Literature DB >> 33852591

Impact of COVID-19 on the hospitalization, treatment, and outcomes of intracerebral and subarachnoid hemorrhage in the United States.

Vijay M Ravindra1,2, Ramesh Grandhi2, Alen Delic3, Samuel Hohmann4, Ernie Shippey4, David Tirschwell5, Jennifer A Frontera6, Shadi Yaghi6,7, Jennifer J Majersik3, Mohammad Anadani8, Adam de Havenon3.   

Abstract

OBJECTIVE: To examine the outcomes of adult patients with spontaneous intracranial and subarachnoid hemorrhage diagnosed with comorbid COVID-19 infection in a large, geographically diverse cohort.
METHODS: We performed a retrospective analysis using the Vizient Clinical Data Base. We separately compared two cohorts of patients with COVID-19 admitted April 1-October 31, 2020-patients with intracerebral hemorrhage (ICH) and those with subarachnoid hemorrhage (SAH)-with control patients with ICH or SAH who did not have COVID-19 admitted at the same hospitals in 2019. The primary outcome was in-hospital death. Favorable discharge and length of hospital and intensive-care stay were the secondary outcomes. We fit multivariate mixed-effects logistic regression models to our outcomes.
RESULTS: There were 559 ICH-COVID patients and 23,378 ICH controls from 194 hospitals. In the ICH-COVID cohort versus controls, there was a significantly higher proportion of Hispanic patients (24.5% vs. 8.9%), Black patients (23.3% vs. 20.9%), nonsmokers (11.5% vs. 3.2%), obesity (31.3% vs. 13.5%), and diabetes (43.4% vs. 28.5%), and patients had a longer hospital stay (21.6 vs. 10.5 days), a longer intensive-care stay (16.5 vs. 6.0 days), and a higher in-hospital death rate (46.5% vs. 18.0%). Patients with ICH-COVID had an adjusted odds ratio (aOR) of 2.43 [1.96-3.00] for the outcome of death and an aOR of 0.55 [0.44-0.68] for favorable discharge. There were 212 SAH-COVID patients and 5,029 controls from 119 hospitals. The hospital (26.9 vs. 13.4 days) and intensive-care (21.9 vs. 9.6 days) length of stays and in-hospital death rate (42.9% vs. 14.8%) were higher in the SAH-COVID cohort compared with controls. Patients with SAH-COVID had an aOR of 1.81 [1.26-2.59] for an outcome of death and an aOR of 0.54 [0.37-0.78] for favorable discharge.
CONCLUSIONS: Patients with spontaneous ICH or SAH and comorbid COVID infection were more likely to be a racial or ethnic minority, diabetic, and obese and to have higher rates of death and longer hospital length of stay when compared with controls.

Entities:  

Mesh:

Year:  2021        PMID: 33852591      PMCID: PMC8046225          DOI: 10.1371/journal.pone.0248728

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

The novel coronavirus severe acute respiratory syndrome-CoV-2 (SARS-CoV-2) has led to a global pandemic of patients with COVID-19, the disease caused by the coronavirus, with significant impact [1]. Neurological complications of COVID-19, including encephalopathy, seizure, and cerebrovascular disease, have been described [2-4]; a recent meta-analysis demonstrated that SARS-CoV-2 increased risk of ischemic and cryptogenic stroke [5]. Increased use of anticoagulants in the context of COVID-19–associated thrombotic events, endothelial injury that may predispose vessels to rupture, and COVID-19–related cerebral sinus thrombosis may predispose COVID-19 patients to higher rates of intracranial hemorrhage. The clinical outcomes of patients with intracranial hemorrhage with COVID-19 are not fully known, with only small cohorts of patients with intracranial hemorrhage and comorbid COVID-19 published to date [6-13]. The objective of the current study is to examine the outcomes of adult patients with spontaneous intracerebral hemorrhage (ICH) and those with nontraumatic subarachnoid hemorrhage (SAH) who were diagnosed with comorbid COVID-19 in a national dataset. We hypothesize that concomitant COVID-19 will increase mortality in this population. We also predict that patients with COVID-19 will have a longer length of stay in the intensive care unit (ICU) and in the hospital.

