- Email: [email protected]
Environmental and Clinical Risk Factors for Delirium in a Neurosurgical Center: A Prospective Study
Accepted Manuscript Environmental and Clinical Risk Factors for Delirium in a Neurosurgical Center: A Prospective Study Fumihiro Matano, MD, PhD, Takayuki Mizunari, MD, PhD, Keiko Yamada, RN, Shiro Kobayashi, PhD, Yasuo Murai, MD, PhD, Akio Morita, PhD PII:
S1878-8750(17)30462-X
DOI:
10.1016/j.wneu.2017.03.139
Reference:
WNEU 5508
To appear in:
World Neurosurgery
Received Date: 13 December 2016 Revised Date:
28 March 2017
Accepted Date: 29 March 2017
Please cite this article as: Matano F, Mizunari T, Yamada K, Kobayashi S, Murai Y, Morita A, Environmental and Clinical Risk Factors for Delirium in a Neurosurgical Center: A Prospective Study, World Neurosurgery (2017), doi: 10.1016/j.wneu.2017.03.139. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title:
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Environmental and Clinical Risk Factors for Delirium in a Neurosurgical Center: A
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Prospective Study Fumihiro Matano, MD, PhD1
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Takayuki Mizunari, MD, PhD1
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Keiko Yamada, RN1
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Shiro Kobayashi, PhD1
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Yasuo Murai, MD, PhD2
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Akio Morita, PhD2
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Department of Neurosurgery, Chiba Hokusoh Hospital
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Department of Neurological Surgery, Nippon Medical School
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Fumihiro Matano, MD, PhD
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Department of Neurosurgery, Chiba Hokusoh Hospital 1715 Kamagari, Inzai
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Chiba, 270-1694, Japan
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Telephone: 476-99-1111
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Fax: 476-99-1906
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Email: [email protected]
Keywords: delirium, environmental, neurosurgical center, risk factor, white matter signal
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abnormalities
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Running title: Environmental risk factors of delirium
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Abstract
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Introduction
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Few reports of delirium-related risk factors have focused on environmental risk factors and clinical
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risk factors, such as white matter signal abnormalities on magnetic resonance imaging (MRI) FLAIR
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images.
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Material & Methods
We prospectively enrolled 253 patients admitted to our neurosurgical center between December
2014 and June 2015 and analyzed a total of 220 patients (100 males; mean age, 64.1 years; age range, 17–92 years). The upper 4 points of the Intensive Care Delirium Screening Checklist (ICDSC)
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indicated delirium. We evaluated patient factors consisting of baseline characteristics and related
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factors, such as white matter lesions (WMLs), and we also evaluated the surrounding environment.
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Delirium occurred in 29/220 cases (13.2%) in the present study. Regarding the baseline characteristics, there were significant statistical correlations between delirium and age (P = 0.0187),
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Hasegawa Dementia Scale-Revised score (P = 0.0022) on admission, and WMLs (P <0.0001).
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WMLs were related to age (P < 0.0001) and atherosclerotic disease (P = 0.004). Regarding related
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factors, there were significant statistical correlations between delirium and staying at a neurosurgical
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care unit (NCU; P = 0.0245). Multivariate logistic regression analyses showed statistically
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significant correlations of delirium with WMLs (P <0.0001) and surrounding delirium patients (P =
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0.026).
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Conclusion
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WMLs in patients and the environmental situation are risk factors for delirium in a neurosurgical center. Therefore, to prevent delirium, we must recognize risk factors, such as high
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grade WMLs, and manage environmental factors, such as the surrounding situation.
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Introduction
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Delirium is defined as a syndrome of acute change in mental status accompanied by
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inattention and marked by a fluctuating course.(19, 30) Many reports of related risk factors of
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delirium, particularly those focusing on the case of an intensive care unit (ICU) stay(20, 34) or on
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postoperative cases, have shown that anemia(10, 17) and surgical operation time(30, 39) are risk
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factors for delirium. However, few reports of delirium-related risk factors have focused on a
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neurosurgical center and environmental and clinical risk factors, such as white matter signal
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abnormalities on magnetic resonance imaging (MRI) FLAIR images.
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We conducted a prospective study analyzing the environmental and clinical risk factors in a neurosurgical center in our hospital.
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Material and Methods
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Methods
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The study protocol was approved by the Nippon Medical School Chiba Hokusoh Hospital
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Research Committee (Chiba, Japan) (R-433), and written informed consent was obtained from all
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patients and their families.
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Patients and clinical characteristic
We prospectively enrolled 253 patients admitted to our neurosurgical center between
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December 2014 and June 2015. We included cases in which the Japan Coma Scale (JCS) score(25,
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31) was less than 10 points. For the purpose of distinguishing acute changing status and disturbance
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of consciousness, dementia, and delirium at admission, we excluded cases in which the intensive
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care delirium screening checklist (ICDSC) score (3) was less than 4 points, the JCS score was in the
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upper 10 points, and the Hasegawa Dementia Scale revised (HDS-R)(33) score was less than 10
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points, as well as those with a psychiatric disorder and those for whom informed consent was not
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acquired at administration. A total of 33 patients were excluded by these criteria. Therefore, we
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subsequently analyzed a total of 220 patients (120 males; mean age: 64.1 years, range: 17–92 years).
