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Abstract

Background

Aneurysmal subarachnoid hemorrhage (SAH) continues to be associated with significant morbidity and mortality despite treatment advancements. Although high blood pressure (BP) remains a significant risk factor in aneurysmal SAH and rerupture, the role of BP parameters and fluctuation in prognostication remains unclear. We sought to define how BP parameters and variability within 24 hours of hospitalization in acute-onset SAH affects patient discharge outcomes.

METHODS

We retrospectively analyzed a prospectively collected cohort of SAH patients. Hourly BP parameters, including systolic BP (SBP), diastolic BP, pulse pressure (PP), and their corresponding variability (delineated by SD), were collected to investigate associations with the primary endpoint of discharge disposition.

RESULTS

One hundred and seventy-four SAH patients were included in the study. On bivariate analysis, Hunt–Hess (HH) score, Fisher grade, intraventricular hemorrhage, external ventricular drain placement, and SBP and PP variability were significantly associated with a poor disposition. Poor disposition was significantly associated with age, HH score, intraventricular hemorrhage, and PP variability on multivariate analysis. PP variability remained an independent predictor for poor disposition (odds ratio 1.11, 95% confidence interval, 1.02–1.21, P = 0.02) when adjusting for potential confounders.

Conclusions

Increased BP and PP variability within the first 24 hours of admission portends a poor discharge disposition for aneurysmal SAH patients.

blood pressure, blood pressure variability, disposition, hypertension, pulse pressure, subarachnoid hemorrhage

Subarachnoid hemorrhage (SAH) is associated with significant morbidity and mortality. Even with current treatment advances, 30-day mortality remains high at 35%.1 Prognosis has been linked to several measures including hemorrhage volume, rerupture, presenting neurological examination, and delayed cerebral ischemia from vasospasm. It is common for patients presenting with SAH to have high blood pressure (BP),2 and intensive BP management is an immediate goal in the intensive care unit for any patient suspected of aneurysmal SAH due to the high mortality associated with aneurysm rerupture.3 Although there are variations in parameter goals and agents used,4 management typically necessitates continuously administered intravenous antihypertensives.5

Beyond having an association with rerupture risk, prior studies have shown that BP and BP variability (BPV) may also predict cerebral ischemia and patient outcome. While there is evidence supporting this association in patients with intracerebral hemorrhage and acute ischemic stroke,6–8 the results are less clear in SAH.9 One study demonstrated a clear relationship between elevated BP on admission and poor outcomes; receiver operating characteristic curve analysis suggested that systolic BP (SBP) >189 mm Hg portended poor outcomes.2 Recently, it was suggested that BPV has a stronger association with outcomes than absolute BP values in SAH patients.5,10 Additionally, BPV has been shown to be a risk factor for stroke11,12 and aneurysmal rebleeds.13

To date, no study has systematically evaluated the role of variability in SBP, diastolic BP (DBP), and pulse pressure (PP) on outcomes in acute phase aneurysmal SAH patients.5,10 We aimed to study the association of these parameters in the acute phase with disposition and functional outcomes. We hypothesized that increased PP variability would lead to worse outcomes as determined by functional status and disposition.

METHODS

Patient population

We performed a retrospective review of all SAH patients admitted at our institution between March 2017 and December 2019. This study was approved by the institutional review board and a HIPAA waiver was obtained due to the retrospective study design.

Inclusion criteria included all patients with spontaneous SAH attributed to an aneurysmal source or spontaneous SAH with negative angiographic studies with hemorrhages beyond the perimesencephalic region. Diagnosis of SAH was made by computed tomography (CT) of the brain or cerebrospinal fluid examination via lumbar puncture. All patients included in the study underwent a CT angiogram or digital subtraction angiography to evaluate possible causes of SAH. Patients with traumatic SAH and SAH associated with arteriovenous malformation or dural arteriovenous fistulas were excluded. Additionally, patients who died within 24 hours of admission were excluded.

On admission, all patients with an aneurysmal pattern of SAH and unsecured aneurysm receive strict SBP goals of <140 mm Hg, with SBPs measured at least every hour. If SBPs are consistently over 140 mm Hg or if a nicardipine drip is instituted, then an arterial line is placed. Aneurysms are routinely secured by first-line coil or clip during the first 24 hours after admission.

