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A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery
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A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery Nozomi Harasawa *, Toshiko Mizuno Tokyo Women’s Medical University, School of Nursing, Tokyo, Japan
A R T I C L E I N F O
A B S T R A C T
Article history: Received 22 July 2013 Received in revised form 2 May 2014 Accepted 8 May 2014 Available online xxx
Purpose: The purpose of this study was to develop and test a scale for predicting POD in patients undergoing cerebrovascular surgery. Methods: The predictive scale for POD was composed of 32 items reflecting the strongest risk factors as determined by a literature review. The NEECHAM Confusion Scale determined POD onset and severity. Results: Delirium developed in 38 (31.1%) of the 122 patients in our sample. Logistic regression revealed the following risk factors: dehydration, age, disturbance of consciousness, underlying illness, and anxiety or depression. The final scale was weighted by referring to odds ratios. The area under the curve was 0.844 (95% CI = 0.766–0.921). The possible total score on this scale was 20 points. A cutoff score of 11 was set for risk of POD (patients scoring over 12 were considered at higher risk). The median score was 8 (range: 4–9) in the non-delirium group and 13 (range: 9–16) in the delirium group (U = 499.0; df = 120; p < 0.001). Scale scores were moderately correlated with delirium duration (r = 0.532; p < 0.001). Conclusion: The present scale is a promising a tool for predicting POD but needs to be studied further. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: Delirium Prediction Scale Cerebrovascular surgery Elderly
1. Introduction Delirium is associated with multiple adverse outcomes including functional decline, new institutionalization, persistent cognitive impairments, and mortality (Dubljanin-Raspopovic et al., 2012; Inouye, 1999; Pandharipande et al., 2013; Siddiqi, House, & Holmes, 2006). These problems are often accompanied by financial losses, lower patient quality of life (QOL), and prolonged hospital stays (Rubin, Neal, Fenlon, Hassan, & Inouye, 2011). In the case of severe delirium—with extreme autochthonous delusions or with several hallucinations and violent behaviors— recovery is slowed, and the effectiveness of interventions decreases (Inouye et al., 1999). Therefore, it appears necessary to treat early stage delirium in the same manner as any physical (e.g., electrolyte abnormality) or environmental (e.g., aggravating noise) factor that could limit recovery. However, especially in older adults with multiple medical problems, several factors intertwine, complicating associations between medical problems and delirium (Inouye, 1998). Numerous factors can easily trigger acute-stage delirium. For instance, changes in circulatory
* Corresponding author at: 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan. Tel.: +81 3 3357 4804x6463; fax: +81 3 3341 8832. E-mail address: [email protected] (N. Harasawa).
dynamics and psychosomatic pain commonly occur, especially after surgery. Therefore, it is necessary to determine whether these are actually symptoms of delirium. If it is possible to determine preoperative factors of delirium before surgery, we could reduce the incidence of delirium. One precaution that can be taken for managing delirium is to assess whether the individual is at high risk before undergoing surgery. Previous attempts have been made to predict the onset of delirium, and some predictive models have been developed (Bakker, Osse, Tulen, Kappetein, & Bogers, 2012; Bo¨hner et al., 2003; Freter et al., 2005; Goldenberg et al., 2006; Guenther et al., 2013; Inouye, Viscoli, Horwitz, Hurst, & Tinetti, 1993; Koster, Hensens, Schuurmans, & van der Palen, 2013; Leung, Sands, Lim, Tsai, & Kinjo, 2013; Leung, Tsai, & Sands, 2011; Litaker, Locala, Franco, Bronson, & Tannous, 2001; Marcantonio et al., 1994; Patti, Saitta, Cusumano, Termine, & Di Vita, 2011; Rudolph et al., 2009). However, these were predictive models or scales about cardiovascular surgery (Bakker et al., 2012; Bo¨hner et al., 2003; Koster et al., 2013; Rudolph et al., 2009), orthopedic surgery (Freter et al., 2005; Goldenberg et al., 2006; Marcantonio et al., 1994), or alimentary disease-related surgery (Patti, Saitta, Cusumano, Termine, & Di Vita, 2011); thus far, we have been unable to find a predictive scale for cerebrovascular surgery. Additionally, these predictive models primarily depend on physical health indices, such as nutritional status, electrolyte abnormality, and disease severity. Such indices
http://dx.doi.org/10.1016/j.archger.2014.05.007 0167-4943/ß 2014 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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do not take into account risks relevant to environmental (e.g., room transfer) and psychological (e.g., anxiety) factors; however, Rudolph et al. (2009) and Leung et al. (2011) did include the Geriatric Depression Scale scores in their study. In a systematic review, Dasgupta and Dumbrell, 2006 observed that depression and institutional residence were risk factors for delirium. Because delirium can be associated with several factors, additional assessment methods are needed above and beyond those that rely on visible physical symptoms. Lipowski (1990) showed that the etiology of delirium should include three classes of causative factor types: predisposing, facilitating, and precipitating. Since these facilitating factors may be mitigated through nursing care, we attempted to find them, and combine them to form a predictive sale. We aimed for the predictive scale to be easily used by nurses while they deal with elderly patients. Inouye et al. (1993) and Marcantonio et al. (1994) developed a predictive system of delirium. However, cerebrovascular disorders cases were excluded from their studies (Inouye et al., 1993; Marcantonio et al., 1994). We predicted that the prophylactic treatment of cerebrovascular disorders related to aging, such as arteriosclerosis, will increase as with the growth of the ‘‘over 65 years old’’ population demographic. Previous studies have shown that cerebrovascular disorders were factors in delirium (Hatano et al., 2013; Litaker et al., 2001). Because the incidence of delirium after brain surgery is 20–30% (Harasawa, Mizuno, & Shimizu, 2011; Oh, Kim, Chun, & Yi, 2008), this represents a major public health problem. The current study aimed (1) to develop a scale for predicting POD in patients undergoing cerebrovascular revascularization surgery, and (2) to determine the cutoff value for assessing POD based on this scale. In this study, the NEECHAM Confusion Scale was used to measure (a) the presence of delirium and (b) delirium severity. 2. Methods 2.1. Study design and objectives 2.1.1. Literature review (Phase 1) During Phase 1, we extracted POD items using factor analysis and existing predictive scales. To extract items, we first retrieved documents through a full-text search within the MEDLINE (1966 to December 2008) and CINAHL (1937 to December 2008) databases. We used two combinations of key words: (a) ‘‘delirium’’ and ‘‘risk assessment’’ and (b) ‘‘delirium’’ and ‘‘prediction.’’ Inclusion criteria were papers that only assessed adult patients (over 19 years old), had full-text versions of the papers online, and examined delirium
among patients was not related to alcohol withdrawal. We retrieved 11 papers through MEDLINE and 2 papers through CINAHL. Our literature review was also supplemented by a manual search. Finally, we selected 9 papers (Aohda et al., 2007; Bo¨hner et al., 2003; Freter et al., 2005; Goldenberg et al., 2006; Inouye et al., 1993; Litaker et al., 2001; Marcantonio et al., 1994; Oh et al., 2008; Yamamoto, Nishizaki, Nakashima, & Yokoyama, 1999) in addition to our previous study (Harasawa et al., 2011; Table 1). In order to select risk factors of delirium for use in the POD scale, we set conditions for whether these studies revealed predictive model factors. In a univariate analysis, we selected factors with p values less than 0.05. We used Lipowski’s framework (Lipowski, 1990) so that nurses could address underlying POD factors. The extracted items were then divided into three classifications: precipitating factors (e.g., primary cerebral disease, systemic secondary diseases affecting the brain), predisposing factors (e.g., age 60 or over, brain damage), and facilitating factors (e.g., psychosocial stress, sleep deprivation). We then created the POD scale that included 32 items (Table 2). Researchers evaluated these 32 items for patients after the latter were hospitalized before surgery. 2.1.2. Validation cohort (Phase 2) During Phase 2, we administered this scale to an open cohort sample. The current study was conducted using patients hospitalized for cerebrovascular surgery within a 1423-bed teaching hospital in Tokyo. We excluded patients with Moyamoya disease, since this disease is not related to age. Between August 2009 and March 2011, all 182 patients were electively undergoing one of the following procedures: extracranial or intracranial bypasses, clipping techniques (including trapping techniques), coating for unruptured cerebral aneurysms, bypass operations, internal carotid artery endarterectomies (CEA), or superficial temporal artery/middle cerebral artery anastomosis surgeries for ischemic cerebrovascular disease (Table 3). Five patients declined participation, and 42 patients were excluded because we were unable to meet with them before their surgeries. Of the remaining 135 patients, 13 were excluded due to missing information (n = 8), needing long-term management in the intensive care unit after surgery (n = 2), and surgery cancelation (n = 3). Patients who were suspected to have experienced delirium before surgery were also excluded. Thus, our sample included 122 patients (67.0% of the initial sample; see Table 3 for a summary). The median age was 66 (range = 58–72) years, and 59% were female. The median number of preoperative days in the hospital was 5 (range = 4–6), and the
Table 1 Final selected preference studies for extracted scale items. Study author
Department
Sample size
Delirium assessment method
Incidence of delirium (%)
Statistical test
Inouye et al.