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The clinical characteristics of the patients included cerebral infarction in 88 cases, intracerebral
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hemorrhage in 30, subarachnoid hemorrhage in 10, head trauma in 24, elective operation in 36, and
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other features in 32 (Table 1). We examined the white matter lesions (WMLs) on MRI FLAIR image
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in all patients in the first week from admission and evaluated them using the Fazekas classification:
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grade 0, no lesion; grade 1, punctate lesions, with a maximum diameter of 9 mm for a single lesion
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and of 20 mm for grouped lesions; grade 2, early confluent lesions, with single lesions between 10
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and 20 mm, grouped lesion more than 20 mm in any diameter, and no more than connecting bridges
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between individual lesions; grade 3, confluent lesions with single or grouped lesions of 20 mm or
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more in any diameter and connecting bridges between individual lesions. We defined grades 0 and 1
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as mild lesions and grades 2 and 3 as severe lesions (Figure). In cases of difficult-to-evaluate WMLs,
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such as those with intracerebral hemorrhage, we referred to the contralateral side WMLs and judged
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the lesions using the Fazekas classification. We defined atherosclerotic disease as the presence of
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one of the following: hypertension, dyslipidemia, diabetes mellitus, and smoking, and we
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investigated the relationship according to Fazekas grading and atherosclerotic disease.
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Evaluation of delirium
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The patients were evaluated twice each day (morning and evening) from the day of
admission until 1 week by a specialist nurse trained in the evaluation of delirium using the ICDSC. The upper 4 points of the ICDSC indicated delirium in the present study. We evaluated patient
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factors of aphasia; emergency admission; a stay in the neurosurgical care unit (NCU); insertion of a
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monitor, such as an electrocardiogram and oxygen saturation monitor; need for oral suction; need for
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changing position to prevent decubiti; inhibition of four limbs and the trunk; surgery performed; use
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of an intra-arterial or venous line; and drainage, such as wound suction or an intraventricular drain.
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The inclusion criteria were patients in whom a drain was inserted, those who required bedrest, and
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those who required close examination, such as stroke cases, postoperative cases, or those with severe
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head trauma admitted to the NCU. The NCU has 8 beds and does not border on one floor, and nurses
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are continuously present. Adaptation criteria for inhibition at our institution included patients who
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required bed rest but could not rest in the bed and those with a risk of self-removal of drip infusion
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lines or drains due to disturbance of consciousness, delirium, or insufficient awakening anesthesia.
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In addition, we evaluated the surrounding environmental factors, such as being in the same room as
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patients with delirium, emergency admission, presence of an insertion monitor, need for oral suction,
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and need for changing position to prevent decubiti. We investigated the factors related to severe
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Fazekas grading, such as age, sex, HDS-R, and atherosclerotic disease.
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Statistical analysis
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Statistical analysis was performed using the Statistical Package for the Social Sciences
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(SPSS) for Mac (V.21.0; SPSS, Armonk, NY, USA). Variables are expressed as mean ± standard
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deviation (SD), median [interquartile ratio (IQR), 25th–75th percentile] or number of patients (%),
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where appropriate. Multivariate logistic regression analysis was performed using variables that were
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significantly associated with bypass patency on univariate analysis (P < 0.10). Differences were
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considered significant at a P value of <0.05.
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Results Delirium occurred in 29/220 cases (13.2%) in the present study. Regarding baseline characteristics, there were significant statistical correlations between delirium and age (P =
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0.0187), HDS-R score (P = 0.0022) on admission, WMLs (P <0.0001 ) and subarachnoid
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hemorrhage (P = 0.0293), and an elective operation showed a low risk of delirium (P = 0.05) in
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univariate regression analysis (Table 2). Regarding related factors, there were significant statistical
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correlations between delirium and staying at an NCU (P = 0.0245), need for suction (P = 0.0043),
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changing position (P = 0.019), and inhibition (P < 0.0001) in univariate regression analysis. (Table
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3). Regarding the surrounding environment, there were significant statistical correlations delirium
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and surrounding needing suction patient (P = 0.0253) in univariate regression analysis. We
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performed multivariate logistic regression analyses using variables that were significantly associated
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with delirium. Multivariate logistic regression analyses showed statistically significant correlations
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between delirium and WMLs (P <0.0001), inhibition of four limbs and the trunk (P = 0.001),
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surrounding monitor (P = 0.035), and surrounding delirium patients (P = 0.026).
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The case of severe lesion in Fazekas grading related age (P < 0.0001), HSD-R (P = 0.001), and
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atherosclerotic disease (P = 0.004). (Table5)
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(Table 4).
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Discussion
In the present study, not only the clinical condition and WMLs of the patients but also the environmental situation were shown to be risk factors for delirium in a neurosurgical center.
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Therefore, to prevent delirium in the case of admission to a neurosurgical center, management of
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environmental factors, such as the surrounding situation in the same room and inhibition, should be
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considered.
