Abstract
Background and Purpose
Risk factors for infections after intracerebral hemorrhage (ICH) and their association with outcomes are unknown. We hypothesized there are predictors of post-stroke infection and infections drive worse outcomes.
Methods
We determined prevalence of infections in a multicenter, triethnic study of ICH. We performed univariate and multivariate analyses to determine the association of infection with admission characteristics and hospital complications. We performed logistic regression on association of infection with outcomes after controlling for known determinants of prognosis after ICH (volume, age, infratentorial location, intraventricular hemorrhage, Glasgow Coma Score).
Results
Among 800 patients, infections occurred in 245 (31%). Admission characteristics associated with infection in multivariable models were ICH volume (OR 1.02 per mL, 95% CI 1.01–1.03), lower GCS (OR 0.91 per point, 95% CI 0.87–0.95), deep location (reference lobar, OR 1.90, 95% CI 1.28–2.88), and black race (reference white, OR 1.53, 95% CI 1.01–2.32). In a logistic regression of admission and hospital factors, infections were associated with intubation (OR 3.1, 95% CI 2.1–4.5), dysphagia (with PEG, OR 3.19, 95% CI 2.03–5.05; without PEG, OR 2.11, 95% CI 1.04–4.23), pulmonary edema (OR 3.71, 95% CI 1.29–12.33), and DVT (OR 5.6, 95% CI 1.86–21.02), but not ICH volume or GCS. Infected patients had higher discharge mortality (16% vs. 8%, p=0.001) and worse 3-month outcomes (mRS≥3, 80% vs. 51%, p<0.001). Infection was an independent predictor of poor 3-month outcome (OR 2.6, 95% CI 1.8–3.9).
Conclusions
There are identifiable risk factors for infection after ICH, and infections predict poor outcomes.
Introduction
Medical complications are an important contributor to mortality and morbidity after stroke, accounting for approximately half of all deaths.1 There is an increasing body of literature on the importance of infection preceding and following acute ischemic stroke (AIS), but information on infections following intracerebral hemorrhage (ICH) is limited.2 A recent meta-analysis of post-stroke infection in a mixed group of over 130,000 ischemic and hemorrhagic stroke patients found a 30% infection rate, with pneumonia and urinary tract infection (UTI) rates of 10% each.3 Other studies have identified risk factors for post-stroke infection, including age, stroke severity, volume of infarct, pre-morbid dependence, and enteral feeding.4–7 Specific risk factors have been identified for particular sites of infection, such as intubation, dysphagia, congestive heart failure, and male sex for pneumonia, and female sex and prior strokes for UTI.8,9 One study proposed the risk of infection is not only due to hospitalization but also due to a post-stroke “immunodepression.”10 Nosocomial infections have a deleterious effect on outcomes in mixed stroke cohorts, with odds ratios for poor long-term outcome ranging from 3 to 11.3
No large studies have examined the incidence of post-stroke infections or their impact on outcomes in a pure ICH cohort. Three small studies (n=62 to 148) using single-institution databases found post-stroke infection rates of 51–58%, though two studies were ICU specific.11–13 A study of 201 ICH patients from the Virtual International Stroke Trials Archive found a much lower rate of post-stroke infection (11%).14 In multivariate analyses, post-ICH infection was associated with high NIH Stroke Scale (NIHSS) scores, age, CReactive Protein (CRP) levels, and invasive procedures.
Given the lack of robust data on infection after ICH, we sought to determine the rate of post-stroke infections and their impact on outcomes of patients enrolled in the Ethnic/Racial Variations of Intracerebral Hemorrhage (ERICH) Study, a large, prospective, NINDS-funded multicenter study of ICH. We hypothesized factors present on admission are associated with developing nosocomial infection, and that such infections would be deleterious to outcomes.
Methods
Study Protocol
A detailed methodology of ERICH has been published.15 The ERICH study is a multi-center case-control study of ICH that aims to identify genetic variation and differences in the distribution of risk factors and imaging characteristics, that may affect risk of ICH in a triethnic group of white, black, and Hispanic patients. A prospective hot-pursuit method of subject enrollment, in which each recruitment center reviews admission, emergency room, and ICU logs for potential ICH cases, was utilized to limit survival bias.15 The study was approved by the Institutional Review Boards of University of Cincinnati and each enrolling site. Informed consent was obtained from all subjects or legal representatives.