Data collection

We collected demographic information, Glasgow coma scale, Fisher grade, Hunt–Hess (HH) score, comorbidities, admission labs (heart rate, international normalized ratio, hemoglobin, platelet count, creatinine, leukocyte count, and blood glucose). Our BP variables included hourly SBP, DBP, and PP. Corresponding BPV for all 3 BP parameters was calculated as the SD.5 If the aneurysm was secured via coil or clip within this 24-hour span, pre-op and post-op BP measurements were collected. Information regarding etiology, treatment modality, hydrocephalus, respiratory failure, external ventricular drain placement, tracheostomy placement, ventriculoperitoneal shunt placement, and vasospasm was collected. Collected outcome variables included mortality, modified Rankin scale (mRS) at discharge and 3-month follow-up, disposition after discharge, and delayed cerebral ischemia.

Our primary measure was functional outcome at discharge as determined by mRS and disposition. Disposition was dichotomized into “good disposition” (home or acute rehabilitation) vs. “poor disposition” (nursing home, hospice, or death). This was done based on the rationale that patients who are placed in acute rehabilitation settings typically go home within days to weeks compared with those placed in skilled nursing facilities. Functional outcome was dichotomized into mRS 0–2 vs. 3–6, based on functional independence. Secondary outcomes included mortality and functional outcome at 3 months.

Statistical analysis

Using disposition as our primary outcome measure, a bivariate analysis was completed comparing those with poor disposition (discharge to a nursing facility, hospice, or death) to those with good disposition (discharge to home, or acute rehabilitation center). Univariable and backward selection multivariable logistic regression analyses were used to determine independent predictors of dichotomized discharge disposition. A P value of less than 0.05 was used to determine statistical significance. Predictor variables that were significant at P < 0.05 were retained in the multiple logistic regression model. Statistical analyses were conducted using SPSS statistical software (version 25.0, IBM SPSS, Chicago, IL).

RESULTS

Study population

We evaluated 196 SAH patients at our institution during the study period. One hundred and seventy-four patients met inclusion criteria and were diagnosed with aneurysmal SAH. Mean age was 55.0 years and 58.6% (102/174) of patients were male. 25.3% (43/174) presented with a HH score greater than 3 and median Fisher grade was 3 (Table 1). Outcomes summaries are presented in Table 2.

Table 1.
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Admission baseline characteristics and bivariate associations based on dichotomized Poor Dispositiona of aneurysmal subarachnoid hemorrhage patients

