http://informahealthcare.com/bij ISSN: 0269-9052 (print), 1362-301X (electronic) Brain Inj, 2014; 28(2): 155–160 ! 2014 Informa UK Ltd. DOI: 10.3109/02699052.2013.860468
ORIGINAL ARTICLE
Smoking and outcome of traumatic brain injury Anna O¨stberg & Olli Tenovuo Department of Neurology, University of Turku and Turku University Central Hospital, Turku, Finland
Abstract
Keywords
Objective: There is evidence that the cholinergic system is involved in cognitive sequels of traumatic brain injury (TBI). Nicotinic acetylcholine receptors (nAChRs) are known to have a major role in cognitive functions. Smokers have up-regulation of these receptors. This study investigated whether smoking is associated with the outcome from TBI. Methods: A specific questionnaire was sent, after checking inclusion and exclusion criteria, to 1022 subjects with TBI who had visited the neurological outpatient clinic of a university hospital during a 14-year period. Of these, 689 (67.4%) responded, forming the final study population. Associations between demographic variables, injury severity and outcome and smoking history were analysed using multivariate methods. Results: Smokers were more often men (p50.001), younger at the time of the injury (p ¼ 0.008) and had less education (p50.0001). In univariate analysis, non-smokers did not differ for outcome of TBI by GOS-E (p ¼ 0.08). Furthermore, in multivariate analysis, no association was found between smoking history and TBI outcome. Conclusions: This study does not suggest that smoking affects the outcome of TBI.
Neurological, outcome, traumatic brain injury
Acetylcholine has an important role in cognitive functions, including attention, learning and memory [1, 2], which are commonly affected after TBI. Experimental studies suggest that in TBI an initial period of cholinergic hyperactivity is followed by a more chronic state of cholinergic hypoactivity [3, 4]. Both human and animal studies have demonstrated that TBI produces chronic changes in the brain’s cholinergic functions and that these abnormalities may contribute to the cognitive consequences of TBI [5–10] Accordingly, several trials have suggested that subjects with TBI-related cognitive deficits may benefit from acetylcholinesterase inhibitors (AChEIs), although the cumulated evidence is still preliminary [11, 12]. NAChRs are important for a variety of neurobehavioural and cognitive functions [13]. Changes in the density of nAChRs have been postulated to play a role in the pathophysiology of several neurodegenerative disorders, e.g. Alzheimer’s disease (AD) and Parkinson disease [14, 15]. Experimental studies have also shown changes in the expression of 7 nAChRs after TBI [16, 17]. Smokers have a widespread up-regulation of nAChRs, probably related to a desensitization of these receptors from nicotine exposure [18]. Nicotine has predominantly stimulant effects that increase the release of many neurotransmitters, e.g. acetylcholine [19]. Nicotine improves vigilance and rapid information processing during smoking deprivation. It may improve attention even in ¨ stberg, Turku Central University Hospital Correspondence: Dr Anna O and University of Turku, Neurology, Turku, Finland. Email: [email protected]
Received 23 July 2012 Revised 23 September 2013 Accepted 25 October 2013 Published online 21 January 2014
the absence of nicotine withdrawal and in non-smokers, although the results have been somewhat conflicting [20–23]. Some epidemiological studies have found a negative correlation between smoking and the development of neurodegenerative disorders [24]. Neuroprotective actions of nicotine have been demonstrated in both in vitro and in vivo animal models of neural toxicity [25, 26] and these actions could be mediated by stimulation of the 7-receptors [27]. Various studies demonstrate an inverse correlation between tobacco use and Parkinson’s disease [28, 29]. On the other hand, smoking history has been associated with an increased risk of AD [30], an action which is thought to be mediated through substances from tobacco smoke other than nicotine. In theory, nicotine could be used to enhance the cholinergic system. Nicotine may improve attention in patients with AD or age-associated memory impairment [29, 30], but it has not been found to improve memory in AD patients [31–33]. In Parkinson’s disease accumulating evidence suggests that nicotine may have therapeutic value [34]. A study in rats showed that nicotine administration attenuates cognitive impairment after TBI [35]. The aim of this study was to evaluate whether smoking history is associated with the outcome of TBI.
