Abstract
There is an increased risk of venous thromboembolism among patients with COVID-19 infection, with the risk being higher among those needing the intensive level of care. Existing data is, however, limited regarding the outcomes of patients admitted with concurrent COVID-19 infection and pulmonary embolism (PE). All acute PE admissions were identified from the National Inpatient Sample database during 2020 using ICD-10 codes. Patients were subsequently classified into those with and without COVID-19 infection. The primary outcome of interest was in-hospital mortality. Using multivariate logistic regression, the predictors of mortality were assessed for patients with concurrent acute PE and COVID-19. The database query generated 278,840 adult patients with a primary diagnosis of PE. Of these, 4580 patients had concurrent PE and COVID-19 infection. The concurrent PE and COVID-19 infection group had a higher proportion of Black-American and Hispanic patients, and those living in the zip codes associated with the lowest annualized income compared to the PE alone group. Furthermore, patients in the concurrent PE and COVID-19 infection group had an increased risk of in-hospital mortality (adjusted odds ratio [aOR]:1.62; 95% CI: 1.17-2.24; P = 0.004), septic shock (aOR: 1.66; 95% CI 1.10-2.52; P = 0.016), respiratory failure (aOR: 1.78; 95% CI 1.53-2.06; P = 0.001), and a longer hospital stay [5.5 days vs 4.59 days; P = 0.001). Concurrent COVID-19 and PE admissions is associated with an increased in-hospital mortality, risk of septic shock and respiratory failure, and a longer length of hospital stay.
Introduction
SARS-COVID-19 infection, a devastating global pandemic with > 663 million cases worldwide and responsible for >1.1 million deaths in the United States, is known to be associated with worse outcomes in patients with cardiovascular co-morbidities.1, 2, 3, 4 Isolated untreated PE is known to have a mortality as high as 30%, and up to 10% of patients die suddenly.5 Studies have shown that persons with severe COVID-19 disease requiring hospitalization are at a higher risk of venous thromboembolism (VTE), especially when requiring ICU care.6, 7, 8, 9 Pulmonary embolism (PE) incidence in hospitalized patients with COVID-19 infection was reported to be around 12.8%-14.1%, increasing to 22% to 24% in ICU patients.7 , 10 Furthermore, in a national registry analysis, the risk ratio of developing PE after COVID-19 infection was found to be 33.05 compared to the general population.11 Although not completely understood, the effect of the viral infection on all 3 pathways in the Virchow triad (stasis, hypercoagulability, and endothelial injury), has been postulated to be cardinal to the development of VTE in severe COVID-19 infection.12
Despite this increased incidence of PE among patients hospitalized with COVID-19 infection, there is limited data on the impact of COVID-19 infection on the in-hospital outcomes of patients admitted with concurrent COVID-19 and PE. Therefore, this study aims to bridge this existing gap in the literature.
Methods
Data were pulled from the National Inpatient Sample (NIS) Database year 2020. The Agency supported the Healthcare Cost Utilization Project (HCUP) for Healthcare Research and Quality (AHRQ), and the NIS is a portion of a family of databases generated by the HCUP. The NIS was designed and sustained by the AHRQ and is the largest publicly available all-payer inpatient database designed to produce U.S. regional and national estimates of patient utilization, admission, fee, quality, and outcomes. It was designed as a stratified probability sample representing all non-federal acute care hospitals nationwide. The attributes of the design and description of NIS can be encountered at https://www.hcup-us.ahrq.gov/nisoverview.jsp.
Discharge details comprise patient demographics, primary payer, hospital features, principal diagnosis, secondary diagnoses, and procedural diagnoses. Next, hospitals are stratified according to ownership/control, bed dimensions, teaching class, urban/rural location, and geographic area. A 20% probability sample of all hospitals within each stratum is collected. Finally, all discharges from these hospitals are recorded and weighted to represent nationally. Data from 49 statewide data organizations (48 states plus the District of Columbia) encompassing more than 97% of the U.S. population was included in the NIS sampling frame.