General medicine
CAM
General surgery, Orthopedic surgery, Gynecology services Orthopedic surgery Vascular Surgery Orthopedic surgery, Gynecology, Neurosurgery, etc. Orthopedic surgery Orthopedic surgery Neurosurgery General medicine, Orthopedic surgery, etc. Neurosurgery
CAM
25 17 9
Proportional hazards model
Marcantonio et al.
Development cohort 107 Validation cohort 174 1341 71 153 500
DRS DSM-IV DSM-IV
26.8 39.2 11.4
Multivariate analysis Multivariate analysis Logistic regression analysis
132 77 224 461
CAM CAM CAM DRS-N
13.6 48.1 21.4 20.8
Logistic regression analysis Logistic regression analysis Logistic regression analysis Univariate analysis
121
NEECHAM Confusion Scale
33.1
Univariate analysis
Yamamoto et al. Bo¨hner et al. Litaker et al. Freter et al. Goldenberg et al. Oh et al. Aohda at al. Harasawa et al.
Logistic regression analysis
CAM: Confusion Assessment Method; DRS: Delirium Rating Scale; DSM-IV: Diagnostic and Statistical Manual of Mental Disorders; DRS-N: Delirium Rating scale for Nurses (Minamikawa et al., 2002).
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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AGG-3007; No. of Pages 8 N. Harasawa, T. Mizuno / Archives of Gerontology and Geriatrics xxx (2014) xxx–xxx Table 2 Selected risk factors.
Table 3 Description of subjects. n = 122
Criteria Precipitating factors Lesion Surgical site Technique Taking multiple internal medications Dehydration Anemia Hypoalbuminemia Predisposing factors Age Gender Cognitive impairment Disturbance of consciousness Underlying disease Hypertension Respiratory disease Liver dysfunction Diabetes Renal disease Heart disorder Cerebral infarction History of delirium Facilitating factors Depression Anxiety Decreased visual acuity Insomnia Tension Using orientation reinforcement Visit of key person Room transfer Hospitalization history ADL disorder Use of sensor-type nurse call Defecation trouble Urination trouble
3
Operative method
Clipping CEA Bypass Male
78 25 19 50
Age (years) Preoperative period (days) Postoperative period (days) Operative time (min) Bleeding (mL)
M (inter-quartile range)
66 (58–72) 10.8 5 (4–6)
Surgical site
Internal carotid
Sex Taking over 3 delirium medications Nitrogen/Creatinine ratio 18 Hematocrit level 33% Albumin level 3.5 g/dl Over 70 years old NM scale scores 47 points Except Clear level of JCS
HADS Depression scores 11 points HADS Anxiety scores 11 points
Likert scale 1–4, Ease (1, 2) or Tension (3, 4)
(%) (63.9) (20.5) (15.6) (41.0)
9 (8–11) 191 (160–235) 30 (20–50)
artery (Left) Internal carotid artery (Right) Anterior communicating artery Middle cerebral artery (Left) Middle cerebral artery (Right) Vertebral artery Basilar artery Other Total
n = 129a 44
(%) (36.1)
25
(20.5)
16
(13.1)
13
(10.7)
15
(12.3)
5 2 9 129
(4.1) (1.6) (7.4)
Mann–Whitney U test, seven cases required multiple surgery sites, giving a total of 129 sites for 122 individuals. CEA: internal carotid artery endarterectomy, M: median.
2.2. Measurements and procedures
indicating delirium. The scale can measure the severity of delirium, with 0–19 points = moderate to severe; 20–24 points = mild delirium to early; 25–26 points = at-risk; and 27–30 points = normal. The scale had high inter-rater agreement between a researcher and nurses (k = 0.65), and internal consistency was high (a = 0.90). Participants with scores of 24 points or lower were placed into a ‘‘delirium’’ group. The scale has been shown to have high sensitivity with this cutoff (Neelon, Champagne, Carlson, & Funk, 1996). Patients were evaluated on postoperative days 1 through 3 or until they received a NEECHAM score of 27 for more than two consecutive days. Nurses from a neurological ward at the hospital, as well as a researcher, filled out the scale. The assessors examined patients over 24 h and completed the measures through a brief patient interview, observation, and a review of both the nursing record and the patient’s medical records.
2.2.1. Delirium assessment The NEECHAM Confusion Scale V (Neelon, Champagne, McConnell, Carlson, & Funk, 1992) determined POD onset and severity. The NEECHAM Confusion Scale is composed of nine items divided into three subscales: information processing, behavior, and performance. Subscale I, information processing (score range = 0–14 points), evaluates components of cognitive status: attention and alertness, verbal and motor response, and memory and orientation. Subscale II, behavior (score range = 0–10 points), evaluates observed behavior and performance ability: general appearance and posture, sensory–motor performance, and verbal responses. Subscale III, performance (score range = 0–16 points), assesses vital functions: vital signs, oxygen saturation level, and urinary incontinence. The total NEECHAM scale score is the sum of the three subscale scores. The scale can be completed in 10 min on the basis of vital sign observation and measurement. Scores can range from 0 (minimal function) to 30 (normal function); the cut-off point is 24, with scores from 0 to 24 points
2.2.2. Measures in the POD predictive scale We used the following measurement tools, keeping in mind that a nurse from the general ward would be best suited to detect patients with delirium because they are constantly providing direct care to patients. Furthermore, we wanted to use measures that the nurse could fill out while performing daily care duties, because this would reduce the burden on study patients, and would not interrupt nurses’ duties. Levels of consciousness were assessed using the Japan Coma Scale (JCS) (Ohta, 2005). The JCS is widely used in Japanese medical settings. The JCS assesses three levels of consciousness by evaluating patient responses. This measure has 9 subscales. A JCS score of 0 indicates lucidity; single-digit JCS scores (e.g., 1, 2, 3) indicate that the patient is drowsy but can be awakened without the use of stimulants; double-digit JCS scores (10, 20, 30) indicate patients who are drowsy but can be roused with the use of stimulants; and triple-digit JCS scores (100, 200, 300) indicate that the patient is comatose.
N-ADL scores 48
NM scale: Mental status standard for N-type elderly; JCS: Japan Coma Scale; HADS: Hospital Anxiety and Depression Scale; ADL: Activities of daily living; N-ADL: Ability scale for N-style elderly activities of daily living.
median number of postoperative days in the hospital was 9 (range = 8–11). The type of operative methods included clipping techniques (63.9%), CEA (20.5%), and bypass (15.6%). The most common surgical sites were the left (36.1%) and right internal carotid artery (20.5%).
