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Prognostic models for neurological functional outcomes in aneurysmal subarachnoid hemorrhage patients with intracranial hematoma
Clinical Neurology and Neurosurgery 191 (2020) 105691
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Prognostic models for neurological functional outcomes in aneurysmal subarachnoid hemorrhage patients with intracranial hematoma
T
Zhen Wanga,1, Jingyi Zhoua,1, Feng Lianga,1, Shenbin Xua, Xiaobo Yua, Jianmin Zhanga,b,c,*, Ligen Shia,* a
Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China Brain Research Institute, Zhejiang University, Hangzhou, Zhejiang, China c Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, Zhejiang, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Aneurysmal subarachnoid hemorrhage Intracerebral hematoma Predictive factors Multivariable logistic regression Retrospective cohort study
Objectives: Current guidelines paid little attention to a unique severe disease about intracranial hematoma owing to aneurysm rupture. We attempted to explore the predictive factors for prognosis in these poor patient population. Patients and Methods: One hundred twenty-one aneurysmal subarachnoid hemorrhage combined with intracerebral hematoma patients discharged between 2013 and 2016 were reviewed in this retrospective study. Unfavorable outcome was defined as a modified Rankin Scale (mRS) score of 3, 4, 5, or 6 at 6 months. Multivariable logistic regression was performed to evaluate the association of unfavorable outcome with preoperative and postoperative clinical characteristics. Results: Of 121 patients with intact follow-up data, 34 (28.10 %) had an unfavorable prognosis. The preoperative prognostic model included patients’ age, respiratory rate, Hunt-Hess scale, red cell distribution width, and serum sodium at admission. The postoperative prognostic model included patients’ age, respiratory rate, red cell distribution width, serum sodium, postoperative delayed cerebral ischemia, and pulmonary infection. Both preoperative and postoperative prognostic models had excellent discrimination with Area Under The Curve (AUC) of 0.864 (P < .001) and 0.898 (P < .001), respectively. Conclusion: In clinical practice, we should pay more attention to those old patients with worse admission HuntHess score, presenting deep-slow respiratory and lower serum sodium. Reduction of postoperative delayed cerebral ischemia and pulmonary infection might improve outcomes after aneurysmal SAH with intracerebral hematoma.
1. Introduction Intracerebral hematoma suffering from subarachnoid hemorrhage (SAH) occurs in 2 of every 10 patients with ruptured intracerebral aneurysms [1], and shows a 38 %–58 % increase in the risk of poor outcome compared with pure SAH patients [2,3]. Currently, no guidelines are available pertaining to clinical features and management approach to these patients. Addressing questions regarding how much preoperative and postoperative factors could predict outcomes in aneurysmal SAH patients may contribute to provide better patient management.
Few studies have explored the association of clinical characteristics and outcomes in patients with aneurysmal SAH and additional intracerebral hematoma [4–6]. Large hematoma volume was previously regarded as an independent prognostic factor for poor outcomes because of its mass effect on intracranial pressure leading to serious early brain injury [3,7]. However, it is now recognized that favorable outcome is also possible in those aneurysmal SAH patients with huge intracerebral hematoma [4]. Although intracerebral hematoma locations [8], midline shift [9], admission neurological scores [2], management approach [10], and intervention time [4] have been reported to influence the prognosis, none of these factors could independently predict
Abbreviations: SAH, subarachnoid hemorrhage; ACA, anterior cerebral artery; MCA, middle cerebral artery; DCI, delayed cerebral ischemia; mRS, modified Rankin score; GCS, Glasgow Coma Scale; WFNS, World Federation of Neurosurgical Societies; CT, computed tomography; SD, standard deviation; IQR, inter quartile range; ROC, receiver operating characteristic curve; AUC, area under receiver operating characteristic curve; ORs, odds ratios; CIs, confidence intervals ⁎ Corresponding authors at: 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. E-mail addresses: [email protected] (J. Zhang), [email protected] (L. Shi). 1 These authors contributed equally to the work. https://doi.org/10.1016/j.clineuro.2020.105691 Received 21 November 2019; Received in revised form 12 January 2020; Accepted 20 January 2020 Available online 22 January 2020 0303-8467/ © 2020 Elsevier B.V. All rights reserved.
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2.5. Variables definition
the neurological functional outcomes. In addition, previous studies have ignored the contribution of postoperative complications in functional recover. Hence, it’s very necessary to develop an efficient multivariate prognostic model including all preoperative and postoperative factors to predict poor outcome in patients with aneurysmal SAH and additional intracerebral hematoma. The main objective of this present study was to determine the independent predictive factors of clinical characteristics and develop preoperative and postoperative prognostic models to predict poor outcome at 6 months.
Neurological deficit was assessed by Glasgow Coma Scale (GCS), World Federation of Neurosurgical Societies (WFNS) scale, and HuntHess scale at baseline. The degree of neurological deficit was defined by GCS scores: 3–8 for severe, 9–12 for moderate, and 13–15 for mild neurological impairment. Laboratory variables were examined at the presentation of patients to hospital. The severity of SAH was assessed by modified Fisher grades. Hematoma volume was measured by the formula ABC/2, where A is the greatest hemorrhage diameter by computed tomography (CT), B is the diameter 90° to A, and C is the approximate number of CT slices with hemorrhage multiplied by the slice thickness [12]. Outcome was assessed by mRS, and favorable outcome was defined as mRS score between 0 and 2 at six months.
2. Material and methods 2.1. Study design
2.6. Statistical analysis
This study was approved by the Institutional Ethics Committee of the Second Affiliated Hospital, School of Medicine, Zhejiang University, and carried out in accordance with The Code of Ethics of the World Medical Association. This was a single-center, retrospective study of patients with aneurysmal SAH and additional intracerebral hematoma discharged between January 2013 and December 2016.
All statistical analyses were performed using SPSS software (version 22.0; International Business Machines Corporation, Armonk, New York). Continuous variable was present as mean ± standard deviation (SD). Binary variable was present as median with inter quartile range (IQR). Patients were divided into favorable and unfavorable outcome groups according to six-month mRS scores. Differences in binary variable were detected by Pearson's chi-squared test between patients with a favorable outcome and those with an unfavorable outcome. As for continuous variable, we performed independent t tests, or MannWhitney U analysis according to data normality. All variables with P values < 0.05 were entered into the multivariate logistic regression analysis. A preoperative prognostic model included baseline demographic characteristics, initial neurological grade, laboratory variables, and imaging data. A postoperative prognostic model contained baseline demographic characteristics, initial neurological grade, laboratory variables, imaging data, different surgical procedures, and postoperative complications. Both prognostic models were developed to identify independent predictors of an unfavorable outcome in the multivariate regression models using the backward logistic regression method. The model’s prediction ability was tested by the receiver operating characteristic curve (ROC). The area under ROC (AUC) over 0.80 was regarded as excellent discrimination. Odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated. A P value < 0.05 was considered statistically significant.
2.2. Study setting All data pertaining to these patients treated at our center were entered into a prospectively collected database. All subjects or their relatives have signed written informed consents. This database was established in 2013 as a project to improve the survival quality of aneurysmal SAH patients. We prospectively collected demographic characteristics including patients’ age, gender, and race; clinical characteristics pertaining to past medical history, basic vital signs, and neurological scores at admission; physiologic variables detecting blood routine tests, serum biochemical examination, blood coagulation function, and so on; imaging data evaluating aneurysmal and intracerebral hematoma characteristics; operative information regarding therapeutic approach and operative time; postoperative complications including delayed cerebral ischemia (DCI), rebleeding, and postoperative infection; and six-month functional outcome assessed by modified Rankin scale (mRS). All patients were received standard medical treatment according to current SAH guidelines [11], including control intracranial pressure, antivasospasm therapy, infection prevention, and so on.
3. Results
2.3. Participant population
3.1. Patient characteristics
In this present study, we reviewed data pertaining to patients diagnosed as aneurysmal SAH with intracerebral hematoma. Inclusion criteria were 1) patients with CT confirmed intracerebral hematoma and SAH, 2) CTA or DSA certified intracerebral aneurysms, 3) age above 18 years old, and 4) treated with 48 h in our center between 2013 and 2016. Exclusion criteria were 1) patients with SAH due to other reasons, 2) combined with intraventricular hemorrhage, 3) combined with arteriovenous malformation, 4) combined with malignant tumor, 5) died prior to operation, and 6) missing data.
Total 1027 patients with aneurysmal SAH were included in the database between January 2013 and December 2016. Approximately 214 patients (20.84 % of all aneurysmal SAH patients) were diagnosed as aneurysmal SAH with intracerebral hematoma. After reviewing all these patients’ data, we excluded 93 patients including 35 patients combined intraventricular hemorrhage, 23 patients died prior to operation, 18 patients with incomplete follow-up data, 13 patients combined malignant tumor, and 4 patients combined arteriovenous malformation. Finally, 121 patients were enrolled in statistics in the present study (Fig. 1). Baseline characteristics of the 121 patients were shown in the Table 1. The mean age of these patients was 56.31 years old, and almost half of these patients were male. Regarding medical history, 44 patients were suffered from hypertension, and 24 patients were smokers. The average value of temperature, respiratory rate, heart rate, and blood pressure were within normal limits. These patients showed moderate neurological deficit assessed by several scales including GCS, WFNS, and Hunt-Hess scales. The vast majority of intracerebral hemorrhage were from the rupture of the anterior cerebral artery (ACA) and middle cerebral artery (MCA) aneurysms.