Inclusion Criteria and Study Variables
Our team conducted a retrospective study design of hospitalizations using NIS year 2020 with a principal diagnosis of pulmonary embolism (PE), who were further stratified with COVID-19 vs without COVID-19 in acute-care hospitals across the United States. Hospitalizations were selected from the NIS database (online at https://www.hcup-us.ahrq.gov). The study population consisted of all inpatient hospitalizations recorded in the NIS for the year 2020 for patients 18 years and above, meeting our diagnostic criteria (Fig 1 ). Study variables included age, gender, race, hospital characteristics (teaching vs nonteaching, bed size; small, medium, and large), hospital region (northeast, midwest, south, and west), insurance (medicare, medicaid, private, and others), median annual income expected for patient's zip code, medical comorbidities, primary and secondary outcomes (outlined below). We used the following ICD-10 codes to identify PE (I26) and COVID-19 diagnoses (U071) (Supplemental Table 1). In addition, we studied baseline characteristics, inpatient mortality predictors, and outcomes (primary and secondary).
FIG 1.
Flow chart of patients selection from the NIS database. (Color version of figure is available online.)
Outcomes Measured
The primary outcome was a mortality comparison in an acute hospital setting among patients principally admitted for PE with vs without COVID-19. The secondary outcomes were (a) cardiogenic shock, (b) NSTEMI, (c) use of a cardiovascular assist device (intra-aortic balloon pump (d) septic shock, (e) acute kidney injury, (f) resource utilization associated with hospitalization defined by the average length of hospital stay, and patients charges (g) independent predictors for mortality. Baseline patient characteristics included demographics (age, sex, race), primary expected payer, median household income for the patient's ZIP code, hospital characteristics (teaching vs nonteaching, bed size; small, medium, and large), hospital region (northeast, midwest, south, and west), Elixhauser comorbidities as defined by the AHRQ, which include: congestive heart failure, myocardial infarction, peripheral vascular disease, valvular heart disease, cardiac arrhythmias, cerebrovascular disease, dementia, chronic pulmonary disease, obesity, rheumatic disease, peptic ulcer disease, liver disease, diabetes without chronic complication, diabetes with chronic complication, hypothyroidism, coagulopathy, fluid and electrolyte disorders, hemiplegia or paraplegia, renal disease, any malignancy (solid, leukemia, lymphoma except skin malignancy), metastatic tumor, AIDS/HIV, alcohol abuse, drug abuse, depression, and psychoses.
Statistical Analysis
We performed our analysis using STATA (Statistics and Data Science), version 17.0 NP-Parallel Edition (Stata Corp, TX). We created Table 1 by analyzing continuous variables using the independent student t-test, while the fisher exact test was used for proportional variables. Logistic regression was used for binary or dichotomous, or categorical variables. Poisson regression was used for discrete variables due to unequal variable distribution. Linear regression was used for continuous variables. A univariate logistic regression, linear regression, and Poisson regression model analysis were used for the unadjusted outcomes measured. A univariate model was used to calculate unadjusted odds ratios (ORs) for the primary and secondary outcomes, while a multivariable logistic, linear, and Poisson regression model was used to calculate adjusted odds ratios (ORs) for the primary and secondary outcomes. Variables with P-values <0.1 within our univariate model were added to our multivariable logistic regression model. All P-values were two-sided, and a P-values <0.05 was considered statistically significant in the multivariable analysis model.
TABLE 1.
Baseline characteristics of patients admitted with pulmonary embolism with and without Concurrent Covid-19 infection
| Variables | PE with COVID-19 (N = 4580) | PE without COVID-19 (N = 274,260) | P-value |
|---|---|---|---|
| Mean age, in years | [60.3 SD ± 16.1] | [63.6 SD ± 15.6] | |
| Age group | 0.001 | ||
| 18 - 44 y | 840 (18.34%) | 35,335 (12.88%) | |