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Delirium is defined as an acute state of confusion characterized by inattention, cognitive impairment, and fluctuating consciousness.(21, 35) Development of delirium is dependent on
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complex inter-relationships between vulnerable patients with several predisposing factors and
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exposure to noxious insults or precipitating factors.(15) However, the correct pathophysiology
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remains unclear.
Patients who experience delirium are at an increased risk of dying during admission,
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having longer stays in the hospital, and experiencing cognitive impairment after discharge.(11, 16,
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28) Therefore, prevention of delirium is extremely important not only from the viewpoint of patient
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outcome but also medical economy. (15, 24, 29) It has been reported that addressing a single risk
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factor is unlikely to resolve delirium.(15) In fact, prior reports have shown that there are many
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possible risk factors for delirium, which cause mental status changes, such as advanced age;
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dementia or depression;(1, 38), physical condition(14, 27), use of medication, such as major
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tranquilisers(13) or benzodiazepines;(23) and results of laboratory investigations, such as low
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albumin(36) or high or low sodium.(23) Higher age and higher HDS-R score were risk factors in the
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present study as they were in a previous report. (2) However, to date, there have been few reports on
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WMLs associated with delirium. In the present study, the case of a severe lesion according to
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Fazekas grading was related to age and presence of atherosclerotic disease. White matter signal
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abnormalities on MRI FLAIR image are associated with various geriatric disorders, including
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cerebrovascular disease, cerebral blood flow, (9) cognitive function, (7) and risk of dementia.(26)
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Small and large artery elastic indices are markers of arteriosclerosis and atherosclerosis respectively
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and liked to WMLs.(37) Decreased elasticity due to aging is associated with a reduction in blood
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flow toward the brain.(18) Brain volume, activity, and cerebral neurotransmitters all decrease in
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elderly patients,(2) and some elderly patients have complicated intracranial atherosclerotic
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disease.(32) Therefore, hypoxia in the brain might be associated with delirium.(4)
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Subarachnoid hemorrhage, high-risk disease, and elective operation were of low risk for
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delirium in the present study. Lara et al.(5) reported that intraventricular bleeding, hydrocephalus,
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and frontal hematomas were risk factors for delirium in subarachnoid hemorrhage cases and reported
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delirium in acute subarachnoid patients triggered by medication, pain, vasospasm, or early ischemia.
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Anxiety also may be related to postoperative delirium.(6) Therefore, patients undergoing an elective
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operation may tend to experience postoperative delirium. On the other hand, inhibition also was at a
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high risk for delirium and may be related to anxiety of the patients.
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There are few report on the relationship of delirium and surrounding factors in a neurosurgical center to date. In the present study, staying at the NCU, presence of surrounding
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sensors, and surrounding delirium patients are high risk factors for delirium in multivariate logistic
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regression analysis. To date, there are no reports on categorizing acutely confused patients together
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as a risk for delirium; however, there are correlations between noise levels and arousal frequencies in
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the intensive care unit. (8, 22) Excessive noise is a potentially modifiable risk factor that may
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contribute to sleep disruption and occurrence of delirium. Patients frequently cite noise as a main
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contributor to sleep disruption.(12)
Delirium may be prevented in a neurosurgical center if the medical staff can recognize
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high-risk patients, especially those with a high Fazekas grade, correctly and administer medicine to
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prevent insomnia. For correctable risk factors, such as putting delirium patients together, we may
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have to arrange the room to ensure a suitably quiet environment, such as an individual cubicle.
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However, we did not investigate whether removing the risk factors evaluated in our study, such as
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inhibition or surrounding factors, results in decreased delirium. Further prospective study should be
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required to support our claim.
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Limitation This study had some limitations. First, we did not investigate the detailed background of
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the patients, such as constant medication or results of laboratory data, and we did not follow the
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patient who had delirium and did not investigate the clinical outcome. Second, inhibition was a risk
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factor for delirium in the present study; however, the criteria of inhibition itself may include the risk
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factors for delirium. Third, removal of risk factors to prevent delirium was not evaluated in the
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present study. Fourth, intracranial lesions interfered with the interpretation of WMLs. Finally, we did
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not investigate patients’ sleep status, surrounding noise, and anxiety objectively.
4 Conclusion
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To prevent delirium in a neurosurgical center, we should recognize the clinical risk factors. WMLs
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are the risk factors for delirium, and inhibition should be avoided as much as possible.
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Environmental factors, such as the surrounding situation in the same room, should be managed.
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Conflicts of interest
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None of authors have any conflicts of interest.
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Figure legends
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Degree of WMLs according to Fazekas classification.
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A. Grade 0, no lesion
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B. Grade 1, punctate lesions, with a maximum diameter of 9 mm for a single lesion and of 20 mm
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for grouped lesions
C. Grade 2, early confluent lesions, with single lesions between 10 and 20 mm, grouped lesions
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more than 20 mm in any diameter, and no more than connecting bridges between individual
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lesions.
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D. Grade 3, confluent lesions, with single or grouped lesions of 20 mm or more in any diameter and connecting bridges between individual lesions.
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24 25 26 27 28 29 30 31 32
Table 1. Characteristics of patients
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Table 2. Clinical characteristics of patients with delirium
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Table 3. Results of statistical analysis of the risk factors for delirium
Table 4. Multivariate logistic regression analysis for risk factors of delirium.