Total (%)Poor Disposition
Variable(Total n = 174)Yes (n = 45)No (n = 129)P value
Age55.0 ± 15.959.8 ± 16.553.5 ± 15.50.021
Male sex102 (58.6)31 (68.9)71 (55.5)0.16
Race/ethnicity
 White/non-Hispanic42 (24.1)11 (24.4)31 (24.2)
 African American105 (60.3)24 (53.3)80 (62.5)
 Asian5 (2.9)3 (6.7)2 (1.6)
 Hispanic14 (8.0)4 (8.9)10 (7.8)
 Other8 (4.6)3 (6.7)5 (3.9)
Body mass index (kg/m2)29.2 ± 7.528.0 ± 6.029.7 ± 7.90.20
Obese (BMIb >30 kg/m2)65 (37.8)15 (34.9)50 (39.1)0.76
Hunt–Hess score >343 (25.3)28 (62.2)15 (12.1)<0.001
Fisher score (median, IQR)3 (3–4)4 (3–4)3 (2–4)
Hypertension97 (55.7)26 (57.8)71 (55.5)0.93
Diabetes27 (15.6)6 (13.3)21 (16.5)0.79
Chronic kidney disease7 (4)3 (6.7)4 (3.1)0.55
Congestive heart failure3 (1.7)2 (4.4)1 (0.8)0.34
Smoker44 (25.4)8 (18.2)36 (28.1)0.27
Coronary artery disease10 (5.7)2 (4.4)8 (6.2)0.94
End stage renal disease6 (3.5)4 (8.9)2 (1.6)0.066
Prior acute ischemic stroke5 (2.9)1 (2.2)4 (3.1)>0.99
Prior intracerebral hemorrhage4 (2.3)2 (4.4)2 (1.6)0.60
Prehospital statin use18 (10.3)6 (13.3)12 (9.4)0.64
Prehospital antiplatelet use20 (11.5)5 (11.1)15 (11.7)>0.99
Admission heart rate81.1 ± 18.283.29 ± 20.880.5 ± 17.30.38
Admission international normalized ratio1.05 ± 0.31.07 ± 0.21.04 ± 0.40.72
Admission hemoglobin13.26 ± 1.913.18 ± 1.913.27 ± 2.00.79
Admission platelet247.11 ± 72.5244.47 ± 72.8248.05 ± 72.90.78
Admission creatinine0.99 ± 1.21.07 ± 1.30.97 ± 1.10.62
Admission blood glucose154.33 ± 59.7167.24 ± 53.1150.09 ± 61.60.098
Admission white blood count17.47 ± 60.515.6 ± 7.118.21 ± 70.50.81
Intraventricular hemorrhage at admission97 (56.1)38 (86.4)59 (46.1)<0.001
External ventricular drain placement104 (60.1)38 (84.4)66 (51.6)<0.001
Nicardipine use107 (61.5)27 (60.0)80 (62.5)0.79
Nicardipine Maximum usec41 (23.6)10 (22.2)31 (24.2)0.76
Total (%)Poor Disposition
Variable(Total n = 174)Yes (n = 45)No (n = 129)P value
Age55.0 ± 15.959.8 ± 16.553.5 ± 15.50.021
Male sex102 (58.6)31 (68.9)71 (55.5)0.16
Race/ethnicity
 White/non-Hispanic42 (24.1)11 (24.4)31 (24.2)
 African American105 (60.3)24 (53.3)80 (62.5)
 Asian5 (2.9)3 (6.7)2 (1.6)
 Hispanic14 (8.0)4 (8.9)10 (7.8)
 Other8 (4.6)3 (6.7)5 (3.9)
Body mass index (kg/m2)29.2 ± 7.528.0 ± 6.029.7 ± 7.90.20
Obese (BMIb >30 kg/m2)65 (37.8)15 (34.9)50 (39.1)0.76
Hunt–Hess score >343 (25.3)28 (62.2)15 (12.1)<0.001
Fisher score (median, IQR)3 (3–4)4 (3–4)3 (2–4)
Hypertension97 (55.7)26 (57.8)71 (55.5)0.93
Diabetes27 (15.6)6 (13.3)21 (16.5)0.79
Chronic kidney disease7 (4)3 (6.7)4 (3.1)0.55
Congestive heart failure3 (1.7)2 (4.4)1 (0.8)0.34
Smoker44 (25.4)8 (18.2)36 (28.1)0.27
Coronary artery disease10 (5.7)2 (4.4)8 (6.2)0.94
End stage renal disease6 (3.5)4 (8.9)2 (1.6)0.066
Prior acute ischemic stroke5 (2.9)1 (2.2)4 (3.1)>0.99
Prior intracerebral hemorrhage4 (2.3)2 (4.4)2 (1.6)0.60
Prehospital statin use18 (10.3)6 (13.3)12 (9.4)0.64
Prehospital antiplatelet use20 (11.5)5 (11.1)15 (11.7)>0.99
Admission heart rate81.1 ± 18.283.29 ± 20.880.5 ± 17.30.38
Admission international normalized ratio1.05 ± 0.31.07 ± 0.21.04 ± 0.40.72
Admission hemoglobin13.26 ± 1.913.18 ± 1.913.27 ± 2.00.79
Admission platelet247.11 ± 72.5244.47 ± 72.8248.05 ± 72.90.78
Admission creatinine0.99 ± 1.21.07 ± 1.30.97 ± 1.10.62
Admission blood glucose154.33 ± 59.7167.24 ± 53.1150.09 ± 61.60.098
Admission white blood count17.47 ± 60.515.6 ± 7.118.21 ± 70.50.81
Intraventricular hemorrhage at admission97 (56.1)38 (86.4)59 (46.1)<0.001
External ventricular drain placement104 (60.1)38 (84.4)66 (51.6)<0.001
Nicardipine use107 (61.5)27 (60.0)80 (62.5)0.79
Nicardipine Maximum usec41 (23.6)10 (22.2)31 (24.2)0.76

Bold values denote statistical significance.

aTransferred to nursing home, hospice, or death.

bBody mass index.

cNicardipine Maximum use defined as continuous infusion at 15 mg/hour.