| 45 - 64 y | 1775 (38.76%) | 95,805 (34.93%) | |
| 65 - 89 y | 1884 (41.16) | 135,280 (49.33%) | |
| ≥ 90 y | 80 (1.75%) | 7839 (2.86%) | |
| Race | 0.001 | ||
| White | 2195 (49.33%) | 181,595 (67.86%) | |
| Black | 1400 (31.46%) | 54,690 (20.44%) | |
| Hispanic | 705 (15.84%) | 19,800 (7.40%) | |
| Asian | 41 (0.9%) | 4020 (1.50%) | |
| Others | 110 (2.47%) | 7500 (2.8%) | |
| Gender | 0.601 | ||
| Male | 2400 (52.4%) | 141,385 (51.55%) | |
| Female | 2180 (47.6%) | 132,875 (48.45%) | |
| Insurance | 0.001 | ||
| Medicare | 2150 (46.99%) | 154,505 (56.41%) | |
| Medicaid | 655 (14.32%) | 33,140 (12.10%) | |
| Private including HMO | 1400 (30.60%) | 68,559 (25.03%) | |
| Self-pay | 145 (3.17%) | 9550 (3.49%) | |
| Others/uninsured | 225 (4.92%) | 8124 (2.97%) | |
| Hospital bed size | 0.690 | ||
| Small | 890 (19.43%) | 55,545 (20.25%) | |
| Medium | 1320 (28.82%) | 75,765 (27.63%) | |
| Large | 2,370 (51.75%) | 142,950 (52.12%) | |
| Hospital teaching status | 0.309 | ||
| Nonteaching hospital | 1024 (22.38%) | 65,415 (23.85%) | |
| Teaching hospital | 3,555 (77.62%) | 208,845 (76.15%) | |
| Hospital region | 0.005 | ||
| Northeast | 935 (20.41%) | 50,540 (18.43%) | |
| Midwest | 1230 (26.86%) | 64,940 (23.68%) | |
| South | 1,775 (38.78%) | 109,350 (39.87%) | |
| West | 640 (13.97%) | 49,430 (18.02%) | |
| Median annual income expected for patient's zip code, US$# | 0.001 | ||
| $1-$45,999 | 1625 (35.95%) | 81,610 (30.23%) | |
| $46,000-$58,999 | 1160 (25.66%) | 73,735 (27.31%) | |
| $59,000-$78,999 | 1030 (22.79%) | 62,955 (23.32%) | |
| >= $79,000 | 705 (15.60%) | 51,705 (19.15%) | |
| Weekend admission | 0.103 | ||
| Yes | 1110 (24.24%) | 60,235 (21.96%) | |
| No | 3470 (75.76%) | 215,025 (78.04%) | |
| Comorbidities | |||
| Coronary artery disease | 795 (17.36%) | 79,590 (29.02%) | 0.001 |
| Congestive heart failure | 845 (18.45%) | 67,285 (24.53) | 0.001 |
| Previous myocardial infarct | 170 (3.71%) | 25,475 (9.29%) | 0.001 |
| Previous PCI | 10 (0.22%) | 1800 (0.66%) | 0.100 |
| Peripheral vascular disease | 130 (2.84%) | 10,660 (3.89%) | 0.135 |
| History of CABG | 150 (1.2%) | 16,130 (2.62%) | 0.001 |
| Atrial fibrillation | 575 (12.55%) | 43,220 (15.76%) | 0.011 |
| Atrial flutter | 130 (2.84%) | 5490 (2.17%) | 0.190 |
| Pulmonary hypertension | 280 (6.11%) | 27,735 (10.11%) | 0.001 |
| Chronic kidney disease | 1250 (27.29%) | 68,570 (25%) | 0.122 |
| Hypertension | 1475 (32.21%) | 97,250 (35.46%) | 0.036 |
| Previous stroke | 25 (0.55%) | 2385 (0.87%) | 0.294 |
| Carotid artery disease | 30 (0.66%) | 3710 (1.35%) | 0.067 |
| Dyslipidemia | 1680 (36.68%) | 127,490 (46.49%) | 0.001 |
| Diabetes | 1555 (33.95%) | 86,870 (31.67%) | 0.143 |
| Chronic liver disease | 290 (6.33%) | 15,055 (5.49%) | 0.270 |
| Obesity | 1255 (27.4%) | 69,575 (25.37%) | 0.158 |
| Anemia | 1554 (33.95%) | 88,460 (32.25%) | 0.270 |
| Nicotine use | 1250 (27.29%) | 115,360 (42.06%) | 0.001 |
| DVT | 15 (0.33%) | 2084 (0.76%) | 0.124 |
| Protein energy malnutrition | 185 (4.04%) | 14,905 (5.43%) | 0.065 |
| Frailty | 5 (0.11%) | 740 (0.27%) | 0.351 |
| Long-term anticoagulation use | 740 (16.16%) | 53,160 (19.38%) | 0.016 |
| Aspirin use | 785 (17.14%) | 66,430 (24.22%) | 0.001 |
| Previous pacemaker implant | 55 (1.20%) | 7180 (2.62%) | 0.007 |
| Dementia | 390 (8.52%) | 14,060 (5.13%) | 0.001 |
| History of malignancy | 40 (0.87%) | 3810 (1.39%) | 0.183 |
| Interventions | |||
| Catheter direct thrombolysis | 120 (2.62%) | 6640 (2.42%) | 0.693 |
| Surgical / Mechanical thrombectomy | 145 (3.17%) | 6280 (2.29%) | 0.073 |
| Concurrent thrombotic events | |||
| Ischemic stroke | 60 (1.31%) | 3370 (1.23%) | 0.589 |
| DVT | 15 (0.33%) | 2084 (0.76%) | 0.124 |
| Elixhauser comorbidity index | 0.001 | ||
| 0-2 | 990 (21.62%) | 47,370 (17.27%) | |
| 3-4 | 1645 (35.92%) | 95,650 (34.88%) | |
| ≥ 5 | 1945 (42.47%) | 131,240 (47.85%) | |
| Elective admission | 0.001 | ||
| Yes | 160 (3.49%) | 25,515 (9.31%) | |
| No | 4420 (96.51%) | 248,505 (90.69%) |
Analyses used Pearson's χ2 test and 1-way analysis of variance for categorical and continuous variables respectively. PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft; ESRD, end stage renal disease.
