key: cord-0716169-ensafqmf
authors: da Silva, Juliana F; Hernandez-Romieu, Alfonso C; Browning, Sean D; Bruce, Beau B; Natarajan, Pavithra; Morris, Sapna B; Gold, Jeremy A W; Neblett Fanfair, Robyn; Rogers-Brown, Jessica; Rossow, John; Szablewski, Christine M; Oosmanally, Nadine; D’Angelo, Melissa Tobin; Drenzek, Cherie; Murphy, David J; Hollberg, Julie; Blum, James M; Jansen, Robert; Wright, David W; Sewell, William; Owens, Jack; Lefkove, Benjamin; Brown, Frank W; Burton, Deron C; Uyeki, Timothy M; Patel, Priti R; Jackson, Brendan R; Wong, Karen K
title: COVID-19 clinical phenotypes: presentation and temporal progression of disease in a cohort of hospitalized adults in Georgia, United States
date: 2020-12-07
journal: Open Forum Infect Dis
DOI: 10.1093/ofid/ofaa596
sha: 773da85633d34ae3d68d03cfa4ceb3e883ce64fc
doc_id: 716169
cord_uid: ensafqmf

BACKGROUND: The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients requires further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize healthcare resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. METHODS: Retrospective cohort study of 305 adult patients hospitalized with COVID-19 in eight academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower’s dissimilarity matrix between each patient’s clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. RESULTS: One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, white blood cell count and lower minimum lymphocyte count, compared with other phenotypes (p<0.01, all comparisons). CONCLUSIONS: Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform the prognosis and tailored clinical management for COVID-19.

A c c e p t e d M a n u s c r i p t 5 Background:

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 10 million infections and 239,590 deaths in the United States 1 . As the number of cases increases, the pandemic continues to place the U.S. healthcare system under pressure. Detailed descriptions of the natural history of coronavirus disease 19 (COVID-19) among hospitalized patients are paramount to optimize healthcare resource utilization, and the detection of different clinical presentations may allow more tailored clinical management strategies. Clinicians have recognized that in severely ill patients, COVID-19 may present with non-pulmonary manifestations rarely seen with other respiratory viruses [2] [3] [4] [5] ; direct cytopathic viral effects in organs, such as the brain and kidneys, and an exaggerated proinflammatory response have been proposed as possible mechanisms to explain this difference 6 .

While advanced age, comorbid conditions, and inflammatory markers have been identified as independent risk factors for severe disease [7] [8] [9] [10] [11] [12] [13] [14] [15] , how these risk factors interact and their relationship to severe pulmonary and extra-pulmonary complications remains poorly understood. Recognizing patterns of clinical progression based on host characteristics, physiologic and laboratory measurements, complications, and outcomes will help clinicians better understand the spectrum and natural history of COVID-19 and can influence decisions about clinical management. Methods to identify heterogeneous phenotypes have been used for other broad disease entities, such as sepsis and asthma [16] [17] [18] .

We collected longitudinal clinical data on 305 hospitalized patients with laboratory-confirmed SARS-CoV-2 infection in the U.S. state of Georgia. Our primary objective was to identify and describe COVID-19 clinical phenotypes based on demographics, comorbidities, presenting signs and symptoms, laboratory values, complications, and outcomes. Our secondary objectives were to: 1) differentiate clinical syndromes at hospital presentation, 2) characterize temporal trends of vital A c c e p t e d M a n u s c r i p t 6 signs and laboratory parameters by disease severity, and 3) describe medical complications and outcomes among hospitalized patients.

The U.S. Centers for Disease Control and Prevention (CDC), the Georgia Department of Public Health (DPH), and three hospital networks within Georgia partnered to review patient medical records at eight Georgia hospitals. Seven hospitals were in metropolitan Atlanta and one was in southern Georgia; they comprised academic medical centers, a public teaching hospital, and community hospitals; all provided tertiary care.

Hospitals provided lists of patients with a positive SARS-CoV-2 reverse transcription-polymerase chain reaction test who were admitted during March 1-March 30, 2020 (n=698). Clinicians reviewed electronic medical records on a convenience sample of sequentially identified adults aged ≥18 years from each hospital's patient list 19 . We conducted as many initial chart abstractions as possible through April 20 (N=305). Transfers between participating hospitals and multiple admissions of the same patient (10 patients had a single readmission, and 1 had two readmissions) were analyzed as a single hospitalization.