Methods

We performed a retrospective analysis using the Vizient Clinical Data Base, a healthcare analytics platform employed by 568 participating US hospitals for the purpose of benchmarking clinical performance, costs, and outcomes [14]. Institutional review board approval was not required to use this deidentified data set. We identified one cohort of adult patients who were hospitalized between April 1 and October 31, 2020, with International Classification of Diseases, 10th revision (ICD-10) codes for ICH (I61*) as a primary or secondary discharge diagnosis and excluded those with ICD-10 coding for SAH (I60*) or ischemic stroke (I63*, H34.1). We identified a second cohort of adult patients who were hospitalized during the same period with SAH who did not have ICH or ischemic stroke. Patients with elective admission or receiving hospice care at admission were excluded. We identified patients who had comorbid COVID-19, determined by the ICD-10 code U07.1, which is reserved for laboratory-confirmed infection with SARS-CoV-2. We compared the patients with COVID-19 in each group to similar groups (ICH or SAH) without COVID-19 treated at the same hospitals in 2019. The primary outcome was in-hospital death. Favorable discharge, defined as discharge home or to acute rehabilitation, was a secondary outcome. Conversely, unfavorable discharge was defined as discharge to skilled nursing facility or death. We also evaluated length of hospital and ICU stay. We report descriptive statistics and test for differences using the chi-squared test, Student’s t-test, or Wilcoxon rank sum tests. We stratified our cohorts by US Census region and hospital size (≤150 beds, 151–250 beds, 251–500 beds, and ≥500 beds) to investigate for differences in our outcomes based on the stratifications. To account for patient clustering by hospital, we fit mixed-effects logistic regression models to our outcomes. The mixed-effects model estimates a separate intercept for each hospital to account for between-hospital differences, such as case volume or proportion of cases with COVID, thus treating each hospital as its own sample in the statistical analysis. The models were adjusted for patient age, sex, race/ethnicity, Elixhauser comorbidity score, intubation, acute coronary syndrome, acute renal failure, pulmonary embolism, diabetes, congestive heart failure, obesity, and smoking. In a sensitivity analysis, we restricted the cohort to patients who had a diagnosis of ICH or SAH that was present on hospital admission. For all analyses, significance was set at p≤0.05 and analysis was performed using Stata 16.0 (StataCorp, College Park, TX).

Results

ICH-COVID

For the ICH cohort, we included 559 ICH-COVID patients and 23,379 controls from 194 hospitals, of which 56 were in the Northeast Census region, 47 in the Midwest, 63 in the South, and 28 in the West. The control cohort was comprised of mostly white patients (57.1%), whereas in the ICH-COVID cohort there were higher proportions of Hispanic patients (24.5% vs. 8.9%), Asian patients (6.1% vs. 4.7%), and Black patients (23.3% vs. 20.9) (Table 1). There were also significantly higher proportions of patients with obesity, diabetes, atrial fibrillation, or congestive heart failure, more male patients, and more patients <75 years in the ICH-COVID cohort (Table 1). There was a lower proportion of smokers in the ICH-COVID cohort. In the control cohort, the ICH was present on admission in 93.0% of patients, it was present on admission in only 53.5% of the ICH-COVID cohort (p<0.001). This suggests that almost half of ICH in patients with COVID-19 happened during the hospitalization. A significantly higher proportion of patients in the ICH-COVID cohort were administered heparin, enoxaparin, and dalteparin during hospitalization (69.8% vs. 55.4%, p<0.001). In the ICH-COVID patients, the proportion who received an anticoagulant (heparin, enoxaparin, or dalteparin) was higher in patients who did not have ICH present on admission than those who did (83.5% vs. 57.9%, p<0.001). In patients with ICH and a documented NIHSS, there were 93 ICH-COVID patients and 6,865 controls, with a median (IQR) NIHSS of 15 (5–24) versus 9 (3–19) (p<0.001).
Table 1

Baseline demographics and outcomes of patients with intracerebral hemorrhage with or without COVID-19 infection.

