Early clinical and sociodemographic experience with patients hospitalized with COVID-19 at a large American healthcare system
Abstract
Background
Despite over 4 million cases of novel coronavirus disease 2019 (COVID-19) in the United States, limited data exist including socioeconomic background and post-discharge outcomes for patients hospitalized with this disease.
Methods
In this case series, we identified patients with COVID-19 admitted to 3 Partners Healthcare hospitals in Boston, Massachusetts between March 7th, 2020, and March 30th, 2020. Patient characteristics, treatment strategies, and outcomes were determined.
Findings
A total of 247 patients hospitalized with COVID-19 were identified; the median age was 61 (interquartile range [IQR]: 50–76 years), 58% were men, 30% of Hispanic ethnicity, 21% enrolled in Medicaid, and 12% dual-enrolled Medicare/Medicaid. The median estimated household income was $66,701 [IQR: $50,336-$86,601]. Most patients were treated with hydroxychloroquine (72%), and statins (76%; newly initiated in 34%). During their admission, 103 patients (42%) required intensive care. At the end of the data collection period (June 24, 2020), 213 patients (86.2%) were discharged alive, 2 patients (0.8%) remain admitted, and 32 patients (13%) have died. Among those discharged alive (n = 213), 70 (32.9%) were discharged to a post-acute facility, 31 (14.6%) newly required supplemental oxygen, 19 (8.9%) newly required tube feeding, and 34 (16%) required new prescriptions for antipsychotics, benzodiazepines, methadone, or opioids. Over a median post-discharge follow-up of 80 days (IQR, 68–84), 22 patients (10.3%) were readmitted.
Interpretation
Patients hospitalized with COVID-19 are frequently of vulnerable socioeconomic status and often require intensive care. Patients who survive COVID-19 hospitalization have substantial need for post-acute services.
Keywords
Evidence before this study
We searched PubMed on July 3rd, 2020, for articles that examined the socioeconomic characteristics, morbidity, and post-discharge outcomes of patients with coronavirus disease 2019 (COVID-19), using the search terms (“novel coronavirus” OR “SARS-CoV-2″ OR “COVID-19″). Data have emerged regarding racial disparities; however, information on socioeconomic status is limited. Furthermore, data regarding rates of off-label prescriptions of hydroxychloroquine and statins among patients hospitalized with COVID-19 early in the pandemic is lacking. Lastly, the morbidity of patients with COVID-19 on discharge and their post-discharge outcomes are uncertain.
Added value of this study
In this case series of 247 patients hospitalized with COVID-19 in 3 hospitals in Boston, Massachusetts, we found that a substantial number of patients were Hispanic (30%) and of low socioeconomic status as suggested by the proportion of patients insured by Medicaid or dual-eligible for Medicare and Medicaid (33%). Many patients were either retired (36%) or unemployed (8.5%). We found prescriptions of hydroxychloroquine (72%), and statins (76%; newly initiated in 34% of all patients) were very common early in the pandemic. The majority of patients have survived their hospitalization (86%), however, approximately one third required post-acute care, and one in seven required supplemental oxygen, one in eleven required tube feeding, and one in six required new prescriptions for antipsychotics, benzodiazepines, methadone, or opioids on discharge. Among patients readmitted over the follow-up period (10%), 41% were due to respiratory symptoms.
Implications of all the available evidence
Greater public health efforts are needed to prevent the spread of SARS-COV-2 among people of vulnerable socioeconomic status. These early results also underscore the urgent need for post-acute systems of care and surveillance for respiratory symptoms following discharge.
1. Introduction
]; however, information on socioeconomic status, insurance, and professional occupations is lacking. This information may inform urgent policy needs.
In this study, a detailed physician-reviewed registry was created to evaluate inpatients with COVID-19 within an integrated healthcare network in Boston, Massachusetts. The goals of the registry were to define characteristics including demographics and professions, treatment strategies, clinical outcomes, and adverse event rates (inclusive of subclinical outcomes from electrocardiographic data), that may inform patient care, public policy, and clinical trials.