Table 1.
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Admission baseline characteristics and bivariate associations based on dichotomized Poor Dispositiona of aneurysmal subarachnoid hemorrhage patients

Total (%)Poor Disposition
Variable(Total n = 174)Yes (n = 45)No (n = 129)P value
Age55.0 ± 15.959.8 ± 16.553.5 ± 15.50.021
Male sex102 (58.6)31 (68.9)71 (55.5)0.16
Race/ethnicity
 White/non-Hispanic42 (24.1)11 (24.4)31 (24.2)
 African American105 (60.3)24 (53.3)80 (62.5)
 Asian5 (2.9)3 (6.7)2 (1.6)
 Hispanic14 (8.0)4 (8.9)10 (7.8)
 Other8 (4.6)3 (6.7)5 (3.9)
Body mass index (kg/m2)29.2 ± 7.528.0 ± 6.029.7 ± 7.90.20
Obese (BMIb >30 kg/m2)65 (37.8)15 (34.9)50 (39.1)0.76
Hunt–Hess score >343 (25.3)28 (62.2)15 (12.1)<0.001
Fisher score (median, IQR)3 (3–4)4 (3–4)3 (2–4)
Hypertension97 (55.7)26 (57.8)71 (55.5)0.93
Diabetes27 (15.6)6 (13.3)21 (16.5)0.79
Chronic kidney disease7 (4)3 (6.7)4 (3.1)0.55
Congestive heart failure3 (1.7)2 (4.4)1 (0.8)0.34
Smoker44 (25.4)8 (18.2)36 (28.1)0.27
Coronary artery disease10 (5.7)2 (4.4)8 (6.2)0.94
End stage renal disease6 (3.5)4 (8.9)2 (1.6)0.066
Prior acute ischemic stroke5 (2.9)1 (2.2)4 (3.1)>0.99
Prior intracerebral hemorrhage4 (2.3)2 (4.4)2 (1.6)0.60
Prehospital statin use18 (10.3)6 (13.3)12 (9.4)0.64
Prehospital antiplatelet use20 (11.5)5 (11.1)15 (11.7)>0.99
Admission heart rate81.1 ± 18.283.29 ± 20.880.5 ± 17.30.38
Admission international normalized ratio1.05 ± 0.31.07 ± 0.21.04 ± 0.40.72
Admission hemoglobin13.26 ± 1.913.18 ± 1.913.27 ± 2.00.79
Admission platelet247.11 ± 72.5244.47 ± 72.8248.05 ± 72.90.78
Admission creatinine0.99 ± 1.21.07 ± 1.30.97 ± 1.10.62
Admission blood glucose154.33 ± 59.7167.24 ± 53.1150.09 ± 61.60.098
Admission white blood count17.47 ± 60.515.6 ± 7.118.21 ± 70.50.81
Intraventricular hemorrhage at admission97 (56.1)38 (86.4)59 (46.1)<0.001
External ventricular drain placement104 (60.1)38 (84.4)66 (51.6)<0.001
Nicardipine use107 (61.5)27 (60.0)80 (62.5)0.79
Nicardipine Maximum usec41 (23.6)10 (22.2)31 (24.2)0.76
Total (%)Poor Disposition
Variable(Total n = 174)Yes (n = 45)No (n = 129)P value
Age55.0 ± 15.959.8 ± 16.553.5 ± 15.50.021
Male sex102 (58.6)31 (68.9)71 (55.5)0.16
Race/ethnicity
 White/non-Hispanic42 (24.1)11 (24.4)31 (24.2)
 African American105 (60.3)24 (53.3)80 (62.5)
 Asian5 (2.9)3 (6.7)2 (1.6)
 Hispanic14 (8.0)4 (8.9)10 (7.8)
 Other8 (4.6)3 (6.7)5 (3.9)
Body mass index (kg/m2)29.2 ± 7.528.0 ± 6.029.7 ± 7.90.20
Obese (BMIb >30 kg/m2)65 (37.8)15 (34.9)50 (39.1)0.76
Hunt–Hess score >343 (25.3)28 (62.2)15 (12.1)<0.001
Fisher score (median, IQR)3 (3–4)4 (3–4)3 (2–4)
Hypertension97 (55.7)26 (57.8)71 (55.5)0.93
Diabetes27 (15.6)6 (13.3)21 (16.5)0.79
Chronic kidney disease7 (4)3 (6.7)4 (3.1)0.55
Congestive heart failure3 (1.7)2 (4.4)1 (0.8)0.34
Smoker44 (25.4)8 (18.2)36 (28.1)0.27
Coronary artery disease10 (5.7)2 (4.4)8 (6.2)0.94
End stage renal disease6 (3.5)4 (8.9)2 (1.6)0.066
Prior acute ischemic stroke5 (2.9)1 (2.2)4 (3.1)>0.99
Prior intracerebral hemorrhage4 (2.3)2 (4.4)2 (1.6)0.60
Prehospital statin use18 (10.3)6 (13.3)12 (9.4)0.64
Prehospital antiplatelet use20 (11.5)5 (11.1)15 (11.7)>0.99
Admission heart rate81.1 ± 18.283.29 ± 20.880.5 ± 17.30.38
Admission international normalized ratio1.05 ± 0.31.07 ± 0.21.04 ± 0.40.72
Admission hemoglobin13.26 ± 1.913.18 ± 1.913.27 ± 2.00.79
Admission platelet247.11 ± 72.5244.47 ± 72.8248.05 ± 72.90.78
Admission creatinine0.99 ± 1.21.07 ± 1.30.97 ± 1.10.62
Admission blood glucose154.33 ± 59.7167.24 ± 53.1150.09 ± 61.60.098
Admission white blood count17.47 ± 60.515.6 ± 7.118.21 ± 70.50.81
Intraventricular hemorrhage at admission97 (56.1)38 (86.4)59 (46.1)<0.001
External ventricular drain placement104 (60.1)38 (84.4)66 (51.6)<0.001
Nicardipine use107 (61.5)27 (60.0)80 (62.5)0.79
Nicardipine Maximum usec41 (23.6)10 (22.2)31 (24.2)0.76