Table 5. Results of statistical analysis of the risk factors for severe Fazekas grading
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Table1: Characteristics patients
Enrolled 253 cases Exclude 33 cases (ICDSC<4,JCS>20,HDS-R<10, psychiatric disease+) Included 220 cases 17-92 (mean;64.1)
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Age Sex (male : female)
120 : 100
Cerebral Infarction; 88 case, Intracerebral hemorrhage; 30 case Subarachnoid hemorrhage; 10 case, Head trauma; 24 case
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Elective operation; 36 case, Others; 32 case
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Table2: Clinical characteristic of patients with delirium
Delirium Present (n=29)
Base line characteristic 64.1 (13.7)
63.3(15.5)
Age > 60 yrs (%)
151 (79.1)
25 (86.2)
Sex,male (%)
100 (45.6)
14 (48.3)
HDS-R (SD)
24.7 (5.7)
20.7 (5.8)
Fazekas classification severe (%)
38 (17.3)
Atherosclerotic disease (%)
162 (73.6)
Type of disease
Absent (n=191)
p Value
60.9 (2.1)
0.0187
126 (65.9)
0.0316
86 (45)
0.842
24.8 (7.5)
0.0022
15 (51.7)
23 (12)
< 0.0001
26 (89.6)
136 (71.2)
0.079
10 (34.5)
78 (40.8)
0.5493
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Mean age,yrs (SD)
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total (n=220)
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Variable
88 (40)
Intracerebral hemorrhage (%)
30 (13.6)
5 (17.2)
25 (13.1)
0.5621
Subarachnoid hemorrhage (%)
10 (4.5)
4 (13.8)
6 (3.1)
0.0293
Head trauma (%)
24 (10.9)
5 (17.2)
19 (9.9)
0.3319
Elective operation (%)
36 (16.4)
1 (3.4)
35 (18.3)
0.05
4 (13.8)
28 (14.7)
0.2175
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Others (%)
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Cerebral infarction (%)
32 (14.5)
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Data are expressed as number of patients (%), unless otherwise indicated. Variables showing significant difference by univariate analysis (p<0.05) are indicated by boldface.
OR (95%CI)
3.22 (1.08-9.661)
7.826 (3.348-18.29)
4.933 (1.3-18.7) 6.282 (0.82-47)
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Table3: Results of statistical analysis of the risk factors for delirium
Delirium total (n=220)
p Value
OR (95%CI)
0.0198
4.9 (1.13-21.4)
107 (56)
0.0245
3.0 (1.2-7.7)
163 (85)
0.139
9 (31)
19 (9.9)
0.0043
4.1 (1.6-10)
17 (58)
63 (32)
0.019
2.9 (1.3-6.4)
17 (58)
17 (8.9)
<0.0001
14.5 (5.9-35)
10 (34)
55 (28)
0.5199
Present (n=29)
167 (75)
27 (93)
NCU (%)
130 (59)
23 (79)
Monitor (%)
191 (86)
28 (96)
Suction (%)
28 (12)
Position changing (%)
80 (36)
Inhibition (%)
34 (15)
Operation (%)
65 (29)
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Surrounding factors
140 (73)
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Emergency admission (%)
Absent (n=191)
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Related factors
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Variable
48 (21)
9 (31)
39 (20)
0.2277
Emergency admission (%)
91 (41)
11 (37)
81 (42)
0.6911
Suction (%)
132 (60)
23 (79)
109 (57)
0.0253
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delirium (%)
Data are expressed as number of patients (%), unless otherwise indicated.
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2.8 (1.1-7.4)
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Table4: Multivariate logistic regression analysis for risk factors of delirium
p Value
Age>60
0.525 15 (2-134)
0.001
Subarachnoid hemorrhage
0.357
Elective operation
0.091
NCU
0.23
Inhibition
8 (1-75)
0.001
Surrounding monitor
6 (1-32)
0.035
Surrounding delirium patients
14 (2-75)
0.026
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Fazekas classification (severe)
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OR (95%CI)
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Table5: Results of statistical analysis of the risk factors for severe Fazekas grading
total (n=220)
Mean age,yrs (SD)
Fazekas grading
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Variable
p Value
Severe lesion (grade2,3) (n=38)
64.1 (13.7)
61.7(11.5)
75.5 (4.4)
<0.0001
Sex,male (%)
100 (45.6)
83 (45.6)
17 (44.7)
1.00
HDS-R (SD)
24.7 (5.7)
24.1 (8.8)
15.8 (8.6)
0.001
Atherosclerotic disease (%)
162 (73.6)
127 (69.8)
35 (92.1)
0.004
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Mild lesion (grade0,1) (n=182)
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Variables showing significant difference by univariate analysis (p<0.05) are indicated by boldface.
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Highlights
1. We analyzed the environmental and clinical risk factors in a neurosurgical center. 2. WMLs and environmental situation are risk factors for delirium in a neurosurgical center.
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3. To prevent delirium, management of environmental factors should be considered.