2. Methods
2.1 Study population
Patients were included if hospitalized with confirmed SARS-CoV-2 infection at three Partners Healthcare hospitals (Massachusetts General Hospital, Brigham and Women’s Hospital, and Newton-Wellesley Hospital) between March 7, 2020 and March 30, 2020. These represent three of the largest hospitals of the largest health care system in New England. Healthcare workers with personal connections to the investigators were excluded (one patient).
Patient demographics, baseline characteristics, symptoms, home medications, laboratory data, electrocardiogram (EKG) data, imaging, and treatment strategies were obtained by physician chart review. In order to improve standardization, one physician (C.M.C) reviewed a random 10% sample of the other reviewer’s data collection to ensure consistency in data collection. Physicians reviewed patient charts across each of the three hospitals which were connected through a shared electronic health record system. Median zip code household income based on 2014 census data was used a proxy for household income. Employment and insurance status were recorded. The first laboratory tests within 48 h of admission were considered the presenting values. When serial laboratory testing was performed, the peak value was recorded.
]. For those with a prolonged baseline QTc interval, further prolongation was considered to be an increase of 60 ms or more or an increase above 500 ms. Among patients with a left or right bundle branch block, the QTc was adjusted according to the following formula: QTc= QTc – (QRS – 100 ms). For patients admitted to the intensive care unit (ICU), use of paralytic agents, vasopressors, inhaled nitric oxide, epoprostenol, and mechanical circulatory support devices was recorded. Data collection ended on June 24, 2020.
2.2 Outcomes
]. Other non-cardiovascular outcomes included acute kidney injury (AKI) according to the Kidney Disease: Improving Global Outcomes Clinical Practice Guidelines and acute respiratory distress syndrome (ARDS), according to the Berlin definition [
,
]. Requirement for supplemental oxygen, tube feeding, and new prescriptions for opioids, benzodiazepines, and antipsychotics on discharge were recorded. Post-discharge Emergency Department presentations and readmission were determined.
2.3 Statistical analysis
Descriptive statistics were used to summarize data. Baseline characteristics, diagnostic testing, treatments, and outcomes were reported as counts and percentages, medians and 1st and 3rd quantiles, or means and standard deviations, as appropriate. Patients who either died or were admitted to the ICU were compared with those who were discharged alive without an ICU stay using Wilcoxon rank sum test for continuous variables or Fisher’s exact test for categorical measurements.
Statistical tests were 2-sided, with p<0.05 considered significant. All analyses were performed using R software (version 3.6.2). The study was approved by the Partners Healthcare Institutional Review Board and informed consent was waived based on secondary use of data from medical record review.
2.4 Role of funding source
The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
3. Results
A total of 247 patients with COVID-19 were identified over the study period; 147 patients (59.5%) were admitted to Massachusetts General Hospital, 52 patients (21.1%) to Brigham and Women’s Hospital, and 48 patients (19.4%) to Newton Wellesley Hospital. Three patients were diagnosed with COVID-19 at outside facilities, the remaining 244 were diagnosed by SARS-CoV-2 PCR within the system. Among patients hospitalized with COVID-19, 51 (20.6%) were admitted directly to the ICU from Emergency Departments.
3.1 Baseline characteristics
Table 1Baseline characteristics of hospitalized patients with COVID-19.
Abbreviations: ACEi, angiotensin-converting-enzyme inhibitors; ARB, angiotensin II receptor blockers; BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; ICU, intensive care unit; IQR, interquartile range; MI, myocardial infarction; N, number of patients; NSAIDs, non-steroidal anti-inflammatory drugs; OSA, obstructive sleep apnea; OHS, obesity hypoventilation syndrome; PAD, peripheral artery disease; TIA, transient ischemic attack.
Commonly reported symptoms were cough (83%), fever (81.8%), shortness of breath (70.9%), altered sense of taste (9.7%) and smell (6.1%). Median time from onset of symptoms to admission was 7 days (IQR, 4–10). Obesity or being overweight was common (n = 187; 75.7%) and 81 patients (32.8%) had current or former tobacco use. Hypertension (n = 128; 51.8%), hyperlipidemia (n = 109; 44.1%), diabetes mellitus (n = 68; 27.5%), and a history of malignancy (n = 46; 18.6%) were also prevalent. On admission, 59 patients (23.9%) were taking an angiotensin-converting-enzyme inhibitor (ACEi) or angiotensin II receptor blockers (ARB), 53 patients (21.5%) were taking a beta-receptor antagonist, and 29 patients (11.7%) were taking a non-steroidal anti-inflammatory drug (NSAID).