Bold values denote statistical significance.

aTransferred to nursing home, hospice, or death.

bBody mass index.

cNicardipine Maximum use defined as continuous infusion at 15 mg/hour.

Table 2.
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Outcomes in aneurysmal SAH patients

Outcomesn (%)
(Total n = 174)
Length of stay (mean, days)23.0 ± 18.2
Microsurgical clipping16 (9.2)
Endovascular treatment108 (62.1)
Aneurysm rerupture during hospitalization10 (5.7)
Sustained intracranial pressure crisis22 (12.6)
Respiratory failure77 (44.2)
Vasospasm (detected radiographically or clinically)61 (35.0)
Decompression for cerebral edema/intracranial pressure crisis13 (7.5)
Norepinephrine use57 (32.8)
Ventriculoperitoneal shunt26 (14.9)
Tracheostomy23 (13.2)
Discharge mRSa >267 (38.5)
Three-month mRSa >242 (24.1)
In-hospital mortality28 (16.1)
Outcomesn (%)
(Total n = 174)
Length of stay (mean, days)23.0 ± 18.2
Microsurgical clipping16 (9.2)
Endovascular treatment108 (62.1)
Aneurysm rerupture during hospitalization10 (5.7)
Sustained intracranial pressure crisis22 (12.6)
Respiratory failure77 (44.2)
Vasospasm (detected radiographically or clinically)61 (35.0)
Decompression for cerebral edema/intracranial pressure crisis13 (7.5)
Norepinephrine use57 (32.8)
Ventriculoperitoneal shunt26 (14.9)
Tracheostomy23 (13.2)
Discharge mRSa >267 (38.5)
Three-month mRSa >242 (24.1)
In-hospital mortality28 (16.1)

Abbreviation: SAH, subarachnoid hemorrhage.

aModified Rankin score.

Table 2.
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Outcomes in aneurysmal SAH patients

Outcomesn (%)
(Total n = 174)
Length of stay (mean, days)23.0 ± 18.2
Microsurgical clipping16 (9.2)
Endovascular treatment108 (62.1)
Aneurysm rerupture during hospitalization10 (5.7)
Sustained intracranial pressure crisis22 (12.6)
Respiratory failure77 (44.2)
Vasospasm (detected radiographically or clinically)61 (35.0)
Decompression for cerebral edema/intracranial pressure crisis13 (7.5)
Norepinephrine use57 (32.8)
Ventriculoperitoneal shunt26 (14.9)
Tracheostomy23 (13.2)
Discharge mRSa >267 (38.5)
Three-month mRSa >242 (24.1)
In-hospital mortality28 (16.1)
Outcomesn (%)
(Total n = 174)
Length of stay (mean, days)23.0 ± 18.2
Microsurgical clipping16 (9.2)
Endovascular treatment108 (62.1)
Aneurysm rerupture during hospitalization10 (5.7)
Sustained intracranial pressure crisis22 (12.6)
Respiratory failure77 (44.2)
Vasospasm (detected radiographically or clinically)61 (35.0)
Decompression for cerebral edema/intracranial pressure crisis13 (7.5)
Norepinephrine use57 (32.8)
Ventriculoperitoneal shunt26 (14.9)
Tracheostomy23 (13.2)
Discharge mRSa >267 (38.5)
Three-month mRSa >242 (24.1)
In-hospital mortality28 (16.1)

Abbreviation: SAH, subarachnoid hemorrhage.

aModified Rankin score.