Case Definition
This manuscript represents an analysis of the first 1400 cases enrolled in the ERICH study. All cases met the following eligibility criteria: diagnosis of spontaneous ICH (including warfar in-related and peripartum ICH); age ≥18; resides near recruiting center; non-Hispanic white, non-Hispanic black, or Hispanic by self-report; and ability of patient or legal representative to provide informed consent. Glasgow Coma Scale (GCS) scores were recorded for patients who presented to the emergency department (ED) of the recruitment site; GCS was unavailable for patients who were directly admitted, transferred from outside EDs, or inpatients at ictus.
In order to assess risk factors for infection and their impact on outcomes, the analytical design involved the following exclusion criteria: death, withdrawal of care, or discharge to hospice within 72 hours of admission; infection within 2 weeks of admission; pre-morbid modified Rankin Scale (mRS) ≥ 3. Patients with missing key data (CT results, GCS, 3-month outcomes) were also excluded.
CT Scans
Intracerebral hemorrhage location was assigned by the blinded central imaging center which was blinded to clinical data. Intracerebral hemorrhage volume was measured using Analyze 9.0 (Mayo Clinic) using previously described methods.16
Ascertainment of infections
Nosocomial infections were identified throughout the entire hospital stay by treating physicians and reported on chart abstraction forms upon hospital discharge. Research staff involved in chart abstraction received instructions on how to fill out data collection forms, including specifying infections, although guidance was limited. Categories of infection included respiratory, urinary, bloodstream, meningitis/ventriculitis, and other. The chart abstraction form and pertinent material from the manual of procedures are available in the online appendix.
Hospital Complications
Major neurosurgical procedures were defined as craniotomy/craniectomy for clot evacuation, stereotactic clot aspiration, or thrombolytic injection into ventricles. External ventricular drain (EVD) and ventriculoperitoneal shunt (VPS) placement were not considered as major neurosurgical procedures.
Outcomes
Three-month outcomes were assessed using the modified Rankin Score (mRS). We defined “good outcome” as an mRS of 0–2, which has been shown to be as effective as shift analysis in large trials.17
Statistical Analysis
Comparisons of patients with and without infection were based on chi-square tests, t-tests, Wilcoxon sign rank tests, or Cochran-Armitage trend test as appropriate. Associations of admission characteristics and hospital-related complications with risk of infection were tested by logistic regression. Logistic regression models were used to test the association of nosocomial infection and the components of the ICH Score18 with poor outcome (mRS≥3) at discharge and 3 months. Receiver Operating Characteristic (ROC) curves were generated for outcome models based on ICH Score components alone plus addition of post-stroke infection. All analyses were performed using SAS version 9.3 (Cary, NC).
Results
Description of the cohort
We enrolled 1400 individuals into ERICH between 9/10/2010 and 12/31/2012, of which 600 patients were excluded from this analysis (counts not mutually exclusive): 222 by design (73 with death, withdrawal of care, or discharge to hospice within 72 hours; 86 with recent infection; 97 with pre-morbid mRS ≥ 3); and 378 due to missing data (133 with incomplete CT data; 166 with unavailable GCS; and 237 lost to follow-up at 3-months). Patients excluded due to missing data did not differ from included patients with respect to known predictors of ICH outcome (ICH size and location, IVH, and presenting GCS), though there was a trend towards smaller bleed volumes in excluded patients (8.8 vs. 11.1mL, p=0.06). The mean age of the included 800 subjects was 60 (±14) years; 60% of the subjects were male, and the racial/ethnic distribution was 25% white, 42% black, and 33% Hispanic.
Prevalence and predictors of infections
Post-stroke infections occurred in 245of the800patients (31%). Respiratory infections were the most common infection followed by UTI (17% and 16% respectively). Multiple infections were seen in 8% of patients. Figure 1 shows rates of infection by type and race. Infection rates were higher in blacks than in whites and Hispanics (36% vs. 26% and 27%, p=0.03), largely driven by UTIs (21% vs. 11% and 13%, p=0.005).
Figure 1.
Post-ICH Infection by Type and Race
Admission characteristics are reported in Table 1. Admission factors associated with infection in univariate analyses (p<.05) included ICH volume (17mL vs. 9mL, p<0.0001), black race (49% vs 39%, p=0.03), location (deep, 68% vs. 58%, p=0.04), admission GCS (13 vs. 15, p<0.0001), WBC count (>10K, 43% vs. 33%, p=0.006), and glucose (138 vs. 126 mg/dL, p=0.0003). In logistic regression models, post-stroke infections were associated with ICH volume (OR 1.02 per mL, 95% CI 1.01–1.03), GCS (per point, OR 0.91, 95% CI 0.87–0.95), deep location (reference lobar, OR 1.90, 95% CI 1.28–2.88), and black race (reference white, OR 1.53, 95% CI 1.01–2.32).