Elixhauser comorbidity index was used to adjust for comorbidity burden for the primary and secondary outcomes. The severity of comorbidities was quantified using the Elixhauser comorbidity index. The original weighted Elixhauser scores, developed by Van Walraven et al.,12 were computed and further stratified into groups (0-2, 3-4, and ≥ 5). The comorbidity score was then calculated for each patient by summing the individual weights of all comorbidities. Weighted estimates were calculated by applying discharge weight to the unweighted discharge records. Weighted estimates were used for all statistical analyses.
We adjusted for covariates in our model with age, gender, race, insurance, hospital teaching status, hospital bed size, use of aspirin, long-term use of anticoagulation, nicotine use, baseline use of oxygen, and Elixhauser comorbidity index. Elixhauser comorbidity index includes congestive heart failure, myocardial infarction, peripheral vascular disease, valvular heart disease, cardiac arrhythmias, cerebrovascular disease, dementia, chronic pulmonary disease, obesity, rheumatic disease, peptic ulcer disease, liver disease, diabetes without chronic complication, diabetes with chronic complication, hypothyroidism, coagulopathy, fluid and electrolyte disorders, hemiplegia or paraplegia, renal disease, any malignancy (solid, leukemia, lymphoma except skin malignancy), metastatic tumor, AIDS/HIV, alcohol abuse, drug abuse, depression, and psychoses.
Result
Patient Characteristics
Of 32,355,827 patients in the NIS year 2020 registry, a total of 278,840 patients 18 years or older admitted with acute PE were identified. They were stratified into 2 cohorts, 4580 with concurrent PE and COVID-19 infection and 274,260 with PE only. The baseline characteristics of the 2 groups are described in Table 1 and Table 2 .
TABLE 2.
Disposition of patients admitted with pulmonary embolism with and without concurrent COVID-19 infection
| Disposition of patients | PE with COVID-19 (N = 4580) | PE without COVID-19 (N = 274,260) |
|---|---|---|
| Home | 2775 (60.59%) | 174,740 (63.73%) |
| Transfer to short-term hospital | 110 (2.4%) | 5965 (2.18%) |
| Skilled Nursing facility, Intermediate care, and another type of facility | 775 (16.92%) | 33,305 (12.15%) |
| Home with home health care | 650 (14.19%) | 48,900 (17.83%) |
| Left against medical advice | 55 (1.2%) | 2945 (1.07%) |
| Died in hospital | 215 (4.69%) | 8330 (3.04%) |
Patients with concurrent PE and COVID-19 infection were noted to be younger (mean age of 60.3 years) compared to those with PE alone (mean age of 63.6 years). Among patients with concurrent PE and COVID-19 infection, 47.6% were females while 48.45% were females in the PE without COVID-19 group. There were more blacks ((31.6% vs 20.44%,) and Hispanics (15.84% vs 7.40%) in the PE/COVID-19 group while the PE alone group had more white population (67.86% vs 49.33%). Furthermore, there was a higher proportion of patients living in the zip codes associated with the lowest expected annual income in the concurrent COVID-19 and PE group (35.95%) compared to the group with PE alone (30.23%)
With regards to the baseline comorbidities, patients with concurrent PE and COVID-19 were only more likely to be ESRD patients on dialysis (15.17% vs 9.79%; P = 0.001) and to have dementia (8.52% vs 5.13%; P = 0.001) compared to patients with PE only. In addition, patients with concurrent PE and COVID-19 infection were more likely to have Elixhauser comorbidity index 0-2 (21.62% vs 17.27%) and 3-4 (35.92% vs 34.88%). In contrast, patients with PE without concurrent COVID-19 infection were likely to have congestive heart failure (24.53 vs 18.45%; P = 0.001), pulmonary hypertension (10.11% vs 6.11%; P = 0.001), COPD (14.32% vs 7.97%; P = 0.001), to have long term anticoagulation use (19.38% vs 16.16%; P = 0.016) and Elixhauser comorbidity index ≥ 5 (47.85% vs 42.47%; P = 0.001).
