex (n=2,490) ICU = intensive care unit; BIPAP = bilevel positive airway pressure; CPAP = continuous positive airway pressure; HFNC = high flow nasal cannula; RRT = renal replacement therapy 25 11 2 7 9 3 3 34 19 4 5 15 5 10 35 23 4 8 19 5 29 0 10 20 30 40 50 60 70 80 90 100 ICU* Invasive mechanical ventilation* BIPAP/CPAP HFNC* Vasopressor* RRT In-hospital death* Percent (%) Interventions and outcomes 18-49 years 50-64 years 65+ years 36 21 4 8 17 5 18 28 15 3 6 13 4 15 0 10 20 30 40 50 60 70 80 90 100 ICU* Invasive mechanical ventilation* BIPAP/CPAP HFNC* Vasopressor* RRT In-hospital death* Percent (%) Interventions and outcomes Male Female for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 9 C. Race/Ethnicity (n=2,490) *p-value <0.05 ICU = intensive care unit; BIPAP = bilevel positive airway pressure; CPAP = continuous positive airway pressure; HFNC = high flow nasal cannula; RRT = renal replacement therapy * For mechanical ventilation, BIPAP/CPAP, and HFNC, patients are assigned based on the highest level of respiratory support required during hospitalization (i.e. invasive mechanical ventilation followed by BIPAP or CPAP, followed by high flow nasal cannula). 32 18 4 8 15 3 20 33 20 3 5 16 8 16 28 16 1 5 13 4 8 0 10 20 30 40 50 60 70 80 90 100 ICU Invasive mechanical ventilation BIPAP/CPAP* HFNC* Vasopressor RRT* In-hospital death* Percent (%) Interventions and outcomes Non-Hispanic White Non-Hispanic Black Hispanic for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 10 Table 3. Risk factors for ICU admission and in-hospital mortality — COVID-NET, 13 sites (N=2,491) A. ICU Admission (N=2,490)* Characteristic Category Not in ICU (n=1,692) In ICU (n=798) Total (N=2,490) P-value Unadjusted Risk Ratio 95% CI Adjusted Risk Ratio 95% CI Age category <0.0001 18–39 years 240 (14) 62 (8) 302 Reference N/A Reference N/A 40–49 years 227 (13) 91 (11) 318 1.38 (1.08, 1.75) 1.22 (0.96, 1.56) 50–64 years 488 (29) 256 (32) 744 1.64 (1.36, 1.99) 1.53 (1.28, 1.83) 65–74 years 298 (18) 180 (23) 478 1.80 (1.50, 2.16) 1.65 (1.34, 2.03) 75–84 years 248 (15) 149 (19) 397 1.80 (1.52, 2.14) 1.84 (1.6, 2.11) 85+ years 191 (11) 60 (8) 251 1.16 (0.87, 1.54) 1.43 (1.00, 2.04)† Sex <0.0001 Female 844 (50) 320 (40) 1164 Reference N/A Reference N/A Male 848 (50) 478 (60) 1326 1.30 (1.20, 1.42) 1.34 (1.20, 1.50) Race/ethnicity 0.63 NH White 800 (47) 378 (47) 1178 Reference Reference N/A NH Black 503 (30) 251 (31) 754 1.03 (0.93, 1.13) 1.01 (0.89, 1.15) Hispanic 219 (13) 87 (11) 306 0.86 (0.69, 1.09) 0.96 (0.76, 1.21) NH Other 105 (6) 53 (7) 158 1.03 (0.89, 1.18) 1.06 (0.83, 1.37) Unknown 64 (4) 29 (4) 93 0.97 (0.77, 1.22) 0.93 (0.74, 1.15) Smoker 0.0001 No/Unknown 1185 (70) 512 (64) 1697 Reference Reference N/A Former 395 (23) 247 (31) 642 1.26 (1.10, 1.43) 1.07 (0.92, 1.24) Current 112 (7) 38 (5) 150 0.84 (0.67, 1.06) 0.81 (0.61, 1.06) Hypertension 0.03 No 746 (44) 314 (39) 1060 Reference Reference N/A Yes 945 (56) 483 (61) 1428 1.13 (1.01, 