VariableControlICH-COVIDp value
(2019)(April–October 2020)
(n = 23,378)(n = 559)
Age category in years  <0.001
<18383 (1.6%)suppressed*
18–504,224 (18.1%)120 (21.5%)
51–646,547 (28.0%)200 (35.8%)
65–745,399 (23.1%)126 (22.5%)
75–792,434 (10.4%)41 (7.3%)
≥804,391 (18.8%)68 (12.2%)
Age <75 years16,553 (70.8%)450 (80.5%)<0.001
Male sex12,794 (54.7%)345 (61.7%)0.001
Race/Ethnicitya  <0.001
White13,350 (57.1%)171 (30.6%)
Black4,893 (20.9%)130 (23.3%)
Hispanic2,081 (8.9%)137 (24.5%)
Asian1,090 (4.7%)34 (6.1%)
Other/Unknown1,964 (8.4%)87 (15.6%)
ICH diagnosis present on admission21,738 (93.0%)299 (53.5%)<0.001
Elixhauser score   
Median, IQR3, 2–54, 3–5<0.001
Mean±SD3.3±2.04.2±2.3<0.001
Obese3,145 (13.5%)174 (31.1%)<0.001
Smoker2,696 (11.5%)18 (3.2%)<0.001
Atrial fibrillation4,474 (19.1%)132 (23.6%)0.008
Hypertension15,854 (67.8%)354 (63.3%)0.025
Diabetes6,657 (28.5%)242 (43.3%)<0.001
Dyslipidemia9,628 (41.2%)235 (42.0%)0.685
Congestive heart failure3,334 (14.3%)107 (19.1%)0.001
Interfacility transfer10,450 (44.7%)245 (43.8%)0.228
Seen in emergency department (n = 23,289)15,588 (68.5%)339 (63.7%)0.019
Intubated7,038 (30.1%)358 (64.0%)<0.001
Administered heparin, enoxaparin, or dalteparin during hospitalization12,945 (55.4%)390 (69.8%)<0.001
Acute coronary syndrome1,195 (5.1%)79 (14.1%)<0.001
Acute renal failure4,854 (20.8%)308 (55.1%)<0.001
Pulmonary embolus598 (2.6%)39 (7.0%)<0.001
Venous sinus or deep vein thrombosis467 (2.0%)10 (1.8%)0.727
Length of hospital stay in days (mean±SD)10.5±15.921.6±21.0<0.001
Length of ICU stay in daysb (mean±SD)6.0±9.416.5±16.7<0.001
Favorable discharge12,040 (51.5)146 (26.1)<0.001
In-hospital death4,216 (18.0)260 (46.5)<0.001

Binary variables presented as n, %; ordinal variables as median, IQR; interval variables as mean (SD). P values calculated with the chi-squared test for binary variables, the Wilcoxon rank sum test for ordinal variables, and Student’s t-test for interval variables.

*Value suppressed for low count.

aWhite and Black racial categories are non-Hispanic.

bLength of ICU stay restricted to patients with >24 hours spent in intensive care.

Binary variables presented as n, %; ordinal variables as median, IQR; interval variables as mean (SD). P values calculated with the chi-squared test for binary variables, the Wilcoxon rank sum test for ordinal variables, and Student’s t-test for interval variables. *Value suppressed for low count. aWhite and Black racial categories are non-Hispanic. bLength of ICU stay restricted to patients with >24 hours spent in intensive care. Additionally, a significantly higher proportion of the ICH-COVID patients were intubated (64.0% vs. 30.1%) or experienced acute coronary syndrome (14.1% vs. 5.1%), acute renal failure (55.1% vs. 20.8%), or pulmonary embolus (7.0% vs. 2.6%) during hospitalization. No differences were observed in the rates of venous sinus or deep vein thrombosis. In the ICH-COVID cohort, there was a longer hospital stay (21.6 days vs. 10.5 days, p<0.001) and longer ICU stay (16.5 days vs. 6.0 days, p<0.001) (Fig 1), lower rate of favorable discharge (26.1% vs. 51.5%, p<0.001), and higher rate of in-hospital death (46.5% vs. 18.0%, p<0.001). We did not observe differences in our primary or secondary outcomes in the stratifications by Census region (p>0.4 for in-hospital death and favorable discharge) or hospital size (p>0.2 for both outcomes). The mixed-effects logistic regression model demonstrated that patients with ICH-COVID had an adjusted odds ratio (aOR) of 2.64 [95% CI, 2.01–3.47] for the outcome of death and an aOR of 0.55 [95% CI, 0.41–0.74] for favorable discharge (Table 2). These associations remained true in the sensitivity analysis where we only included patients who had ICH that was present on hospital admission (aOR 2.01 and 0.69, respectively; p<0.001 for both).
Fig 1

(A) Bar graph demonstrating rates of in-hospital death and favorable discharge among ICH-COVID and ICH cohorts. (B) Bar graph demonstrates rates of length of ICU and hospital stay among ICH-COVID and ICH cohorts.

Table 2

Mixed-effects logistic regression fit to in-hospital death and favorable discharge, showing odds ratios for ICH patients with comorbid COVID-19 compared with controls.