3.2 Diagnostic testing
Table 2Laboratory data on admission.
Abbreviations: ALT, alanine transaminase; AST, aspartate aminotransferase; CRP, C-reactive protein; CK, creatine kinase; ESR, erythrocyte sedimentation rate; hs troponin, high-sensitivity cardiac troponin; ICU, intensive care unit; IQR, interquartile range; LDH, lactate dehydrogenase; N, number of patients; NT-proBNP, N-terminal pro-b-type natriuretic peptide.
3.3 Treatments
Table 3Treatment strategies.
Abbreviations: ACEi, angiotensin-converting-enzyme inhibitors; ARB, angiotensin II receptor blockers; EKG, electrocardiogram; ICU, intensive care unit; IVIG, intravenous immunoglobulin; n=number of patients.
Prescription of a statin was common: 187 patients (75.7%) received a statin during admission (initiated during hospitalization in 85 patients; 45.5%). Discontinuation of statin was common (55 of 187 patients; 29.4%: 28 [15%] due to a creatine kinase elevation and 27 [14.4%] due to elevated liver biochemical test levels). Peak creatine kinase levels were higher among those who received a statin (median, 282 U/L: IQR, 116–737) than those who did not (median, 156 U/L: IQR, 84–342, p = 0.02). Among those who received a statin, peak creatine kinase levels were higher among those who received azithromycin (median, 344 U/L: IQR, 122–872) than those who received a statin alone (median, 207 U/L: IQR, 98–306.5, p = 0.02).
The majority of patients received venous thromboembolism prophylaxis or therapeutic anticoagulation (n = 238; 96.4%). A glucocorticoid was prescribed to 52 patients (21.1%) at least once during their admission (newly initiated in 36; 69.2%), and tocilizumab was prescribed to 12 patients (4.9%). Enrollment in a clinical trial was common; 57 patients (23.1%) for remdesivir, 7 patients (2.8%) for sarilumab, and 21 patients (8.5%) received inhaled nitric oxide, of which 11 patients were enrolled in a clinical trial.
3.4 In-hospital outcomes
A computed tomographic (CT) pulmonary angiogram or extremity venous doppler ultrasound was performed in 57 patients (23.1%) to assess for VTE, which was detected in 12 patients (4.9% of all patients and 21.1% of those who underwent testing for suspicion of VTE). Among those who underwent imaging evaluation for VTE (n = 57), serum d-dimer concentration was higher among patients diagnosed with a thromboembolism compared with others: 5761 ng/mL (IQR, 3374–9955) versus 1595 ng/mL (IQR, 996–3971), p = 0.003. AKI was common, occurring in 86 patients (34.8%); 17 patients (6.9%) required renal replacement therapy. AKI occurred in 57 patients (55.3%) who required an ICU stay (N = 103) and in 29 patients (20.1%) who did not require an ICU stay (N = 144).
3.5 Morbidity at discharge and post-discharge outcomes
Among those discharged alive (n = 213), the median length of stay was 9 days (IQR, 5–18). Delirium was diagnosed in 62 patients (29.1%). Among those who required intensive care (n = 78; 36.6%), 20 (25.6%) were diagnosed with critical illness myopathy, and 28 patients (35.9%) received medications to prevent or treat sedative withdrawal.
At discharge, 31 patients (14.6%) newly required supplemental oxygen, 19 patients (8.9%) required tube feeding (11 via percutaneous gastrostomy tube and 8 via nasogastric tube), 14 patients (5.7%) had a tracheostomy, and one patient (0.5%) required hemodialysis. On discharge, 18 patients (8.5%) were newly prescribed antipsychotics, 7 (3.3%) benzodiazepines, 4 (1.9%) methadone, and 5 (2.3%) opioids.