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Introduction
History
Materials and methods Subjects The study subjects were recruited from outpatients with TBI diagnosis (n ¼ 1151) who had been treated at the Department of Neurology, Turku University Central Hospital, between
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January 1993 and June 2006. The reasons for referral to a neurologist were a recent TBI or disability from an earlier TBI. Some patients had had more than one TBI, although the last one had been the reason for referral. It is known that earlier TBIs may increase the risk for poor recovery, but in most patients the presence or especially the severity of earlier TBIs was impossible to assess reliably, which is why this information was not included in the analysis. Inclusion criteria for this study were: (1) information about the severity of the injury, either as a Glasgow coma score (GCS) at admission or duration of post-traumatic amnesia (PTA), available in the medical records and (2) age 15 years at injury. Exclusion criteria were: (1) uncertain TBI diagnosis: the criteria for a TBI diagnosis [36] had to be fulfilled; and (2) non-traumatic neurological disorders which would prevent the outcome evaluation of TBI. The severity of TBI was classified using the duration of PTA as mild (1–24 hours), moderate (1–7 days), severe (1–4 weeks) or very severe (44 weeks) and, according to the GCS, as mild (scores 13–15), moderate (9–12) or severe (3–8). The following data were collected from all subjects: (1) age at the time of the injury and at the time of the survey, (2) gender, (3) severity of TBI and (4) outcome of TBI. The outcome of TBI was classified using the extended version of the Glasgow Outcome Scale (GOS-E) [37]. Forming of the study group is shown in Figure 1. Of the original group, 52 had died, 36 had an uncertain TBI diagnosis, in 12 the medical records were missing, 25 were 515 years at the time of the injury and four patients had a non-traumatic neurological condition preventing the outcome evaluation. Thus, the actual study group consisted of 1022 subjects. A questionnaire was sent to these 1022 by mail and 689 of them (67.4%) replied, forming the final study group. The demographic data of the study population are presented in Table I. Compared to the initial material of 1151 subjects, the subjects of the study group were more often women (p ¼ 0.0048), older at the time of injury (p ¼ 0.0301) and had milder trauma by PTA (p ¼ 0.0478). Study methods A specially designed questionnaire (Appendix), including 10 closed questions, was sent to the study subjects. The questions concerned the subjective evaluation of the functional outcome of TBI, smoking and drinking history, smoking and alcohol effects and changes after the injury and eventual use and effects of AChEIs. Results concerning alcohol consumption, possible changes in alcohol and tobacco effect after injury and possible change in AChEI effect after TBI will be published later in separate articles. In the analyses of this article, a division into smokers and non-smokers is made based on smoking at the time of injury. The duration of formal education was asked for as complementary background information. An information sheet explaining the purpose of the study and a consent form were included in the posting, the latter to be returned with the questionnaire. The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland.
Brain Inj, 2014; 28(2): 155–160
Figure 1. The flow diagram of study design.
Statistical analysis Statistical analyses were conducted with the SAS System for Windows (V9.1). To test for the differences in categorical variables, Chi-Square tests or, if necessary, Fisher’s exact tests were applied. Continuous variables were analysed with oneway ANOVA, and the Kruskal-Wallis test was applied to test the coherence of the analysis. Further analyses were done with logistic regression analyses. The odds ratios (OR) with 95% confidence intervals (95% CI) were also calculated. The level of significance was p50.05 in all analyses.
Results As always in studies using medical records, there were missing data concerning some variables in individual subjects. Their frequencies are included in Table I. One approach to handling missing values is a complete-case analysis, which requires exclusion of all subjects with missing data. Such an approach would have resulted in a 20% reduction in the sample size for the present study and would have markedly reduced the statistical power and the reliability of the statistical model. Additionally, this exclusion would have
Smoking and outcome of traumatic brain injury
DOI: 10.3109/02699052.2013.860468
Table I. Demographics of study population.
Table II. Demographics, injury severity and outcome statistics for smokers and non-smokers groups.