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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The JCS is strongly correlated with the Glasgow Coma Scale (Ono, Wada, Takahara, & Shirotani, 2007). We used a simple behavioral rating scale (the mental status standard for N-type elderly, NM scale; Nishimura et al., 1993) to screen for dementia and determine its severity. Another simple behavioral rating scale was employed to assess activities of daily living (the Ability for N-style elderly activities of daily living measure; N-ADLs, Nishimura et al., 1993). These scales have been widely used in Japan and can be administered by any hospital personnel (as opposed to requiring a psychiatrist or clinical psychologist). According to Nishimura et al. (1993), classifications for scores are ‘‘normal’’ (50–48), ‘‘borderline’’ (47–43), ‘‘mild dementia’’ (42–31), ‘‘moderate dementia’’ (30–17), and ‘‘severe dementia’’ (16–0). A strong and significant negative correlation (r = 0.947) has been observed between the intellectual function score on the Gottfries–Brane–Steen (GBS) scale and the NM scale, with a strong and significant positive correlation (r = 0.944) between the motor function score on the GBS scale and N-ADLs score (Nishimura et al., 1993). Because the GBS scale is used to assess dementia worldwide, these results indicate that the NM scale and N-ADLs are valid scales for assessing dementia and ADLs. Additionally, since the NM scale and N-ADLs are both based on observations of daily living behaviors, these assessments are appropriate in both clinical and residential settings. We used the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983) to assess anxiety and depression symptoms. The HADS was developed to evaluate anxiety and depression among patients with physical symptoms. Each Likert-type item is scored on a scale from 0 to 3 with 0–21 as the range for possible depression and anxiety scores. Scores for each subscale are interpreted as ‘‘normal’’ (0–7), ‘‘mild’’ (8–10), ‘‘moderate’’ (11–14), or ‘‘severe’’ (15–21). Scores for the entire instrument (emotional distress) range from 0 to 42 with higher scores indicating more distress. This scale is self-reported and can be completed in 5 min. 2.3. Ethical considerations Participants gave informed consent after a researcher explained the study. Participants were assured that their participation or refusal would not affect their medical or nursing care. The ethical committee of the Faculty of Medicine at Tokyo Women’s Medical University approved this study. 2.4. Statistical analysis Comparisons were made between delirious and non-delirious groups via a chi-square test or Fisher’s exact test for categorical data and Student’s t-test for continuous data. If continuous data were skewed based on the Kolmogorov–Smirnov test, we used the Mann–Whitney U-test. We also performed a factor analysis of all
scale items, except the items that had an onset frequency of 0; we classified these items according to the three-factor model of delirium by Lipowski. We used the generalized least squares method of extraction, and used a promax rotation method with Kaiser normalization. A logistic regression analysis was performed to explore the relationship between preoperative factors and post-operative delirium. Preoperative factors were analyzed with a stepwise logistic regression at p < 0.25 (Bendel & Afifi, 1977). The p-value was expanded to 0.25 so that non-significant factors could be included in the logistic regression procedure. Five to seven covariates are considered suitable for a logistic regression analysis that uses R2 and mean square error approximation parameters. Anything higher may cause the model to become unstable (Dowdy & Wearden, 1991). To determine which variables to place into the logistic regression using the stepwise method, we confirmed the multicollinearity between variables. We assessed the fitness of the logistic regression model and chose the model with the best fit. We confirmed these parameters through structural equation modeling for final scale items. Items were weighted according to odds ratios obtained from each logistic regression model. The odds ratio for each significant independent variable was rounded to the nearest whole integer to create a scoring system that could be easily calculated by the medical care personnel. Receiver operating characteristics (ROC) analysis was used to determine whether confirmed scores on the POD predictive scale were actual predictors of delirium. Furthermore, we compared the sensitivity, specificity, and the positive likelihood ratio associated with each score distribution to determine the cutoff value for the predictive scale. We used Youden’s index (sensitivity + specificity 1) to indicate the maximum (Sackett, Haynes, & Tugwell, 1985). Because these data were nonparametrical, we used Spearman’s rank correlation coefficients to examine the association between the POD predictive scale score and sustained days of delirium. The threshold for statistical significance was set at p < 0.05. IBM SPSS Statistics 21.0 software (IBM Japan, Ltd., Tokyo, Japan) was used for statistical analyses. 3. Results 3.1. Generation status and risk factors of delirium Delirium was diagnosed in 38 patients (31.1% of the sample) using the NEECHAM Confusion Scale. A significant difference was found between the delirium and the non-delirium groups for both the number of preoperative and postoperative days in the hospital, and for perioperative bleeding (Table 4). We also conducted a chi-square test to assess the presence or absence of delirium onset (Table 5). Results indicated significant differences for surgical technique (x2 = 8.982, p = 0.011), age over 70 years
Table 4 Presence or absence of delirium onset and hospitalization and peri/postoperative information (N = 122). Delirium
Preoperative hospitalization (days) M (inter-quartile range) Postoperative Hospitalization (days) Operative time (min) Bleeding (mL) Days until postoperative walk initiation (days)
Present
Absent
(n = 38)
(n = 84)
U value
p
5 (4–7)
5 (4–5)
2.180
0.029
10 (8–15)
9 (8–10)
2.293
0.022
1.516 2.128 2.647
0.130 0.033 0.008
205 (164–257.5) 50 (21–50) 3 (2–4)
189 (158.75–224.5) 30 (19.5–50) 2 (2–3)
Mann–Whitney U test; M: median.
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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Table 5 Univariate analysis results for POD onset prediction scale items and delirium onset (N = 122).
x2
Delirium
Variables
Present (n = 38)
Absent (n = 84)
Lesion Surgical site
Inner skull Anterior communicating artery Internal carotid artery Right Internal carotid artery Left Middle cerebral artery Right Middle cerebral artery Left Basilar artery Vertebral artery Others
29 6 11 12 1 5 0 1 2
63 10 14 29 14 8 2 4 7
Technique Taking multiple internal medications Dehydration Anemia Hypoalbuminemia Age Gender Cognitive impairment Disturbance of consciousness
Clipping/CEA/Bypass
18/9/11 3 30 1 0 22 19 7 17
Underlying disease Hypertension Respiratory disease Liver dysfunction Diabetes Renal disease Heart disorder Cerebral infarction History of delirium Depression/Anxiety Depression Anxiety Decreased visual acuity Insomnia Tension Using orientation reinforcement (calendar or watch) Visit by key person Room transfer Hospitalization history ADL disorder Use of the sensor-type nurse call Defecation trouble Urination trouble
Nitrogen/Creatinine ratio 18
Over 70 years old Male Less than 47 points on NM scale Over JCSI-1
Over 11 points on HADS Depression or Anxiety items HADS Depression scores 11 HADS Anxiety scores 11
p
0.024 0.347 2.420 0.278 4.779a 0.363a 0.920a 0.302a 0.361a
0.876 0.556 0.120 0.598 0.036 0.541 1.000 1.000 0.719
61/16/8 1 54 2 1 20 31 3 8
8.982 3.708a 2.623 0.007a 0.456a 15.344 1.855 7.670a 19.912
0.011 0.089 0.105 1.000 1.000 <0.001 0.173 0.010 <0.001
35 25 8 9 7 4 6 15 2 9
53 40 11 16 14 7 11 19 1 5
10.954a 3.470 1.260 0.436 0.057 0.153a 0.158 3.697 1.809a 8.098a
0.001 0.062 0.262 0.509 0.801 0.738 .691 0.054 0.229 0.006
6 6 15 6 16 15 23 3 30 1 0 1 1
1 4 41 22 33 37 44 6 56 3 0 2 0
10.310a 4.228a 0.918 1.601 0.897 0.224 0.701 0.022a 4.006 0.007a – 0.007a 2.229a
0.004 0.069 0.338 0.206 0.826 0.636 0.402 1.000 0.045 1.000 – 1.000 0.311
a Fischer’s exact test, CEA: internal carotid artery endarterectomy; JCS: Japan Coma Scale; NM scale: Mental status standard for N-type elderly; HADS: Hospital Anxiety and Depression Scale; ADL: activities of daily living.
(x2 = 15.344, p < 0.001), disturbance of consciousness (as indicated by the JCS score; x2 = 19.912, p < 0.001), underlying disease (x2 = 10.954, p = 0.001), depression (x2 = 10.310, p = 0.004), right middle cerebral artery as the surgical site (x2 = 4.779, p = 0.036), cognitive impairment (as indicated by a score less than 47 points on the NM scale; x2 = 7.670, p = 0.010), and a history of hospitalization (x2 = 4.006, p = 0.045). The duration of delirium ranged from 0 to 14 days (median = 1.00; mean = 1.69; SD = 2.50). Additionally, we performed a principal component analysis for all items in order to confirm the three-factor structure of delirium by Lipowski. As a result, nine factors were extracted (see Appendix 1). 3.2. Logistic regression analysis All variables that yielded p-values of less than 0.25 in a chisquare test for the presence or absence of delirium were included in the logistic regression analysis; the following variables were added: right internal carotid artery surgical site (x2 = 2.420, p = 0.120), dehydration (x2 = 2.623, p = 0.105), gender (x2 = 1.855, p = 0.173), cerebral infarction (x2 = 3.697, p = 0.054), anxiety (x2 = 4.228, p = 0.069), and insomnia (x2 = 1.601,
p = 0.206). However, we excluded participants taking multiple internal medications (x2 = 3.708, p = 0.089) and those with a history of delirium (x2 = 1.809, p = 0.229), because so few participants in our sample met these conditions. The right middle cerebral artery (x2 = 4.779, p = 0.036) was also excluded because only one person developed delirium in this manner. We also checked for multicollinearity, and combined items/factors. The correlation between the HADS depression and anxiety was r = 0.347, p < 0.001. The HADS depression and anxiety scores were combined into one factor. The HADS scores were assessed when depression or anxiety scores were greater than 11 points (x2 = 8.098, p = 0.006). Finally, we entered technique, age, disturbance of consciousness, cognitive impairment, underlying disease, cerebral infarction, depression or anxiety, dehydration, gender, insomnia, and hospitalization history into a logistic regression analysis. We ran logistic regression analyses for various combinations of these variables. We also entered variables into the logistic regression model, examining the how the model fit changed with each variable added. This resulted in our choosing dehydration (OR = 4.065, 95% CI = 1.295–12.755), age (OR = 4.380, 95% CI = 1.619–11.852), disturbance of consciousness (OR = 4.296,
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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6 Table 6 Logistic regression model.
b Dehydration Age Disturbance of consciousness Underlying disease Depression/Anxiety Constant
Pointsa
Odds ratio
95% confidence interval
1.402 1.477 1.458
4.065 4.380 4.296
1.295–12.755 1.619–11.852 1.372–13.446
0.016 0.004 0.012
4 4 4
1.681 1.171 6.998
5.372 3.225 0.001
1.306–22.093 1.246–8.347
0.020 0.016 <0.001
5 3 –
p
Model Chi-square test x2 = 46.792; p < 0.001, R2 = 0.448, Hit ratio = 81.1%, Hosmer– Lemeshow test, p = 0.672, -2 Logarithm likelihood = 104.555. a Calculated by rounding odds ratio to the nearest integer. We removed variables linked to non-contributory items that we assessed through a logistic regression (p > 0.25).