1.27) 0.92 (0.79, 1.07) Obesity (BMI≥30) 0.0013 No 821 (53) 347 (46) 1168 Reference N/A Reference N/A Yes 739 (47) 415 (54) 1154 1.25 (1.14, 1.37) 1.31 (1.16, 1.47) for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 11 Diabetes <0.0001 No 1176 (70) 491 (62) 1667 Reference N/A Reference N/A Yes 513 (30) 306 (38) 819 1.20 (1.08, 1.34) 1.13 (1.03, 1.24) Chronic Lung Disease <0.0001 No 1222 (72) 515 (65) 1737 Reference N/A Reference N/A Yes 466 (28) 281 (35) 747 1.25 (1.09, 1.44) 1.17 (1.00, 1.37)‡ Cardiovascular Disease 0.006 No 1136 (67) 491 (62) 1627 Reference N/A Reference N/A Yes 553 (33) 306 (38) 859 1.17 (1.09, 1.26) 0.98 (0.88, 1.09) Neurologic 0.06 No 1297 (77) 639 (80) 1936 Reference N/A Reference N/A Yes 390 (23) 158 (20) 548 0.88 (0.76, 1.03) 0.85 (0.70, 1.04) Renal 0.004 No 1453 (86) 649 (81) 2102 Reference N/A Reference N/A Yes 238 (14) 148 (19) 386 1.23 (1.11, 1.37) 1.05 (0.94, 1.16) Immuno- suppression <0.0001 No 1541 (91) 683 (86) 2224 Reference N/A Reference N/A Yes 149 (9) 114 (14) 263 1.39 (1.22, 1.58) 1.29 (1.13, 1.47) Gastro- intestinal or Liver 0.87 No 1609 (95) 759 (95) 2368 Reference N/A N/A N/A Yes 81 (5) 37 (5) 118 0.98 (0.80, 1.20) N/A N/A Hematologic 0.87 No 1632 (97) 771 (97) 2403 Reference N/A N/A N/A Yes 55 (3) 25 (3) 80 0.96 (0.76, 1.22) N/A N/A Rheumatologic or Autoimmune 0.87 No 1637 (97) 772 (97) 2409 Reference N/A N/A N/A Yes 53 (3) 24 (3) 77 0.96 (0.74, 1.26) N/A N/A Outpatient ACE-Inhibitor Use 0.99 No/Unknown 1063 (83) 513 (83) 1576 Reference N/A N/A N/A Yes 213 (17) 103 (17) 316 0.99 (0.84, 1.18) N/A N/A for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 12 Outpatient Angiotensin Receptor Blocker Use 0.04 No/Unknown 1118 (88) 520 (84) 1638 Reference N/A Reference N/A Yes 159 (12) 98 (16) 257 1.18 (1.06, 1.31) 1.07 (0.95, 1.21) ICU = intensive care unit; CI = confidence interval; N/A = not applicable; ACE = angiotensin-converting-enzyme *Final model included age, sex, race/ethnicity, smoking status, hypertension, obesity, diabetes, chronic lung disease, cardiovascular disease, neurologic disease, renal disease, immunosuppression, and outpatient use of an angiotensin receptor blocker. †p=0.0494 ‡p=0.0524 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 13 B. In-hospital mortality (N=2,490)* Characteristic Category Discharged Alive (n=2070) In-Hospital Death (n=420) Total (N=2490) P-value Unadjusted Risk Ratio 95% CI Adjusted Risk Ratio 95% CI Age <0.0001 18-39 years 296 (14) 6 (1) 302 Reference N/A Reference N/A 40–49 years 308 (15) 10 (2) 318 1.51 (0.59, 3.85) 1.23 (0.51, 2.99) 50-64 years 670 (32) 74 (18) 744 4.62 (2.1, 10.18) 3.11 (1.50, 6.46) 65–74 years 375 (18) 103 (25) 478 9.88 (4.28, 22.85) 5.77 (2.64, 12.64) 75–84 years 277 (13) 120 (29) 397 13.89 (6.12, 31.52) 7.67 (3.35, 17.59) 85+ years 144 (7) 107 (25) 251 19.46 (9.39, 40.35) 10.98 (5.09, 23.69) Sex 0.03 Female 988 (48) 176 (42) 1164 Reference N/A Reference N/A Male 1082 (52) 244 (58) 1326 1.22 (1.02, 1.47) 