Variable
Mean
SD
Range
n
Age at TBI Age at survey Time between TBI and survey Gender Men Women Education (years)a GCSb Mild Moderate Severe PTAc Mild Moderate Severe Very severe GOS-Ed Upper Good Recovery Lower Good Recovery Upper Moderate Disability Lower Moderate Disability Upper Severe Disability Lower Severe Disability Subjective outcomee Upper Good Recovery Lower Good Recovery Upper Moderate Disability Lower Moderate Disability Upper Severe Disability Lower Severe Disability
39.8 49.1 9.3
14.9 15.1 8.4
15–84 17–92 1–55
689 689 450 239
12.2
3.7
157
% Variable b
65.3 34.7
2–30 447 56 134
70.0 9.0 21.0
235 165 149 78
37.5 26.3 23.8 12.4
142 177 170 131 40 4
21.4 26.7 25.6 19.7 6.0 0.6
114 160 209 139 46 19
16.6 23.3 30.4 20.2 6.7 2.8
Notes: Severity of TBI classified according to GCS as follows: 15–13 mild, 12–9 moderate and 8 and PTA as follows: 524 hours mild, 1–7 days moderate, 1–4 weeks severe and 44 weeks very severe. a 19 observations missing, b52 observations missing, c62 observations missing, d25 observations missing, e2 observations missing.
led to a biased subject pool that would have been less representative of the total study population. To handle missing values this study has used mean substitution. Univariate analysis Initial analyses involved the whole study group. Younger subjects had more severe injuries (both GCS and PTA p50.0001, Kruskal-Wallis test). As expected, outcome of TBI by GOS-E differed according to the initial TBI severity by GCS (p50.0001) or PTA classes (p50.0001). Of all study subjects, 43.9% were smokers at the time of TBI and 56.3% had ever smoked before the injury. Those who smoked at the time of injury were considered as smokers in further analyses. Table II shows a univariate analysis between smokers and non-smokers. Smokers were more often men (p50.001), younger at the time of injury (p50.0001) and had less education (p ¼ 0.008). Non-smokers and smokers did not show differences in their TBI severity (p ¼ 0.14 with GCS, p ¼ 0.06 with PTA). In further analyses, age at TBI, education, gender and smoking were selected as predictor variables for outcome by GOS-E. Outcome did not differ between sexes. Because younger patients had more severe injuries, the outcome differed significantly according to age at TBI (p50.0001, Kruskal-Wallis test). No significant associations were found between education and GOS-E (p ¼ 0.76, Kruskal-Wallis
Age at injury, mean SD Age at follow-up, mean SDb Education, mean SDb Sex, %a Female Male PTA classes, % (n)a 424 hours 1–7 days 1–4 weeks 44 weeks GCS classes, % (n)a 15–13 12–9 8 GOS-E, % (n)a Upper Good Recovery Lower Good Recovery Upper Moderate Disability Lower Moderate Disability Severe Disability
Smokers (n ¼ 295)
Non-smokers (n ¼ 392)
36.7 13.1 45.1 13.7 11.8 3.2
42.0 15.6 52.1 15.2 12.5 4.1
24.1 75.9
42.9 57.1
p 50.0001 0.004 0.008 50.0001 0.06
32.3 27.9 24.2 15.6
(87) (75) (65) (42)
41.2 25.2 23.5 10.1
(147) (90) (84) (36) 0.14
66.7 (180) 11.1 (30) 22.2 (60)
72.7 (266) 7.1 (26) 20.2 (74)
19.1 24.5 25.9 21.3 9.2
23.1 28.4 25.3 18.7 4.5
0.08 (54) (69) (73) (60) (26)
(88) (108) (96) (71) (17)
Notes: aChi-square test. Kruskal-Wallis test.
b
test), nor was there any difference in the outcome of TBI between non-smokers and smokers by GOS-E (p ¼ 0.08). Multivariate analysis As smoking is strongly associated with age, education and gender—factors which might also have a relationship with the outcome of TBI—a multivariate analysis was used to study a possible independent association between smoking and TBI outcome. Because in this study material the severity of injury was associated with the age at injury, the PTA classes were analysed separately to control for the effect of this association. Ignorance of this phenomenon in statistical analyses would have led to skewed results in which the TBI outcome of smokers would have seemed to be poorer than that of non-smokers. As in univariate analyses, also in multivariate analysis, age at TBI, education, gender and smoking history were nominated as predictor variables for GOS-E. Education was classified into three categories: 0–9 years, 10–12 years and 13 years or more. Age was grouped into 18–25, 26–40 and 41–55 years of age; subjects who were 17 years or 56 years were excluded from the logistic regression analysis. Simple and multiple logistic regression analyses were performed to assess each predictor’s relationship to the outcome, with and without adjustment for the effects of all other predictors. GOS-E was divided into five classes so that the lower severe disability class was fused with the upper severe disability because of the small number of subjects in the lowest GOS-E classes. The unadjusted ORs, CIs and probability values for each predictor, as well as the adjusted ORs, CIs and p-values are presented in Table III. Unadjusted ORs depict each predictor’s effect without adjustment with other predictors, while adjusted ORs depict each predictor’s unique effect after adjustment for all others predictors.