2.4. Data sources Although the database included all patients with aneurysmal SAH, we only retrospectively reviewed data regarding patients with aneurysmal SAH and intracerebral hematoma in this present study. Demographic and clinical characteristics were extracted by two authors (FL and SBX). Patients’ physiologic variables were automatic export from our database. Imaging data were evaluated by two authors (XBY and JYZ) in a blinded fashion. All the extracted data were checked by the primary author (LGS). 2
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Fig. 1. Flow chart of primary analysis population selection. aSAH = aneurysmal subarachnoid hemorrhage.
logistic regression analysis. The preoperative prognostic model included patients’ age, temperature, respiratory rate, GCS score, WFNS score, Hunt-Hess score, Fisher score, C-reactive protein, serum sodium, and RDW. The postoperative prognostic model included patients’ age, temperature, respiratory rate, GCS score, WFNS score, Hunt-Hess score, Fisher score, C-reactive protein, serum sodium, RDW, surgical procedure, DCI, and pulmonary infection. In the postoperative prognostic model, patients’ age (P = .002), admission respiratory rate (P = .026), serum sodium (P = .013), RDW (P = .010), postoperative DCI (P = .001), and postoperative pulmonary infection (P = .001) were independent predictors of unfavorable outcome. The preoperative prognostic model including patients age (P < .001), respiratory rate (P = .012), serum sodium(P = .043), RDW(P = .076), and Hunt-Hess score (P = .047) showed excellent discrimination with an AUC of 0.864 (95 % CI 0.790 - 0.937, P < .001). The postoperative prognostic model had better discriminatory performance with an AUC of 0.898 (95 % CI 0.829 - 0.967, P < .001) compared with preoperative prognostic model (Fig. 3).
3.2. Preoperative factors for unfavorable outcomes Several differences were observed in preoperative demographic and clinical characteristics between patients with unfavorable outcome and those with favorable outcome (Table 1). Unfavorable outcome was more likely to occur in those patients with older ages (t = -4.647, P < .001), higher temperature (χ2 = -2.089, P = .037), and lower respiratory rate at admission (Z = 2.307, P = .021). In addition, the severe neurological defect assessed by GCS (Z = 2.588, P = .010, Fig. 2A), WFNS (Z = -2.347, P = .019), Hunt-Hess score (Z = -3.300, P = .001), and Fisher score (Z = -2.779, P = .005) scales were associated with unfavorable outcome during six-month follow-up period. Preoperative blood parameters, high C-reactive protein (Z = -2.405, P = .016), high serum sodium (Z = -2.757, P = .006), and high red cell distribution width (RDW) (Z = -2.999, P = .003) were associated with unfavorable outcome (Table 1). No significant difference was observed in aneurysmal location and size, hematoma location and volume, and middle shift between patients with unfavorable outcome and those with favorable outcome (Table 1). The location of ruptured aneurysms has no effects on both unfavorable outcome (Fig. 2B) and hematoma volume (Fig. 2C).
4. Discussions Based on the data from our study of SAH patients, we firstly conducted prognostic models for outcomes in aneurysmal SAH patients with intracranial hematoma. The preoperative prognostic model including patients’ age, admission respiratory rate, serum sodium, RDW, and Hunt-Hess score made it possible to estimate prognosis before the treatment, which was helpful in determining therapeutic methods and informing the family about prognosis. The postoperative prognostic model including the preoperative factors as well as postoperative delayed cerebral ischemia and pulmonary infection showed excellent discrimination. This postoperative model could be important, because it indicated that the reduction of the postoperative complications, especially delayed cerebral ischemia and pulmonary infection, could improve the outcome. We developed these prognostic models in response to clinical needs for studies that target those patients diagnosed with aneurysmal SAH and intracranial hematoma for research studies aimed at understanding predictive factors for outcome and in providing suggestions for managing these patients. Our prognostic models are the first to focus on prediction of outcome, which could play important roles in either clinical practice or future research settings of this type. First, previous studies have indicated that not all these patients had poor final
3.3. Postoperative factors for unfavorable outcomes Patient operative and postoperative characteristics were shown in the Table 2. About three quarters of these patients were received craniotomy for aneurysm clipping. Moreover, additional surgical operations were carried out according to the patient’s condition, including hematoma removal in 56 patients, decompressive craniectomy in 17 patients, external ventricular drainage in 6 patients, lumbar drainage in 66 patients, and intracranial pressure monitoring in 24 patients. The average of onset to operation time was 32 h. Univariate analysis for unfavorable outcome indicated that surgical procedures (P = .040), postoperative delayed cerebral ischemia (χ2 = 17.674, P < .001), and postoperative pulmonary infection (χ2 = 15.309, P < .001) were associated with unfavorable outcome. 3.4. Multivariate analyses for predictors of unfavorable outcomes Multivariable logistic regression for predictors of unfavorable outcome at six months was presented in the Fig. 3. We included all the significant factors from the univariate analysis into the multivariable 3
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Table 1 Patient Baseline Demographic and Clinical Characteristics. Characteristics
All patients (n = 121)
Demographic Characteristics Age, year, mean ± SD 56.31 ± 11.24 Male, n (%) 48 (39.67) Hypertension, n (%) 44 (36.36) Diabetes, n (%) 4 (3.30) Coronary disease, n (%) 0 (0) Smoking, n (%) 24 (19.83) Clinical Characteristics Temperature, °C 37.10 (37.00–37.40) Respiratory rate, min−1 19 (17–20) Heart rate, min−1 77 (70–83) SBP, mmHg, mean ± SD 137(123–156.5) DBP, mmHg, mean ± SD 78.70 ± 13.94 Neurological scores, median (IQR) GCS score 14 (9.50–15) WFNS score 2(1–4) Hunt-Hess score 3(2–3.50) Fisher score 3 (3–4) Aneurysm location, n (%) ACA 36(29.75 %) MCA 52(42.98 %) PCA 19(15.70 %) Other locations 14(11.57 %) Multiple aneurysms 4 (3.36) Aneurysm size, mm 4.0 (3.0–5.8) Hematoma location, n (%) Frontal, n (%) 47(38.84 %) Temporal, n (%) 66(54.55 %) Frontotemporal, n (%) 8(6.61 %) Pre hematoma, ml 7.08 (3.68–13.80) Post hematoma, ml 3.57 (1.43–6.60) Clearance rate, % 43.92 (17.80–73.97) Blood Parameters Values Obtained at Admission Leukocyte, 109/L 12.9 (10.5–17.1) Hemoglobin, g/L 127.37 ± 19.14 C-reactive protein, mg/L 20.20 (9.40–42.25) Glucose, mmol/L 7.19 (6.29–8.62) Serum potassium, mmol/L 3.63 (3.47–3.99) Serum sodium, mmol/L 139.6 (137.7–141.9) Serum calcium, mmol/L 2.13 (1.99–2.24) PT, s 13.1 (12.5–13.7) APTT, s 32.7 (29.2–36.5) INR 1.02 (0.98–1.09) RDW 13.2 (12.7–13.8)
Favorable outcome (n = 87)
Unfavorable outcome (n = 34)
Statistics
P value
53.56 ± 10.87 39 (44.83) 29 (33.33) 2 (2.30) 0 (0) 19 (21.84)
63.32 ± 9.00 9 (26.47) 15 (44.12) 2 (5.88) 0 (0.00) 5 (14.71)
t = -4.647 χ2 = 3.442 χ2 = 1.229 χ2 = 0.982 NA χ2 = 0.782
0.000 0.064 0.268 0.314 NA 0.376
37.10 (37.00–37.30) 19 (18–20) 75 (70–83) 137(122–157) 80.07 ± 14.15
37.30 (37.10–37.50) 18 (15–20) 78 (71.3–89.5) 138.5(123–156.3) 75.21 ± 12.93
Z = -2.089 Z = 2.307 Z = -1.455 Z = 0.037 t = 1.739
0.037 0.021 0.146 0.970 0.085
14 (11–15) 2(1–4) 2(2–3) 3 (3-3)
12.5 (7–14.25) 3(1.75–4) 3(2.75–4) 3 (3–4)
Z = 2.588 Z = -2.347 Z = -3.300 Z = -2.779
0.010 0.019 0.001 0.005
28(32.18 %) 37(42.53 %) 10(11.49 %) 12(13.79 %) 3 (3.49) 4.0 (3.0–5.8)
8(23.53 %) 15(44.12 %) 9(26.47 %) 2(5.88 %) 1 (3.03) 4.0 (2.8–6.0)
χ2 = 5.444
0.142
χ2 = 0.015 Z = -0.277
1.000 0.782
36(41.38 %) 48(55.17 %) 3(3.45 %) 6.92 (3.56–13.27) 3.38 (1.30–6.21) 44.62 (24.25–80.05)
11(32.35 %) 18(2.94 %) 5(14.71 %) 9.24 (4.27–16.50) 4.86 (1.92–9.79) 42.67 (11.27–71.34)
χ2=5.221
0.073
Z = -1.264 Z = -1.891 Z = 0.839
0.206 0.059 0.402
12.9 (10.2–17.0) 128.45 ± 18.89 19.00 (8.80–37.10) 7.03 (6.09–8.57) 3.7 (3.47–4.0) 139.3 (137.5–141.5) 2.15 (2.02–2.25) 13.1 (12.5–13.6) 32.7 (28.5–36.9) 1.03 (0.98–1.09) 13.0 (12.6–13.6)
12.9 (11.13–17.48) 124.62 ± 19.78 31.55 (14.60–77.45) 7.64 (6.60–8.75) 3.55 (3.39–3.83) 141.3 (138.9–144.0) 2.10 (1.81–2.21) 13.2 (12.7–13.7) 32.7 (30.0–35.7) 1.01 (0.98–1.10) 13.5 (13.1–14.2)
Z=-0.753 t = 0.990 Z=-2.405 Z=-1.652 Z=1.892 Z=-2.757 Z=1.445 Z=-0.603 Z=-0.208 Z=0.364 Z=-2.999
0.452 0.324 0.016 0.099 0.059 0.006 0.148 0.546 0.836 0.716 0.003
SD = Standard Deviation; n = Number; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; IQR = Inter Quartile Range; GCS = Glasgow Coma Scale; WFNS = World Federation of Neurosurgical Societies; ACA = Anterior Cerebral Artery; MCA = Middle Cerebral Artery; PCA = Posterior Cerebral Artery; PT = Prothrombin Time; APTT = Activated Partial Thromboplastin Time; INR = International Normalized Ratio; RDW = Red Cell Distribution Width.