Table 7 Structural equation model of final scale items. Precipitating factors Predisposing Facilitating factors factors 2.12 Dehydration Age Disturbance of consciousness Underlying disease Depression/Anxiety Correlation between factors
Precipitating Precipitating Predisposing
x2 = 121.188,
df = 7,
p < 0.001,
GFI = 0.836,
0.91 0.72 0.69 1.62 Predisposing 0.111
*
Facilitating 0.003 0.124*
AGFI = 0.508,
RMSEA = 0.367,
AIC = 147.188. * p < 0.05.
95% CI = 1.372–13.446), underlying disease (OR = 5.372, 95% CI = 1.306–22.093), and depression or anxiety (OR = 3.225, 95% CI = 1.246–8.347) as predictors (Table 6). We confirmed these models through structural equation modeling. The final model showed the structure to be divided into three factors (Table 7). This structure was same as Lipowski’s model. Fitness values for this model were as follows: GFI = 0.836, AGFI = 0.508, and RMSEA = 0.367. 3.3. POD predictive scale scores Scale items were weighted based on the odds ratio of each variable as determined by the logistic regression analysis. Scores were defined as follows: underlying disease was 5 points; dehydration, age, and disturbance of consciousness were 4 points; and depression or anxiety, was 3 points (Table 8). When plotting the ROC curve, the area under the curve (AUC) was 0.844 (95% CI = 0.766–0.921). We established a cut-off of 12 points that Youden’s index (sensitivity + specificity 1) indicated as the maximum. The sensitivity of the POD predictive scale was 71.1%, and the specificity was 85.7%. The POD predictive scale scores ranged from 0 to 20 points, with a median of 9 (range 5–13) points. The NEECHAM scores were negatively correlated with the delirium predictive scale scores Table 8 Association of NEECHAM score and POD predictive score. POD predictive score 12, n/N (%) NEECAHM score
POD: postoperative delirium.
Normal At-risk Moderate Severe
6/51 (11.8%) 6/33 (18.2%) 18/28 (64.3%) 9/10 (90.0%)
(r = 0.513; p < 0.001). When we compared the median according to the presence or absence of delirium onset, the non-delirium group’s score was 8 (range 4–9) points, and the delirium group’s score was 13 (range: 9–16) points; this difference was statistically significant (U = 499.0, df = 120, p < 0.001). The duration of delirium had a moderate positive correlation with POD predictive scale scores (r = 0.532; p < 0.001). After confirming that the ratio became 12 points on the predictive scales or more according to severity of delirium, normal was 11.8%, at-risk was 18.2%, moderate was 64.3%, and severe was 90% (Table 7). 4. Discussion The present study revealed that the incidence of delirium from cranial nerve disease was 22.2–33.1% (Harasawa et al., 2011; Oh et al., 2008), similar to what has been observed in the existing literature. In our systematic review of the literature on delirium from cerebral infarctions, we found an incidence rate of 26% (95% CI = 19–33%) in the case of acute stroke (Carin-Levy, Mead, Nicol, Rush, & van Wijck, 2012). Therefore, we believe that the present study’s sample (122 patients) was suitable for analyzing delirium in patients undergoing cerebrovascular surgery. The advantage of this scale is that it is the first such measure for use in surgical operations of cerebrovascular disease. It is based on previously validated predictive scales, from which we extracted a number of the items. Furthermore, we aimed to develop a scale that could be used to evaluate nursing interventions, and considered triggers while extracting items. However, the structure of the items that we extracted through our literature review did not necessarily accord with the three classifications of Lipowski’s model. We think that we could classify these nine factors into two factors; one is affected by physical factors such as underlying disease, and cerebral infarction, while the other is affected by environmental factors such as mental stress, anxiety, ADL disorder, etc. We contained only depression/anxiety as a facilitating factor. Because triggering factors are more important than facilitating factors themselves, and it is important to identify protective measures, we feel that other physical factors strongly influence delirium. Still, we note that patient preparatory states were beneficial to our methodology, because patients were hospitalized pre-surgery; this vitiated environmental and mental confounds. However, this study showed that intervention was possible, even if delirium had a direct physical cause such as brain disease. The current POD predictive scale revealed five factors: underlying disease, depression or anxiety, age, disturbance of consciousness, and dehydration. These factors were similar to those observed in previous studies. The discriminant hit ratio was 81.1%, and the area under the curve (AUC) after weighting scale items was 0.844 (95% CI = 0.766–0.921). Thus, the current POD is perhaps a better predictive scale of delirium than the measures previously developed by Inouye et al. (1993; AUC = 0.74) and Litaker et al. (2001; AUC = 0.78). Litaker et al.’s scale had a higher sensitivity (73.7%) than the present scale (71.1%); however, the current scale has a higher specificity (86.9%). If scale specificity is high, we are able to address the possible presence of delirium among cerebrovascular surgical patients in a valid manner. Additionally, the present scale demonstrated a high likelihood of predicting delirium severity, since predictive scale scores were higher when delirium became more severe. This indicates that the current scale could be successfully used to prophylactically reduce delirium by prompting interventions in the event of a high scale score. Furthermore, the current scale could be effectively used for preoperative screening to predict POD among cerebrovascular disease patients. However, we must first determine an appropriate and cost-effective cut-off point for treating patients who appear to
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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be at increased risk for delirium. Since hospitalizations are shortened by an average of 2.15 days when delirium is prevented (saving an average of $1340; Rubin et al., 2011), and the prevention of delirium removes a great burden from patients, patients’ families, and nursing staff, the POD predictive scale appears to be a cost-effective solution. In line with recent guidelines for preventing delirium (O’Mahony, Murthy, Akunne, & Young, 2011), we believe that the POD predictive scale can play an important preventive and predictive role. Our scale design makes it easy to implement this measure in a clinical setting, even among staff who are not experts or highly experienced in delirium. The five factors included in the POD predictive scale can be useful indicators of possible effective interventions. In the Hospital Elder Life Program (HELP), Inouye Bogardus, Baker, Leo-Summers, and Cooney (2000) stated that effective protection against delirium requires an interdisciplinary team approach that includes rehabilitation, diet, and general care. When dealing with patients undergoing surgery, it can be difficult to maintain such an integrated and tailored team approach. We hope that this scale will help medical staff identify patients who are at an elevated risk of POD, especially those who are at risk of severe delirium. Conversely, because this scale is high in specificity, it can also distinguish those patients for whom delirium is less likely to develop, enabling more efficient nursing management and use of resources.
7
Limitations of the current study included the fact that results were obtained from a single institution, and the fact that delirium was not assessed blindly between the POD predictive scale and the NEECHAM Confusion Scale. Additional studies testing the POD predictive scale need to be validated in a different sample. We hope to conduct future investigations confirming the ideal cutoff value for the current scale, which should maximize costeffectiveness and clinical utility of the POD scale. We would also like to test a delirium preventive care program based on our POD predictive scale. Finally, it would be useful to determine the ways that the POD predictive scale can be implemented by studying the outcomes for patients who were administered this scale.
Conflict of interest The authors have no financial or personal conflicts of interest to declare. Acknowledgments This work was supported by JSPS KAKENHI Grant Number 24890254.
Appendix 1. Factor analysis for POD predictive factors Factor loadings 2
1 Technique Lesion Cerebral infarction Gender History of delirium Taking multiple internal medications Depression/Anxiety Urination trouble Defecation trouble Activity of daily living Cognitive impairment Room transfer Tension Visiting by key person Disturbance of consciousness Age Underlying disease Dehydration Insomnia Using orientation reinforcement Decreased visual acuity
3
4
5
6
7
8
9
0.819 0.809 0.631 0.611 0.834 0.806 0.791 0.854 0.731 0.791 0.542 0.338 0.800 0.726 0.588 0.421 0.360 0.573 0.330 0.307
0.607 0.320
Extraction Method: Generalized least squares. Rotation Method: Promax with Kaiser Normalization. Anemia, Hypoalbuminemia, and Hospitalization factor loadings did not achieve the cut-off of >0.300.