Because some patients were still hospitalized at the time of initial data abstraction, all patient records were re-examined on May 8 to assess outcomes. Study data were collected and managed using REDcap (Research Electronic Data Capture) tools hosted at CDC 20, 21 . Medical records were examined for clinical, laboratory, and radiologic data, and audits were performed on all chart abstractions to correct data missingness and identify implausible values. This investigation was determined by CDC and DPH to be public health surveillance 22 . A c c e p t e d M a n u s c r i p t 7

To identify patterns in the clinical course, we grouped patients into clusters based upon demographic characteristics (age, sex); comorbidities (diabetes mellitus, cardiovascular disease, hypertension, chronic lung disease, extreme obesity *BMI ≥40 kg/m 2 ], neurologic disorders) 23 [AKI], acute liver injury, and bacterial coinfection). Comorbidities, presenting symptoms, and complications were selected based on known or suspected risk factors, frequency observed in prior studies, and associations with severe COVID-19 8, 25, 26 . Laboratory values were selected to represent markers of inflammation, hypercoagulability, and end organ damage, and we used peak or nadir values to capture the highest severity of illness. Race was not included as a factor in the clustering analysis because of high prevalence of black race (83.2%) 24 . Eighteen patients initially admitted for reasons other than COVID-19 (e.g., trauma, post-surgical complication, device-related issues) were excluded from the analysis. The median proportion of imputed data per patient was 11.8%, and all imputed values were removed after clustering for subsequent analysis and visualization (see

We computed Gower's dissimilarity matrix 27 between observations, which allows a similarity metric to be calculated from continuous and categorical variables. Because we wanted to emphasize clinical A c c e p t e d M a n u s c r i p t 8 over demographic characteristics in the phenotypes, age and sex were downweighted by 50%. We used the partitioning around medoids algorithm 28 to cluster observations. For visualization, we calculated standard deviations for categorical variables and median absolute deviations for numeric variables.

Admission vital signs and laboratory values were abstracted for all patients. For serial vital signs and laboratory data, patients were stratified by phenotype, and we plotted the daily average among patients by stratum inversely weighting observations by the number of observations per patient per day. Wilcoxon rank-sum tests were used to evaluate differences in distributions.

Analyses and visualizations were conducted in R software version 3.6.2 (Vienna, Austria).

Baseline characteristics of patients have been previously described 24 . Among 305 patients, median age was 60 years (interquartile range [IQR] 46-69), 51% were female, and 83% were non-Hispanic Black. 119 patients (39%) were admitted to the intensive care unit (ICU), 92 (30%) received invasive mechanical ventilation (IMV), and 51 (17%) died. 287 (94%) were initially admitted for confirmed or suspected COVID-19. Among 18 patients not originally admitted with suspected COVID-19, SARS-CoV-2 was detected by RT-PCR at median hospital day 2; 7 were diagnosed ≥5 days after admission (range 5-38).

Among the subset of 287 patients whose initial reason for admission was COVID-19, median time from symptom onset to admission was 7 days (IQR 4.0-9.0). While most presented with fever or respiratory symptoms (Figure 1 ), there were 10 (3%) patients who did not present with fever, cough, A c c e p t e d M a n u s c r i p t 9 or shortness of breath. Of these 10, 7 developed a fever within 2 days of admission, and 5, all of whom were >70 years old, presented with new or worsened altered mental status. After febrile respiratory syndromes, syndromes involving myalgia, fatigue, or gastrointestinal symptoms were the most common. Diarrhea occurred in 75 (26%) patients, usually in combination with fever or respiratory symptoms. Nineteen (7%) patients presented with altered mental status (median age 79 years, range 62-95). Among 119 ICU patients, 13 (11%) underwent prone positioning while ventilated, 6 (5%) received a tracheostomy, and 2 (2%) received extracorporeal membrane oxygenation.

In addition to supportive therapy, hydroxychloroquine was administered to 117 (38%) patients, of whom 37 did not require intensive care. Corticosteroids were initiated in 17% of patients. Eight patients received lopinavir/ritonavir, and none received tocilizumab. No patients received immunomodulatory therapies (e.g., IL-1 inhibitors and IL-6 blockers). Antibiotics covering common community-acquired pathogens were administered to 95% within 48 hours after admission (99% of ICU patients); 51% received antibiotics after 48 hours, mostly as an escalation of empiric antibiotics.

Data on anticoagulation use was not available for analysis. Table 1) .

The partitioning around medoids analysis grouped patients into six patterns (clusters A-F, Figure 3 ).

Mortality was highest in cluster A (49%, n=53) compared with other clusters (<20%). Proportions of IMV (89%), shock (81%), and AKI (87%) were also highest in cluster A. This cluster had the highest median age (67 years), the highest prevalence of cardiovascular disease (64%) and was predominantly male (60%). Cluster A patients had elevated median peak CRP, LDH, CPK, D-dimer, PTT, AST, ALT, and Creatinine compared with the cohort (Supplementary Figure 1) . The median nadir absolute lymphocyte count for this cluster was relatively low (0.72 cells/mm 3 ).

Clusters B (n=34) and C (n=39) had high prevalence of AKI (>70%) and similar mortality rates (19% and 18%, respectively). Cluster B was distinguished by high prevalence of diabetes (79%) and diarrhea as a presenting symptom (79%), and cluster C patients had a high prevalence of chills (69%).