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Brain Inj, 2014; 28(2): 155–160
Table III. Adjusted results of logistic regression analysis categorized by PTA classes.
Predictor
Multiple logistic regression
Simple logistic regression
Comparison
OR (95% CI)
p-Value
OR (95% CI)
41–55, 18–25 26–40, 18–25
1.37 (0.60–3.17) 0.98 (0.39–2.44)
0.30 0.61
1.46 (0.64–3.32) 1.02 (0.14–2.52)
1.61 1.44 1.37 1.08
(0.76–3.42) (0.72–2.88) (0.60–3.17) (0.58–2.00)
0.21 0.29 0.50 0.82
1.65 1.39 1.10 1.11
2.14 (0.90–5.12) 1.95 (0.79–4.77)
0.21 0.41
1.97 (0.88–4.42) 1.81 (0.76–4.27)
1.23 1.44 0.77 0.87
0.61 0.34 0.44 0.70
1.38 1.38 1.00 0.77
4.47 (1.76–11.4) 1.97 (0.84–4.63)
0.003 0.83
4.53 (1.83–11.2) 2.01 (0.87–4.67)
1.30 1.13 0.82 0.51
(0.50–3.38) (0.51–2.49) (0.41–1.68) (0.24–1.11)
0.59 0.76 0.59 0.09
1.39 0.98 1.15 0.57
0.97 (0.34–2.82) 0.54 (0.16–1.77)
0.56 0.26
1.02 (0.36–2.87) 0.54 (0.17–1.73)
1.38 1.40 0.97 1.11
0.62 0.54 0.81 0.84
1.51 1.54 0.87 1.10
(1) PTA mild (n ¼ 157) Age at TBI (years) Education (years) Smoking at time of TBI Gender (2) PTA moderate (n ¼ 132) Age at TBI
0–9, 13 10–12, 13 Non-smoker, smoker male, female 41–55, 18–25 26–40, 18–25
(0.79–3.45) (0.70–2.75) (0.61–1.97) (0.61–2.01)
Education Smoking at time of TBI Gender (3) PTA severe (n ¼ 120) Age at TBI
2–9, 16–30 10–15, 16–30 Non-smoker, smoker male, female 41–55, 18–25 26–40, 18–25
(0.55–2.77) (0.68–3.05) (0.40–1.50) (0.43–1.76)
(0.63–3.02) (0.67–2.85) (0.55–1.84) (0.40–1.47)
Education Smoking at time of TBI Gender (4) PTA very severe (n ¼ 64) Age at TBI
2–9, 16–30 10–15, 16–30 Non-smoker, smoker male, female 41–55, 18–25 26–40, 18–25
(0.56–3.44) (0.48–2.03) (0.60–2.20) (0.28–1.15)
Education Smoking at time of TBI Gender
2–9, 16–30 10–15, 16–30 Non-smoker, smoker male, female
(0.38–4.98) (0.48–4.05) (0.34–2.82) (0.42–2.94)
(0.45–5.16) (0.56–4.20) (0.36–2.11) (0.43–2.81)
p-Value 0.47 0.23 0.63 0.37 0.18 0.34 0.75 0.74 0.24 0.23 0.46 0.61 0.42 0.38 0.99 0.43 0.004 0.002 0.87 0.74 0.48 0.96 0.67 0.12 0.48 0.48 0.23 0.66 0.51 0.40 0.75 0.84
PTA categorized as follows: 524 hours mild, 1–7 days moderate, 1–4 weeks severe and 44 weeks very severe.