Variables affecting disposition

25.9% (45/174) of patients were categorized as having poor disposition. There was no significant discrepancy in demographic variables between the 2 disposition groups (Table 1). However, those in the poor disposition group had a higher proportion of patients with a HH score greater than 3 (62.2% vs. 12.1%, P < 0.001) and higher median Fisher grade (4 vs. 3; P < 0.001). The poor disposition group had higher rates of intraventricular hemorrhage on initial CT scan (86.4% vs. 46.1%; P < 0.001) and were more likely to undergo external ventricular drain placement (84.4% vs. 51.6%; P < 0.001).

When comparing BP parameters and BPV, increased SBP variability (P = 0.011) and PP variability (P = 0.042) were significantly associated with poor disposition on bivariate analysis (Table 3).

Table 3.
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Bivariate associations of dichotomized Poor Dispositiona and admission blood pressure measurements obtained within the first 24 hours

Poor Disposition
VariableMeanYes (n = 45)No (n = 129)P value
SBPb
 SBPb mean133.94 ± 13.9135.36 ± 17.8133.54 ± 12.250.45
 SBPb variability17.6 ± 7.019.9 ± 7.916.94 ± 6.480.014
DBPc
 DBPc mean65.59 ± 9.266.09 ± 11.165.41 ± 8.480.67
 DBPc variability10.09 ± 5.310.91 ± 4.69.83 ± 5.50.24
PPd
 PPd mean68.26 ± 13.369.18 ± 16.468.03 ± 12.120.62
 PPd variability13.19 ± 5.214.59 ± 6.312.76 ± 4.730.042
Poor Disposition
VariableMeanYes (n = 45)No (n = 129)P value
SBPb
 SBPb mean133.94 ± 13.9135.36 ± 17.8133.54 ± 12.250.45
 SBPb variability17.6 ± 7.019.9 ± 7.916.94 ± 6.480.014
DBPc
 DBPc mean65.59 ± 9.266.09 ± 11.165.41 ± 8.480.67
 DBPc variability10.09 ± 5.310.91 ± 4.69.83 ± 5.50.24
PPd
 PPd mean68.26 ± 13.369.18 ± 16.468.03 ± 12.120.62
 PPd variability13.19 ± 5.214.59 ± 6.312.76 ± 4.730.042

Bold values denote statistical significance.

aTransferred to nursing home, hospice, or death.

bSystolic blood pressure.

cDiastolic blood pressure.

dPulse pressure.

Table 3.
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Bivariate associations of dichotomized Poor Dispositiona and admission blood pressure measurements obtained within the first 24 hours

Poor Disposition
VariableMeanYes (n = 45)No (n = 129)P value
SBPb
 SBPb mean133.94 ± 13.9135.36 ± 17.8133.54 ± 12.250.45
 SBPb variability17.6 ± 7.019.9 ± 7.916.94 ± 6.480.014
DBPc
 DBPc mean65.59 ± 9.266.09 ± 11.165.41 ± 8.480.67
 DBPc variability10.09 ± 5.310.91 ± 4.69.83 ± 5.50.24
PPd
 PPd mean68.26 ± 13.369.18 ± 16.468.03 ± 12.120.62
 PPd variability13.19 ± 5.214.59 ± 6.312.76 ± 4.730.042
Poor Disposition
VariableMeanYes (n = 45)No (n = 129)P value
SBPb
 SBPb mean133.94 ± 13.9135.36 ± 17.8133.54 ± 12.250.45
 SBPb variability17.6 ± 7.019.9 ± 7.916.94 ± 6.480.014
DBPc
 DBPc mean65.59 ± 9.266.09 ± 11.165.41 ± 8.480.67
 DBPc variability10.09 ± 5.310.91 ± 4.69.83 ± 5.50.24
PPd
 PPd mean68.26 ± 13.369.18 ± 16.468.03 ± 12.120.62
 PPd variability13.19 ± 5.214.59 ± 6.312.76 ± 4.730.042

Bold values denote statistical significance.

aTransferred to nursing home, hospice, or death.

bSystolic blood pressure.

cDiastolic blood pressure.

dPulse pressure.