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Abbreviation HDS-R, Hasegawa Dementia Scale-Revised; ICDSC, intensive care delirium screening checklist JCS, Japan Coma Scale; NCU, neurosurgical care unit; SPSS, Statistical Package for the Social
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Sciences
S1878-8750(17)30462-X
DOI:
10.1016/j.wneu.2017.03.139
Reference:
WNEU 5508
To appear in:
World Neurosurgery
Received Date: 13 December 2016 Revised Date:
28 March 2017
Accepted Date: 29 March 2017
Please cite this article as: Matano F, Mizunari T, Yamada K, Kobayashi S, Murai Y, Morita A, Environmental and Clinical Risk Factors for Delirium in a Neurosurgical Center: A Prospective Study, World Neurosurgery (2017), doi: 10.1016/j.wneu.2017.03.139. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title:
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Environmental and Clinical Risk Factors for Delirium in a Neurosurgical Center: A
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Prospective Study Fumihiro Matano, MD, PhD1
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Takayuki Mizunari, MD, PhD1
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Keiko Yamada, RN1
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Shiro Kobayashi, PhD1
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Yasuo Murai, MD, PhD2
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Akio Morita, PhD2
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Department of Neurosurgery, Chiba Hokusoh Hospital
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Department of Neurological Surgery, Nippon Medical School
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Fumihiro Matano, MD, PhD
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Department of Neurosurgery, Chiba Hokusoh Hospital 1715 Kamagari, Inzai
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Chiba, 270-1694, Japan
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Telephone: 476-99-1111
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Fax: 476-99-1906
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Email: [email protected]
Keywords: delirium, environmental, neurosurgical center, risk factor, white matter signal
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abnormalities
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Running title: Environmental risk factors of delirium
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Matano.2
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Abstract
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Introduction
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Few reports of delirium-related risk factors have focused on environmental risk factors and clinical
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risk factors, such as white matter signal abnormalities on magnetic resonance imaging (MRI) FLAIR
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images.
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Material & Methods
We prospectively enrolled 253 patients admitted to our neurosurgical center between December
2014 and June 2015 and analyzed a total of 220 patients (100 males; mean age, 64.1 years; age range, 17–92 years). The upper 4 points of the Intensive Care Delirium Screening Checklist (ICDSC)
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indicated delirium. We evaluated patient factors consisting of baseline characteristics and related
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factors, such as white matter lesions (WMLs), and we also evaluated the surrounding environment.
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Delirium occurred in 29/220 cases (13.2%) in the present study. Regarding the baseline characteristics, there were significant statistical correlations between delirium and age (P = 0.0187),
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Hasegawa Dementia Scale-Revised score (P = 0.0022) on admission, and WMLs (P <0.0001).
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WMLs were related to age (P < 0.0001) and atherosclerotic disease (P = 0.004). Regarding related
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factors, there were significant statistical correlations between delirium and staying at a neurosurgical
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care unit (NCU; P = 0.0245). Multivariate logistic regression analyses showed statistically
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significant correlations of delirium with WMLs (P <0.0001) and surrounding delirium patients (P =
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0.026).
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Conclusion
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WMLs in patients and the environmental situation are risk factors for delirium in a neurosurgical center. Therefore, to prevent delirium, we must recognize risk factors, such as high
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grade WMLs, and manage environmental factors, such as the surrounding situation.
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Introduction
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Delirium is defined as a syndrome of acute change in mental status accompanied by
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inattention and marked by a fluctuating course.(19, 30) Many reports of related risk factors of
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delirium, particularly those focusing on the case of an intensive care unit (ICU) stay(20, 34) or on
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postoperative cases, have shown that anemia(10, 17) and surgical operation time(30, 39) are risk
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factors for delirium. However, few reports of delirium-related risk factors have focused on a
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neurosurgical center and environmental and clinical risk factors, such as white matter signal
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abnormalities on magnetic resonance imaging (MRI) FLAIR images.
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We conducted a prospective study analyzing the environmental and clinical risk factors in a neurosurgical center in our hospital.
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Material and Methods
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Methods
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The study protocol was approved by the Nippon Medical School Chiba Hokusoh Hospital
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Research Committee (Chiba, Japan) (R-433), and written informed consent was obtained from all
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patients and their families.
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Patients and clinical characteristic
We prospectively enrolled 253 patients admitted to our neurosurgical center between
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December 2014 and June 2015. We included cases in which the Japan Coma Scale (JCS) score(25,
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31) was less than 10 points. For the purpose of distinguishing acute changing status and disturbance
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of consciousness, dementia, and delirium at admission, we excluded cases in which the intensive
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care delirium screening checklist (ICDSC) score (3) was less than 4 points, the JCS score was in the
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upper 10 points, and the Hasegawa Dementia Scale revised (HDS-R)(33) score was less than 10
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points, as well as those with a psychiatric disorder and those for whom informed consent was not
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acquired at administration. A total of 33 patients were excluded by these criteria. Therefore, we
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subsequently analyzed a total of 220 patients (120 males; mean age: 64.1 years, range: 17–92 years).