The outcome of injury by GOS-E did not differ between smokers and non-smokers in the multivariate model. Age within these age categories (from 18–55 years) did not seem to have any obvious association with TBI outcome in this study. Only in the moderate and severe injury classes by PTA did there seem to be a trend for younger subjects to have better recovery and in only one analysis was this difference statistically significant. Neither education nor gender had significant associations with outcome in this study.
Discussion The aim of this study was to search for a possible association between smoking history and outcome of TBI, assessed with GOS-E. No association was found between smoking and outcome from TBI. A clear difficulty in this study was that smoking is so intimately associated with many known or suggested predictors of TBI outcome (age, education) [38] that this might easily lead to distorted results. The aim was to take this into account by using multiple logistic regression analyses. There is evidence for both negative [39] and positive [40] effects of smoking on brain function, but this study did not find support for either of these in subjects with TBI.
One might expect that smokers would have poorer outcome from TBI because of the known associations between smoking, alcohol consumption and low level of education [41–43]. On the other hand, smokers are on the average younger and younger patients have generally better outcomes. Accordingly, existing weak or moderate influences of smoking might be hidden because of these strong associations to known outcome predictive factors. For unknown reasons, in this study population, the younger subjects had more severe injuries and this was also the group that smoked most. Without recognition of this fact, it would have seemed that the smoker’s outcome is poorer. On the other hand, taking this into account by analysing the various PTA classes separately reduced the statistical power. Known associations of gender, education and age with smoking, and taking these into account in the analyses, further weakens the statistical power of the study. Some of the weaknesses of this study have already been discussed above. Although most subjects with TBI-related hospitalization from the referral area were included in the initial study material, there is surely an over-representation of subjects who have remained symptomatic after TBI. It is also possible that the most severely injured are under-represented because of their lower capability to answer the questionnaire.
DOI: 10.3109/02699052.2013.860468
Cognitive problems may also have had some influence on the reliability of the answers. Also the long-time gap between the survey and TBI in some subjects may have influenced the reliability of the results. However, it seems unlikely that these issues would have influenced the associations between smoking and outcome. It was also considered unlikely that previous TBIs would affect the eventual effect of smoking on recovery, but, if the frequency of earlier TBIs would for some reason significantly differ in smokers and non-smokers, this could have affected the results. In conclusion, these results cannot as such be generalized to all subjects with TBI and there remains a possibility that smoking could increase the likelihood of a good or full recovery, especially from milder injuries. A negative influence on recovery would probably have been revealed by this study, if it existed.
Acknowledgements We express our warm thanks to Hans Helenius, MSc, Department of Biostatistics, University of Turku, for thorough guidance in statistical matters.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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Appendix: Smoking effect on outcome from TBI Questionnaire Code
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œœœ
We kindly ask you to answer the questions below by choosing the closest accurate option—only one answer to each question. (1) How have you recovered from your traumatic brain injury? perfectly, I am as before I am almost as before, practically no lingering symptoms I have slight problems in daily functions I have clear problems in daily functions I have remarkable difficulties in daily functions I need continuous aid in all daily functions
œ œ œ œ œ œ
(2) Have you ever smoked (occasional episodes doesn’t count)? never (move to question 6) before injury ___ years but stopped smoking the year____ I have started smoking after the injury (move to question 5) I smoked before the injury and do still smoke I smoked before the injury but have stopped smoking after that
œ œ œ œ œ
(3) If you did smoke before the injury, did the effect of smoking change after the injury? no change œ effect of smoking changed to a more negative œ effect of smoking changed to a more positive œ I don’t remember œ (4) If you smoked before the injury, did you smoke during the hospital care? I didn’t get hospital care œ No, I didn’t œ I started smoking immediately when I could raise from bed œ not until the last days at care œ I don’t remember œ (5) If you did smoke before the injury, how much was your average daily tobacco consumption? about _________ packets per day œ I don’t remember œ
(7)
Did the effect of alcohol change after the injury? I haven’t used any alcohol before or after the injury I don’t know because I haven’t used alcohol after the injury no change my tolerance to alcohol became lower my tolerance to alcohol became higher I don’t remember
œ œ œ œ œ œ
Has your alcohol consumption changed after the injury? No I haven’t used alcohol after the injury I have reduced alcohol consumption after the injury I have increased alcohol consumption after the injury
œ œ œ œ
(8)
Have you used or tried AChEI (Exelon/Reminyl/Aricept) medication to treat the symptoms of your traumatic brain injury (so called Alzheimer’s medication)? never œ I don’t remember œ I have tried, but do not use any longer œ Yes, I use them currently œ
(9)
If you have tried AChEI medication, what was the effect of this medication? I don’t remember œ no benefit, no adverse effects œ no benefit, only adverse effects œ little benefit, no adverse effects œ little benefit, but also adverse effects œ lots of benefit, no adverse effects œ lots of benefit, but also adverse effects œ
(10) How many years you have studied in total? _____________ years
Thank you for your participation to this study!