patients [15,16]. Additional intracerebral hematoma was observed an association with the development of delayed cerebral ischemia in aneurysmal SAH patients [6,17]. The underlying mechanism regarding intracerebral hematoma resulting in a high rate of delayed cerebral ischemia might be mass effect of hematoma leading to a long sustained increase in intracranial pressure [7,18]. Theoretically, the earlier the hematoma was removed, the better outcomes the patients obtained. However, the univariate analysis of the present study indicated that hematoma removal was not a predictive factor for outcome (Fig. 3). It should be noted that decompressive craniectomy was observed to improve outcome on univariate analysis (P = 0.061), although it was not significant in the multivariate analysis (Fig. 3). Future studies should focus on predictive factors for postoperative delayed cerebral ischemia in order to control this postoperative complication and improve outcome. Pulmonary complications have been considered as strongly negative factors for morbidity and mortality following aneurysmal SAH [19,20]. Sudden elevation of intracranial pressure caused by aneurysmal SAH will activate the sympathetic nervous system leading to pulmonary edema [21]. Subsequently, it will increase the incidence of ventilator-acquired pneumonia [22]. These pulmonary complications
outcomes [13,14], although additional intracerebral hematoma was regarded to increase the mortality and disability of aneurysmal SAH patients [4]. Hence, it is very necessary to differentiate those patients with predicted poor final outcomes to perform more intensive therapy. The well-validated preoperative prognostic model could accurately predict outcome before the treatment. Individualized treatment performing according to the predicted results by our preoperative prognostic model is important for economizing medical resources and improving individual final recovery. Second, this preoperative prognostic model is useful for selecting participants in further studies. Further researchers need to consider patients’ age, respiratory rate, serum sodium, RDW, and Hunt-Hess score in their study design. The present study indicated that only a minority of patients (34/121) with aneurysmal SAH and intracerebral hematoma had poor outcomes. Our preoperative prognostic model could be used to identify patients most likely to result in poor outcomes, enriching future randomized controlled trials. Third, the postoperative prognostic model draws our attention to postoperative delayed cerebral ischemia and pulmonary infection. Previous studies have indicated that delayed cerebral ischemia after aneurysmal SAH had a negative impact on outcome for the 4
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as primary outcomes. Except this novelty, several other strengths of our study contributed to its high reliability. All these patients in the present study were extracted from our large single-center prospectively collected database containing 1027 SAH patients. All neurological scales, surgical procedures, neurological intensive care, and outcome assessment scores were collected from one single center, which reduced the patient heterogeneity. Besides, we drew up strict inclusion and exclusion criteria to furthest reduce patient heterogeneity prior to this study. We excluded those potential influencing factors for outcomes including intraventricular hemorrhage and malignant tumor. In addition, we detected almost fifty clinical index including baseline demographic and clinical characteristics, blood parameters values at admission, operative information, and postoperative complications in univariate analysis. Our multiple preoperative and postoperative prognostic models showed excellent discrimination with high AUC of 0.864 and 0.898, respectively. Another important superiority of the present study was the practicability of these prognostic models in the clinical work. The preoperative model containing five factors, patients’ age, admission respiratory rate, serum sodium, RDW, and Hunt-Hess score, could timely obtain in the emergency ward. It is useful for clinician to reference the predicted results from our prognostic model to formulate effective therapeutic plan. Importantly, patients’ age is considered as a forceful influence factor for outcome in previous studies involving poorgrade aneurysmal SAH patients [24,25]. There is no doubt about the predictive value of initial Hunt-Hess scores in patients with pure aneurysmal SAH patients [26,27], but whether it still works in predicting for patients with aneurysmal SAH and intracerebral hemorrhage patients wasn’t detected in previous study. The present study indicated that initial Hunt-Hess scores didn’t lose its predictive power in this special patient population. Respiratory rate and serum sodium level are interesting findings, which got less attention in previous studies. In the patients with pure aneurysmal SAH, extracerebral organ dysfunction including respiratory failure was observed to be in correlation of poor neurologic outcome [28]. As for serum sodium, it has been reported that hyponatremia was relatively common among aneurysmal SAH patients, which was proved to have an association with delayed cerebral ischemia and indicate poor prognosis [29–31]. The present study indicated that the respiratory rate and serum sodium level were also suitable for predicting outcome in patients with aneurysmal SAH and intracerebral hematoma. As all studies have limitations, some obvious deficiency was existed in the present study. First, the small sample size might increase the probability of sampling error. In the present study, we only collected data of patients discharged from our hospital in recent four years in order to reduce the heterogeneity of diagnostic and therapeutic level. Hence, we only included 121 patients in the statistical analysis. Secondary, this is a retrospective study with its congenital defect of a high risk of selection bias. Specially, we excluded 23 patients died prior to operation and 18 patients with incomplete follow-up data, which might reduce the severity of this patient population. Thirdly, all the data are from our single center. It’s very necessary to test our models using patients’ clinical characteristics and outcomes by other clinic centers. Finally, the outcome was defined by mRS scores that has been developed and tested only in aneurysmal SAH patients after surgical aneurysm repair [6].
Fig. 2. Clinical characteristics of patients with aneurysmal subarachnoid hemorrhage and intracerebral hemorrhage. (A) Neurological deficit assessments between patients with unfavorable outcome and those with favorable outcome at admission. Scores on Glasgow coma scale: 3–8 for severe, 9–12 for moderate, and 13–15 for mild neurological impairment. (B) outcome in different intracranial artery segments. Similar rates of unfavorable outcome were observed among ACA, MCA, PCA, and ICA. (C) Hematoma volume in different intracranial artery segments. ACA = anterior cerebral artery; MCA = middle cerebral artery; PCA = posterior cerebral artery; ICA = internal cerebral artery.
can cause disturbed oxygenation of the brain, and thereby contribute to poor outcome in these patients [23]. The present study also indicated that postoperative pulmonary infection was an independent predictive factor for poor outcome in patients with aneurysmal SAH and intracerebral hemorrhage. In addition, the surgical procedure was not considered as a significant predictor in the multivariate regression analysis. The decision of surgical procedure was made by our neurosurgeons according to the patients’ conditions. If the patients presented acute cerebral herniation, the neurosurgeons prefer to perform craniotomy clipping. It may result in the unbalance of pre-operative clinical status between clipped and coiled patients. However, our data indicated that there is no significant difference in GCS score between clipped (11.45 ± 3.65) and coiled (12.40 ± 2.87) patients (P = 0.201). Our prognostic models were the first to target neurological recovery
5. Conclusions The present study firstly designed preoperative and postoperative prognostic models to predict the outcome in patients with aneurysmal SAH and intracerebral hematoma. The preoperative prognostic model indicated that we should pay more attention to those old patients with worse admission Hunt-Hess score, presenting deep-slow respiratory and lower serum sodium. And the postoperative prognostic model demonstrated that reduction of postoperative delayed cerebral ischemia and pulmonary infection might improve outcome after aneurysmal SAH 5
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Table 2 Patient Operative and Postoperative Characteristics. Characteristics
Operation Characteristics Clipping, n (%) Coiling, n (%) Hematoma removal, n (%) DCO, n (%) EVD, n (%) Lumbar drainage, n (%) ICP monitoring, n (%) OTO time, h Postoperation Characteristics Rebleeding, n (%) DCI, n (%) Pulmonary infection, n (%)
All patients (n = 121)
Favorable outcome (n = 87)
Unfavorable outcome (n = 34)
Statistics
P value
87(71.90 %) 34(28.10 %) 56(46.28 %) 17(14.05 %) 6(6.90 %) 66(54.55 %) 24(19.83 %) 32(24–68.75)
58(66.67 %) 29(33.33 %) 41(47.13 %) 9(10.34 %) 3(3.45 %) 46(52.87 %) 16(18.39 %) 32(24–72)
29(85.29 %) 5(14.71 %) 15(44.12 %) 8(23.53 %) 3(8.82 %) 20(58.82 %) 8(23.53 %) 36(18.5–49)
χ2 = 4.198
0.040
χ2 = 0.089 χ2 = 3.519 χ2 = 1.499 χ2 = 0.349 χ2 = 0.406 Z = -0.516
0.765 0.061 0.221 0.555 0.524 0.606
9 (7.44 %) 17 (14.05 %) 39 (32.23 %)
4 (4.60 %) 5 (5.75 %) 19 (21.84 %)
5 (14.71 %) 12 (35.29 %) 20 (58.82 %)
χ2 = 3.628 χ2 = 17.674 χ2 = 15.309
0.069 0.000 0.000
HCR = Hematoma Clearance Rate; DCO = Decompressive Craniectomy Operation; EVD = External Ventricular Drainage; ICP = Intracranial Pressure; OTO = Onset To Operation; DCI = Delayed Cerebral Ischemia.
Zhou: Data curation, Methodology, Investigation. Feng Liang: Formal analysis, Visualization, Investigation. Shenbin Xu: Investigation, Visualization. Xiaobo Yu: Validation, Formal analysis. Jianmin Zhang: Project administration, Data curation. Ligen Shi: Data curation, Supervision, Writing - review & editing.
with intracerebral hematoma. Future prospective studies are necessary to test the validity and reliability of these two prognostic models. Funding JM Zhang is supported by the National Key Research and Development Program of China (2017YFC1308500) & Key Program of Science and Technology Development of Zhejiang Province (2017C03021). Z Wang is supported by Basic Public Interests Research Plan of Zhejiang Province (LGF18H090005). JY Zhou is supported by Basic Public Interests Research Plan of Zhejiang Province (LY18H090001).