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The clinical characteristics of the patients included cerebral infarction in 88 cases, intracerebral
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hemorrhage in 30, subarachnoid hemorrhage in 10, head trauma in 24, elective operation in 36, and
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other features in 32 (Table 1). We examined the white matter lesions (WMLs) on MRI FLAIR image
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in all patients in the first week from admission and evaluated them using the Fazekas classification:
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grade 0, no lesion; grade 1, punctate lesions, with a maximum diameter of 9 mm for a single lesion
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and of 20 mm for grouped lesions; grade 2, early confluent lesions, with single lesions between 10
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and 20 mm, grouped lesion more than 20 mm in any diameter, and no more than connecting bridges
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between individual lesions; grade 3, confluent lesions with single or grouped lesions of 20 mm or
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more in any diameter and connecting bridges between individual lesions. We defined grades 0 and 1
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as mild lesions and grades 2 and 3 as severe lesions (Figure). In cases of difficult-to-evaluate WMLs,
2
such as those with intracerebral hemorrhage, we referred to the contralateral side WMLs and judged
3
the lesions using the Fazekas classification. We defined atherosclerotic disease as the presence of
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one of the following: hypertension, dyslipidemia, diabetes mellitus, and smoking, and we
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investigated the relationship according to Fazekas grading and atherosclerotic disease.
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Evaluation of delirium
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The patients were evaluated twice each day (morning and evening) from the day of
admission until 1 week by a specialist nurse trained in the evaluation of delirium using the ICDSC. The upper 4 points of the ICDSC indicated delirium in the present study. We evaluated patient
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factors of aphasia; emergency admission; a stay in the neurosurgical care unit (NCU); insertion of a
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monitor, such as an electrocardiogram and oxygen saturation monitor; need for oral suction; need for
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changing position to prevent decubiti; inhibition of four limbs and the trunk; surgery performed; use
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of an intra-arterial or venous line; and drainage, such as wound suction or an intraventricular drain.
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The inclusion criteria were patients in whom a drain was inserted, those who required bedrest, and
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those who required close examination, such as stroke cases, postoperative cases, or those with severe
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head trauma admitted to the NCU. The NCU has 8 beds and does not border on one floor, and nurses
18
are continuously present. Adaptation criteria for inhibition at our institution included patients who
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required bed rest but could not rest in the bed and those with a risk of self-removal of drip infusion
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lines or drains due to disturbance of consciousness, delirium, or insufficient awakening anesthesia.
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In addition, we evaluated the surrounding environmental factors, such as being in the same room as
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patients with delirium, emergency admission, presence of an insertion monitor, need for oral suction,
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and need for changing position to prevent decubiti. We investigated the factors related to severe
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Fazekas grading, such as age, sex, HDS-R, and atherosclerotic disease.
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Statistical analysis
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Statistical analysis was performed using the Statistical Package for the Social Sciences
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(SPSS) for Mac (V.21.0; SPSS, Armonk, NY, USA). Variables are expressed as mean ± standard
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deviation (SD), median [interquartile ratio (IQR), 25th–75th percentile] or number of patients (%),
30
where appropriate. Multivariate logistic regression analysis was performed using variables that were
31
significantly associated with bypass patency on univariate analysis (P < 0.10). Differences were
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considered significant at a P value of <0.05.
33 34 35 36
Results Delirium occurred in 29/220 cases (13.2%) in the present study. Regarding baseline characteristics, there were significant statistical correlations between delirium and age (P =
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0.0187), HDS-R score (P = 0.0022) on admission, WMLs (P <0.0001 ) and subarachnoid
2
hemorrhage (P = 0.0293), and an elective operation showed a low risk of delirium (P = 0.05) in
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univariate regression analysis (Table 2). Regarding related factors, there were significant statistical
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correlations between delirium and staying at an NCU (P = 0.0245), need for suction (P = 0.0043),
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changing position (P = 0.019), and inhibition (P < 0.0001) in univariate regression analysis. (Table
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3). Regarding the surrounding environment, there were significant statistical correlations delirium
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and surrounding needing suction patient (P = 0.0253) in univariate regression analysis. We
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performed multivariate logistic regression analyses using variables that were significantly associated
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with delirium. Multivariate logistic regression analyses showed statistically significant correlations
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between delirium and WMLs (P <0.0001), inhibition of four limbs and the trunk (P = 0.001),
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surrounding monitor (P = 0.035), and surrounding delirium patients (P = 0.026).
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The case of severe lesion in Fazekas grading related age (P < 0.0001), HSD-R (P = 0.001), and
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atherosclerotic disease (P = 0.004). (Table5)
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(Table 4).
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Discussion
In the present study, not only the clinical condition and WMLs of the patients but also the environmental situation were shown to be risk factors for delirium in a neurosurgical center.
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Therefore, to prevent delirium in the case of admission to a neurosurgical center, management of
19
environmental factors, such as the surrounding situation in the same room and inhibition, should be
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considered.
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Delirium is defined as an acute state of confusion characterized by inattention, cognitive impairment, and fluctuating consciousness.(21, 35) Development of delirium is dependent on
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complex inter-relationships between vulnerable patients with several predisposing factors and
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exposure to noxious insults or precipitating factors.(15) However, the correct pathophysiology
25
remains unclear.