DeathFavorable discharge
Odds ratio95% CISEp valueOdds ratio95% CISEp value
Unadjusted4.103.45–4.870.36<0.0010.320.27–0.370.03<0.001
Adjusted*2.431.96–3.000.26<0.0010.550.44–0.680.06<0.001

* Adjusted for patient age, sex, race/ethnicity, Elixhauser comorbidity score, intubation, acute coronary syndrome, acute renal failure, pulmonary embolism, diabetes congestive heart failure, obesity, and smoking.

(A) Bar graph demonstrating rates of in-hospital death and favorable discharge among ICH-COVID and ICH cohorts. (B) Bar graph demonstrates rates of length of ICU and hospital stay among ICH-COVID and ICH cohorts. * Adjusted for patient age, sex, race/ethnicity, Elixhauser comorbidity score, intubation, acute coronary syndrome, acute renal failure, pulmonary embolism, diabetes congestive heart failure, obesity, and smoking.

SAH-COVID

For the SAH cohort, we included 212 SAH-COVID patients and 5,029 controls from 119 hospitals, of which 35 were in the Northeast Census region, 25 in the Midwest, 40 in the South and 19 in the West. A majority of the control group were white (52.4%), whereas in the SAH-COVID cohort the proportion of white patients was 29.3%, because of larger Hispanic and Black proportions (Table 3). More patients in the SAH-COVID cohort had obesity, atrial fibrillation, diabetes, dyslipidemia, and congestive heart failure comorbidities than in the control group, and fewer were smokers (Table 3). SAH was not present on admission in over half of patients with COVID-19. No significant difference in the proportion of patients who were administered heparin, enoxaparin, or dalteparin during hospitalization was observed. In the 212 SAH-COVID patients, the proportion who received an anticoagulant was higher in patients who did not have SAH present on admission than those who did (80.6% vs. 65.4%, p<0.001).
Table 3

Baseline demographics and outcomes of patients with subarachnoid hemorrhage.

VariableControlSAH-COVIDp value
(2019)(April–July 2020)
(n = 5,029)(n = 212)
Age category in years  0.793
<1874 (1.5%)suppressed*
18–501,471 (29.3%)58 (27.4%)
51–641,704 (33.9%)78 (36.8%)
65–74996 (19.8%)41 (19.3%)
75–79305 (6.1%)16 (7.8%)
≥80479 (9.5%)17 (8.0%)
Age <75 years4,245 (84.4%)179 (84.4%)0.993
Male sex2,127 (42.3%)107 (50.5%)0.018
Race/Ethnicitya  <0.001
White2,637 (52.4%)62 (29.3%)
Black910 (18.1%)44 (20.8%)
Hispanic645 (12.8%)63 (29.7%)
Asian198 (3.9%)suppressed
Other/Unknown639 (12.7%)34 (16.0%)
SAH diagnosis present on admission4,633 (92.1%)104 (49.1%)<0.001
Elixhauser score   
Median, IQR2, 1–44, 2–5<0.001
Mean±SD2.8±2.13.8±2.2<0.001
Obese740 (14.7%)66 (31.1%)<0.001
Smoker835 (16.6%)13 (6.1%)<0.001
Atrial fibrillation625 (12.4%)35 (16.5%)0.079
Hypertension3,214 (63.9%)122 (57.6%)0.059
Diabetes1,015 (20.2%)99 (46.7%)<0.001
Dyslipidemia1,684 (33.5%)93 (43.9%)0.002
Congestive heart failure634 (12.6%)48 (22.6%)<0.001
Interfacility transfer2,895 (57.6%)86 (40.6%)<0.001
Seen in emergency department (n = 5,016)2,869 (59.7%)135 (65.5%)0.091
Intubated1,591 (31.6%)144 (67.9%)<0.001
Administered heparin, enoxaparin, or dalteparin during hospitalization3,472 (69.0%)155 (73.1%)0.208
Acute coronary syndrome325 (6.5%)31 (14.6%)<0.001
Acute renal failure917 (18.2%)117 (55.2%)<0.001
Pulmonary embolus96 (1.9%)23 (10.9%)<0.001
Venous sinus or deep vein thrombosis102 (2.0%)suppressed0.421
Length of hospital stay in days (mean±SD)13.4±15.826.9±23.4<0.001
Length of ICU stay in days (mean±SD)*9.6±9.821.9±19.0<0.001
Favorable discharge3,292 (65.5%)88 (31.1%)<0.001
In-hospital death744 (14.8%)91 (42.9%)<0.001

Binary variables presented as n, %; ordinal variables as median, IQR; interval variables as mean (SD). P values calculated with the chi-squared test for binary variables, the Wilcoxon rank sum test for ordinal variables, and Student’s t-test for interval variables.