Table 1.
Admission Characteristics
| Infection (n=245) | No Infection (n=555) | p | Respiratory (n=134) | UTI (n=125) | Sepsis (n=22) | CNS (n=15) | Other (n=16) | |
|---|---|---|---|---|---|---|---|---|
| Demographic | ||||||||
| Age: Mean (SD) | 59 (15) | 60 (14) | 0.55 | 59 (16) | 61 (15) | 59 (12) | 56 (13) | 53 (17) |
| Sex: Females | 93 (38) | 223 (40) | 0.55 | 41 (31) | 62 (50) | 6 (27) | 4 (27) | 6 (38) |
| Race | ||||||||
| White | 53 (22) | 148 (27) | 32 (24) | 23 (18) | 5 (23) | 3 (20) | 1 (6) | |
| Black | 120 (49) | 216 (39) | 63 (47) | 69 (55) | 11 (50) | 9 (60) | 8 (50) | |
| Hispanic | 72 (29) | 191 (34) | 0.03 | 39 (29) | 33 (26) | 6 (27) | 3 (20) | 7 (44) |
| Past Medical History | ||||||||
| Surgery within 30 days | 5 (2) | 21 (4) | 0.20 | 2 (2) | 3 (2) | ** | ** | ** |
| HIV+ | 5 (2) | 4 (1) | 0.10 | 2 (2) | 1 (1) | 3 (14) | ** | 1 (6) |
| Cancer | 20 (8) | 66 (12) | 0.11 | 8 (6) | 12 (10) | 2 (9) | ** | 2 (13) |
| ESRD/HD | 4 (2) | 8 (1) | 0.84 | 1 (1) | 2 (2) | 2 (9) | ** | 1 (6) |
| Diabetes | 62 (25) | 149 (27) | 0.65 | 30 (22) | 35 (28) | 5 (23) | 3 (20) | 5 (31) |
| Dementia | 13 (5) | 25 (5) | 0.61 | 9 (7) | 5 (4) | ** | ** | ** |
| Social History | ||||||||
| Active Smoker | 55 (23) | 117 (21) | 0.61 | 33 (25) | 27 (22) | 5 (24) | 6 (40) | 5 (31) |
| Heavy Drinker | 29 (13) | 63 (12) | 0.70 | 20 (16) | 14 (12) | 2 (11) | 2 (15) | 1 (7) |
| Urine Toxicology (+) | 32 (16) | 73 (17) | 0.89 | 20 (18) | 17 (17) | 5 (26) | 1 (8) | 4 (27) |
| Case Type | ||||||||
| ICH | 241 (98) | 543 (98) | 0.62 | 133 (99) | 122 (98) | 22 (100) | 15 (100) | 15 (94) |
| Deep | 163 (68) | 317 (58) | 86 (65) | 88 (72) | 16 (73) | 13 (87) | 8 (53) | |
| Lobar | 51 (21) | 162 (30) | 32 (24) | 23 (19) | 4 (18) | ** | 6 (40) | |
| Brainstem | 13 (5) | 22 (4) | 8 (6) | 5 (4) | 1 (5) | 2 (13) | ** | |
| Cerebellum | 14 (6) | 42 (8) | 0.04 | 7 (5) | 6 (5) | 1 (5) | ** | 1 (7) |
| Primary IVH | 4 (2) | 12 (2) | 0.62 | 3 (2) | 3 (2) | ** | ** | ** |
| ICH (primary) with SAH | 9 (4) | 15 (3) | 0.46 | 7 (5) | 3 (2) | 1 (5) | ** | 1 (6) |
| ICH Volume: Median (IQR) | 17 (7–39) | 9 (3–20) | <0.001 | 19 (8–40) | 15 (5–30) | 17 (8–44) | 20 (5–31) | 27 (19–51) |
| ED Data | ||||||||
| Initial temp: Mean (SD) | 97.9 (1.2) | 97.8 (1.4) | 0.25 | 97.9 (1.4) | 98.0 (1.1) | 97.9 (0.8) | 97.9 (1.1) | 97.9 (0.8) |
| GCS-Total: Median (IQR) | 13 (8–15) | 15 (13–15) | <0.001 | 11 (6–15) | 14 (10–15) | 13 (11–15) | 8 (6–12) | 14 (11–15) |
| Coma/Posturing | 61 (26) | 52 (10) | <0.001 | 43 (33) | 26 (21) | 4 (20) | 2 (13) | 3 (19) |
| Initial Labs | ||||||||
| WBC >10 | 103 (43) | 177 (33) | 0.006 | 58 (44) | 45 (37) | 9 (43) | 5 (33) | 9 (56) |
| Serum Blood Sugar: Median (IQR) | 138 (115–177) | 126 (105–162) | <0.001 | 140 (118–182) | 135 (111–174) | 122 (105–149) | 134 (121–178) | 149 (120–235) |
| HbA1C: Median (IQR) | 5.7 (5.3–6.6) | 5.9 (5.4–6.6) | 0.10 | 5.7 (5.4–6.6) | 5.8 (5.4–6.7) | 5.6 (5.4–6.7) | 5.8 (5.3–6.8) | 5.5 (5.1–7.3) |