Patients who experience delirium are at an increased risk of dying during admission,
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having longer stays in the hospital, and experiencing cognitive impairment after discharge.(11, 16,
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28) Therefore, prevention of delirium is extremely important not only from the viewpoint of patient
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outcome but also medical economy. (15, 24, 29) It has been reported that addressing a single risk
30
factor is unlikely to resolve delirium.(15) In fact, prior reports have shown that there are many
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possible risk factors for delirium, which cause mental status changes, such as advanced age;
32
dementia or depression;(1, 38), physical condition(14, 27), use of medication, such as major
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tranquilisers(13) or benzodiazepines;(23) and results of laboratory investigations, such as low
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albumin(36) or high or low sodium.(23) Higher age and higher HDS-R score were risk factors in the
35
present study as they were in a previous report. (2) However, to date, there have been few reports on
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WMLs associated with delirium. In the present study, the case of a severe lesion according to
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Fazekas grading was related to age and presence of atherosclerotic disease. White matter signal
2
abnormalities on MRI FLAIR image are associated with various geriatric disorders, including
3
cerebrovascular disease, cerebral blood flow, (9) cognitive function, (7) and risk of dementia.(26)
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Small and large artery elastic indices are markers of arteriosclerosis and atherosclerosis respectively
5
and liked to WMLs.(37) Decreased elasticity due to aging is associated with a reduction in blood
6
flow toward the brain.(18) Brain volume, activity, and cerebral neurotransmitters all decrease in
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elderly patients,(2) and some elderly patients have complicated intracranial atherosclerotic
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disease.(32) Therefore, hypoxia in the brain might be associated with delirium.(4)
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Subarachnoid hemorrhage, high-risk disease, and elective operation were of low risk for
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delirium in the present study. Lara et al.(5) reported that intraventricular bleeding, hydrocephalus,
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and frontal hematomas were risk factors for delirium in subarachnoid hemorrhage cases and reported
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delirium in acute subarachnoid patients triggered by medication, pain, vasospasm, or early ischemia.
13
Anxiety also may be related to postoperative delirium.(6) Therefore, patients undergoing an elective
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operation may tend to experience postoperative delirium. On the other hand, inhibition also was at a
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high risk for delirium and may be related to anxiety of the patients.
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There are few report on the relationship of delirium and surrounding factors in a neurosurgical center to date. In the present study, staying at the NCU, presence of surrounding
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sensors, and surrounding delirium patients are high risk factors for delirium in multivariate logistic
19
regression analysis. To date, there are no reports on categorizing acutely confused patients together
20
as a risk for delirium; however, there are correlations between noise levels and arousal frequencies in
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the intensive care unit. (8, 22) Excessive noise is a potentially modifiable risk factor that may
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contribute to sleep disruption and occurrence of delirium. Patients frequently cite noise as a main
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contributor to sleep disruption.(12)
Delirium may be prevented in a neurosurgical center if the medical staff can recognize
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high-risk patients, especially those with a high Fazekas grade, correctly and administer medicine to
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prevent insomnia. For correctable risk factors, such as putting delirium patients together, we may
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have to arrange the room to ensure a suitably quiet environment, such as an individual cubicle.
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However, we did not investigate whether removing the risk factors evaluated in our study, such as
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inhibition or surrounding factors, results in decreased delirium. Further prospective study should be
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required to support our claim.
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Limitation This study had some limitations. First, we did not investigate the detailed background of
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the patients, such as constant medication or results of laboratory data, and we did not follow the
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patient who had delirium and did not investigate the clinical outcome. Second, inhibition was a risk
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factor for delirium in the present study; however, the criteria of inhibition itself may include the risk
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factors for delirium. Third, removal of risk factors to prevent delirium was not evaluated in the
2
present study. Fourth, intracranial lesions interfered with the interpretation of WMLs. Finally, we did
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not investigate patients’ sleep status, surrounding noise, and anxiety objectively.
4 Conclusion
6
To prevent delirium in a neurosurgical center, we should recognize the clinical risk factors. WMLs
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are the risk factors for delirium, and inhibition should be avoided as much as possible.
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Environmental factors, such as the surrounding situation in the same room, should be managed.
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Conflicts of interest
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None of authors have any conflicts of interest.
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Figure legends
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Degree of WMLs according to Fazekas classification.
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A. Grade 0, no lesion
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B. Grade 1, punctate lesions, with a maximum diameter of 9 mm for a single lesion and of 20 mm
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for grouped lesions
C. Grade 2, early confluent lesions, with single lesions between 10 and 20 mm, grouped lesions
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more than 20 mm in any diameter, and no more than connecting bridges between individual
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lesions.
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D. Grade 3, confluent lesions, with single or grouped lesions of 20 mm or more in any diameter and connecting bridges between individual lesions.
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24 25 26 27 28 29 30 31 32
Table 1. Characteristics of patients
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Tables
Table 2. Clinical characteristics of patients with delirium
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Table 3. Results of statistical analysis of the risk factors for delirium
Table 4. Multivariate logistic regression analysis for risk factors of delirium.