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AGG-3007; No. of Pages 8 Archives of Gerontology and Geriatrics xxx (2014) xxx–xxx
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A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery Nozomi Harasawa *, Toshiko Mizuno Tokyo Women’s Medical University, School of Nursing, Tokyo, Japan
A R T I C L E I N F O
A B S T R A C T
Article history: Received 22 July 2013 Received in revised form 2 May 2014 Accepted 8 May 2014 Available online xxx
Purpose: The purpose of this study was to develop and test a scale for predicting POD in patients undergoing cerebrovascular surgery. Methods: The predictive scale for POD was composed of 32 items reflecting the strongest risk factors as determined by a literature review. The NEECHAM Confusion Scale determined POD onset and severity. Results: Delirium developed in 38 (31.1%) of the 122 patients in our sample. Logistic regression revealed the following risk factors: dehydration, age, disturbance of consciousness, underlying illness, and anxiety or depression. The final scale was weighted by referring to odds ratios. The area under the curve was 0.844 (95% CI = 0.766–0.921). The possible total score on this scale was 20 points. A cutoff score of 11 was set for risk of POD (patients scoring over 12 were considered at higher risk). The median score was 8 (range: 4–9) in the non-delirium group and 13 (range: 9–16) in the delirium group (U = 499.0; df = 120; p < 0.001). Scale scores were moderately correlated with delirium duration (r = 0.532; p < 0.001). Conclusion: The present scale is a promising a tool for predicting POD but needs to be studied further. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: Delirium Prediction Scale Cerebrovascular surgery Elderly
1. Introduction Delirium is associated with multiple adverse outcomes including functional decline, new institutionalization, persistent cognitive impairments, and mortality (Dubljanin-Raspopovic et al., 2012; Inouye, 1999; Pandharipande et al., 2013; Siddiqi, House, & Holmes, 2006). These problems are often accompanied by financial losses, lower patient quality of life (QOL), and prolonged hospital stays (Rubin, Neal, Fenlon, Hassan, & Inouye, 2011). In the case of severe delirium—with extreme autochthonous delusions or with several hallucinations and violent behaviors— recovery is slowed, and the effectiveness of interventions decreases (Inouye et al., 1999). Therefore, it appears necessary to treat early stage delirium in the same manner as any physical (e.g., electrolyte abnormality) or environmental (e.g., aggravating noise) factor that could limit recovery. However, especially in older adults with multiple medical problems, several factors intertwine, complicating associations between medical problems and delirium (Inouye, 1998). Numerous factors can easily trigger acute-stage delirium. For instance, changes in circulatory
* Corresponding author at: 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan. Tel.: +81 3 3357 4804x6463; fax: +81 3 3341 8832. E-mail address: [email protected] (N. Harasawa).
dynamics and psychosomatic pain commonly occur, especially after surgery. Therefore, it is necessary to determine whether these are actually symptoms of delirium. If it is possible to determine preoperative factors of delirium before surgery, we could reduce the incidence of delirium. One precaution that can be taken for managing delirium is to assess whether the individual is at high risk before undergoing surgery. Previous attempts have been made to predict the onset of delirium, and some predictive models have been developed (Bakker, Osse, Tulen, Kappetein, & Bogers, 2012; Bo¨hner et al., 2003; Freter et al., 2005; Goldenberg et al., 2006; Guenther et al., 2013; Inouye, Viscoli, Horwitz, Hurst, & Tinetti, 1993; Koster, Hensens, Schuurmans, & van der Palen, 2013; Leung, Sands, Lim, Tsai, & Kinjo, 2013; Leung, Tsai, & Sands, 2011; Litaker, Locala, Franco, Bronson, & Tannous, 2001; Marcantonio et al., 1994; Patti, Saitta, Cusumano, Termine, & Di Vita, 2011; Rudolph et al., 2009). However, these were predictive models or scales about cardiovascular surgery (Bakker et al., 2012; Bo¨hner et al., 2003; Koster et al., 2013; Rudolph et al., 2009), orthopedic surgery (Freter et al., 2005; Goldenberg et al., 2006; Marcantonio et al., 1994), or alimentary disease-related surgery (Patti, Saitta, Cusumano, Termine, & Di Vita, 2011); thus far, we have been unable to find a predictive scale for cerebrovascular surgery. Additionally, these predictive models primarily depend on physical health indices, such as nutritional status, electrolyte abnormality, and disease severity. Such indices
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Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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2
do not take into account risks relevant to environmental (e.g., room transfer) and psychological (e.g., anxiety) factors; however, Rudolph et al. (2009) and Leung et al. (2011) did include the Geriatric Depression Scale scores in their study. In a systematic review, Dasgupta and Dumbrell, 2006 observed that depression and institutional residence were risk factors for delirium. Because delirium can be associated with several factors, additional assessment methods are needed above and beyond those that rely on visible physical symptoms. Lipowski (1990) showed that the etiology of delirium should include three classes of causative factor types: predisposing, facilitating, and precipitating. Since these facilitating factors may be mitigated through nursing care, we attempted to find them, and combine them to form a predictive sale. We aimed for the predictive scale to be easily used by nurses while they deal with elderly patients. Inouye et al. (1993) and Marcantonio et al. (1994) developed a predictive system of delirium. However, cerebrovascular disorders cases were excluded from their studies (Inouye et al., 1993; Marcantonio et al., 1994). We predicted that the prophylactic treatment of cerebrovascular disorders related to aging, such as arteriosclerosis, will increase as with the growth of the ‘‘over 65 years old’’ population demographic. Previous studies have shown that cerebrovascular disorders were factors in delirium (Hatano et al., 2013; Litaker et al., 2001). Because the incidence of delirium after brain surgery is 20–30% (Harasawa, Mizuno, & Shimizu, 2011; Oh, Kim, Chun, & Yi, 2008), this represents a major public health problem. The current study aimed (1) to develop a scale for predicting POD in patients undergoing cerebrovascular revascularization surgery, and (2) to determine the cutoff value for assessing POD based on this scale. In this study, the NEECHAM Confusion Scale was used to measure (a) the presence of delirium and (b) delirium severity. 2. Methods 2.1. Study design and objectives 2.1.1. Literature review (Phase 1) During Phase 1, we extracted POD items using factor analysis and existing predictive scales. To extract items, we first retrieved documents through a full-text search within the MEDLINE (1966 to December 2008) and CINAHL (1937 to December 2008) databases. We used two combinations of key words: (a) ‘‘delirium’’ and ‘‘risk assessment’’ and (b) ‘‘delirium’’ and ‘‘prediction.’’ Inclusion criteria were papers that only assessed adult patients (over 19 years old), had full-text versions of the papers online, and examined delirium
among patients was not related to alcohol withdrawal. We retrieved 11 papers through MEDLINE and 2 papers through CINAHL. Our literature review was also supplemented by a manual search. Finally, we selected 9 papers (Aohda et al., 2007; Bo¨hner et al., 2003; Freter et al., 2005; Goldenberg et al., 2006; Inouye et al., 1993; Litaker et al., 2001; Marcantonio et al., 1994; Oh et al., 2008; Yamamoto, Nishizaki, Nakashima, & Yokoyama, 1999) in addition to our previous study (Harasawa et al., 2011; Table 1). In order to select risk factors of delirium for use in the POD scale, we set conditions for whether these studies revealed predictive model factors. In a univariate analysis, we selected factors with p values less than 0.05. We used Lipowski’s framework (Lipowski, 1990) so that nurses could address underlying POD factors. The extracted items were then divided into three classifications: precipitating factors (e.g., primary cerebral disease, systemic secondary diseases affecting the brain), predisposing factors (e.g., age 60 or over, brain damage), and facilitating factors (e.g., psychosocial stress, sleep deprivation). We then created the POD scale that included 32 items (Table 2). Researchers evaluated these 32 items for patients after the latter were hospitalized before surgery. 2.1.2. Validation cohort (Phase 2) During Phase 2, we administered this scale to an open cohort sample. The current study was conducted using patients hospitalized for cerebrovascular surgery within a 1423-bed teaching hospital in Tokyo. We excluded patients with Moyamoya disease, since this disease is not related to age. Between August 2009 and March 2011, all 182 patients were electively undergoing one of the following procedures: extracranial or intracranial bypasses, clipping techniques (including trapping techniques), coating for unruptured cerebral aneurysms, bypass operations, internal carotid artery endarterectomies (CEA), or superficial temporal artery/middle cerebral artery anastomosis surgeries for ischemic cerebrovascular disease (Table 3). Five patients declined participation, and 42 patients were excluded because we were unable to meet with them before their surgeries. Of the remaining 135 patients, 13 were excluded due to missing information (n = 8), needing long-term management in the intensive care unit after surgery (n = 2), and surgery cancelation (n = 3). Patients who were suspected to have experienced delirium before surgery were also excluded. Thus, our sample included 122 patients (67.0% of the initial sample; see Table 3 for a summary). The median age was 66 (range = 58–72) years, and 59% were female. The median number of preoperative days in the hospital was 5 (range = 4–6), and the
Table 1 Final selected preference studies for extracted scale items. Study author
Department
Sample size
Delirium assessment method
Incidence of delirium (%)
Statistical test
Inouye et al.