Declaration of Competing Interest The authors declare no conflicts of interest.
Acknowledgments
CRediT authorship contribution statement
We want to pay our respects to all the patients involved in this study.
Zhen Wang: Conceptualization, Writing - original draft. Jingyi
Fig. 3. AUCs of the predictive value of outcome according to prognostic models. (A) The preoperative prognostic model including patients age, respiratory rate, serum sodium, RDW, and Hunt-Hess score showed excellent discrimination with an AUC of 0.864. (B) The postoperative prognostic model including patients’ age, admission respiratory rate, serum sodium, RDW, postoperative DCI, and postoperative pulmonary infection were independent predictors of unfavorable outcome. The postoperative prognostic model had better discriminatory performance with an AUC of 0.898 compared with preoperative prognostic model. RDW = red cell distribution width, DCI = delayed cerebral ischemia, AUC = area under the curve. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 6
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References [17]
[1] X. Liu, G.J. Rinkel, Aneurysmal and clinical characteristics as risk factors for intracerebral haematoma from aneurysmal rupture, J. Neurol. 258 (2011) 862–865, https://doi.org/10.1007/s00415-010-5855-2. [2] A. Wan, B.N. Jaja, T.A. Schweizer, et al., Clinical characteristics and outcome of aneurysmal subarachnoid hemorrhage with intracerebral hematoma, J. Neurosurg. 125 (2016) 1344–1351, https://doi.org/10.3171/2015.10.jns151036. [3] Y. Tokuda, T. Inagawa, Y. Katoh, et al., Intracerebral hematoma in patients with ruptured cerebral aneurysms, Surg. Neurol. 43 (1995) 272–277, https://doi.org/10. 1016/0090-3019(95)80013-7. [4] E. Guresir, J. Beck, H. Vatter, et al., Subarachnoid hemorrhage and intracerebral hematoma: incidence, prognostic factors, and outcome, Neurosurgery 63 (2008) 1088–1093, https://doi.org/10.1227/01.neu.0000335170.76722.b9 discussion 1093-1084. [5] B.N. Bohnstedt, H.S. Nguyen, C.G. Kulwin, et al., Outcomes for clip ligation and hematoma evacuation associated with 102 patients with ruptured middle cerebral artery aneurysms, World Neurosurg. 80 (2013) 335–341, https://doi.org/10.1016/ j.wneu.2012.03.008. [6] J. Platz, E. Guresir, M. Wagner, et al., Increased risk of delayed cerebral ischemia in subarachnoid hemorrhage patients with additional intracerebral hematoma, J. Neurosurg. 126 (2017) 504–510, https://doi.org/10.3171/2015.12.jns151563. [7] F.A. Sehba, R.M. Pluta, J.H. Zhang, Metamorphosis of subarachnoid hemorrhage research: from delayed vasospasm to early brain injury, Mol. Neurobiol. 43 (2011) 27–40, https://doi.org/10.1007/s12035-010-8155-z. [8] E.R. Smith, B.S. Carter, C.S. Ogilvy, Proposed use of prophylactic decompressive craniectomy in poor-grade aneurysmal subarachnoid hemorrhage patients presenting with associated large sylvian hematomas, Neurosurgery 51 (2002) 117–124, https://doi.org/10.1227/01.NEU.0000017192.85679.38 discussion 124. [9] P.D. Le Roux, A.T. Dailey, D.W. Newell, et al., Emergent aneurysm clipping without angiography in the moribund patient with intracerebral hemorrhage: the use of infusion computed tomography scans, Neurosurgery 33 (1993) 189–197 discussion 197. doi:. [10] G. Talamonti, M. Nichelatti, A.A. Al Mashni, et al., Life-threatening cerebral hematoma owing to aneurysm rupture, World Neurosurg. 85 (2016) 215–227, https://doi.org/10.1016/j.wneu.2015.08.082. [11] E.S. Connolly Jr., A.A. Rabinstein, J.R. Carhuapoma, et al., Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/american Stroke Association, Stroke 43 (2012) 1711–1737, https://doi.org/10.1161/STR.0b013e3182587839. [12] R.U. Kothari, T. Brott, J.P. Broderick, et al., The ABCs of measuring intracerebral hemorrhage volumes, Stroke 27 (1996) 1304–1305 doi: WOS:A1996VA09200007. [13] C.S. Lee, J.U. Park, J.G. Kang, et al., The clinical characteristics and treatment outcomes of patients with ruptured middle cerebral artery aneurysms associated with intracerebral hematoma, J. Cerebrovasc. Endovasc. Neurosurg. 14 (2012) 181–185, https://doi.org/10.7461/jcen.2012.14.3.181. [14] K.M. Abbed, C.S. Ogilvy, Intracerebral hematoma from aneurysm rupture, Neurosurg. Focus 15 (2003) E4. doi:. [15] A.S. Sarrafzadeh, P. Vajkoczy, P. Bijlenga, et al., Monitoring in Neurointensive Care - the challenge to detect delayed cerebral ischemia in high-grade aneurysmal SAH, Front. Neurol. 5 (2014) 134, https://doi.org/10.3389/fneur.2014.00134. [16] R.L. Macdonald, Delayed neurological deterioration after subarachnoid
[18] [19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27] [28]
[29]
[30]
[31]
7
haemorrhage, Nat. Rev. Neurol. 10 (2014) 44–58, https://doi.org/10.1038/ nrneurol.2013.246. E. Crobeddu, M.K. Mittal, S. Dupont, et al., Predicting the lack of development of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage, Stroke 43 (2012) 697–701, https://doi.org/10.1161/STROKEAHA.111.638403. R.L. Macdonald, B.K. Weir, A review of hemoglobin and the pathogenesis of cerebral vasospasm, Stroke 22 (1991) 971–982 doi: WOS:A1991GB31200001. J.M. Kahn, E.C. Caldwell, S. Deem, et al., Acute lung injury in patients with subarachnoid hemorrhage: incidence, risk factors, and outcome, Crit. Care Med. 34 (2006) 196–202, https://doi.org/10.1097/01.CCM.0000194540.44020.8E. J.A. Friedman, M.A. Pichelmann, D.G. Piepgras, et al., Pulmonary complications of aneurysmal subarachnoid hemorrhage, Neurosurgery 52 (2003) 1025–1031, https://doi.org/10.1227/01.NEU.0000058222.59289.F1 discussion 1031-1022. F.A. Sehba, J. Hou, R.M. Pluta, et al., The importance of early brain injury after subarachnoid hemorrhage, Prog. Neurobiol. 97 (2012) 14–37, https://doi.org/10. 1016/j.pneurobio.2012.02.003. J.B. Cui, Q.Q. Chen, T.T. Liu, et al., Risk factors for early-onset ventilator-associated pneumonia in aneurysmal subarachnoid hemorrhage patients, Braz. J. Med. Biol. Res. 51 (2018) e6830, , https://doi.org/10.1590/1414-431x20176830. W.J. Schuiling, P.J. Dennesen, J.T. Tans, et al., Troponin I in predicting cardiac or pulmonary complications and outcome in subarachnoid haemorrhage, J. Neurol Neurosurg Psychiatry 76 (2005) 1565–1569, https://doi.org/10.1136/jnnp.2004. 060913. J. Mocco, E.R. Ransom, R.J. Komotar, et al., Preoperative prediction of long-term outcome in poor-grade aneurysmal subarachnoid hemorrhage, Neurosurgery 59 (2006) 529–538, https://doi.org/10.1227/01.NEU.0000228680.22550.A2 discussion 529-538.. C.J. Taylor, F. Robertson, D. Brealey, et al., Outcome in poor grade subarachnoid hemorrhage patients treated with acute endovascular coiling of aneurysms and aggressive intensive care, Neurocrit. Care 14 (2011) 341–347, https://doi.org/10. 1007/s12028-010-9377-7. T. Inagawa, M. Shibukawa, F. Inokuchi, et al., Primary intracerebral and aneurysmal subarachnoid hemorrhage in Izumo City, Japan. Part II: management and surgical outcome, J. Neurosurg. 93 (2000) 967–975, https://doi.org/10.3171/jns. 2000.93.6.0967. D.S. Rosen, R.L. Macdonald, Subarachnoid hemorrhage grading scales: a systematic review, Neurocrit. Care 2 (2005) 110–118, https://doi.org/10.1385/NCC:2:2:110. A. Gruber, A. Reinprecht, U.M. Illievich, et al., Extracerebral organ dysfunction and neurologic outcome after aneurysmal subarachnoid hemorrhage, Crit. Care Med. 27 (1999) 505–514, https://doi.org/10.1097/00003246-199903000-00026. M.J. McGirt, R. Blessing, S.M. Nimjee, et al., Correlation of serum brain natriuretic peptide with hyponatremia and delayed ischemic neurological deficits after subarachnoid hemorrhage, Neurosurgery 54 (2004) 1369–1373, https://doi.org/10. 1227/01.NEU.0000125016.37332.50 discussion 1373-1364. A.I. Qureshi, M.F. Suri, G.Y. Sung, et al., Prognostic significance of hypernatremia and hyponatremia among patients with aneurysmal subarachnoid hemorrhage, Neurosurgery 50 (2002) 749–755, https://doi.org/10.1097/00006123-20020400000012 discussion 755-746. D. Hasan, E.F. Wijdicks, M. Vermeulen, Hyponatremia is associated with cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage, Ann. Neurol. 27 (1990) 106–108, https://doi.org/10.1002/ana.410270118.