*Value suppressed for low count.

aWhite and Black racial categories are non-Hispanic.

bLength of ICU stay restricted to patients with >24 hours spent in intensive care.

Binary variables presented as n, %; ordinal variables as median, IQR; interval variables as mean (SD). P values calculated with the chi-squared test for binary variables, the Wilcoxon rank sum test for ordinal variables, and Student’s t-test for interval variables. *Value suppressed for low count. aWhite and Black racial categories are non-Hispanic. bLength of ICU stay restricted to patients with >24 hours spent in intensive care. Similarly, significantly higher proportions of patients in the SAH-COVID cohort were intubated and experienced acute coronary syndrome, acute renal failure, and pulmonary embolus. No differences were observed among rates of venous sinus or deep vein thrombosis. In the SAH-COVID cohort, there was a longer hospital stay (26.9 vs. 13.4 days, p<0.001), longer ICU stay (21.9 vs. 9.6 days, p<0.001), lower rate of favorable discharge (31.1% vs. 65.5%, p<0.001), and more in-hospital death (42.9% vs. 14.8%, p<0.001) (Fig 2). In patients with SAH and a documented NIHSS, there were 15 SAH-COVID patients and 851 controls, with a median (IQR) NIHSS of 13 (7–26) versus 3 (0–14) (p<0.001). We did not find differences in outcome rates by hospital size, but there was a higher rate of favorable discharge in SAH-COVID patients in the West and South than Northeast or Midwest (Northeast: 21.6%, Midwest 25.6%, South: 45.3%, West: 31.4%, p = 0.022). The mixed-effects logistic regression model demonstrated that patients with SAH-COVID had an aOR of 1.81 [1.26–2.59] for the outcome of death and an aOR of 0.54 [0.37–0.78] for favorable discharge (Table 4). In the sensitivity analysis where we only included patients with SAH present on admission, the association between SAH-COVID and in-hospital mortality remained significant (aOR 2.11, 1.22–3.65), but was not for the favorable discharge (aOR 0.71, 0.44–1.15).
Fig 2

(A) Bar graph demonstrating rates of in hopital death and favorable discharge among SAH-COVID and SAH cohorts. (B) Bar graph demonstrates rates of length of ICU and hospital stay among SAH-COVID and SAH cohorts.

Table 4

Mixed-effects logistic regression fit to in-hospital death and favorable discharge, showing odds ratios for SAH patients with comorbid COVID-19 compared with controls.

DeathFavorable discharge
Odds ratio95% CISEp valueOdds ratio95% CISEp value
Unadjusted4.343.26–5.780.64<0.0010.250.18–0.330.04<0.001
Adjusted*1.811.26–32.590.330.0010.540.37–0.780.100.001

* Adjusted for patient age, sex, race/ethnicity, Elixhauser comorbidity score, intubation, acute coronary syndrome, acute renal failure, pulmonary embolism, diabetes congestive heart failure, obesity, and smoking.

(A) Bar graph demonstrating rates of in hopital death and favorable discharge among SAH-COVID and SAH cohorts. (B) Bar graph demonstrates rates of length of ICU and hospital stay among SAH-COVID and SAH cohorts. * Adjusted for patient age, sex, race/ethnicity, Elixhauser comorbidity score, intubation, acute coronary syndrome, acute renal failure, pulmonary embolism, diabetes congestive heart failure, obesity, and smoking.