Primary outcomes
Among patients with concurrent PE and COVID-19, 4.69% died during the hospitalization compared to 3.04% of patients who had PE without COVID-19 with an adjusted odds ratio of 1.62 (95% CI 1.17-2.24; P = 0.004] for in-hospital mortality. Table 3
TABLE 3.
Comparative in-hospital outcome of patients admitted with pulmonary embolism with and without concurrent COVID-19 infection
| Outcomes | PE with COVID-19 | PE without COVID-19 | aOR/aIRR* | 95% CI | p-value |
|---|---|---|---|---|---|
| In-hospital Mortality | (4.69%) | (3.04%) | 1.62 | 1.17-2.24 | 0.004 |
| Cardiogenic shock | 65 (1.42%) | 4950 (1.8%) | 0.80 | 0.43-1.47 | 0.465 |
| IABP | 10 (0.22%) | 840 (0.31%) | 0.79 | 0.20-3.17 | 0.742 |
| NSTEMI | 105 (2.29%) | 10,525 (3.84%) | 0.65 | 0.42-1.02 | 0.062 |
| Ischemic stroke | 60 (1.31%) | 3370 (1.23%) | 1.07 | 0.60-1.89 | 0.824 |
| Septic shock | 125 (2.73%) | 4430 (1.62%) | 1.66 | 1.10-2.52 | 0.016 |
| Acute kidney injury | 610 (13.32%) | 41,315 (15.06%) | 0.95 | 0.78-1.17 | 0.660 |
| Respiratory failure | 1470 (32.1%) | 62,560 (22.81%) | 1.78 | 1.53-2.06 | 0.001 |
| RF with intubation | 20 (0.44%) | 490 (0.18%) | 2.48 | 0.88-6.98 | 0.084 |
| Length of Stay | 5.5 days | 4.59 days | 1.23* | 1.14-1.33 | 0.001 |
| Average patient charges, USD | $ 74,313.4 | $80,012.2 | 0.95* | 0.86-1.04 | 0.281 |
aOR, adjusted odd ratio, aIRR, adjusted incidence rate ratio, aMD, adjusted mean difference, CI, confidence interval.
Outcomes adjusted for age, gender, race, insurance status, hospital characteristics (teaching status, & bed size), baseline oxygen use, anticoagulation and antiplatelet use, nicotine use, and comorbidities measured using the Elixhauser comorbidity index.
Secondary Outcomes
With regards to the secondary outcomes, patients with concurrent PE and COVID-19 infection had an increased risk of septic shock (aOR: 1.66; 95% CI 1.10-2.52; P = 0.016), respiratory failure (aOR = 1.78; 95% CI 1.53-2.06; P = 0.001), and a longer length of hospital stay (5.5 days vs 4.59 days; aIRR: 1.23; 95% CI 1.14-1.33; P = 0.001) compared to patients with PE alone. Meanwhile, there was no difference in the risk of cardiogenic shock (aOR: 0.80; 95% CI 0.43-1.47; P = 0.465), need for intra-aortic balloon pump use (aOR: 0.79; 95% CI 0.20-3.17; P = 0.742), Non-ST-elevation myocardial infarction (aOR: 0.65; 95% CI 0.42-1.02; P = 0.062), acute Kidney Injury (aOR: 0.95; 95% CI 0.78-1.17; P = 0.660), and respiratory failure requiring intubation (aOR: 2.48; 95% CI 0.88-6.98; P = 0.084) between the two groups. Table 3
Predictors of In-Hospital Mortality
Among patients with concurrent PE and Covid-19 infection, the predictors of in-hospital mortality included older age; 65-89 years old (aHR: 4.1; 95% CI: 1.3-14.2; P = 0.020), above 90 years old (aHR: 4.8; 95% CI: 1.5-16.1; P = 0.001), presence of pulmonary hypertension (aHR:3.2; 95% CI: 1.5-6.7; P = 0.002), being transferred in from another type of health facility (aHR: 2.2; 95% CI: 1.1-4.5; P = 0.001), electrolyte abnormalities (aHR: 3.9; 95% CI: 1.1-7.5; P = 0.030), having no medical insurance and unable to self-pay (aHR: 2.4; 95% CI: 1.2-4.4; P = 0.001), Frailty (aHR:1.7; 95% CI: 1.3-2.7; P = 0.001), history of coronary artery bypass grafting (aHR: 2.8; 95% CI 1.4-4.7; P = 0.001), history of percutaneous coronary intervention (aHR: 2.6; 95% CI: 1.3-5.9; P = 0.001), history of pacemaker Insertion (aHR: 5.7; 95% CI: 2.7-12.0; P = 0.001) and an Elixhauser comorbidity index of 5 or above (aHR: 2.8; 95% CI: 1.5-5.1; P = 0.001). Table 4 , and Figure 2 .