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Clinical Neurology and Neurosurgery journal homepage: www.elsevier.com/locate/clineuro
Prognostic models for neurological functional outcomes in aneurysmal subarachnoid hemorrhage patients with intracranial hematoma
T
Zhen Wanga,1, Jingyi Zhoua,1, Feng Lianga,1, Shenbin Xua, Xiaobo Yua, Jianmin Zhanga,b,c,*, Ligen Shia,* a
Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China Brain Research Institute, Zhejiang University, Hangzhou, Zhejiang, China c Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, Zhejiang, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Aneurysmal subarachnoid hemorrhage Intracerebral hematoma Predictive factors Multivariable logistic regression Retrospective cohort study
Objectives: Current guidelines paid little attention to a unique severe disease about intracranial hematoma owing to aneurysm rupture. We attempted to explore the predictive factors for prognosis in these poor patient population. Patients and Methods: One hundred twenty-one aneurysmal subarachnoid hemorrhage combined with intracerebral hematoma patients discharged between 2013 and 2016 were reviewed in this retrospective study. Unfavorable outcome was defined as a modified Rankin Scale (mRS) score of 3, 4, 5, or 6 at 6 months. Multivariable logistic regression was performed to evaluate the association of unfavorable outcome with preoperative and postoperative clinical characteristics. Results: Of 121 patients with intact follow-up data, 34 (28.10 %) had an unfavorable prognosis. The preoperative prognostic model included patients’ age, respiratory rate, Hunt-Hess scale, red cell distribution width, and serum sodium at admission. The postoperative prognostic model included patients’ age, respiratory rate, red cell distribution width, serum sodium, postoperative delayed cerebral ischemia, and pulmonary infection. Both preoperative and postoperative prognostic models had excellent discrimination with Area Under The Curve (AUC) of 0.864 (P < .001) and 0.898 (P < .001), respectively. Conclusion: In clinical practice, we should pay more attention to those old patients with worse admission HuntHess score, presenting deep-slow respiratory and lower serum sodium. Reduction of postoperative delayed cerebral ischemia and pulmonary infection might improve outcomes after aneurysmal SAH with intracerebral hematoma.
1. Introduction Intracerebral hematoma suffering from subarachnoid hemorrhage (SAH) occurs in 2 of every 10 patients with ruptured intracerebral aneurysms [1], and shows a 38 %–58 % increase in the risk of poor outcome compared with pure SAH patients [2,3]. Currently, no guidelines are available pertaining to clinical features and management approach to these patients. Addressing questions regarding how much preoperative and postoperative factors could predict outcomes in aneurysmal SAH patients may contribute to provide better patient management.
Few studies have explored the association of clinical characteristics and outcomes in patients with aneurysmal SAH and additional intracerebral hematoma [4–6]. Large hematoma volume was previously regarded as an independent prognostic factor for poor outcomes because of its mass effect on intracranial pressure leading to serious early brain injury [3,7]. However, it is now recognized that favorable outcome is also possible in those aneurysmal SAH patients with huge intracerebral hematoma [4]. Although intracerebral hematoma locations [8], midline shift [9], admission neurological scores [2], management approach [10], and intervention time [4] have been reported to influence the prognosis, none of these factors could independently predict
Abbreviations: SAH, subarachnoid hemorrhage; ACA, anterior cerebral artery; MCA, middle cerebral artery; DCI, delayed cerebral ischemia; mRS, modified Rankin score; GCS, Glasgow Coma Scale; WFNS, World Federation of Neurosurgical Societies; CT, computed tomography; SD, standard deviation; IQR, inter quartile range; ROC, receiver operating characteristic curve; AUC, area under receiver operating characteristic curve; ORs, odds ratios; CIs, confidence intervals ⁎ Corresponding authors at: 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. E-mail addresses: [email protected] (J. Zhang), [email protected] (L. Shi). 1 These authors contributed equally to the work. https://doi.org/10.1016/j.clineuro.2020.105691 Received 21 November 2019; Received in revised form 12 January 2020; Accepted 20 January 2020 Available online 22 January 2020 0303-8467/ © 2020 Elsevier B.V. All rights reserved.
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2.5. Variables definition
the neurological functional outcomes. In addition, previous studies have ignored the contribution of postoperative complications in functional recover. Hence, it’s very necessary to develop an efficient multivariate prognostic model including all preoperative and postoperative factors to predict poor outcome in patients with aneurysmal SAH and additional intracerebral hematoma. The main objective of this present study was to determine the independent predictive factors of clinical characteristics and develop preoperative and postoperative prognostic models to predict poor outcome at 6 months.
Neurological deficit was assessed by Glasgow Coma Scale (GCS), World Federation of Neurosurgical Societies (WFNS) scale, and HuntHess scale at baseline. The degree of neurological deficit was defined by GCS scores: 3–8 for severe, 9–12 for moderate, and 13–15 for mild neurological impairment. Laboratory variables were examined at the presentation of patients to hospital. The severity of SAH was assessed by modified Fisher grades. Hematoma volume was measured by the formula ABC/2, where A is the greatest hemorrhage diameter by computed tomography (CT), B is the diameter 90° to A, and C is the approximate number of CT slices with hemorrhage multiplied by the slice thickness [12]. Outcome was assessed by mRS, and favorable outcome was defined as mRS score between 0 and 2 at six months.
2. Material and methods 2.1. Study design
2.6. Statistical analysis
This study was approved by the Institutional Ethics Committee of the Second Affiliated Hospital, School of Medicine, Zhejiang University, and carried out in accordance with The Code of Ethics of the World Medical Association. This was a single-center, retrospective study of patients with aneurysmal SAH and additional intracerebral hematoma discharged between January 2013 and December 2016.
All statistical analyses were performed using SPSS software (version 22.0; International Business Machines Corporation, Armonk, New York). Continuous variable was present as mean ± standard deviation (SD). Binary variable was present as median with inter quartile range (IQR). Patients were divided into favorable and unfavorable outcome groups according to six-month mRS scores. Differences in binary variable were detected by Pearson's chi-squared test between patients with a favorable outcome and those with an unfavorable outcome. As for continuous variable, we performed independent t tests, or MannWhitney U analysis according to data normality. All variables with P values < 0.05 were entered into the multivariate logistic regression analysis. A preoperative prognostic model included baseline demographic characteristics, initial neurological grade, laboratory variables, and imaging data. A postoperative prognostic model contained baseline demographic characteristics, initial neurological grade, laboratory variables, imaging data, different surgical procedures, and postoperative complications. Both prognostic models were developed to identify independent predictors of an unfavorable outcome in the multivariate regression models using the backward logistic regression method. The model’s prediction ability was tested by the receiver operating characteristic curve (ROC). The area under ROC (AUC) over 0.80 was regarded as excellent discrimination. Odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated. A P value < 0.05 was considered statistically significant.
2.2. Study setting All data pertaining to these patients treated at our center were entered into a prospectively collected database. All subjects or their relatives have signed written informed consents. This database was established in 2013 as a project to improve the survival quality of aneurysmal SAH patients. We prospectively collected demographic characteristics including patients’ age, gender, and race; clinical characteristics pertaining to past medical history, basic vital signs, and neurological scores at admission; physiologic variables detecting blood routine tests, serum biochemical examination, blood coagulation function, and so on; imaging data evaluating aneurysmal and intracerebral hematoma characteristics; operative information regarding therapeutic approach and operative time; postoperative complications including delayed cerebral ischemia (DCI), rebleeding, and postoperative infection; and six-month functional outcome assessed by modified Rankin scale (mRS). All patients were received standard medical treatment according to current SAH guidelines [11], including control intracranial pressure, antivasospasm therapy, infection prevention, and so on.
3. Results
2.3. Participant population
3.1. Patient characteristics
In this present study, we reviewed data pertaining to patients diagnosed as aneurysmal SAH with intracerebral hematoma. Inclusion criteria were 1) patients with CT confirmed intracerebral hematoma and SAH, 2) CTA or DSA certified intracerebral aneurysms, 3) age above 18 years old, and 4) treated with 48 h in our center between 2013 and 2016. Exclusion criteria were 1) patients with SAH due to other reasons, 2) combined with intraventricular hemorrhage, 3) combined with arteriovenous malformation, 4) combined with malignant tumor, 5) died prior to operation, and 6) missing data.
Total 1027 patients with aneurysmal SAH were included in the database between January 2013 and December 2016. Approximately 214 patients (20.84 % of all aneurysmal SAH patients) were diagnosed as aneurysmal SAH with intracerebral hematoma. After reviewing all these patients’ data, we excluded 93 patients including 35 patients combined intraventricular hemorrhage, 23 patients died prior to operation, 18 patients with incomplete follow-up data, 13 patients combined malignant tumor, and 4 patients combined arteriovenous malformation. Finally, 121 patients were enrolled in statistics in the present study (Fig. 1). Baseline characteristics of the 121 patients were shown in the Table 1. The mean age of these patients was 56.31 years old, and almost half of these patients were male. Regarding medical history, 44 patients were suffered from hypertension, and 24 patients were smokers. The average value of temperature, respiratory rate, heart rate, and blood pressure were within normal limits. These patients showed moderate neurological deficit assessed by several scales including GCS, WFNS, and Hunt-Hess scales. The vast majority of intracerebral hemorrhage were from the rupture of the anterior cerebral artery (ACA) and middle cerebral artery (MCA) aneurysms.