Discussion

ICH and SAH in patients with comorbid COVID-19 infection are associated with a higher rate of in-hospital death and a lower rate of favorable discharge when compared with control patients without COVID-19. We also found that half of ICH and SAH in COVID-19 patients occurred after the patients had been admitted to the hospital and were more likely in patients who had received anticoagulation. This information could be useful as emerging therapies for COVID-19 are developed and warrant consideration for use in this at-risk population. Using a national dataset, we have estimated the impact of concomitant COVID-19 infection by assessing the resultant outcomes in the setting of ICH and SAH. Initially during the COVID-19 pandemic, there was a decrease in the incidence of hospitalization for acute SAH across multiple countries [15]; however, outcomes for those admitted with SAH with and without COVID-19 during this period are lacking. Importantly, the neurological manifestations of COVID-19 continue to emerge. The most common manifestation is toxic metabolic encephalopathy, while the most commonly reported stroke type thus far has been ischemic, which is thought to be associated with the overall hypercoagulable state [16, 17]. In a multinational study on hospitalized patients with SARS-CoV-2 infection, Shahjouei et al. [18] demonstrated an overall stroke risk of 0.5% (pooled 0.9%) with ischemic heart disease (OR 2.5, p = 0.006) and mechanical ventilaton (OR 1.9, p = 0.03) as independent predictors. Although less common, a subset of neurologic complications has been thought to be due a cytokine storm and multisystem inflammation resulting in acute demyelination, vasculitis, necrotizing encephalopathy, and posterior reversible encephalopathy syndrome [19]. Tsivgoulis et al. [20] undertook a review of neurological manifestations during the COVID-19 pandemic and found predominantly mild neurological manifestations in the majority of infected patients, while multiple comorbidites were present with severe neurological disease. In this study, we demonstrated that patients in the ICH-COVID cohort were significantly more likely to be Hispanic, Asian, or Black than patients in the control group, which mostly comprised white patients. The COVID-19 pandemic has placed a disproportionate burden of morbidity and mortality on the Black and Hispanic populations [21], which is similar to previous viral pandemics [22-24]. Parcha et al. [25] studied geographic variation and racial disparities in COVID-19 and demonstrated a threefold higher cumulative incidence rate (per 100,000) in Blacks compared with Whites, with a twofold higher crude mortality rate. In both the ICH-COVID and SAH-COVID comparisons, we found higher proportions of Blacks, Asians, and Hispanics than in the control groups. Douglas and Subica [21] demonstrated that a higher percentage of Blacks and Hispanics predicted more total COVID-19 tests per 1000 persons (p<0.05), but with lower predicted hospitalizations and ICU bed access. Their findings indicate that Blacks and Hispanics are not only at risk secondary to inequitable access to healthcare resources in disadvantaged communities but also that social disadvantage may drive inequities related to access to care. The predisposition for higher proportions of Blacks and Hispanics to be affected with COVID-19 is mirrored in this sample examining patients experiencing ICH or SAH, likely with similar socioeconomic driving forces. Further inquiry into this phenomenon is warranted and necessary. A higher proportion of patients in the SAH-COVID and ICH-COVID cohorts had obesity, atrial fibrillation, diabetes, and congestive heart failure than in the control cohorts. Smati et al. [26] found that overweight status and obesity carried a poorer early prognosis in patients with type II diabetes and COVID-19, but the impact of obesity diminished in those greater than 75 years of age. Interestingly, in both the ICH-COVID and SAH-COVID cohorts in our study, there were lower proportions of patients who smoked versus controls. This may indicate that COVID-19–related morbidity substitutes as a pulmonary risk factor and creates an additive effect in the setting of spontaneous intracranial hemorrhage, either ICH or SAH, and a poorer outcome. This may also be a reflection of the virulence of COVID-19 to affect patients with presumably normal pulmonary function as a nonsmoker. The strength of this study is that the Vizient Clinical Data Base is a large, validated database that comprises a sample of the population presenting to both community and teaching/academic hospitals in different regions of the country, making the findings more generalizable than single-center cohort studies [27, 28]. Administrative data from Vizient has previously been used to examine large cohorts of patients with stroke [29, 30]. On the other hand, there are also several limitations to the current investigation. The study cohort is derived from an administrative database, so data were queried using billing codes, which have varying accuracy. The Vizient database is primarily used by academic institutions, which could introduce bias and limit generalizability to community-based practices. We also used a historical cohort from 2019, which represents an adequate comparison, but may limit the generalizability. Additionally, coding for ICH and SAH may be mixed, despite different clinical implications for each. In an effort to overcome this limitation, we only included patients with unique codes of ICH or SAH. A total of 4,378 cases were excluded secondary to mixed coding; inclusion of these patients in the analysis did not change the overall results of the investigation, so mixed cases were censored to isolate the analysis to the conditions of interest. The severity of ICH (volume, intraventricular extension) and SAH were not available through this dataset, and severity of hemorrhage likely plays a role that was thus undetected in this investigation. Although our data suggest more severe ICH and SAH in patients with COVID based on NIHSS, the high potential for selection bias limits the conclusions that can be drawn from this finding.The use of anticoagulants was limited to identify heparin, enoxaparin, or dalteparin; other medications may have been missed via this method. We also do not have dosing information. With emerging evidence regarding outcomes and heterogeneity of COVID-19, it is challenging to generalize the results revealed from this investigation. Despite these limitations, we have demonstrated compelling evidence that concomitant COVID-19 infection significantly impacts outcomes in patients experiencing ICH or SAH.