Table 5. Results of statistical analysis of the risk factors for severe Fazekas grading
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Table1: Characteristics patients
Enrolled 253 cases Exclude 33 cases (ICDSC<4,JCS>20,HDS-R<10, psychiatric disease+) Included 220 cases 17-92 (mean;64.1)
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Age Sex (male : female)
120 : 100
Cerebral Infarction; 88 case, Intracerebral hemorrhage; 30 case Subarachnoid hemorrhage; 10 case, Head trauma; 24 case
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Elective operation; 36 case, Others; 32 case
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Table2: Clinical characteristic of patients with delirium
Delirium Present (n=29)
Base line characteristic 64.1 (13.7)
63.3(15.5)
Age > 60 yrs (%)
151 (79.1)
25 (86.2)
Sex,male (%)
100 (45.6)
14 (48.3)
HDS-R (SD)
24.7 (5.7)
20.7 (5.8)
Fazekas classification severe (%)
38 (17.3)
Atherosclerotic disease (%)
162 (73.6)
Type of disease
Absent (n=191)
p Value
60.9 (2.1)
0.0187
126 (65.9)
0.0316
86 (45)
0.842
24.8 (7.5)
0.0022
15 (51.7)
23 (12)
< 0.0001
26 (89.6)
136 (71.2)
0.079
10 (34.5)
78 (40.8)
0.5493
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Mean age,yrs (SD)
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total (n=220)
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Variable
88 (40)
Intracerebral hemorrhage (%)
30 (13.6)
5 (17.2)
25 (13.1)
0.5621
Subarachnoid hemorrhage (%)
10 (4.5)
4 (13.8)
6 (3.1)
0.0293
Head trauma (%)
24 (10.9)
5 (17.2)
19 (9.9)
0.3319
Elective operation (%)
36 (16.4)
1 (3.4)
35 (18.3)
0.05
4 (13.8)
28 (14.7)
0.2175
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Others (%)
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Cerebral infarction (%)
32 (14.5)
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Data are expressed as number of patients (%), unless otherwise indicated. Variables showing significant difference by univariate analysis (p<0.05) are indicated by boldface.
OR (95%CI)
3.22 (1.08-9.661)
7.826 (3.348-18.29)
4.933 (1.3-18.7) 6.282 (0.82-47)
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Table3: Results of statistical analysis of the risk factors for delirium
Delirium total (n=220)
p Value
OR (95%CI)
0.0198
4.9 (1.13-21.4)
107 (56)
0.0245
3.0 (1.2-7.7)
163 (85)
0.139
9 (31)
19 (9.9)
0.0043
4.1 (1.6-10)
17 (58)
63 (32)
0.019
2.9 (1.3-6.4)
17 (58)
17 (8.9)
<0.0001
14.5 (5.9-35)
10 (34)
55 (28)
0.5199
Present (n=29)
167 (75)
27 (93)
NCU (%)
130 (59)
23 (79)
Monitor (%)
191 (86)
28 (96)
Suction (%)
28 (12)
Position changing (%)
80 (36)
Inhibition (%)
34 (15)
Operation (%)
65 (29)
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Surrounding factors
140 (73)
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Emergency admission (%)
Absent (n=191)
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Related factors
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Variable
48 (21)
9 (31)
39 (20)
0.2277
Emergency admission (%)
91 (41)
11 (37)
81 (42)
0.6911
Suction (%)
132 (60)
23 (79)
109 (57)
0.0253
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delirium (%)
Data are expressed as number of patients (%), unless otherwise indicated.
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2.8 (1.1-7.4)
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Table4: Multivariate logistic regression analysis for risk factors of delirium
p Value
Age>60
0.525 15 (2-134)
0.001
Subarachnoid hemorrhage
0.357
Elective operation
0.091
NCU
0.23
Inhibition
8 (1-75)
0.001
Surrounding monitor
6 (1-32)
0.035
Surrounding delirium patients
14 (2-75)
0.026
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Fazekas classification (severe)
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OR (95%CI)
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Variable
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Variables showing significant difference by univariate analysis (p<0.05) are indicated by boldface.
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Table5: Results of statistical analysis of the risk factors for severe Fazekas grading
total (n=220)
Mean age,yrs (SD)
Fazekas grading
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Variable
p Value
Severe lesion (grade2,3) (n=38)
64.1 (13.7)
61.7(11.5)
75.5 (4.4)
<0.0001
Sex,male (%)
100 (45.6)
83 (45.6)
17 (44.7)
1.00
HDS-R (SD)
24.7 (5.7)
24.1 (8.8)
15.8 (8.6)
0.001
Atherosclerotic disease (%)
162 (73.6)
127 (69.8)
35 (92.1)
0.004
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Data are expressed as number of patients (%), unless otherwise indicated.
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Mild lesion (grade0,1) (n=182)
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Variables showing significant difference by univariate analysis (p<0.05) are indicated by boldface.
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Highlights
1. We analyzed the environmental and clinical risk factors in a neurosurgical center. 2. WMLs and environmental situation are risk factors for delirium in a neurosurgical center.
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3. To prevent delirium, management of environmental factors should be considered.
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Abbreviation HDS-R, Hasegawa Dementia Scale-Revised; ICDSC, intensive care delirium screening checklist JCS, Japan Coma Scale; NCU, neurosurgical care unit; SPSS, Statistical Package for the Social
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Sciences