2.4. Data sources Although the database included all patients with aneurysmal SAH, we only retrospectively reviewed data regarding patients with aneurysmal SAH and intracerebral hematoma in this present study. Demographic and clinical characteristics were extracted by two authors (FL and SBX). Patients’ physiologic variables were automatic export from our database. Imaging data were evaluated by two authors (XBY and JYZ) in a blinded fashion. All the extracted data were checked by the primary author (LGS). 2
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Fig. 1. Flow chart of primary analysis population selection. aSAH = aneurysmal subarachnoid hemorrhage.
logistic regression analysis. The preoperative prognostic model included patients’ age, temperature, respiratory rate, GCS score, WFNS score, Hunt-Hess score, Fisher score, C-reactive protein, serum sodium, and RDW. The postoperative prognostic model included patients’ age, temperature, respiratory rate, GCS score, WFNS score, Hunt-Hess score, Fisher score, C-reactive protein, serum sodium, RDW, surgical procedure, DCI, and pulmonary infection. In the postoperative prognostic model, patients’ age (P = .002), admission respiratory rate (P = .026), serum sodium (P = .013), RDW (P = .010), postoperative DCI (P = .001), and postoperative pulmonary infection (P = .001) were independent predictors of unfavorable outcome. The preoperative prognostic model including patients age (P < .001), respiratory rate (P = .012), serum sodium(P = .043), RDW(P = .076), and Hunt-Hess score (P = .047) showed excellent discrimination with an AUC of 0.864 (95 % CI 0.790 - 0.937, P < .001). The postoperative prognostic model had better discriminatory performance with an AUC of 0.898 (95 % CI 0.829 - 0.967, P < .001) compared with preoperative prognostic model (Fig. 3).
3.2. Preoperative factors for unfavorable outcomes Several differences were observed in preoperative demographic and clinical characteristics between patients with unfavorable outcome and those with favorable outcome (Table 1). Unfavorable outcome was more likely to occur in those patients with older ages (t = -4.647, P < .001), higher temperature (χ2 = -2.089, P = .037), and lower respiratory rate at admission (Z = 2.307, P = .021). In addition, the severe neurological defect assessed by GCS (Z = 2.588, P = .010, Fig. 2A), WFNS (Z = -2.347, P = .019), Hunt-Hess score (Z = -3.300, P = .001), and Fisher score (Z = -2.779, P = .005) scales were associated with unfavorable outcome during six-month follow-up period. Preoperative blood parameters, high C-reactive protein (Z = -2.405, P = .016), high serum sodium (Z = -2.757, P = .006), and high red cell distribution width (RDW) (Z = -2.999, P = .003) were associated with unfavorable outcome (Table 1). No significant difference was observed in aneurysmal location and size, hematoma location and volume, and middle shift between patients with unfavorable outcome and those with favorable outcome (Table 1). The location of ruptured aneurysms has no effects on both unfavorable outcome (Fig. 2B) and hematoma volume (Fig. 2C).
4. Discussions Based on the data from our study of SAH patients, we firstly conducted prognostic models for outcomes in aneurysmal SAH patients with intracranial hematoma. The preoperative prognostic model including patients’ age, admission respiratory rate, serum sodium, RDW, and Hunt-Hess score made it possible to estimate prognosis before the treatment, which was helpful in determining therapeutic methods and informing the family about prognosis. The postoperative prognostic model including the preoperative factors as well as postoperative delayed cerebral ischemia and pulmonary infection showed excellent discrimination. This postoperative model could be important, because it indicated that the reduction of the postoperative complications, especially delayed cerebral ischemia and pulmonary infection, could improve the outcome. We developed these prognostic models in response to clinical needs for studies that target those patients diagnosed with aneurysmal SAH and intracranial hematoma for research studies aimed at understanding predictive factors for outcome and in providing suggestions for managing these patients. Our prognostic models are the first to focus on prediction of outcome, which could play important roles in either clinical practice or future research settings of this type. First, previous studies have indicated that not all these patients had poor final
3.3. Postoperative factors for unfavorable outcomes Patient operative and postoperative characteristics were shown in the Table 2. About three quarters of these patients were received craniotomy for aneurysm clipping. Moreover, additional surgical operations were carried out according to the patient’s condition, including hematoma removal in 56 patients, decompressive craniectomy in 17 patients, external ventricular drainage in 6 patients, lumbar drainage in 66 patients, and intracranial pressure monitoring in 24 patients. The average of onset to operation time was 32 h. Univariate analysis for unfavorable outcome indicated that surgical procedures (P = .040), postoperative delayed cerebral ischemia (χ2 = 17.674, P < .001), and postoperative pulmonary infection (χ2 = 15.309, P < .001) were associated with unfavorable outcome. 3.4. Multivariate analyses for predictors of unfavorable outcomes Multivariable logistic regression for predictors of unfavorable outcome at six months was presented in the Fig. 3. We included all the significant factors from the univariate analysis into the multivariable 3
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Table 1 Patient Baseline Demographic and Clinical Characteristics. Characteristics
All patients (n = 121)
Demographic Characteristics Age, year, mean ± SD 56.31 ± 11.24 Male, n (%) 48 (39.67) Hypertension, n (%) 44 (36.36) Diabetes, n (%) 4 (3.30) Coronary disease, n (%) 0 (0) Smoking, n (%) 24 (19.83) Clinical Characteristics Temperature, °C 37.10 (37.00–37.40) Respiratory rate, min−1 19 (17–20) Heart rate, min−1 77 (70–83) SBP, mmHg, mean ± SD 137(123–156.5) DBP, mmHg, mean ± SD 78.70 ± 13.94 Neurological scores, median (IQR) GCS score 14 (9.50–15) WFNS score 2(1–4) Hunt-Hess score 3(2–3.50) Fisher score 3 (3–4) Aneurysm location, n (%) ACA 36(29.75 %) MCA 52(42.98 %) PCA 19(15.70 %) Other locations 14(11.57 %) Multiple aneurysms 4 (3.36) Aneurysm size, mm 4.0 (3.0–5.8) Hematoma location, n (%) Frontal, n (%) 47(38.84 %) Temporal, n (%) 66(54.55 %) Frontotemporal, n (%) 8(6.61 %) Pre hematoma, ml 7.08 (3.68–13.80) Post hematoma, ml 3.57 (1.43–6.60) Clearance rate, % 43.92 (17.80–73.97) Blood Parameters Values Obtained at Admission Leukocyte, 109/L 12.9 (10.5–17.1) Hemoglobin, g/L 127.37 ± 19.14 C-reactive protein, mg/L 20.20 (9.40–42.25) Glucose, mmol/L 7.19 (6.29–8.62) Serum potassium, mmol/L 3.63 (3.47–3.99) Serum sodium, mmol/L 139.6 (137.7–141.9) Serum calcium, mmol/L 2.13 (1.99–2.24) PT, s 13.1 (12.5–13.7) APTT, s 32.7 (29.2–36.5) INR 1.02 (0.98–1.09) RDW 13.2 (12.7–13.8)
Favorable outcome (n = 87)
Unfavorable outcome (n = 34)
Statistics
P value
53.56 ± 10.87 39 (44.83) 29 (33.33) 2 (2.30) 0 (0) 19 (21.84)
63.32 ± 9.00 9 (26.47) 15 (44.12) 2 (5.88) 0 (0.00) 5 (14.71)
t = -4.647 χ2 = 3.442 χ2 = 1.229 χ2 = 0.982 NA χ2 = 0.782
0.000 0.064 0.268 0.314 NA 0.376
37.10 (37.00–37.30) 19 (18–20) 75 (70–83) 137(122–157) 80.07 ± 14.15
37.30 (37.10–37.50) 18 (15–20) 78 (71.3–89.5) 138.5(123–156.3) 75.21 ± 12.93
Z = -2.089 Z = 2.307 Z = -1.455 Z = 0.037 t = 1.739
0.037 0.021 0.146 0.970 0.085
14 (11–15) 2(1–4) 2(2–3) 3 (3-3)
12.5 (7–14.25) 3(1.75–4) 3(2.75–4) 3 (3–4)
Z = 2.588 Z = -2.347 Z = -3.300 Z = -2.779
0.010 0.019 0.001 0.005
28(32.18 %) 37(42.53 %) 10(11.49 %) 12(13.79 %) 3 (3.49) 4.0 (3.0–5.8)
8(23.53 %) 15(44.12 %) 9(26.47 %) 2(5.88 %) 1 (3.03) 4.0 (2.8–6.0)
χ2 = 5.444
0.142
χ2 = 0.015 Z = -0.277
1.000 0.782
36(41.38 %) 48(55.17 %) 3(3.45 %) 6.92 (3.56–13.27) 3.38 (1.30–6.21) 44.62 (24.25–80.05)
11(32.35 %) 18(2.94 %) 5(14.71 %) 9.24 (4.27–16.50) 4.86 (1.92–9.79) 42.67 (11.27–71.34)
χ2=5.221
0.073
Z = -1.264 Z = -1.891 Z = 0.839
0.206 0.059 0.402
12.9 (10.2–17.0) 128.45 ± 18.89 19.00 (8.80–37.10) 7.03 (6.09–8.57) 3.7 (3.47–4.0) 139.3 (137.5–141.5) 2.15 (2.02–2.25) 13.1 (12.5–13.6) 32.7 (28.5–36.9) 1.03 (0.98–1.09) 13.0 (12.6–13.6)
12.9 (11.13–17.48) 124.62 ± 19.78 31.55 (14.60–77.45) 7.64 (6.60–8.75) 3.55 (3.39–3.83) 141.3 (138.9–144.0) 2.10 (1.81–2.21) 13.2 (12.7–13.7) 32.7 (30.0–35.7) 1.01 (0.98–1.10) 13.5 (13.1–14.2)
Z=-0.753 t = 0.990 Z=-2.405 Z=-1.652 Z=1.892 Z=-2.757 Z=1.445 Z=-0.603 Z=-0.208 Z=0.364 Z=-2.999
0.452 0.324 0.016 0.099 0.059 0.006 0.148 0.546 0.836 0.716 0.003
SD = Standard Deviation; n = Number; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; IQR = Inter Quartile Range; GCS = Glasgow Coma Scale; WFNS = World Federation of Neurosurgical Societies; ACA = Anterior Cerebral Artery; MCA = Middle Cerebral Artery; PCA = Posterior Cerebral Artery; PT = Prothrombin Time; APTT = Activated Partial Thromboplastin Time; INR = International Normalized Ratio; RDW = Red Cell Distribution Width.