Multivariate analysis

A multivariate analysis was performed using all significant variables including age, external ventricular drain placement, intraventricular hemorrhage, HH score, and SBP variability. Age (odds ratio (OR) 1.03, 95% confidence interval (CI), 1.00–1.06, P = 0.028), intraventricular hemorrhage (OR 3.98, 95% CI, 1.41–12.73, P = 0.027), HH score (OR 13.58, 95% CI, 5.3–38.4, P = 0.027), and PP variability (OR 1.11, 95% CI, 1.02–1.21, P = 0.015) remained significantly associated with poor disposition as independent risk factors (Table 4). Given the significant association between SBP variability and disposition, a second multivariate analysis was performed using SBP variability which also demonstrated that age (OR 1.03, 95% CI, 1.00–1.06, P = 0.027), intraventricular hemorrhage (OR 3.49, 95% CI, 1.01–8.69, P = 0.022), HH score (OR 12.14, 95% CI, 4.82–33.4, P < 0.0001), and SBP variability (OR 1.07, 95% CI, 1.01–1.14, P = 0.019) remained significantly associated with poor disposition as independent risk factors (Supplementary Table S1 online).

Table 4.
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Multivariable logistic regression analysis using dichotomized Poor Dispositiona as outcome and admission baseline characteristics and pulse pressure variability

VariableOdds ratio95% confidence intervalP value
Age1.0311.003–1.0600.028
External ventricular drain2.5010.8879–7.6970.0922
Intraventricular hemorrhage3.9821.413–12.730.013
Hunt–Hess score >313.585.303–38.37<0.0001
Pulse pressure variability1.1101.023–1.2120.015
VariableOdds ratio95% confidence intervalP value
Age1.0311.003–1.0600.028
External ventricular drain2.5010.8879–7.6970.0922
Intraventricular hemorrhage3.9821.413–12.730.013
Hunt–Hess score >313.585.303–38.37<0.0001
Pulse pressure variability1.1101.023–1.2120.015

Bold values denote statistical significance.

aTransferred to nursing home, hospice, or death.

Table 4.
Open in new tab

Multivariable logistic regression analysis using dichotomized Poor Dispositiona as outcome and admission baseline characteristics and pulse pressure variability

VariableOdds ratio95% confidence intervalP value
Age1.0311.003–1.0600.028
External ventricular drain2.5010.8879–7.6970.0922
Intraventricular hemorrhage3.9821.413–12.730.013
Hunt–Hess score >313.585.303–38.37<0.0001
Pulse pressure variability1.1101.023–1.2120.015
VariableOdds ratio95% confidence intervalP value
Age1.0311.003–1.0600.028
External ventricular drain2.5010.8879–7.6970.0922
Intraventricular hemorrhage3.9821.413–12.730.013
Hunt–Hess score >313.585.303–38.37<0.0001
Pulse pressure variability1.1101.023–1.2120.015

Bold values denote statistical significance.

aTransferred to nursing home, hospice, or death.

Discussion

Acute BP management is a cornerstone of treatment for patients with SAH. However, there are no current clinical guidelines pertaining to stabilization of highly variable BP in the acute phase. These guidelines may become more important in the future as data accumulates demonstrating a role for BP parameters as markers for short- and long-term patient outcomes.

In this single-center retrospective study of adult patients with SAH, BPV prior to treatment or within the first 24 hours after arrival was associated with poor disposition. Specifically, we demonstrate a significant positive correlation between PP and SBP variability and poor disposition. On multivariate analysis, this relationship remained significant with PP and SBP variability remaining independent predictors for poor disposition when adjusting for potential confounders. The use of disposition as a suitable primary endpoint is supported by its significant associations with poor HH scores, intraventricular hemorrhage, and age in our study population, which correlate with poor outcomes.