All data n (%) unless noted
p-values are for Infection vs. No Infection
No observations
Hospital-related complications (Table 2) associated with infection with p<0.001 included major neurosurgical procedures (35% vs. 14%, p<0.0001), external ventricular drain (EVD) placement (37% vs. 15%, p<0.0001), intubation (63% vs. 25%, p<0.0001), bowel-bladder dysfunction (7% vs. 1%, p<0.0001), pulmonary edema (6% vs. 1%, p<0.0001), decubitus ulcer (4% vs 1%, p=0.0008), DVT (8% vs. 1%, p<0.0001), and dysphagia requiring PEG (34% vs. 8%, p<0.0001). In multivariate models including both admission and hospital-related factors, infections were associated with intubation (OR 3.08, 95% CI 2.1–4.51), dysphagia with and without PEG (with PEG OR 3.19, 95% CI 2.03–5.05; without PEG OR 2.11, 95% CI 1.04–4.23), pulmonary edema (OR 3.71, 95% CI 1.29–12.33), and DVT (OR 5.6, 95% CI 1.86–21.02), whereas ICH volume, location, and GCS did not reach statistical significance.
Table 2.
Hospital-Related Complications
| Infection (n=245) | No Infection (n=555) | p | Respiratory (n=134) | UTI (n=125) | Sepsis (n=22) | CNS (n=15) | Other (n=16) | |
|---|---|---|---|---|---|---|---|---|
| Interventions | ||||||||
| Major Neurosurgical Procedures | 86 (35) | 79 (14) | <0.001 | 50 (37) | 37 (30) | 6 (27) | 9 (60) | 11 (69) |
| Craniotomy for evacuation | 37 (15) | 35 (6) | <0.001 | 22 (16) | 13 (10) | 3 (14) | 2 (13) | 7 (44) |
| Thombolytic Injection into Ventricles | 11 (5) | 9 (2) | 0.02 | 7 (5) | 7 (6) | 1 (5) | 2 (13) | ** |
| EVD | 91 (37) | 85 (15) | <0.001 | 58 (43) | 34 (27) | 4 (18) | 14 (93) | 8 (50) |
| Intubation | 154 (63) | 140 (25) | <0.001 | 102 (76) | 58 (46) | 17 (77) | 12 (80) | 12 (80) |
| Clinical Course | ||||||||
| Patient made DNR | 45 (19) | 44 (8) | <0.001 | 34 (26) | 16 (13) | 2 (9) | 3 (20) | 2 (14) |
| Patient made DNI | 23 (10) | 18 (3) | <0.001 | 15 (12) | 13 (10) | 1 (5) | ** | 2 (14) |
| Withdrawal of Care after 72 hours | 24 (10) | 29 (5) | 0.02 | 20 (15) | 6 (5) | 1 (5) | 2 (13) | 1 (7) |
| Subsequent stroke/TIA | 8 (3) | 3 (1) | 0.002 | 2 (2) | 3 (2) | 1 (5) | 1 (7) | 2 (13) |
| Complications | ||||||||
| Bowel-Bladder dysfunction | 18 (7) | 8 (1) | <0.001 | 12 (9) | 6 (5) | 1 (5) | ** | 2 (13) |
| Cardiac arrest | 6 (3) | 4 (1) | 0.04 | 5 (4) | 2 (2) | 1 (5) | ** | ** |
| Pulmonary Edema | 15 (6) | 5 (1) | <0.001 | 11 (8) | 2 (2) | 2 (9) | 1 (7) | 3 (19) |
| Decubitus Ulcer | 9 (4) | 3 (1) | <0.001 | 4 (3) | 3 (2) | ** | 2 (13) | 3 (19) |
| DVT | 18 (8) | 4 (1) | <0.001 | 11 (8) | 9 (7) | 3 (14) | 2 (13) | 2 (13) |
| Dysphagia without PEG/NG | 18 (8) | 24 (4) | 0.08 | 10 (8) | 8 (6) | 2 (9) | 2 (13) | 2 (13) |
| Dysphagia with PEG/NG | 84 (34) | 46 (8) | <0.001 | 52 (39) | 39 (31) | 5 (23) | 5 (33) | 6 (38) |
| Hyperglycemia | 19 (8) | 29 (5) | 0.16 | 9 (7) | 11 (9) | 2 (9) | 2 (13) | 1 (6) |
| Acute MI on EKG | 5 (2) | 5 (1) | 0.18 | 2 (2) | 4 (3) | 1 (5) | ** | ** |