patients [15,16]. Additional intracerebral hematoma was observed an association with the development of delayed cerebral ischemia in aneurysmal SAH patients [6,17]. The underlying mechanism regarding intracerebral hematoma resulting in a high rate of delayed cerebral ischemia might be mass effect of hematoma leading to a long sustained increase in intracranial pressure [7,18]. Theoretically, the earlier the hematoma was removed, the better outcomes the patients obtained. However, the univariate analysis of the present study indicated that hematoma removal was not a predictive factor for outcome (Fig. 3). It should be noted that decompressive craniectomy was observed to improve outcome on univariate analysis (P = 0.061), although it was not significant in the multivariate analysis (Fig. 3). Future studies should focus on predictive factors for postoperative delayed cerebral ischemia in order to control this postoperative complication and improve outcome. Pulmonary complications have been considered as strongly negative factors for morbidity and mortality following aneurysmal SAH [19,20]. Sudden elevation of intracranial pressure caused by aneurysmal SAH will activate the sympathetic nervous system leading to pulmonary edema [21]. Subsequently, it will increase the incidence of ventilator-acquired pneumonia [22]. These pulmonary complications
outcomes [13,14], although additional intracerebral hematoma was regarded to increase the mortality and disability of aneurysmal SAH patients [4]. Hence, it is very necessary to differentiate those patients with predicted poor final outcomes to perform more intensive therapy. The well-validated preoperative prognostic model could accurately predict outcome before the treatment. Individualized treatment performing according to the predicted results by our preoperative prognostic model is important for economizing medical resources and improving individual final recovery. Second, this preoperative prognostic model is useful for selecting participants in further studies. Further researchers need to consider patients’ age, respiratory rate, serum sodium, RDW, and Hunt-Hess score in their study design. The present study indicated that only a minority of patients (34/121) with aneurysmal SAH and intracerebral hematoma had poor outcomes. Our preoperative prognostic model could be used to identify patients most likely to result in poor outcomes, enriching future randomized controlled trials. Third, the postoperative prognostic model draws our attention to postoperative delayed cerebral ischemia and pulmonary infection. Previous studies have indicated that delayed cerebral ischemia after aneurysmal SAH had a negative impact on outcome for the 4
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as primary outcomes. Except this novelty, several other strengths of our study contributed to its high reliability. All these patients in the present study were extracted from our large single-center prospectively collected database containing 1027 SAH patients. All neurological scales, surgical procedures, neurological intensive care, and outcome assessment scores were collected from one single center, which reduced the patient heterogeneity. Besides, we drew up strict inclusion and exclusion criteria to furthest reduce patient heterogeneity prior to this study. We excluded those potential influencing factors for outcomes including intraventricular hemorrhage and malignant tumor. In addition, we detected almost fifty clinical index including baseline demographic and clinical characteristics, blood parameters values at admission, operative information, and postoperative complications in univariate analysis. Our multiple preoperative and postoperative prognostic models showed excellent discrimination with high AUC of 0.864 and 0.898, respectively. Another important superiority of the present study was the practicability of these prognostic models in the clinical work. The preoperative model containing five factors, patients’ age, admission respiratory rate, serum sodium, RDW, and Hunt-Hess score, could timely obtain in the emergency ward. It is useful for clinician to reference the predicted results from our prognostic model to formulate effective therapeutic plan. Importantly, patients’ age is considered as a forceful influence factor for outcome in previous studies involving poorgrade aneurysmal SAH patients [24,25]. There is no doubt about the predictive value of initial Hunt-Hess scores in patients with pure aneurysmal SAH patients [26,27], but whether it still works in predicting for patients with aneurysmal SAH and intracerebral hemorrhage patients wasn’t detected in previous study. The present study indicated that initial Hunt-Hess scores didn’t lose its predictive power in this special patient population. Respiratory rate and serum sodium level are interesting findings, which got less attention in previous studies. In the patients with pure aneurysmal SAH, extracerebral organ dysfunction including respiratory failure was observed to be in correlation of poor neurologic outcome [28]. As for serum sodium, it has been reported that hyponatremia was relatively common among aneurysmal SAH patients, which was proved to have an association with delayed cerebral ischemia and indicate poor prognosis [29–31]. The present study indicated that the respiratory rate and serum sodium level were also suitable for predicting outcome in patients with aneurysmal SAH and intracerebral hematoma. As all studies have limitations, some obvious deficiency was existed in the present study. First, the small sample size might increase the probability of sampling error. In the present study, we only collected data of patients discharged from our hospital in recent four years in order to reduce the heterogeneity of diagnostic and therapeutic level. Hence, we only included 121 patients in the statistical analysis. Secondary, this is a retrospective study with its congenital defect of a high risk of selection bias. Specially, we excluded 23 patients died prior to operation and 18 patients with incomplete follow-up data, which might reduce the severity of this patient population. Thirdly, all the data are from our single center. It’s very necessary to test our models using patients’ clinical characteristics and outcomes by other clinic centers. Finally, the outcome was defined by mRS scores that has been developed and tested only in aneurysmal SAH patients after surgical aneurysm repair [6].
Fig. 2. Clinical characteristics of patients with aneurysmal subarachnoid hemorrhage and intracerebral hemorrhage. (A) Neurological deficit assessments between patients with unfavorable outcome and those with favorable outcome at admission. Scores on Glasgow coma scale: 3–8 for severe, 9–12 for moderate, and 13–15 for mild neurological impairment. (B) outcome in different intracranial artery segments. Similar rates of unfavorable outcome were observed among ACA, MCA, PCA, and ICA. (C) Hematoma volume in different intracranial artery segments. ACA = anterior cerebral artery; MCA = middle cerebral artery; PCA = posterior cerebral artery; ICA = internal cerebral artery.
can cause disturbed oxygenation of the brain, and thereby contribute to poor outcome in these patients [23]. The present study also indicated that postoperative pulmonary infection was an independent predictive factor for poor outcome in patients with aneurysmal SAH and intracerebral hemorrhage. In addition, the surgical procedure was not considered as a significant predictor in the multivariate regression analysis. The decision of surgical procedure was made by our neurosurgeons according to the patients’ conditions. If the patients presented acute cerebral herniation, the neurosurgeons prefer to perform craniotomy clipping. It may result in the unbalance of pre-operative clinical status between clipped and coiled patients. However, our data indicated that there is no significant difference in GCS score between clipped (11.45 ± 3.65) and coiled (12.40 ± 2.87) patients (P = 0.201). Our prognostic models were the first to target neurological recovery
5. Conclusions The present study firstly designed preoperative and postoperative prognostic models to predict the outcome in patients with aneurysmal SAH and intracerebral hematoma. The preoperative prognostic model indicated that we should pay more attention to those old patients with worse admission Hunt-Hess score, presenting deep-slow respiratory and lower serum sodium. And the postoperative prognostic model demonstrated that reduction of postoperative delayed cerebral ischemia and pulmonary infection might improve outcome after aneurysmal SAH 5
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Table 2 Patient Operative and Postoperative Characteristics. Characteristics
Operation Characteristics Clipping, n (%) Coiling, n (%) Hematoma removal, n (%) DCO, n (%) EVD, n (%) Lumbar drainage, n (%) ICP monitoring, n (%) OTO time, h Postoperation Characteristics Rebleeding, n (%) DCI, n (%) Pulmonary infection, n (%)
All patients (n = 121)
Favorable outcome (n = 87)
Unfavorable outcome (n = 34)
Statistics
P value
87(71.90 %) 34(28.10 %) 56(46.28 %) 17(14.05 %) 6(6.90 %) 66(54.55 %) 24(19.83 %) 32(24–68.75)
58(66.67 %) 29(33.33 %) 41(47.13 %) 9(10.34 %) 3(3.45 %) 46(52.87 %) 16(18.39 %) 32(24–72)
29(85.29 %) 5(14.71 %) 15(44.12 %) 8(23.53 %) 3(8.82 %) 20(58.82 %) 8(23.53 %) 36(18.5–49)
χ2 = 4.198
0.040
χ2 = 0.089 χ2 = 3.519 χ2 = 1.499 χ2 = 0.349 χ2 = 0.406 Z = -0.516
0.765 0.061 0.221 0.555 0.524 0.606
9 (7.44 %) 17 (14.05 %) 39 (32.23 %)
4 (4.60 %) 5 (5.75 %) 19 (21.84 %)
5 (14.71 %) 12 (35.29 %) 20 (58.82 %)
χ2 = 3.628 χ2 = 17.674 χ2 = 15.309
0.069 0.000 0.000
HCR = Hematoma Clearance Rate; DCO = Decompressive Craniectomy Operation; EVD = External Ventricular Drainage; ICP = Intracranial Pressure; OTO = Onset To Operation; DCI = Delayed Cerebral Ischemia.
Zhou: Data curation, Methodology, Investigation. Feng Liang: Formal analysis, Visualization, Investigation. Shenbin Xu: Investigation, Visualization. Xiaobo Yu: Validation, Formal analysis. Jianmin Zhang: Project administration, Data curation. Ligen Shi: Data curation, Supervision, Writing - review & editing.
with intracerebral hematoma. Future prospective studies are necessary to test the validity and reliability of these two prognostic models. Funding JM Zhang is supported by the National Key Research and Development Program of China (2017YFC1308500) & Key Program of Science and Technology Development of Zhejiang Province (2017C03021). Z Wang is supported by Basic Public Interests Research Plan of Zhejiang Province (LGF18H090005). JY Zhou is supported by Basic Public Interests Research Plan of Zhejiang Province (LY18H090001).