Upon leaving the hospital, 143 (67.1%) were discharged home and 70 (32.9%) to a facility. The median post-discharge follow-up was 80 (IQR, 68–84) days. Following discharge, 36 patients (16.9%) re-presented to the Emergency Department, and 22 (10.3%) were readmitted. The median time from discharge to readmission was 19 (IQR, 8–32) days. The three most common reasons for readmission (Supplementary Table 5) were persistent COVID-19 symptoms (4/22, 18.2%), pneumonia (3/22, 13.6%), and pulmonary embolism (2/22, 9.1%). Over the follow-up period, four patients died.
4. Discussion
]. Frequent emergency department utilizers at Massachusetts General Hospital have reported a ~72% proportion of white patients, [
] compared with only 51% in this study. The proportion of Hispanic patients admitted with COVID-19 in our cohort is also higher than in New York City (23%) [
]. We extend those findings and report that one third of patients were insured by Medicaid or dual-enrolled, although the median estimated household income of $66,701 is only slightly lower than the state median household income for that year ($71,681) []. Because the two ways we estimated wealth produce different results, we believe the zip-code estimates may mask ecological-inference bias (i.e., poorer patients in middle-class zip codes). The high proportion of Medicaid patients and hospitality workers could be related to financial vulnerability requiring patients to work and limiting their ability to self-isolate. Because nearly half of the sample were retired or unemployed, these results also suggest the possibility of non-occupational spread; however, this requires further evaluation through contract tracing and mechanistic studies. It is also possible that this cohort were more vulnerable to severe infection (requiring hospitalization) due to differences in demographics.
,
] use of statin therapy to treat COVID-19 was common. However, in a noteworthy number of patients the drug was discontinued due to adverse events; which may be exacerbated when combined with azithromycin [
]. Furthermore, following demonstration of in vitro activity of hydroxychloroquine in inhibiting SARS-CoV-2, [
] early attention had shifted toward this anti-malarial agent. In this early experience, we found high proportions of patients treated with both hydroxychloroquine and azithromycin. QTc prolongation was common and there was one non-fatal episode of polymorphic ventricular tachycardia. These findings dovetail with more recent concerns about the safety profile of chloroquine [
] and recent guidance from the National Institutes of Health advising against hydroxychloroquine or statins outside of a trial []. Off-label use of these agents is now uncommon at our institutions, highlighting the dynamic nature of COVID-19 evidence. Encouragingly, enrollment in clinical trials was common early in the pandemic, occurring in approximately one-third of patients in this report.
] and California study [
] but lower than another study from New York (although one in which over half of the study population was still admitted at the time of the report) [
]. ICU stay rates among COVID-19 hospitalized patients also appear similarly high across the United States. Comparable to data from California [
] and New York City [
], we found high ICU admission rates, with 42% of study patients spending time in the ICU. Notably, patients typically presented to the hospital 7 days after onset of symptoms, and 21% of patients were admitted directly to ICUs from the Emergency Department.
]. Arrhythmias were more common; 8.5% had new atrial fibrillation/flutter and ventricular tachycardia occurred in 9.3%. Precipitating factors for these arrhythmias may be multifactorial with contributions from myocardial injury (present in more than half on admission), systemic inflammation, and critical illness.
]; however, patients included in prior studies have often lacked VTE prophylaxis [
] or the frequencies of VTE prophylaxis and image testing were not reported to contextualize the results [
]. We found a relatively low rate of VTE at 4.9%. The majority of patients were either on VTE prophylaxis or therapeutic anticoagulation for various indications. Although our low event rate could be explained by under-ascertainment, a reasonable proportion of patients (23%) underwent evaluation for VTE with imaging during admission. Most patients with COVID-19 had an elevated serum d-dimer on admission, potentially limiting the diagnostic utility of this biomarker. Nevertheless, at the time of testing for VTE, the d-dimer concentration was significantly higher among those who were diagnosed with a VTE.
Our analyses emphasize that even survivors of COVID-19 have substantial needs after discharge. One quarter of patients discharged alive had experienced delirium during their admission, and among those who had an ICU stay, a quarter were diagnosed with critical illness myopathy, and over a third received medications to prevent or treat sedative withdrawal. Nearly a third of patients required post-acute care, and one in seven required supplemental oxygen, one in eleven required tube feeding, and one in six required new prescriptions for antipsychotics, benzodiazepines, methadone, or opioids on discharge. Among patients who required readmission, 41% were due to persistent COVID-19 symptoms, pneumonia, or pulmonary embolism.