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ORIGINAL ARTICLE
Smoking and outcome of traumatic brain injury Anna O¨stberg & Olli Tenovuo Department of Neurology, University of Turku and Turku University Central Hospital, Turku, Finland
Abstract
Keywords
Objective: There is evidence that the cholinergic system is involved in cognitive sequels of traumatic brain injury (TBI). Nicotinic acetylcholine receptors (nAChRs) are known to have a major role in cognitive functions. Smokers have up-regulation of these receptors. This study investigated whether smoking is associated with the outcome from TBI. Methods: A specific questionnaire was sent, after checking inclusion and exclusion criteria, to 1022 subjects with TBI who had visited the neurological outpatient clinic of a university hospital during a 14-year period. Of these, 689 (67.4%) responded, forming the final study population. Associations between demographic variables, injury severity and outcome and smoking history were analysed using multivariate methods. Results: Smokers were more often men (p50.001), younger at the time of the injury (p ¼ 0.008) and had less education (p50.0001). In univariate analysis, non-smokers did not differ for outcome of TBI by GOS-E (p ¼ 0.08). Furthermore, in multivariate analysis, no association was found between smoking history and TBI outcome. Conclusions: This study does not suggest that smoking affects the outcome of TBI.
Neurological, outcome, traumatic brain injury
Acetylcholine has an important role in cognitive functions, including attention, learning and memory [1, 2], which are commonly affected after TBI. Experimental studies suggest that in TBI an initial period of cholinergic hyperactivity is followed by a more chronic state of cholinergic hypoactivity [3, 4]. Both human and animal studies have demonstrated that TBI produces chronic changes in the brain’s cholinergic functions and that these abnormalities may contribute to the cognitive consequences of TBI [5–10] Accordingly, several trials have suggested that subjects with TBI-related cognitive deficits may benefit from acetylcholinesterase inhibitors (AChEIs), although the cumulated evidence is still preliminary [11, 12]. NAChRs are important for a variety of neurobehavioural and cognitive functions [13]. Changes in the density of nAChRs have been postulated to play a role in the pathophysiology of several neurodegenerative disorders, e.g. Alzheimer’s disease (AD) and Parkinson disease [14, 15]. Experimental studies have also shown changes in the expression of 7 nAChRs after TBI [16, 17]. Smokers have a widespread up-regulation of nAChRs, probably related to a desensitization of these receptors from nicotine exposure [18]. Nicotine has predominantly stimulant effects that increase the release of many neurotransmitters, e.g. acetylcholine [19]. Nicotine improves vigilance and rapid information processing during smoking deprivation. It may improve attention even in ¨ stberg, Turku Central University Hospital Correspondence: Dr Anna O and University of Turku, Neurology, Turku, Finland. Email: [email protected]
Received 23 July 2012 Revised 23 September 2013 Accepted 25 October 2013 Published online 21 January 2014
the absence of nicotine withdrawal and in non-smokers, although the results have been somewhat conflicting [20–23]. Some epidemiological studies have found a negative correlation between smoking and the development of neurodegenerative disorders [24]. Neuroprotective actions of nicotine have been demonstrated in both in vitro and in vivo animal models of neural toxicity [25, 26] and these actions could be mediated by stimulation of the 7-receptors [27]. Various studies demonstrate an inverse correlation between tobacco use and Parkinson’s disease [28, 29]. On the other hand, smoking history has been associated with an increased risk of AD [30], an action which is thought to be mediated through substances from tobacco smoke other than nicotine. In theory, nicotine could be used to enhance the cholinergic system. Nicotine may improve attention in patients with AD or age-associated memory impairment [29, 30], but it has not been found to improve memory in AD patients [31–33]. In Parkinson’s disease accumulating evidence suggests that nicotine may have therapeutic value [34]. A study in rats showed that nicotine administration attenuates cognitive impairment after TBI [35]. The aim of this study was to evaluate whether smoking history is associated with the outcome of TBI.