TABLE 4.
Predictors of in-hospital mortality in patients admitted with concurrent PE and COVID-19 Infection
| Predictors of PE with COVID-19 mortality | 95% lower CI | 95 % upper CI | aHR | P-value |
|---|---|---|---|---|
| Age Group: 18 - 44 y | ref | ref | ref | ref |
| 45-64 y | 0.4 | 5.6 | 1.6 | 0.497 |
| 65-89 y | 1.3 | 14.2 | 4.1 | 0.020 |
| ≥ 90 y | 1.5 | 16.1 | 4.8 | 0.001 |
| Pulmonary hypertension | 1.5 | 6.7 | 3.2 | 0.002 |
| Transferred in from another type of health facility | 1.1 | 4.5 | 2.2 | 0.001 |
| Electrolyte abnormalities | 1.1 | 7.5 | 3.9 | 0.030 |
| Uninsured/others | 1.2 | 4.4 | 2.4 | 0.001 |
| Frailty | 1.3 | 2.7 | 1.7 | 0.001 |
| Prior CABG for CAD | 1.4 | 4.7 | 2.8 | 0.001 |
| Prior percutaneous coronary intervention | 1.3 | 5.9 | 2.6 | 0.001 |
| Prior pacemaker insertion | 2.7 | 12.0 | 5.7 | 0.001 |
| Elixhauser comorbidity index ≥5 †(Ref: ≤4) | 1.5 | 5.1 | 2.8 | 0.001 |
aHR, adjusted hazard ratio, CI, confidence interval; PE, Pulmonary Embolism.
FIG 2.
Predictors of Mortality In Patients Admitted With Concurrent COVID-19 and pulmonary embolism. (Color version of figure is available online.)
Discussion
In this largest and first-of-its-kind nationwide analysis of 278,840 patients, we sought to assess the in-hospital outcomes, and the predictors of mortality in patients admitted with concurrent PE and COVID-19. The major findings in our study can be summarized as follows; (1) Patients with concurrent PE and COVID-19 infection were more likely to be younger, African American, Hispanics, and of low socioeconomic background. (2) Patients admitted with PE and concurrent COVID-19 infection have higher mortality compared to patients with PE only. (3) They are also at higher odds of septic shock, respiratory failure and have a longer duration of hospital stay. (4) Increasing age, frailty, Elixhauser comorbidity index ≥5, and being uninsured were major predictors of in-hospital mortality among patients admitted with concurrent PE and COVID-19 infection.
The pathogenesis of hypercoagulability in COVID-19 is multifaceted. Endothelial damage directly via COVID-19 or through complement pathway, hyperimmune response, cytokine storm, and activation of coagulation cascade have been proposed as the mechanisms responsible for the hypercoagulable state in COVID-19 infection.8 , 13, 14, 15 Deriving from previous studies, the true reported incidence of PE in the patients who succumbed to COVID-19 infection is probably an underestimation, given that not all patients undergo diagnostic imaging and autopsy of patients showed a higher incidence of PE even in those with low suspicion.16, 17, 18 Therefore, the exact burden of VTE remains unknown. Our present analysis shows that the patient group with concurrent COVID-19 infection and PE had a higher proportion of dialysis-dependent patients, Blacks, and Hispanics, and compared to the group with PE alone. The higher proportion of ESRD patients in the COVID/PE group can be explained by the need for these patients to attend their life-sustaining dialysis sessions in often crowded dialysis centers. In addition, our study aligns with previously reported racial differences in the rates of COVID-19 infection and its associated morbidity and mortality both in the United States and across the world.19, 20, 21 In addition to the thrombogenic nature of COVID-19 infection, this noted racial difference can be partly explained by already known genetic differences in hemostatic parameters and predisposition to thrombogenicity in Blacks.22, 23, 24 Furthermore, similar to previous studies noting disproportionate COVID-19 prevalence and mortality in low-income communities, we also note a higher proportion of patients with concurrent COVID-19 infection and PE were from the zip codes associated with the lowest annualized gross income.19 , 20 This can be partly explained by the fact that patients in these zip codes, due to the financial constraints had to work through the pandemic and unable to isolate due to their living conditions. These findings, further reiterate the need for strategic and intentional efforts to mitigate healthcare disparities across racial and socioeconomic divides.