| Seizure | 14 (6) | 18 (2) | 0.10 | 11 (8) | 4 (3) | ** | ** | 2 (13) |
| Mortality | ||||||||
| Dead at Discharge | 38 (16) | 44 (8) | 0.001 | 29 (22) | 10 (8) | 3 (14) | 4 (27) | 2 (13) |
| Cause of Death: | ||||||||
| Brain Death | 6 (18) | 11 (28) | 6 (21) | 1 (11) | ** | ** | ** | |
| Withdrawal of care after 72 hours | 20 (61) | 27 (68) | 15 (54) | 7 (78) | 1 (33) | 3 (75) | 1 (50) | |
| Cardiac Arrest | 7 (21) | 2 (5) | 0.10 | 5 (18) | 1 (11) | ** | ** | 1 (50) |
All data n (%) unless noted
p-values are for Infection vs. No Infection
No observations
Infections as a predictor of outcomes
Patients with infection were more likely to die in the hospital (16% vs. 8%, p=0.001). Figure 2 demonstrates 3-month outcomes by infection status and type. The presence of infection had a significant impact on 3-month outcomes—80% of patient with infections had poor 3-month outcomes versus 51% in those without infection (p<0.0001). The infection type also impacted outcomes—patients with lone respiratory infections had worse outcomes versus those with lone urinary infections (86% vs. 69%, p=0.005). Logistic regression (Table 3) that included post-stroke infection along with the components of the ICH Score revealed that post-stroke infection remained strongly predictive of 3-month poor outcome (OR 2.6, 95% CI 1.8–3.9). Respiratory infections had a greater impact on poor 3-month outcomes than urinary infections (respiratory, OR 4.0, 95% CI 2.3–7.3; urinary, OR 1.7, 95% CI 1.1–2.8). ROC curves based on ICH Score components plus post-stroke infection were improved compared with curves based on ICH Score components alone; the c-statistic increased from 0.747 to 0.778 for discharge outcomes and from 0.788 to 0.806 for 3-month outcomes (Figure 3).
Figure 2.
3-month Functional Outcome by Infection Status
Table 3.
Multivariate Analyses for 3-month Outcomes by Infection Status and Type
| Probability of 3-Month mRS ≥3 (N=800)
| ||||||
|---|---|---|---|---|---|---|
| Variable | Multivariate Models
|
|||||
| Any Infection | Respiratory Infection | Urinary Infection | ||||
|
| ||||||
| OR (CI) | p-value | OR (CI) | p-value | OR (CI) | p-value | |
| Infection Type | ||||||
| Any Infection | 2.6 (1.8,3.9) | <0.0001 | -- | -- | ||
| Respiratory | -- | 4.0 (2.3,7.3) | <0.0001 | -- | ||
| Urinary | -- | -- | 1.7 (1.1,2.8) | 0.03 | ||
| Age (≥80) | 11.6 (5.2,30.9) | <0.0001 | 11.7 (5.2,31.2) | <0.0001 | 11.6 (5.2,31.0) | <0.0001 |
| GCS: | ||||||
| 5–12 | 3.5 (2.2,5.5) | <0.0001 | 3.5 (2.3,5.6) | <0.0001 | 3.9 (2.5,6.1) | <0.0001 |
| 3–4 | 2.1 (0.9,5.7) | 0.1 | 1.8 (0.7,5.0) | 0.2 | 2.7 (1.1,7.1) | 0.03 |
| ICH Volume ≥ 30 mL | 5.4 (3.1,9.9) | <0.0001 | 5.6 (3.2,10.3) | <0.0001 | 6.0 (3.4,11.0) | <0.0001 |
| Infratentorial | 2.2 (1.3,3.7) | 0.002 | 2.2 (1.3,3.7) | 0.002 | 2.2 (1.3,3.6) | 0.002 |
| IVH | 2.3 (1.6,3.3) | <0.0001 | 2.5 (1.7,3.5) | <0.0001 | 2.4 (1.7,3.4) | <0.0001 |
Figure 3. Receiver Operator Curves for ICH Score Alone and for ICH Score + Infection.