1.30 (1.14, 1.49) Race/ethnicity <0.0001 NH White 944 (46) 234 (56) 1178 Reference N/A Reference N/A NH Black 637 (31) 117 (28) 754 0.75 (0.57, 0.99) 1.07 (0.85, 1.35) Hispanic 281 (14) 25 (6) 306 0.42 (0.32, 0.56) 1.17 (0.91, 1.51) NH Other 132 (6) 26 (6) 158 0.82 (0.54, 1.25) 1.26 (0.86, 1.82) Unknown 76 (4) 17 (4) 93 0.91 (0.44, 1.88) 1.30 (0.74, 2.31) Smoker <0.0001 No/Unknown 1461 (71) 236 (56) 1697 Reference N/A Reference NA Former 481 (23) 161 (38) 642 1.75 (1.51, 2.04) 1.14 (0.98, 1.33) Current 128 (6) 22 (5) 150 1.06 (0.75, 1.49) 1.22 (0.99, 1.51) Hypertension <0.0001 No 955 (46) 105 (25) 1060 Reference N/A Reference NA Yes 1114 (54) 314 (75) 1428 2.18 (1.66, 2.86) 1.07 (0.79, 1.45) Obesity (BMI≥30) 0.001 No 944 (49) 224 (58) 1168 Reference N/A Reference N/A Yes 992 (51) 162 (42) 1154 0.73 (0.61, 0.86) 1.09 (0.92, 1.30) Diabetes <0.0001 for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 14 No 1423 (69) 244 (58) 1667 Reference N/A Reference N/A Yes 644 (31) 175 (42) 819 1.44 (1.2, 1.72) 1.19 (1.01, 1.40 Chronic Lung Disease <0.0001 No 1489 (72) 248 (59) 1737 Reference N/A Reference N/A Yes 577 (28) 170 (41) 747 1.56 (1.27, 1.91) 1.31 (1.13, 1.52) Cardiovascular Disease <0.0001 No 1462 (71) 165 (39) 1627 Reference N/A Reference N/A Yes 605 (29) 254 (61) 859 2.85 (2.42, 3.36) 1.28 (1.03, 1.58) Neurologic <0.0001 No 1674 (81) 262 (63) 1936 Reference N/A Reference N/A Yes 391 (19) 157 (37) 548 2.10 (1.72, 2.56) 1.25 (1.04, 1.50) Renal <0.0001 No 1814 (88) 288 (69) 2102 Reference N/A Reference N/A Yes 255 (12) 131 (31) 386 2.45 (2.04, 2.93) 1.33 (1.10, 1.61) Immuno- suppression <0.0001 No 1873 (91) 351 (84) 2224 Reference N/A Reference N/A Yes 195 (9) 68 (16) 263 1.58 (1.33, 1.88) 1.39 (1.13, 1.70) Gastro- intestinal or Liver 0.12 No 1975 (96) 393 (94) 2368 Reference N/A N/A N/A Yes 92 (4) 26 (6) 118 1.30 (1.00, 1.70) N/A N/A Hematologic 0.02 No 2005 (97) 398 (95) 2403 Reference N/A Reference N/A Yes 59 (3) 21 (5) 80 1.54 (1.11, 2.12) 1.33 (0.93, 1.9) Rheumatologic or Autoimmune 0.03 No 2010 (97) 399 (95) 2409 Reference N/A Reference N/A Yes 57 (3) 20 (5) 77 1.53 (1.03, 2.29) 0.87 (0.66, 1.16) Outpatient ACE- Inhibitor Use 0.99 No/Unknown 1312 (83) 264 (83) 1576 Reference N/A N/A N/A Yes 263 (17) 53 (17) 316 0.98 (0.74, 1.30) N/A N/A Outpatient Angiotensin 0.11 No/Unknown 1372 (87) 266 (84) 1638 Reference N/A N/A N/A for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint 15 Receptor Blocker Use Yes 205 (13) 52 (16) 257 1.19 (0.91, 1.57) N/A N/A CI = confidence interval; N/A = not applicable; ACE = angiotensin-converting-enzyme *Final model adjusted for age, sex, race/ethnicity, smoker, hypertension, obesity, diabetes, chronic lung disease, cardiovascular disease, neurologic disease, renal disease, immunosuppression, hematologic disorders, and rheumatologic or autoimmune disease. for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 22, 2020. ; https://doi.org/10.1101/2020.05.18.20103390doi: medRxiv preprint