General medicine
CAM
General surgery, Orthopedic surgery, Gynecology services Orthopedic surgery Vascular Surgery Orthopedic surgery, Gynecology, Neurosurgery, etc. Orthopedic surgery Orthopedic surgery Neurosurgery General medicine, Orthopedic surgery, etc. Neurosurgery
CAM
25 17 9
Proportional hazards model
Marcantonio et al.
Development cohort 107 Validation cohort 174 1341 71 153 500
DRS DSM-IV DSM-IV
26.8 39.2 11.4
Multivariate analysis Multivariate analysis Logistic regression analysis
132 77 224 461
CAM CAM CAM DRS-N
13.6 48.1 21.4 20.8
Logistic regression analysis Logistic regression analysis Logistic regression analysis Univariate analysis
121
NEECHAM Confusion Scale
33.1
Univariate analysis
Yamamoto et al. Bo¨hner et al. Litaker et al. Freter et al. Goldenberg et al. Oh et al. Aohda at al. Harasawa et al.
Logistic regression analysis
CAM: Confusion Assessment Method; DRS: Delirium Rating Scale; DSM-IV: Diagnostic and Statistical Manual of Mental Disorders; DRS-N: Delirium Rating scale for Nurses (Minamikawa et al., 2002).
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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AGG-3007; No. of Pages 8 N. Harasawa, T. Mizuno / Archives of Gerontology and Geriatrics xxx (2014) xxx–xxx Table 2 Selected risk factors.
Table 3 Description of subjects. n = 122
Criteria Precipitating factors Lesion Surgical site Technique Taking multiple internal medications Dehydration Anemia Hypoalbuminemia Predisposing factors Age Gender Cognitive impairment Disturbance of consciousness Underlying disease Hypertension Respiratory disease Liver dysfunction Diabetes Renal disease Heart disorder Cerebral infarction History of delirium Facilitating factors Depression Anxiety Decreased visual acuity Insomnia Tension Using orientation reinforcement Visit of key person Room transfer Hospitalization history ADL disorder Use of sensor-type nurse call Defecation trouble Urination trouble
3
Operative method
Clipping CEA Bypass Male
78 25 19 50
Age (years) Preoperative period (days) Postoperative period (days) Operative time (min) Bleeding (mL)
M (inter-quartile range)
66 (58–72) 10.8 5 (4–6)
Surgical site
Internal carotid
Sex Taking over 3 delirium medications Nitrogen/Creatinine ratio 18 Hematocrit level 33% Albumin level 3.5 g/dl Over 70 years old NM scale scores 47 points Except Clear level of JCS
HADS Depression scores 11 points HADS Anxiety scores 11 points
Likert scale 1–4, Ease (1, 2) or Tension (3, 4)
(%) (63.9) (20.5) (15.6) (41.0)
9 (8–11) 191 (160–235) 30 (20–50)
artery (Left) Internal carotid artery (Right) Anterior communicating artery Middle cerebral artery (Left) Middle cerebral artery (Right) Vertebral artery Basilar artery Other Total
n = 129a 44
(%) (36.1)
25
(20.5)
16
(13.1)
13
(10.7)
15
(12.3)
5 2 9 129
(4.1) (1.6) (7.4)
Mann–Whitney U test, seven cases required multiple surgery sites, giving a total of 129 sites for 122 individuals. CEA: internal carotid artery endarterectomy, M: median.
2.2. Measurements and procedures
indicating delirium. The scale can measure the severity of delirium, with 0–19 points = moderate to severe; 20–24 points = mild delirium to early; 25–26 points = at-risk; and 27–30 points = normal. The scale had high inter-rater agreement between a researcher and nurses (k = 0.65), and internal consistency was high (a = 0.90). Participants with scores of 24 points or lower were placed into a ‘‘delirium’’ group. The scale has been shown to have high sensitivity with this cutoff (Neelon, Champagne, Carlson, & Funk, 1996). Patients were evaluated on postoperative days 1 through 3 or until they received a NEECHAM score of 27 for more than two consecutive days. Nurses from a neurological ward at the hospital, as well as a researcher, filled out the scale. The assessors examined patients over 24 h and completed the measures through a brief patient interview, observation, and a review of both the nursing record and the patient’s medical records.
2.2.1. Delirium assessment The NEECHAM Confusion Scale V (Neelon, Champagne, McConnell, Carlson, & Funk, 1992) determined POD onset and severity. The NEECHAM Confusion Scale is composed of nine items divided into three subscales: information processing, behavior, and performance. Subscale I, information processing (score range = 0–14 points), evaluates components of cognitive status: attention and alertness, verbal and motor response, and memory and orientation. Subscale II, behavior (score range = 0–10 points), evaluates observed behavior and performance ability: general appearance and posture, sensory–motor performance, and verbal responses. Subscale III, performance (score range = 0–16 points), assesses vital functions: vital signs, oxygen saturation level, and urinary incontinence. The total NEECHAM scale score is the sum of the three subscale scores. The scale can be completed in 10 min on the basis of vital sign observation and measurement. Scores can range from 0 (minimal function) to 30 (normal function); the cut-off point is 24, with scores from 0 to 24 points
2.2.2. Measures in the POD predictive scale We used the following measurement tools, keeping in mind that a nurse from the general ward would be best suited to detect patients with delirium because they are constantly providing direct care to patients. Furthermore, we wanted to use measures that the nurse could fill out while performing daily care duties, because this would reduce the burden on study patients, and would not interrupt nurses’ duties. Levels of consciousness were assessed using the Japan Coma Scale (JCS) (Ohta, 2005). The JCS is widely used in Japanese medical settings. The JCS assesses three levels of consciousness by evaluating patient responses. This measure has 9 subscales. A JCS score of 0 indicates lucidity; single-digit JCS scores (e.g., 1, 2, 3) indicate that the patient is drowsy but can be awakened without the use of stimulants; double-digit JCS scores (10, 20, 30) indicate patients who are drowsy but can be roused with the use of stimulants; and triple-digit JCS scores (100, 200, 300) indicate that the patient is comatose.
N-ADL scores 48
NM scale: Mental status standard for N-type elderly; JCS: Japan Coma Scale; HADS: Hospital Anxiety and Depression Scale; ADL: Activities of daily living; N-ADL: Ability scale for N-style elderly activities of daily living.
median number of postoperative days in the hospital was 9 (range = 8–11). The type of operative methods included clipping techniques (63.9%), CEA (20.5%), and bypass (15.6%). The most common surgical sites were the left (36.1%) and right internal carotid artery (20.5%).