A) Discharge Outcomes B) 3-month Outcomes
Discussion
We found that almost a third of the ICH patients in our multicenter, multiethnic cohort study developed infections during acute hospitalization, and that several factors present on admission are predictive of the occurrence of infection. Pneumonias and UTIs comprised most infections, and a quarter of infected patients developed multiple infections. The rate of nosocomial infection in this ICH cohort is consistent with a recent meta-analysis of a mixed ischemic and hemorrhagic stroke cohort.3 We further found that post-stroke infections increase the risk of adverse outcomes, including disability and mortality.
Post-stroke infection was strongly associated with known risk factors for infection: poor GCS, intubation, dysphagia, pulmonary edema, and invasive procedures.11–14 Infection was also associated with DVT, possibly due to DVT-related fever prompting infectious evaluations, or possibly as a marker for long hospital stays. Surprisingly, was not an association with age despite being a well-established general risk factor for nosocomial infection.19
Blacks had substantially higher rates of infection, especially UTIs where the rate was almost double that of whites. While race is not a well-established risk factor for nosocomial infection, there appears to be an ICH-specific race susceptibility to infection. One potential mechanism might be related to the low WBC count seen in blacks.20 While low WBC counts have not conferred an increased nosocomial infection risk amongst blacks generally, it may potentially influence the susceptibility to post-stroke immunodepression. Infections were also more common in deep hemorrhages, which were more common in blacks, than in lobar hemorrhages. While we adjusted for location, it is possible there was some residual confounding. It is also possible that some of the increased infection rate may represent racial bias in ascertainment and surveillance of infection. Further studies are needed to confirm these findings and to investigate possible etiologies of this racial disparity.
Our results highlight the deleterious impact of infections on outcome after ICH. Despite controlling for components of the ICH Score, post-stroke infection remained a strong contributor to poor 3-month outcomes. While respiratory infections were more deleterious than urinary infections, both worsened outcomes. Models including infection and ICH Score components resulted in superior prediction of 3-month outcomes compared with models including only ICH Score components. Infection, therefore, does not appear to be merely a marker of high-grade hemorrhages, but rather contributes independently to outcomes. The contribution of infection to poor outcomes was of similar magnitude as poor GCS, infra tentorial location, or IVH.
Potential mechanisms by which infection may worsen outcomes after ICH include inflammation, promotion of secondary neuronal injury, disruption of neuronal regeneration and neuroplasticity, and cardiopulmonary deconditioning. In ischemic stroke models, lymphocyte deficient mice are protected from ischemia, and reconstitution of the immune system with non-CNS antigen specific lymphocytes is known to worsen the ischemia.21 Infection, by enhancing pro-inflammatory cascades and causing a general activation of lymphocytes, might lead to secondary neuronal injury and worse functional outcomes after ICH.
Our results should encourage stroke practitioners to be vigilant about prevention and treatment of infections. The reduction of mortality in stroke units is often achieved by reducing the morbidity of immobilization and secondary complications—early physical therapy, removal of urinary catheters, and early dysphagia screening.22 One randomized-control trial examined the utility of prophylactic moxifloxacin in ischemic stroke patients with MCA territory lesions and NIHSS >11. This small study of 80 patients showed a reduction in infections in the per protocol analysis (17% vs. 42%), but no improvement in outcomes.23 Whether prophylactic antibiotics could be targeted to a high-risk group to improve outcomes after ICH is a possible future area of inquiry.