There were two clusters (D, n=29, and E, n=61) that were predominantly female. These clusters differed by comorbidities: cluster D had higher prevalence of diabetes, chronic lung disease, and severe obesity (BMI ≥40). Cluster E had the lowest prevalence of mechanical ventilation (5%).

A c c e p t e d M a n u s c r i p t 11 Cluster F (n=71) patients were the youngest (median age 43) and had few comorbidities. There were few complications reported in these patients relative to the cohort. While 15% of these patients required mechanical ventilation, this cluster had the lowest mortality rate (1%).

Treatment and co-infections by cluster are shown in Supplementary Table 2. Antibiotic use overall was >90% among all clusters, although continued antibiotic use >48 hours after admission was higher among Cluster A patients (87%) than in other clusters (range: 33-41%). Cluster A had the 

Final outcomes were known for 297 (97%) patients. Eleven patients were readmitted to the same hospital system during the data collection period; 6 were readmitted with worsening COVID-19 symptoms, 3 with complaints likely unrelated to COVID-19, and in 2 patients cause of readmission was not available. Among 51 patients (17%) with a recorded cause of death, the primary event leading to death was respiratory failure in 26 (51%) and multiorgan system failure or shock in 13 (26%). Among patients who received IMV, mortality was 44%.

Among 246 patients discharged alive, 205 (83%) were discharged to their pre-hospital level of care, and 38 (15%) were discharged to a higher level of care than their pre-hospital baseline (for example, someone who was previously independent at home being discharged home with new services or to a rehabilitation facility) (Supplementary Table 4 ). There were 37 (12%) patients with new oxygen requirements upon discharge. Three patients were discharged for prolonged mechanical ventilation weaning to long term acute care facilities, 4 were discharged with a tracheostomy, and 6 were discharged with new RRT needs.

In a cohort of 305 hospitalized patients with COVID-19 in Georgia, we conducted a cluster analysis to identify clinical phenotypes. Patient characteristics clustered into patterns based on host factors, symptoms, laboratory findings, and complications. One cluster was characterized by high mortality, elevated inflammatory markers, laboratory evidence of end organ damage, shock, and VTE. These features share similarities to the multisystem inflammatory syndrome in children (MIS-C) with COVID-19 in Europe and the United States 29, 30 , and they also share similarities to cytokine release A c c e p t e d M a n u s c r i p t 13 syndrome due to acute infection. Patients with this phenotype were more often older, male, and had a high prevalence of cardiovascular disease compared with other clusters. The phenotype identified in this study may overlap with case reports in adults of an MIS-A phenotype characterized by inflammatory markers and extrapulmonary involvement [31] [32] [33] .

Acute kidney injury was the most common non-respiratory complication among this cohort, and some patients required new renal replacement therapy upon discharge. This finding is consistent with those from other reports 3,10,34 and they suggest that SARS-CoV-2 may have a specific tropism for renal tissue 2 . However, the degree to which acute kidney injury can be attributed to effects of SARS-CoV-2 in our cohort is unclear given the high proportion of shock in some patients. VTE was common, particularly among patients with the severe phenotype. Other studies have described higher rates of VTE among critically ill COVID-19 patients compared with other patients 35 

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This investigation was determined by CDC and DPH to be public health surveillance and thus did not require patient consent as not personal identifiable data was used. The design of the work was approved by CDC ethics committees.A c c e p t e d M a n u s c r i p t 16 A c c e p t e d M a n u s c r i p t Figure 1 : Presenting signs and symptoms among adults hospitalized for COVID-19 whose initial reason for admission was COVID-19 symptoms (n=287) -Georgia, March 2020The blue bars show the number of patients with each mutually exclusive symptom combination indicated by the connected black dots. The black bars show the frequency of each symptom. "Constitutional Symptoms" include myalgia and fatigue. "Any Other" includes sore throat, chills, headaches, anosmia, chest pain, dizziness, rash, or other symptoms.A c c e p t e d M a n u s c r i p t Legend: Cell labels show median cluster values for age, symptom onset to admission days, and laboratory values (CRP peak through Cr peak) and cluster proportions for female sex, comorbidities (diabetes through neurologic disorder), presenting symptoms (fever through altered mental status), complications (shock through bacterial coinfection), and outcomes (acute RRT, mechanical ventilation, death). Cell color indicates the median absolute deviation from the median or standard deviation from the mean. Analysis excludes 18 patients who were initially admitted for reasons other than COVID-19.Abbreviations: AKI, acute kidney injury; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ARDS, acute respiratory distress syndrome; BMI, body mass index; Cr, creatinine; CRP, C-reactive protein; LDH, lactate dehydrogenase; CPK, creatine phosphokinase; PTT, partial thromboplastin time; WBC, white blood cell count; RRT, renal replacement therapy; VTE, venous thromboembolism. The average daily value among patients in Cluster A versus other clusters is shown for four key laboratory values. For patients with multiple observations per day, the average is weighted inversely by the number of observations per patient per day.Abbreviations: AST, aspartate aminotransferase