Our findings are consistent with prior studies evaluating the role of BPV on patient outcomes after SAH, although primary endpoints have varied between studies. Ascanio et al. demonstrated the same level of significance for SBP variability and poor outcomes, defined as a mRS score of ≥3 at last follow-up, with a mean follow-up time of 18 months.10 Our findings also corroborate a study by Yang et al. that organized SBP variability over the first 24 hours into tertiles, where the lowest tertile had more favorable Glasgow Outcome Score at discharge and the highest tertile had significantly fewer favorable scores.5 Furthermore, Kirkness et al. demonstrated poor outcomes using Glasgow Outcome Score at 6-month follow-up in patients with high BPV over a 24-hour period.14 Another study by Lin et al. demonstrated that high SBP variability in the first 24 hours is associated with aneurysmal rebleed, and that these patients have poor outcomes at 3-month follow-up.13

In our study, disposition did not significantly correlate with mean SBP, mean DBP or variability, or mean PP. Multiple studies support the idea that mean SBP does not correlate with functional outcome5,10,14 or aneurysmal rerupture.13 Similarly, Zhang et al. found no significant difference in PP on admission between SAH patients dichotomized by mRS at discharge.12 Given our findings and those in the literature, there is a growing body of evidence that suggests BPV is more predictive of patient outcomes than BP itself in SAH. This has important implications for treatment goals, as guidelines for BP management are often solely based on specific targeted values and do not require the utilization of real-time arterial BP measurements.

Our findings, demonstrating an association between PP and SBP variability and clinical outcome, may support the hypothesis that BPV reflects a worsened state of autoregulation in SAH.10,14 Beyond the relationship in SAH patients, PP variability is also associated with poor functional outcomes in acute ischemic stroke patients treated with intravenous thrombolysis as well as intracerebral hemorrhage patients.6,8 Worse prognosis ensues because of secondary injuries reflected by suboptimal cerebral blood flow. In particular, higher PP variability may reflect dysfunction of autoregulation, as increases in stroke volume can act as a compensatory mechanism for maintaining cerebral perfusion pressure, leading to increases in both SBP and PP.6 The significant associations that we found with PP and SBP variability lead us to surmise that higher PP variability is a reflection of the SBP rather than DBP component.

DBP seems to be less critical in SAH than SBP. Some studies did not include DBP parameters in their analysis,10 and others reported no difference in DBP variability between outcome groups.5,15 Our study agreed with these findings. Faust et al. reported larger increases in mean arterial pressure over the first 8 days in patients with poor Glasgow Outcome Score scores and claimed that these changes were predominantly influenced by DBP changes.16 However, the main focus of their study was to compare BP in the presence or absence of vasospasm; DBP was not evaluated when comparing groups by Glasgow Outcome Score as was performed for change in mean arterial pressure in their study.

Limitations

Our study has several limitations. This is a single-center study with a small number of patients enrolled over a short period of time. There were some inconsistencies in BP measurement, as noninvasive measurements were used when there were no invasive measurements available. However, while invasive measurements are generally considered more accurate, issues in calibration frequently lead to inaccuracy. And as measurements were documented each hour, the more frequent measurements possible with continuous arterial measurements were not incorporated in this study. Another limitation of our study is that the true physiological values for BP and BPV are altered due to our hospital’s protocol for keeping SBP <140 mm Hg for unsecured aneurysms. As a result, patients will receive varying doses of antihypertensive medications to meet SBP goals. However, as noted in Table 1, difference between nicardipine use and maximum nicardipine use between the good and poor disposition groups was not significant. This suggests that intensive BP management itself, although altering the pathophysiology of patients with acute SAH, did not interfere with the inherent pathophysiological mechanisms for BPV that are associated with poor disposition. Additionally, our BP measurements were collected within the first 24 hours of hospitalization, so for a subset of patients, physiology may have been further deranged by the operative course in unanticipated ways.

Finally, our analysis was limited to outcomes at time of discharge; long-term outcomes were not evaluated. In addition, PP variability was not uniformly associated with poor outcome as measured by other variables such as mRS and death at both discharge and 3 months. However, while death is a uniform outcome variable, functional outcome as dichotomized by traditional stroke studies (with dichotomization occurring at either mRS 1 or 2), may not be relevant in SAH, as patients may present with a worse short-term outcome, not due to a focal deficit such as in ischemic stroke, but due to prolonged cognitive decline.17

BPV has been the subject of recent studies for patients diagnosed with ischemic stroke, hemorrhagic stroke, and aneurysmal SAH. We conclude based on our results that increased BPV is an independent predictor for poor disposition in aneurysmal SAH patients and that efforts should be made to prevent drastic BP fluctuations during the acute phase of SAH management. Both SBP and PP variations appear to have more significant associations with outcome than DBP. Further studies, larger in size and prospective in design, are needed to clearly delineate the effects of BPV.

FUNDING

No sources of funding were used for this study.

DISCLOSURE

The authors declared no conflict of interest.

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