20 14
Introduction
History
Materials and methods Subjects The study subjects were recruited from outpatients with TBI diagnosis (n ¼ 1151) who had been treated at the Department of Neurology, Turku University Central Hospital, between
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January 1993 and June 2006. The reasons for referral to a neurologist were a recent TBI or disability from an earlier TBI. Some patients had had more than one TBI, although the last one had been the reason for referral. It is known that earlier TBIs may increase the risk for poor recovery, but in most patients the presence or especially the severity of earlier TBIs was impossible to assess reliably, which is why this information was not included in the analysis. Inclusion criteria for this study were: (1) information about the severity of the injury, either as a Glasgow coma score (GCS) at admission or duration of post-traumatic amnesia (PTA), available in the medical records and (2) age 15 years at injury. Exclusion criteria were: (1) uncertain TBI diagnosis: the criteria for a TBI diagnosis [36] had to be fulfilled; and (2) non-traumatic neurological disorders which would prevent the outcome evaluation of TBI. The severity of TBI was classified using the duration of PTA as mild (1–24 hours), moderate (1–7 days), severe (1–4 weeks) or very severe (44 weeks) and, according to the GCS, as mild (scores 13–15), moderate (9–12) or severe (3–8). The following data were collected from all subjects: (1) age at the time of the injury and at the time of the survey, (2) gender, (3) severity of TBI and (4) outcome of TBI. The outcome of TBI was classified using the extended version of the Glasgow Outcome Scale (GOS-E) [37]. Forming of the study group is shown in Figure 1. Of the original group, 52 had died, 36 had an uncertain TBI diagnosis, in 12 the medical records were missing, 25 were 515 years at the time of the injury and four patients had a non-traumatic neurological condition preventing the outcome evaluation. Thus, the actual study group consisted of 1022 subjects. A questionnaire was sent to these 1022 by mail and 689 of them (67.4%) replied, forming the final study group. The demographic data of the study population are presented in Table I. Compared to the initial material of 1151 subjects, the subjects of the study group were more often women (p ¼ 0.0048), older at the time of injury (p ¼ 0.0301) and had milder trauma by PTA (p ¼ 0.0478). Study methods A specially designed questionnaire (Appendix), including 10 closed questions, was sent to the study subjects. The questions concerned the subjective evaluation of the functional outcome of TBI, smoking and drinking history, smoking and alcohol effects and changes after the injury and eventual use and effects of AChEIs. Results concerning alcohol consumption, possible changes in alcohol and tobacco effect after injury and possible change in AChEI effect after TBI will be published later in separate articles. In the analyses of this article, a division into smokers and non-smokers is made based on smoking at the time of injury. The duration of formal education was asked for as complementary background information. An information sheet explaining the purpose of the study and a consent form were included in the posting, the latter to be returned with the questionnaire. The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland.
Brain Inj, 2014; 28(2): 155–160
Figure 1. The flow diagram of study design.
Statistical analysis Statistical analyses were conducted with the SAS System for Windows (V9.1). To test for the differences in categorical variables, Chi-Square tests or, if necessary, Fisher’s exact tests were applied. Continuous variables were analysed with oneway ANOVA, and the Kruskal-Wallis test was applied to test the coherence of the analysis. Further analyses were done with logistic regression analyses. The odds ratios (OR) with 95% confidence intervals (95% CI) were also calculated. The level of significance was p50.05 in all analyses.
Results As always in studies using medical records, there were missing data concerning some variables in individual subjects. Their frequencies are included in Table I. One approach to handling missing values is a complete-case analysis, which requires exclusion of all subjects with missing data. Such an approach would have resulted in a 20% reduction in the sample size for the present study and would have markedly reduced the statistical power and the reliability of the statistical model. Additionally, this exclusion would have
Smoking and outcome of traumatic brain injury
DOI: 10.3109/02699052.2013.860468
Table I. Demographics of study population.