In our study, a mortality rate of 4.7% was noted among patients with concurrent COVID-19 and PE, with an adjusted OR of 1.62 (CI: 1.17-2.24; P = 0.004). This finding is comparable to that reported by Miro et al in their study involving 62 Spanish emergency departments. Patients with concurrent PE and COVID-19 had a > 2-fold risk of mortality compared to patients with PE alone.25 These findings are similar to the reported higher in-hospital mortality noted in patients admitted with concurrent COVID-19 infection and other cardiovascular co-morbidities and diseases.26 , 27 This can partly be explained by the fact that patients with concurrent COVID-19 infection and PE were at a higher risk of developing in-hospital complications, in specific septic shock and respiratory failure as noted in this present study. Furthermore, the higher rates of these in-hospital complications are a plausible explanation for the longer length of stay noted among this patient population.
Among patients with concurrent PE and COVID-19 infection, major predictors of mortality included increasing age, frailty, and Elixhauser index of >5. Furthermore, being uninsured was noted to be another major predictor of mortality among patients with concurrent PE and COVID-19 infection. This difference can be explained partly by the fact uninsured patients are less likely connected to health, therefore they tend to have undiagnosed comorbidities and illness. This is comparable with previous findings highlighting the existence of significant and actionable socioeconomic disparities in major health outcomes.19 , 20 , 28
Strengths and Limitations
This study is not without limitations and the data presented should be interpreted in light of these. First, this is a retrospective analysis of a claims-based database. Secondly, given the lack of access to each individual patient's data, the findings may possibly be influenced by confounders uncounted for such as medication use (anticoagulation or systemic thrombolytic use). Thirdly, the care and management of COVID-19 infection have evolved over time, including the widespread availability of vaccination which is not accounted for in the present study.
Despite these known challenges, the sample size of the present study, the HCUP-NIS database is a well validate database that is generalizable to the entire US population given its robust internal and external validation procedures. Furthermore, the large sample size used in the analysis further strengthens the validity of this study and may help compensate for the limitations mentioned above.
Conclusion
In this large nationwide analysis, concurrent PE and COVID-19 infection were associated with worse hospital mortality, and a higher risk of developing septic shock, and respiratory failure, with a longer hospital stay. Furthermore, racial and economic differences were noted in the incidence of concurrent PE and COVID-19 infection.
Footnotes
The authors have no conflicts of interest to disclose.
Supplementary material associated with this article can be found in the online version at doi:10.1016/j.cpcardiol.2023.101669.
Appendix. Supplementary materials
References
- 1.Organization, W.H. WHO Coronavirus (COVID-19) Dashboard. 2023 Accessed January 19, 2023; Available from: https://covid19.who.int/.
- 2.Prevention, C.f.D.C.a. COVID-19 Mortality Overview. 2023 Accessed January 19, 2023; Available from: https://www.cdc.gov/nchs/covid19/mortality-overview.htm.