Our study has important limitations. First, because two patients included in this study remained admitted at the end of the study period, event rates may be under-reported. In particular, patients alive at closing of data analysis may die later, which would underestimate the reported fatality rate. Second, because this is a retrospective observational study, data collection is limited to that documented and reported in the electronic medical record. Nevertheless, all data were obtained by physician review as opposed to being queried from either claims data, or EHR data. Third, whether findings in this study are generalizable to other regions of the country is unknown and therefore the results should be interpreted with caution. An important strength of this analysis is the inclusion of multiple hospitals including a community teaching hospital. Fourth, since this was a retrospective analysis, only 45 of the 257 patients had all initial laboratory tests, and as such we could not develop a predictive algorithm using multivariable analysis given the limitations of missing data. Lastly, readmissions to outside facilities and out-of-hospital mortality may not have been captured in our medical record system.
In conclusion, we demonstrate socioeconomic vulnerability and frequent off-label use of medications early in the pandemic among patients hospitalized with COVID-19. Patients who survive COVID-19 hospitalization have substantial morbidity and need for post-acute services.
Declaration of Competing Interest
Dr. Bohula reports personal fees from Servier, personal fees from Merck, personal fees from NIH, personal fees from Lexicon, personal fees from Medscape, personal fees from Academic CME, personal fees from MD Conference Express, personal fees from Paradigm, personal fees from Novartis, grants and personal fees from Amgen, grants from Astra Zeneca, grants from Merck, personal fees from Novo Nordisk, grants from Eisai, personal fees from PriMed, outside the submitted work. Dr. Morrow reports grants from Abbott Laboratories, grants from Amgen, grants and personal fees from AstraZeneca, grants from Eisai, grants from GlaxoSmithKline, grants and personal fees from Merck, grants and personal fees from Novartis, grants and personal fees from Roche Diagnostics, personal fees from Bayer Pharma, personal fees from InCarda, grants from Medicines Company, grants from Takeda, outside the submitted work; he is a member of the TIMI Study Group which has received institutional research grant support through Brigham and Women’s Hospital from: Abbott, Amgen, Anthos Therapeutics, Aralez, AstraZeneca, Bayer HealthCare Pharmaceuticals, Inc., Daiichi-Sankyo, Eisai, GlaxoSmithKline, Intarcia, Janssen, MedImmune, Merck, Novartis, Pfizer, Poxel, Quark Pharmaceuticals, Regeneron, Roche, Siemens, Takeda, The Medicines Company, Zora Biosciences. Dr. Natarajan reports grants from Amgen, grants and personal fees from Apple, personal fees from Blackstone Life Sciences, grants from Boston Scientific, personal fees from Novartis, outside the submitted work. Dr. Wasfy reports grant funding from Harvard Catalyst/National Institutes of Health, American Heart Association, and consulting fees from Pfizer, Biotronik. He is vice-chair of the New England CEPAC. He has received travel funded by the American College of Cardiology and grant support to spouse from the National Institutes of Health; none of these are relevant to this topic. The remaining authors have no conflicts of interest to disclose.
Data sharing statement
Request for access to the data should be made to the corresponding author and will be reviewed on a case to case basis. Data could potentially be made available provided the applicant has appropriate ethics approval and approval from the authors.
Funding
The principal investigator, Dr. Wasfy reports a grant from the American Heart Association (18 CDA 34110215).
Author contributions
Drs. McCarthy, Murphy, Jones-O’Connor, Olshan, Khambhati, Rehman, and Mr. Cadigan performed the data collection. Ms. Cui performed the data analysis. All authors contributed to the study design, literature search, data interpretation, and writing.
Acknowledgments
None.
Appendix. Supplementary materials
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Article Info
Publication History
Published: August 18, 2020
Accepted:
July 28,
2020
Received in revised form:
July 25,
2020
Received:
June 8,
2020
Footnotes
Funding: American Heart Association
Identification
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© 2020 The Authors. Published by Elsevier Ltd.
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