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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The JCS is strongly correlated with the Glasgow Coma Scale (Ono, Wada, Takahara, & Shirotani, 2007). We used a simple behavioral rating scale (the mental status standard for N-type elderly, NM scale; Nishimura et al., 1993) to screen for dementia and determine its severity. Another simple behavioral rating scale was employed to assess activities of daily living (the Ability for N-style elderly activities of daily living measure; N-ADLs, Nishimura et al., 1993). These scales have been widely used in Japan and can be administered by any hospital personnel (as opposed to requiring a psychiatrist or clinical psychologist). According to Nishimura et al. (1993), classifications for scores are ‘‘normal’’ (50–48), ‘‘borderline’’ (47–43), ‘‘mild dementia’’ (42–31), ‘‘moderate dementia’’ (30–17), and ‘‘severe dementia’’ (16–0). A strong and significant negative correlation (r = 0.947) has been observed between the intellectual function score on the Gottfries–Brane–Steen (GBS) scale and the NM scale, with a strong and significant positive correlation (r = 0.944) between the motor function score on the GBS scale and N-ADLs score (Nishimura et al., 1993). Because the GBS scale is used to assess dementia worldwide, these results indicate that the NM scale and N-ADLs are valid scales for assessing dementia and ADLs. Additionally, since the NM scale and N-ADLs are both based on observations of daily living behaviors, these assessments are appropriate in both clinical and residential settings. We used the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983) to assess anxiety and depression symptoms. The HADS was developed to evaluate anxiety and depression among patients with physical symptoms. Each Likert-type item is scored on a scale from 0 to 3 with 0–21 as the range for possible depression and anxiety scores. Scores for each subscale are interpreted as ‘‘normal’’ (0–7), ‘‘mild’’ (8–10), ‘‘moderate’’ (11–14), or ‘‘severe’’ (15–21). Scores for the entire instrument (emotional distress) range from 0 to 42 with higher scores indicating more distress. This scale is self-reported and can be completed in 5 min. 2.3. Ethical considerations Participants gave informed consent after a researcher explained the study. Participants were assured that their participation or refusal would not affect their medical or nursing care. The ethical committee of the Faculty of Medicine at Tokyo Women’s Medical University approved this study. 2.4. Statistical analysis Comparisons were made between delirious and non-delirious groups via a chi-square test or Fisher’s exact test for categorical data and Student’s t-test for continuous data. If continuous data were skewed based on the Kolmogorov–Smirnov test, we used the Mann–Whitney U-test. We also performed a factor analysis of all
scale items, except the items that had an onset frequency of 0; we classified these items according to the three-factor model of delirium by Lipowski. We used the generalized least squares method of extraction, and used a promax rotation method with Kaiser normalization. A logistic regression analysis was performed to explore the relationship between preoperative factors and post-operative delirium. Preoperative factors were analyzed with a stepwise logistic regression at p < 0.25 (Bendel & Afifi, 1977). The p-value was expanded to 0.25 so that non-significant factors could be included in the logistic regression procedure. Five to seven covariates are considered suitable for a logistic regression analysis that uses R2 and mean square error approximation parameters. Anything higher may cause the model to become unstable (Dowdy & Wearden, 1991). To determine which variables to place into the logistic regression using the stepwise method, we confirmed the multicollinearity between variables. We assessed the fitness of the logistic regression model and chose the model with the best fit. We confirmed these parameters through structural equation modeling for final scale items. Items were weighted according to odds ratios obtained from each logistic regression model. The odds ratio for each significant independent variable was rounded to the nearest whole integer to create a scoring system that could be easily calculated by the medical care personnel. Receiver operating characteristics (ROC) analysis was used to determine whether confirmed scores on the POD predictive scale were actual predictors of delirium. Furthermore, we compared the sensitivity, specificity, and the positive likelihood ratio associated with each score distribution to determine the cutoff value for the predictive scale. We used Youden’s index (sensitivity + specificity 1) to indicate the maximum (Sackett, Haynes, & Tugwell, 1985). Because these data were nonparametrical, we used Spearman’s rank correlation coefficients to examine the association between the POD predictive scale score and sustained days of delirium. The threshold for statistical significance was set at p < 0.05. IBM SPSS Statistics 21.0 software (IBM Japan, Ltd., Tokyo, Japan) was used for statistical analyses. 3. Results 3.1. Generation status and risk factors of delirium Delirium was diagnosed in 38 patients (31.1% of the sample) using the NEECHAM Confusion Scale. A significant difference was found between the delirium and the non-delirium groups for both the number of preoperative and postoperative days in the hospital, and for perioperative bleeding (Table 4). We also conducted a chi-square test to assess the presence or absence of delirium onset (Table 5). Results indicated significant differences for surgical technique (x2 = 8.982, p = 0.011), age over 70 years
Table 4 Presence or absence of delirium onset and hospitalization and peri/postoperative information (N = 122). Delirium
Preoperative hospitalization (days) M (inter-quartile range) Postoperative Hospitalization (days) Operative time (min) Bleeding (mL) Days until postoperative walk initiation (days)
Present
Absent
(n = 38)
(n = 84)
U value
p
5 (4–7)
5 (4–5)
2.180
0.029
10 (8–15)
9 (8–10)
2.293
0.022
1.516 2.128 2.647
0.130 0.033 0.008
205 (164–257.5) 50 (21–50) 3 (2–4)
189 (158.75–224.5) 30 (19.5–50) 2 (2–3)
Mann–Whitney U test; M: median.
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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Table 5 Univariate analysis results for POD onset prediction scale items and delirium onset (N = 122).
x2
Delirium
Variables
Present (n = 38)
Absent (n = 84)
Lesion Surgical site
Inner skull Anterior communicating artery Internal carotid artery Right Internal carotid artery Left Middle cerebral artery Right Middle cerebral artery Left Basilar artery Vertebral artery Others
29 6 11 12 1 5 0 1 2
63 10 14 29 14 8 2 4 7
Technique Taking multiple internal medications Dehydration Anemia Hypoalbuminemia Age Gender Cognitive impairment Disturbance of consciousness
Clipping/CEA/Bypass
18/9/11 3 30 1 0 22 19 7 17
Underlying disease Hypertension Respiratory disease Liver dysfunction Diabetes Renal disease Heart disorder Cerebral infarction History of delirium Depression/Anxiety Depression Anxiety Decreased visual acuity Insomnia Tension Using orientation reinforcement (calendar or watch) Visit by key person Room transfer Hospitalization history ADL disorder Use of the sensor-type nurse call Defecation trouble Urination trouble
Nitrogen/Creatinine ratio 18
Over 70 years old Male Less than 47 points on NM scale Over JCSI-1
Over 11 points on HADS Depression or Anxiety items HADS Depression scores 11 HADS Anxiety scores 11
p
0.024 0.347 2.420 0.278 4.779a 0.363a 0.920a 0.302a 0.361a
0.876 0.556 0.120 0.598 0.036 0.541 1.000 1.000 0.719
61/16/8 1 54 2 1 20 31 3 8
8.982 3.708a 2.623 0.007a 0.456a 15.344 1.855 7.670a 19.912
0.011 0.089 0.105 1.000 1.000 <0.001 0.173 0.010 <0.001
35 25 8 9 7 4 6 15 2 9
53 40 11 16 14 7 11 19 1 5
10.954a 3.470 1.260 0.436 0.057 0.153a 0.158 3.697 1.809a 8.098a
0.001 0.062 0.262 0.509 0.801 0.738 .691 0.054 0.229 0.006
6 6 15 6 16 15 23 3 30 1 0 1 1
1 4 41 22 33 37 44 6 56 3 0 2 0
10.310a 4.228a 0.918 1.601 0.897 0.224 0.701 0.022a 4.006 0.007a – 0.007a 2.229a
0.004 0.069 0.338 0.206 0.826 0.636 0.402 1.000 0.045 1.000 – 1.000 0.311
a Fischer’s exact test, CEA: internal carotid artery endarterectomy; JCS: Japan Coma Scale; NM scale: Mental status standard for N-type elderly; HADS: Hospital Anxiety and Depression Scale; ADL: activities of daily living.
(x2 = 15.344, p < 0.001), disturbance of consciousness (as indicated by the JCS score; x2 = 19.912, p < 0.001), underlying disease (x2 = 10.954, p = 0.001), depression (x2 = 10.310, p = 0.004), right middle cerebral artery as the surgical site (x2 = 4.779, p = 0.036), cognitive impairment (as indicated by a score less than 47 points on the NM scale; x2 = 7.670, p = 0.010), and a history of hospitalization (x2 = 4.006, p = 0.045). The duration of delirium ranged from 0 to 14 days (median = 1.00; mean = 1.69; SD = 2.50). Additionally, we performed a principal component analysis for all items in order to confirm the three-factor structure of delirium by Lipowski. As a result, nine factors were extracted (see Appendix 1). 3.2. Logistic regression analysis All variables that yielded p-values of less than 0.25 in a chisquare test for the presence or absence of delirium were included in the logistic regression analysis; the following variables were added: right internal carotid artery surgical site (x2 = 2.420, p = 0.120), dehydration (x2 = 2.623, p = 0.105), gender (x2 = 1.855, p = 0.173), cerebral infarction (x2 = 3.697, p = 0.054), anxiety (x2 = 4.228, p = 0.069), and insomnia (x2 = 1.601,
p = 0.206). However, we excluded participants taking multiple internal medications (x2 = 3.708, p = 0.089) and those with a history of delirium (x2 = 1.809, p = 0.229), because so few participants in our sample met these conditions. The right middle cerebral artery (x2 = 4.779, p = 0.036) was also excluded because only one person developed delirium in this manner. We also checked for multicollinearity, and combined items/factors. The correlation between the HADS depression and anxiety was r = 0.347, p < 0.001. The HADS depression and anxiety scores were combined into one factor. The HADS scores were assessed when depression or anxiety scores were greater than 11 points (x2 = 8.098, p = 0.006). Finally, we entered technique, age, disturbance of consciousness, cognitive impairment, underlying disease, cerebral infarction, depression or anxiety, dehydration, gender, insomnia, and hospitalization history into a logistic regression analysis. We ran logistic regression analyses for various combinations of these variables. We also entered variables into the logistic regression model, examining the how the model fit changed with each variable added. This resulted in our choosing dehydration (OR = 4.065, 95% CI = 1.295–12.755), age (OR = 4.380, 95% CI = 1.619–11.852), disturbance of consciousness (OR = 4.296,
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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6 Table 6 Logistic regression model.
b Dehydration Age Disturbance of consciousness Underlying disease Depression/Anxiety Constant
Pointsa
Odds ratio
95% confidence interval
1.402 1.477 1.458
4.065 4.380 4.296
1.295–12.755 1.619–11.852 1.372–13.446
0.016 0.004 0.012
4 4 4
1.681 1.171 6.998
5.372 3.225 0.001
1.306–22.093 1.246–8.347
0.020 0.016 <0.001
5 3 –
p
Model Chi-square test x2 = 46.792; p < 0.001, R2 = 0.448, Hit ratio = 81.1%, Hosmer– Lemeshow test, p = 0.672, -2 Logarithm likelihood = 104.555. a Calculated by rounding odds ratio to the nearest integer. We removed variables linked to non-contributory items that we assessed through a logistic regression (p > 0.25).