There are significant strengths to this study. It is a multicenter study with a large and diverse population. Infections are diagnosed as part of the usual clinical care of these patients, so the data should be generalizable. Additionally, hot pursuit is used to limit survival bias within the cohort. Limitations of the study include varying definitions and surveillance methods for infection and lack of specifics on infection timing, severity, and treatment. The present analysis was not a pre-specified focus of the ERICH study, and as a secondary analysis of data collected as part of a genetic study, it is therefore hypothesis-generating. Research staff involved in chart abstraction receive intensive training and detailed instructions, repeated on a regular basis, on how to complete data collection forms, including specifying UTI, respiratory, and CNS infections. The design of the study is to recruit equal numbers of white, black, and Hispanic patients. Thus, this is not a population-based study as blacks and Hispanics are over-represented compared with the overall US population. In keeping with the purpose of the study, the current analysis is thus equally powered to detect infection by race/ethnicity and thereby avoid a racial bias towards detection in only non-Hispanic whites. Finally, temperatures were not recorded throughout the study and concurrent fever, a known independent risk factor for poor outcome after ICH, is a potential moderator of the association of infection and poor outcome found in the present analysis.
In conclusion, infection is a common and serious complication after ICH. Some groups, particularly blacks, appear to be at increased risk for infection after ICH. Future studies are warranted to investigate feasibility and efficacy of interventions to reduce infections after ICH.
Supplementary Material
Acknowledgments
Funding Sources
National Institutes for Neurological Disorders and Stroke (U01-069763).
Footnotes
Disclosures
Dr. Rosand discloses fees from Boehringer Ingelheim unrelated to this study. All other authors have no disclosures.
References
- 1.Johnston KC, Li JY, Lyden PD, Hanson SK, Feasby TE, Adams RJ, et al. Medical and Neurological Complications of Ischemic Stroke: Experience From the RANTTAS Trial. Stroke. 1998;29:447–453. doi: 10.1161/01.str.29.2.447. [DOI] [PubMed] [Google Scholar]
- 2.Emsley HC, Hopkins SJ. Acute ischaemic stroke and infection: recent and emerging concepts. Lancet Neurol. 2008;7:341–353. doi: 10.1016/S1474-4422(08)70061-9. [DOI] [PubMed] [Google Scholar]
- 3.Westendorp WF, Nederkoorn PJ, Vermeij JD, Dijkgraaf MG, van de Beek D. Post-stroke infection: a systematic review and meta-analysis. BMC Neurol. 2011;11:110. doi: 10.1186/1471-2377-11-110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kammersgaard LP, Jørgensen HS, Reith J, Nakayama H, Houth JG, Weber UJ, et al. Early infection and prognosis after acute stroke: The Copenhagen Stroke Study. J Stroke Cerebrovasc Dis. 2001;10:217–221. doi: 10.1053/jscd.2001.30366. [DOI] [PubMed] [Google Scholar]
- 5.Pittock SJ, Meldrum D, Hardiman O, Thornton J, Brennan P, Moroney JT. The Oxfordshire Community Stroke Project Classification: Correlation with imaging, associated complications, and prediction of outcome in acute ischemic stroke. J Stroke Cerebrovasc Dis. 2003;12:1–7. doi: 10.1053/jscd.2003.7. [DOI] [PubMed] [Google Scholar]
- 6.Kwan J, Hand P. Infection after acute stroke is associated with poor short-term outcome. Acta Neurol Scand. 2007;115:331–338. doi: 10.1111/j.1600-0404.2006.00783.x. [DOI] [PubMed] [Google Scholar]
- 7.Vargas M, Horcajada JP, Obach V, Revilla M, Cervera A, Torres F, et al. Clinical consequences of infection in patients with acute stroke: is it prime time for further antibiotic trials? Stroke. 2006;37:461–465. doi: 10.1161/01.STR.0000199138.73365.b3. [DOI] [PubMed] [Google Scholar]