- 3.Roth G.A., Vaduganathan M., Mensah G.A. Impact of the COVID-19 pandemic on cardiovascular health in 2020. J Am Coll Cardiol. 2022;80(6):631–640. doi: 10.1016/j.jacc.2022.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhou F., et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020;395(10229):1054–1062. doi: 10.1016/S0140-6736(20)30566-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Shamaki G.R., et al. Endovascular venous interventions - a state-of-the-art review. Current Problems Cardiol. 2023;48(3) doi: 10.1016/j.cpcardiol.2022.101534. [DOI] [PubMed] [Google Scholar]
- 6.Piazza G., et al. Registry of arterial and venous thromboembolic complications in patients with COVID-19. J Am Coll Cardiol. 2020;76(18):2060–2072. doi: 10.1016/j.jacc.2020.08.070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Mansory E.M., Srigunapalan S., Lazo-Langner A. Venous thromboembolism in hospitalized critical and noncritical COVID-19 patients: a systematic review and meta-analysis. TH Open. 2021;5(3):e286–e294. doi: 10.1055/s-0041-1730967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Cui S., et al. Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia. J Thromb Haemost. 2020;18(6):1421–1424. doi: 10.1111/jth.14830. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lo Re V., 3rd, et al. Association of COVID-19 vs influenza with risk of arterial and venous thrombotic events among hospitalized patients. Jama. 2022;328(7):637–651. doi: 10.1001/jama.2022.13072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Nopp S., et al. Risk of venous thromboembolism in patients with COVID-19: a systematic review and meta-analysis. Res Pract Thromb Haemost. 2020;4(7):1178–1191. doi: 10.1002/rth2.12439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Katsoularis I., et al. Risks of deep vein thrombosis, pulmonary embolism, and bleeding after covid-19: nationwide self-controlled cases series and matched cohort study. Bmj. 2022;377 doi: 10.1136/bmj-2021-069590. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hajra A., et al. Management of thrombotic complications in COVID-19: an update. Drugs. 2020;80(15):1553–1562. doi: 10.1007/s40265-020-01377-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Campbell C.M., Kahwash R. Will complement inhibition be the new target in treating COVID-19-related systemic thrombosis? Circulation. 2020;141(22):1739–1741. doi: 10.1161/CIRCULATIONAHA.120.047419. [DOI] [PubMed] [Google Scholar]
- 14.Maier C.L., et al. COVID-19-associated hyperviscosity: a link between inflammation and thrombophilia? Lancet. 2020;395(10239):1758–1759. doi: 10.1016/S0140-6736(20)31209-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Varga Z., et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet. 2020;395(10234):1417–1418. doi: 10.1016/S0140-6736(20)30937-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Dolhnikoff M., et al. Pathological evidence of pulmonary thrombotic phenomena in severe COVID-19. J Thromb Haemost. 2020;18(6):1517–1519. doi: 10.1111/jth.14844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Roncon L., et al. Incidence of acute pulmonary embolism in COVID-19 patients: systematic review and meta-analysis. Eur J Intern Med. 2020;82:29–37. doi: 10.1016/j.ejim.2020.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Wichmann D., et al. Autopsy findings and venous thromboembolism in patients with COVID-19: a prospective cohort study. Ann Intern Med. 2020;173(4):268–277. doi: 10.7326/M20-2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Abedi V., et al. Racial, economic, and health inequality and COVID-19 infection in the United States. J Racial Ethn Health Disparities. 2021;8(3):732–742. doi: 10.1007/s40615-020-00833-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Magesh S., et al. Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis. JAMA Netw Open. 2021;4(11) doi: 10.1001/jamanetworkopen.2021.34147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Knight R., et al. Association of COVID-19 with major arterial and venous thrombotic diseases: a population-wide cohort study of 48 million adults in England and Wales. Circulation. 2022;146(12):892–906. doi: 10.1161/CIRCULATIONAHA.122.060785. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Chaudhary R., et al. Race-related disparities in COVID-19 thrombotic outcomes: beyond social and economic explanations. EClin Med. 2020;29 doi: 10.1016/j.eclinm.2020.100647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kim H.K., et al. Ethnic difference of thrombogenicity in patients with cardiovascular disease: a pandora box to explain prognostic differences. Korean Circ J. 2021;51(3):202–221. doi: 10.4070/kcj.2020.0537. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Lev E.I., et al. Influence of race and sex on thrombogenicity in a large cohort of coronary artery disease patients. J Am Heart Assoc. 2014;3(5) doi: 10.1161/JAHA.114.001167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Miró Ò., et al. Pulmonary embolism in patients with COVID-19: incidence, risk factors, clinical characteristics, and outcome. Eur Heart J. 2021;42(33):3127–3142. doi: 10.1093/eurheartj/ehab314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Goel A., et al. Impact of COVID-19 on outcomes of patients hospitalized with STEMI: a nationwide propensity-matched analysis. Curr Probl Cardiol. 2022;48(4) doi: 10.1016/j.cpcardiol.2022.101547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Isath A., et al. COVID-19, heart failure hospitalizations, and outcomes: a nationwide analysis. Curr Probl Cardiol. 2022;48(4) doi: 10.1016/j.cpcardiol.2022.101541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wadhera R.K., et al. Association of socioeconomic disadvantage with mortality and readmissions among older adults hospitalized for pulmonary embolism in the United States. J Am Heart Assoc. 2021;10(13) doi: 10.1161/JAHA.121.021117. [DOI] [PMC free article] [PubMed] [Google Scholar]
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