Conclusion

Patients experiencing spontaneous ICH or SAH with comorbid COVID-19 infection had worse outcomes with respect to discharge disposition and in-hospital death. Additionally, hospital and ICU length of stay were longer and comorbid events including pulmonary embolism, acute coronary syndrome, and respiratory failure were all higher in the COVID-19 cohort, as were race and ethnic minorities. Half of the patients with COVID-19 developed ICH or SAH during their hospitalization and were more likely to have received anticoagulation. This finding warrants further investigation to improve outcomes for these high-risk cerebrovascular patients.
  27 in total

1.  Racial and Ethnic Disparities in the Utilization of Thrombectomy for Acute Stroke.

Authors:  Lorenzo Rinaldo; Alejandro A Rabinstein; Harry Cloft; John M Knudsen; Leonardo Rangel Castilla; Waleed Brinjikji
Journal:  Stroke       Date:  2019-08-01       Impact factor: 7.914

2.  Acute cerebrovascular disease following COVID-19: a single center, retrospective, observational study.

Authors:  Yanan Li; Man Li; Mengdie Wang; Yifan Zhou; Jiang Chang; Ying Xian; David Wang; Ling Mao; Huijuan Jin; Bo Hu
Journal:  Stroke Vasc Neurol       Date:  2020-07-02

3.  The Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID): Development of Case Report Forms for Global Use.

Authors:  Molly McNett; Ericka L Fink; Michelle Schober; Shraddha Mainali; Raimund Helbok; Courtney L Robertson; Jorge Mejia-Mantilla; Pedro Kurtz; Cássia Righy; Juan D Roa; Carlos Villamizar-Rosales; Valeria Altamirano; Jennifer A Frontera; Nelson Maldonado; David Menon; Jose Suarez; Sherry H Y Chou
Journal:  Neurocrit Care       Date:  2020-09-18       Impact factor: 3.210

4.  COVID-19 treatment resource disparities and social disadvantage in New York City.

Authors:  Jason A Douglas; Andrew M Subica
Journal:  Prev Med       Date:  2020-10-06       Impact factor: 4.018

5.  Impact of COVID-19 on Outcomes in Ischemic Stroke Patients in the United States.

Authors:  Adam de Havenon; John P Ney; Brian Callaghan; Alen Delic; Samuel Hohmann; Ernie Shippey; Gregory J Esper; Eric Stulberg; David Tirschwell; Jennifer Frontera; Shadi Yaghi; Mohammad Anadani; Jennifer J Majersik
Journal:  J Stroke Cerebrovasc Dis       Date:  2020-12-09       Impact factor: 2.136

Review 6.  COVID-19 and cerebrovascular diseases: a comprehensive overview.

Authors:  Georgios Tsivgoulis; Lina Palaiodimou; Ramin Zand; Vasileios Arsenios Lioutas; Christos Krogias; Aristeidis H Katsanos; Ashkan Shoamanesh; Vijay K Sharma; Shima Shahjouei; Claudio Baracchini; Charalambos Vlachopoulos; Rossetos Gournellis; Petros P Sfikakis; Else Charlotte Sandset; Andrei V Alexandrov; Sotirios Tsiodras
Journal:  Ther Adv Neurol Disord       Date:  2020-12-08       Impact factor: 6.570

7.  Neurological emergencies associated with COVID-19: stroke and beyond.

Authors:  Amit Agarwal; Marco Pinho; Karuna Raj; Frank F Yu; Girish Bathla; Michael Achilleos; Thomas ONeill; Michael Still; Joseph Maldjian
Journal:  Emerg Radiol       Date:  2020-08-11

8.  Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 - COVID-NET, 14 States, March 1-30, 2020.