Declaration of Competing Interest The authors declare no conflicts of interest.
Acknowledgments
CRediT authorship contribution statement
We want to pay our respects to all the patients involved in this study.
Zhen Wang: Conceptualization, Writing - original draft. Jingyi
Fig. 3. AUCs of the predictive value of outcome according to prognostic models. (A) The preoperative prognostic model including patients age, respiratory rate, serum sodium, RDW, and Hunt-Hess score showed excellent discrimination with an AUC of 0.864. (B) The postoperative prognostic model including patients’ age, admission respiratory rate, serum sodium, RDW, postoperative DCI, and postoperative pulmonary infection were independent predictors of unfavorable outcome. The postoperative prognostic model had better discriminatory performance with an AUC of 0.898 compared with preoperative prognostic model. RDW = red cell distribution width, DCI = delayed cerebral ischemia, AUC = area under the curve. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 6
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References [17]
[1] X. Liu, G.J. Rinkel, Aneurysmal and clinical characteristics as risk factors for intracerebral haematoma from aneurysmal rupture, J. Neurol. 258 (2011) 862–865, https://doi.org/10.1007/s00415-010-5855-2. [2] A. Wan, B.N. Jaja, T.A. Schweizer, et al., Clinical characteristics and outcome of aneurysmal subarachnoid hemorrhage with intracerebral hematoma, J. Neurosurg. 125 (2016) 1344–1351, https://doi.org/10.3171/2015.10.jns151036. [3] Y. Tokuda, T. Inagawa, Y. Katoh, et al., Intracerebral hematoma in patients with ruptured cerebral aneurysms, Surg. Neurol. 43 (1995) 272–277, https://doi.org/10. 1016/0090-3019(95)80013-7. [4] E. Guresir, J. Beck, H. Vatter, et al., Subarachnoid hemorrhage and intracerebral hematoma: incidence, prognostic factors, and outcome, Neurosurgery 63 (2008) 1088–1093, https://doi.org/10.1227/01.neu.0000335170.76722.b9 discussion 1093-1084. [5] B.N. Bohnstedt, H.S. Nguyen, C.G. Kulwin, et al., Outcomes for clip ligation and hematoma evacuation associated with 102 patients with ruptured middle cerebral artery aneurysms, World Neurosurg. 80 (2013) 335–341, https://doi.org/10.1016/ j.wneu.2012.03.008. [6] J. Platz, E. Guresir, M. Wagner, et al., Increased risk of delayed cerebral ischemia in subarachnoid hemorrhage patients with additional intracerebral hematoma, J. Neurosurg. 126 (2017) 504–510, https://doi.org/10.3171/2015.12.jns151563. [7] F.A. Sehba, R.M. Pluta, J.H. Zhang, Metamorphosis of subarachnoid hemorrhage research: from delayed vasospasm to early brain injury, Mol. Neurobiol. 43 (2011) 27–40, https://doi.org/10.1007/s12035-010-8155-z. [8] E.R. Smith, B.S. Carter, C.S. Ogilvy, Proposed use of prophylactic decompressive craniectomy in poor-grade aneurysmal subarachnoid hemorrhage patients presenting with associated large sylvian hematomas, Neurosurgery 51 (2002) 117–124, https://doi.org/10.1227/01.NEU.0000017192.85679.38 discussion 124. [9] P.D. Le Roux, A.T. Dailey, D.W. Newell, et al., Emergent aneurysm clipping without angiography in the moribund patient with intracerebral hemorrhage: the use of infusion computed tomography scans, Neurosurgery 33 (1993) 189–197 discussion 197. doi:. [10] G. Talamonti, M. Nichelatti, A.A. Al Mashni, et al., Life-threatening cerebral hematoma owing to aneurysm rupture, World Neurosurg. 85 (2016) 215–227, https://doi.org/10.1016/j.wneu.2015.08.082. [11] E.S. Connolly Jr., A.A. Rabinstein, J.R. Carhuapoma, et al., Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/american Stroke Association, Stroke 43 (2012) 1711–1737, https://doi.org/10.1161/STR.0b013e3182587839. [12] R.U. Kothari, T. Brott, J.P. Broderick, et al., The ABCs of measuring intracerebral hemorrhage volumes, Stroke 27 (1996) 1304–1305 doi: WOS:A1996VA09200007. [13] C.S. Lee, J.U. Park, J.G. Kang, et al., The clinical characteristics and treatment outcomes of patients with ruptured middle cerebral artery aneurysms associated with intracerebral hematoma, J. Cerebrovasc. Endovasc. Neurosurg. 14 (2012) 181–185, https://doi.org/10.7461/jcen.2012.14.3.181. [14] K.M. Abbed, C.S. Ogilvy, Intracerebral hematoma from aneurysm rupture, Neurosurg. Focus 15 (2003) E4. doi:. [15] A.S. Sarrafzadeh, P. Vajkoczy, P. Bijlenga, et al., Monitoring in Neurointensive Care - the challenge to detect delayed cerebral ischemia in high-grade aneurysmal SAH, Front. Neurol. 5 (2014) 134, https://doi.org/10.3389/fneur.2014.00134. [16] R.L. Macdonald, Delayed neurological deterioration after subarachnoid
[18] [19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27] [28]
[29]
[30]
[31]
7
haemorrhage, Nat. Rev. Neurol. 10 (2014) 44–58, https://doi.org/10.1038/ nrneurol.2013.246. E. Crobeddu, M.K. Mittal, S. Dupont, et al., Predicting the lack of development of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage, Stroke 43 (2012) 697–701, https://doi.org/10.1161/STROKEAHA.111.638403. R.L. Macdonald, B.K. Weir, A review of hemoglobin and the pathogenesis of cerebral vasospasm, Stroke 22 (1991) 971–982 doi: WOS:A1991GB31200001. J.M. Kahn, E.C. Caldwell, S. Deem, et al., Acute lung injury in patients with subarachnoid hemorrhage: incidence, risk factors, and outcome, Crit. Care Med. 34 (2006) 196–202, https://doi.org/10.1097/01.CCM.0000194540.44020.8E. J.A. Friedman, M.A. Pichelmann, D.G. Piepgras, et al., Pulmonary complications of aneurysmal subarachnoid hemorrhage, Neurosurgery 52 (2003) 1025–1031, https://doi.org/10.1227/01.NEU.0000058222.59289.F1 discussion 1031-1022. F.A. Sehba, J. Hou, R.M. Pluta, et al., The importance of early brain injury after subarachnoid hemorrhage, Prog. Neurobiol. 97 (2012) 14–37, https://doi.org/10. 1016/j.pneurobio.2012.02.003. J.B. Cui, Q.Q. Chen, T.T. Liu, et al., Risk factors for early-onset ventilator-associated pneumonia in aneurysmal subarachnoid hemorrhage patients, Braz. J. Med. Biol. Res. 51 (2018) e6830, , https://doi.org/10.1590/1414-431x20176830. W.J. Schuiling, P.J. Dennesen, J.T. Tans, et al., Troponin I in predicting cardiac or pulmonary complications and outcome in subarachnoid haemorrhage, J. Neurol Neurosurg Psychiatry 76 (2005) 1565–1569, https://doi.org/10.1136/jnnp.2004. 060913. J. Mocco, E.R. Ransom, R.J. Komotar, et al., Preoperative prediction of long-term outcome in poor-grade aneurysmal subarachnoid hemorrhage, Neurosurgery 59 (2006) 529–538, https://doi.org/10.1227/01.NEU.0000228680.22550.A2 discussion 529-538.. C.J. Taylor, F. Robertson, D. Brealey, et al., Outcome in poor grade subarachnoid hemorrhage patients treated with acute endovascular coiling of aneurysms and aggressive intensive care, Neurocrit. Care 14 (2011) 341–347, https://doi.org/10. 1007/s12028-010-9377-7. T. Inagawa, M. Shibukawa, F. Inokuchi, et al., Primary intracerebral and aneurysmal subarachnoid hemorrhage in Izumo City, Japan. Part II: management and surgical outcome, J. Neurosurg. 93 (2000) 967–975, https://doi.org/10.3171/jns. 2000.93.6.0967. D.S. Rosen, R.L. Macdonald, Subarachnoid hemorrhage grading scales: a systematic review, Neurocrit. Care 2 (2005) 110–118, https://doi.org/10.1385/NCC:2:2:110. A. Gruber, A. Reinprecht, U.M. Illievich, et al., Extracerebral organ dysfunction and neurologic outcome after aneurysmal subarachnoid hemorrhage, Crit. Care Med. 27 (1999) 505–514, https://doi.org/10.1097/00003246-199903000-00026. M.J. McGirt, R. Blessing, S.M. Nimjee, et al., Correlation of serum brain natriuretic peptide with hyponatremia and delayed ischemic neurological deficits after subarachnoid hemorrhage, Neurosurgery 54 (2004) 1369–1373, https://doi.org/10. 1227/01.NEU.0000125016.37332.50 discussion 1373-1364. A.I. Qureshi, M.F. Suri, G.Y. Sung, et al., Prognostic significance of hypernatremia and hyponatremia among patients with aneurysmal subarachnoid hemorrhage, Neurosurgery 50 (2002) 749–755, https://doi.org/10.1097/00006123-20020400000012 discussion 755-746. D. Hasan, E.F. Wijdicks, M. Vermeulen, Hyponatremia is associated with cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage, Ann. Neurol. 27 (1990) 106–108, https://doi.org/10.1002/ana.410270118.