Table 7 Structural equation model of final scale items. Precipitating factors Predisposing Facilitating factors factors 2.12 Dehydration Age Disturbance of consciousness Underlying disease Depression/Anxiety Correlation between factors
Precipitating Precipitating Predisposing
x2 = 121.188,
df = 7,
p < 0.001,
GFI = 0.836,
0.91 0.72 0.69 1.62 Predisposing 0.111
*
Facilitating 0.003 0.124*
AGFI = 0.508,
RMSEA = 0.367,
AIC = 147.188. * p < 0.05.
95% CI = 1.372–13.446), underlying disease (OR = 5.372, 95% CI = 1.306–22.093), and depression or anxiety (OR = 3.225, 95% CI = 1.246–8.347) as predictors (Table 6). We confirmed these models through structural equation modeling. The final model showed the structure to be divided into three factors (Table 7). This structure was same as Lipowski’s model. Fitness values for this model were as follows: GFI = 0.836, AGFI = 0.508, and RMSEA = 0.367. 3.3. POD predictive scale scores Scale items were weighted based on the odds ratio of each variable as determined by the logistic regression analysis. Scores were defined as follows: underlying disease was 5 points; dehydration, age, and disturbance of consciousness were 4 points; and depression or anxiety, was 3 points (Table 8). When plotting the ROC curve, the area under the curve (AUC) was 0.844 (95% CI = 0.766–0.921). We established a cut-off of 12 points that Youden’s index (sensitivity + specificity 1) indicated as the maximum. The sensitivity of the POD predictive scale was 71.1%, and the specificity was 85.7%. The POD predictive scale scores ranged from 0 to 20 points, with a median of 9 (range 5–13) points. The NEECHAM scores were negatively correlated with the delirium predictive scale scores Table 8 Association of NEECHAM score and POD predictive score. POD predictive score 12, n/N (%) NEECAHM score
POD: postoperative delirium.
Normal At-risk Moderate Severe
6/51 (11.8%) 6/33 (18.2%) 18/28 (64.3%) 9/10 (90.0%)
(r = 0.513; p < 0.001). When we compared the median according to the presence or absence of delirium onset, the non-delirium group’s score was 8 (range 4–9) points, and the delirium group’s score was 13 (range: 9–16) points; this difference was statistically significant (U = 499.0, df = 120, p < 0.001). The duration of delirium had a moderate positive correlation with POD predictive scale scores (r = 0.532; p < 0.001). After confirming that the ratio became 12 points on the predictive scales or more according to severity of delirium, normal was 11.8%, at-risk was 18.2%, moderate was 64.3%, and severe was 90% (Table 7). 4. Discussion The present study revealed that the incidence of delirium from cranial nerve disease was 22.2–33.1% (Harasawa et al., 2011; Oh et al., 2008), similar to what has been observed in the existing literature. In our systematic review of the literature on delirium from cerebral infarctions, we found an incidence rate of 26% (95% CI = 19–33%) in the case of acute stroke (Carin-Levy, Mead, Nicol, Rush, & van Wijck, 2012). Therefore, we believe that the present study’s sample (122 patients) was suitable for analyzing delirium in patients undergoing cerebrovascular surgery. The advantage of this scale is that it is the first such measure for use in surgical operations of cerebrovascular disease. It is based on previously validated predictive scales, from which we extracted a number of the items. Furthermore, we aimed to develop a scale that could be used to evaluate nursing interventions, and considered triggers while extracting items. However, the structure of the items that we extracted through our literature review did not necessarily accord with the three classifications of Lipowski’s model. We think that we could classify these nine factors into two factors; one is affected by physical factors such as underlying disease, and cerebral infarction, while the other is affected by environmental factors such as mental stress, anxiety, ADL disorder, etc. We contained only depression/anxiety as a facilitating factor. Because triggering factors are more important than facilitating factors themselves, and it is important to identify protective measures, we feel that other physical factors strongly influence delirium. Still, we note that patient preparatory states were beneficial to our methodology, because patients were hospitalized pre-surgery; this vitiated environmental and mental confounds. However, this study showed that intervention was possible, even if delirium had a direct physical cause such as brain disease. The current POD predictive scale revealed five factors: underlying disease, depression or anxiety, age, disturbance of consciousness, and dehydration. These factors were similar to those observed in previous studies. The discriminant hit ratio was 81.1%, and the area under the curve (AUC) after weighting scale items was 0.844 (95% CI = 0.766–0.921). Thus, the current POD is perhaps a better predictive scale of delirium than the measures previously developed by Inouye et al. (1993; AUC = 0.74) and Litaker et al. (2001; AUC = 0.78). Litaker et al.’s scale had a higher sensitivity (73.7%) than the present scale (71.1%); however, the current scale has a higher specificity (86.9%). If scale specificity is high, we are able to address the possible presence of delirium among cerebrovascular surgical patients in a valid manner. Additionally, the present scale demonstrated a high likelihood of predicting delirium severity, since predictive scale scores were higher when delirium became more severe. This indicates that the current scale could be successfully used to prophylactically reduce delirium by prompting interventions in the event of a high scale score. Furthermore, the current scale could be effectively used for preoperative screening to predict POD among cerebrovascular disease patients. However, we must first determine an appropriate and cost-effective cut-off point for treating patients who appear to
Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007
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be at increased risk for delirium. Since hospitalizations are shortened by an average of 2.15 days when delirium is prevented (saving an average of $1340; Rubin et al., 2011), and the prevention of delirium removes a great burden from patients, patients’ families, and nursing staff, the POD predictive scale appears to be a cost-effective solution. In line with recent guidelines for preventing delirium (O’Mahony, Murthy, Akunne, & Young, 2011), we believe that the POD predictive scale can play an important preventive and predictive role. Our scale design makes it easy to implement this measure in a clinical setting, even among staff who are not experts or highly experienced in delirium. The five factors included in the POD predictive scale can be useful indicators of possible effective interventions. In the Hospital Elder Life Program (HELP), Inouye Bogardus, Baker, Leo-Summers, and Cooney (2000) stated that effective protection against delirium requires an interdisciplinary team approach that includes rehabilitation, diet, and general care. When dealing with patients undergoing surgery, it can be difficult to maintain such an integrated and tailored team approach. We hope that this scale will help medical staff identify patients who are at an elevated risk of POD, especially those who are at risk of severe delirium. Conversely, because this scale is high in specificity, it can also distinguish those patients for whom delirium is less likely to develop, enabling more efficient nursing management and use of resources.
7
Limitations of the current study included the fact that results were obtained from a single institution, and the fact that delirium was not assessed blindly between the POD predictive scale and the NEECHAM Confusion Scale. Additional studies testing the POD predictive scale need to be validated in a different sample. We hope to conduct future investigations confirming the ideal cutoff value for the current scale, which should maximize costeffectiveness and clinical utility of the POD scale. We would also like to test a delirium preventive care program based on our POD predictive scale. Finally, it would be useful to determine the ways that the POD predictive scale can be implemented by studying the outcomes for patients who were administered this scale.
Conflict of interest The authors have no financial or personal conflicts of interest to declare. Acknowledgments This work was supported by JSPS KAKENHI Grant Number 24890254.
Appendix 1. Factor analysis for POD predictive factors Factor loadings 2
1 Technique Lesion Cerebral infarction Gender History of delirium Taking multiple internal medications Depression/Anxiety Urination trouble Defecation trouble Activity of daily living Cognitive impairment Room transfer Tension Visiting by key person Disturbance of consciousness Age Underlying disease Dehydration Insomnia Using orientation reinforcement Decreased visual acuity
3
4
5
6
7
8
9
0.819 0.809 0.631 0.611 0.834 0.806 0.791 0.854 0.731 0.791 0.542 0.338 0.800 0.726 0.588 0.421 0.360 0.573 0.330 0.307
0.607 0.320
Extraction Method: Generalized least squares. Rotation Method: Promax with Kaiser Normalization. Anemia, Hypoalbuminemia, and Hospitalization factor loadings did not achieve the cut-off of >0.300.
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Please cite this article in press as: Harasawa, N., Mizuno, T., A novel scale predicting postoperative delirium (POD) in patients undergoing cerebrovascular surgery. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.05.007