- 8.Hilker R, Poetter C, Findeisen N, Sobesky J, Jacobs A, Neveling M, et al. Nosocomial pneumonia after acute stroke: implications for neurological intensive care medicine. Stroke. 2003;34:975–981. doi: 10.1161/01.STR.0000063373.70993.CD. [DOI] [PubMed] [Google Scholar]
- 9.Ovbiagele B, Hills NK, Saver JL, Johnston SC. Frequency and Determinants of Pneumonia and Urinary Tract Infection During Stroke Hospitalization. J Stroke Cerebrovasc Dis. 2006;15:209–213. doi: 10.1016/j.jstrokecerebrovasdis.2006.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Dirnagl U, Klehmet J, Braun JS, Harms H, Meisel C, Ziemssen T, et al. Stroke-induced immunodepression: experimental evidence and clinical relevance. Stroke. 2007;38:770–773. doi: 10.1161/01.STR.0000251441.89665.bc. [DOI] [PubMed] [Google Scholar]
- 11.Sykora M, Diedler J, Poli S, Rizos T, Turcani P, Veltkamp R, et al. Autonomic shift and increased susceptibility to infections after acute intracerebral hemorrhage. Stroke. 2011;42:1218–1223. doi: 10.1161/STROKEAHA.110.604637. [DOI] [PubMed] [Google Scholar]
- 12.Diedler J, Sykora M, Hahn P, Rupp A, Rocco A, Herweh C, et al. C-reactive-protein levels associated with infection predict short- and long-term outcome after supratentorial intracerebral hemorrhage. Cerebrovasc Dis. 2009;27:272–279. doi: 10.1159/000199465. [DOI] [PubMed] [Google Scholar]
- 13.Ohwaki K, Yano E, Nagashima H, Nakagomi T, Tamura A. Impact of infection on length of intensive care unit stay after intracerebral hemorrhage. Neurocrit Care. 2008;8:271–275. doi: 10.1007/s12028-007-9007-1. [DOI] [PubMed] [Google Scholar]
- 14.Ali M, Lyden P, Sacco RL, Shuaib A, Lees KR. Natural history of complications after intracerebral haemorrhage. Eur J Neurol. 2009;16:624–630. doi: 10.1111/j.1468-1331.2009.02559.x. [DOI] [PubMed] [Google Scholar]
- 15.Woo D, Rosand J, Kidwell C, McCauley JL, Osborne J, Brown MW, et al. The Ethnic/Racial Variations of Intracerebral Hemorrhage (ERICH) study protocol. Stroke. 2013;44:e120–5. doi: 10.1161/STROKEAHA.113.002332. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Goldstein JN, Fazen LE, Snider R, Schwab K, Greenberg SM, Smith EE, et al. Contrast extravasation on CT angiography predicts hematoma expansion in intracerebral hemorrhage. Neurology. 2007;68:889–894. doi: 10.1212/01.wnl.0000257087.22852.21. [DOI] [PubMed] [Google Scholar]
- 17.Savitz SI, Lew R, Bluhmki E, Hacke W, Fisher M. Shift analysis versus dichotomization of the modified Rankin scale outcome scores in the NINDS and ECASS-II trials. Stroke. 2007;38:3205–12. doi: 10.1161/STROKEAHA.107.489351. [DOI] [PubMed] [Google Scholar]
- 18.Hemphill JC, Bonovich DC, Besmertis L, Manley GT, Johnston SC, Tuhrim S. The ICH Score: A Simple, Reliable Grading Scale for Intracerebral Hemorrhage. Stroke. 2001;32:891–897. doi: 10.1161/01.str.32.4.891. [DOI] [PubMed] [Google Scholar]
- 19.Freeman J, McGowan JE. Risk factors for nosocomial infection. J Infect Dis. 1978;138:811–9. doi: 10.1093/infdis/138.6.811. [DOI] [PubMed] [Google Scholar]
- 20.Lim E-M, Cembrowski G, Cembrowski M, Clarke G. Race-specific WBC and neutrophil count reference intervals. Int J Lab Hematol. 2010;32:590–597. doi: 10.1111/j.1751-553X.2010.01223.x. [DOI] [PubMed] [Google Scholar]
- 21.Kamel H, Iadecola C. Brain-immune interactions and ischemic stroke: clinical implications. Arch Neurol. 2012;69:576–81. doi: 10.1001/archneurol.2011.3590. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Stroke Unit Trialists’ Collaboration. How Do Stroke Units Improve Patient Outcomes?: A Collaborative Systematic Review of the Randomized Trials. Stroke. 1997;28:2139–2144. doi: 10.1161/01.str.28.11.2139. [DOI] [PubMed] [Google Scholar]
- 23.Harms H, Prass K, Meisel C, Klehmet J, Rogge W, Drenckhahn C, et al. Preventive antibacterial therapy in acute ischemic stroke: a randomized controlled trial. PLoS One. 2008;3:e2158. doi: 10.1371/journal.pone.0002158. [DOI] [PMC free article] [PubMed] [Google Scholar]
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