Authors:  Shikha Garg; Lindsay Kim; Michael Whitaker; Alissa O'Halloran; Charisse Cummings; Rachel Holstein; Mila Prill; Shua J Chai; Pam D Kirley; Nisha B Alden; Breanna Kawasaki; Kimberly Yousey-Hindes; Linda Niccolai; Evan J Anderson; Kyle P Openo; Andrew Weigel; Maya L Monroe; Patricia Ryan; Justin Henderson; Sue Kim; Kathy Como-Sabetti; Ruth Lynfield; Daniel Sosin; Salina Torres; Alison Muse; Nancy M Bennett; Laurie Billing; Melissa Sutton; Nicole West; William Schaffner; H Keipp Talbot; Clarissa Aquino; Andrea George; Alicia Budd; Lynnette Brammer; Gayle Langley; Aron J Hall; Alicia Fry
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2020-04-17       Impact factor: 17.586

9.  A Prospective Study of Neurologic Disorders in Hospitalized Patients With COVID-19 in New York City.

Authors:  Jennifer A Frontera; Sakinah Sabadia; Rebecca Lalchan; Taolin Fang; Brent Flusty; Patricio Millar-Vernetti; Thomas Snyder; Stephen Berger; Dixon Yang; Andre Granger; Nicole Morgan; Palak Patel; Josef Gutman; Kara Melmed; Shashank Agarwal; Matthew Bokhari; Andres Andino; Eduard Valdes; Mirza Omari; Alexandra Kvernland; Kaitlyn Lillemoe; Sherry H-Y Chou; Molly McNett; Raimund Helbok; Shraddha Mainali; Ericka L Fink; Courtney Robertson; Michelle Schober; Jose I Suarez; Wendy Ziai; David Menon; Daniel Friedman; David Friedman; Manisha Holmes; Joshua Huang; Sujata Thawani; Jonathan Howard; Nada Abou-Fayssal; Penina Krieger; Ariane Lewis; Aaron S Lord; Ting Zhou; D Ethan Kahn; Barry M Czeisler; Jose Torres; Shadi Yaghi; Koto Ishida; Erica Scher; Adam de Havenon; Dimitris Placantonakis; Mengling Liu; Thomas Wisniewski; Andrea B Troxel; Laura Balcer; Steven Galetta
Journal:  Neurology       Date:  2020-10-05       Impact factor: 9.910

10.  Endovascular thrombectomy in acute ischemic stroke patients with COVID-19: prevalence, demographics, and outcomes.

Authors:  Adam de Havenon; Shadi Yaghi; Eva A Mistry; Alen Delic; Samuel Hohmann; Ernie Shippey; Eric Stulberg; David Tirschwell; Jennifer A Frontera; Nils H Petersen; Mohammad Anadani
Journal:  J Neurointerv Surg       Date:  2020-09-28       Impact factor: 8.572

View more
  7 in total

1.  Frequency of Neurological Diseases After COVID-19, Influenza A/B and Bacterial Pneumonia.

Authors:  Pardis Zarifkar; Costanza Peinkhofer; Michael E Benros; Daniel Kondziella
Journal:  Front Neurol       Date:  2022-06-23       Impact factor: 4.086

Review 2.  Pneumonia in Nervous System Injuries: An Analytic Review of Literature and Recommendations.

Authors:  Zohreh Erfani; Hesan Jelodari Mamaghani; Jeremy Aaron Rawling; Alireza Eajazi; Douglas Deever; Seyyedmohammadsadeq Mirmoeeni; Amirhossein Azari Jafari; Ali Seifi
Journal:  Cureus       Date:  2022-06-02

Review 3.  Acute Neurologic Complications of COVID-19 and Postacute Sequelae of COVID-19.

Authors:  Neha S Dangayach; Virginia Newcombe; Romain Sonnenville
Journal:  Crit Care Clin       Date:  2022-03-23       Impact factor: 3.879

4.  Intracerebral Hemorrhage in COVID-19 Infection.

Authors:  Jem Atillasoy; Audrey C Leasure; Kevin N Sheth
Journal:  World Neurosurg       Date:  2021-09       Impact factor: 2.104

Review 5.  Acute and chronic neurological disorders in COVID-19: potential mechanisms of disease.

Authors:  Erin F Balcom; Avindra Nath; Christopher Power
Journal:  Brain       Date:  2021-12-31       Impact factor: 13.501

Review 6.  The relationship between COVID-19 infection and intracranial hemorrhage: A systematic review.

Authors:  Samuel R Daly; Anthony V Nguyen; Yilu Zhang; Dongxia Feng; Jason H Huang
Journal:  Brain Hemorrhages       Date:  2021-11-11

Review 7.  Neurological consequences of COVID-19.

Authors:  Waldemar Brola; Maciej Wilski
Journal:  Pharmacol Rep       Date:  2022-09-30       Impact factor: 3.919

  7 in total

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