Table II. Demographics, injury severity and outcome statistics for smokers and non-smokers groups.
Variable
Mean
SD
Range
n
Age at TBI Age at survey Time between TBI and survey Gender Men Women Education (years)a GCSb Mild Moderate Severe PTAc Mild Moderate Severe Very severe GOS-Ed Upper Good Recovery Lower Good Recovery Upper Moderate Disability Lower Moderate Disability Upper Severe Disability Lower Severe Disability Subjective outcomee Upper Good Recovery Lower Good Recovery Upper Moderate Disability Lower Moderate Disability Upper Severe Disability Lower Severe Disability
39.8 49.1 9.3
14.9 15.1 8.4
15–84 17–92 1–55
689 689 450 239
12.2
3.7
157
% Variable b
65.3 34.7
2–30 447 56 134
70.0 9.0 21.0
235 165 149 78
37.5 26.3 23.8 12.4
142 177 170 131 40 4
21.4 26.7 25.6 19.7 6.0 0.6
114 160 209 139 46 19
16.6 23.3 30.4 20.2 6.7 2.8
Notes: Severity of TBI classified according to GCS as follows: 15–13 mild, 12–9 moderate and 8 and PTA as follows: 524 hours mild, 1–7 days moderate, 1–4 weeks severe and 44 weeks very severe. a 19 observations missing, b52 observations missing, c62 observations missing, d25 observations missing, e2 observations missing.
led to a biased subject pool that would have been less representative of the total study population. To handle missing values this study has used mean substitution. Univariate analysis Initial analyses involved the whole study group. Younger subjects had more severe injuries (both GCS and PTA p50.0001, Kruskal-Wallis test). As expected, outcome of TBI by GOS-E differed according to the initial TBI severity by GCS (p50.0001) or PTA classes (p50.0001). Of all study subjects, 43.9% were smokers at the time of TBI and 56.3% had ever smoked before the injury. Those who smoked at the time of injury were considered as smokers in further analyses. Table II shows a univariate analysis between smokers and non-smokers. Smokers were more often men (p50.001), younger at the time of injury (p50.0001) and had less education (p ¼ 0.008). Non-smokers and smokers did not show differences in their TBI severity (p ¼ 0.14 with GCS, p ¼ 0.06 with PTA). In further analyses, age at TBI, education, gender and smoking were selected as predictor variables for outcome by GOS-E. Outcome did not differ between sexes. Because younger patients had more severe injuries, the outcome differed significantly according to age at TBI (p50.0001, Kruskal-Wallis test). No significant associations were found between education and GOS-E (p ¼ 0.76, Kruskal-Wallis
Age at injury, mean SD Age at follow-up, mean SDb Education, mean SDb Sex, %a Female Male PTA classes, % (n)a 424 hours 1–7 days 1–4 weeks 44 weeks GCS classes, % (n)a 15–13 12–9 8 GOS-E, % (n)a Upper Good Recovery Lower Good Recovery Upper Moderate Disability Lower Moderate Disability Severe Disability
Smokers (n ¼ 295)
Non-smokers (n ¼ 392)
36.7 13.1 45.1 13.7 11.8 3.2
42.0 15.6 52.1 15.2 12.5 4.1
24.1 75.9
42.9 57.1
p 50.0001 0.004 0.008 50.0001 0.06
32.3 27.9 24.2 15.6
(87) (75) (65) (42)
41.2 25.2 23.5 10.1
(147) (90) (84) (36) 0.14
66.7 (180) 11.1 (30) 22.2 (60)
72.7 (266) 7.1 (26) 20.2 (74)
19.1 24.5 25.9 21.3 9.2
23.1 28.4 25.3 18.7 4.5
0.08 (54) (69) (73) (60) (26)
(88) (108) (96) (71) (17)
Notes: aChi-square test. Kruskal-Wallis test.
b
test), nor was there any difference in the outcome of TBI between non-smokers and smokers by GOS-E (p ¼ 0.08). Multivariate analysis As smoking is strongly associated with age, education and gender—factors which might also have a relationship with the outcome of TBI—a multivariate analysis was used to study a possible independent association between smoking and TBI outcome. Because in this study material the severity of injury was associated with the age at injury, the PTA classes were analysed separately to control for the effect of this association. Ignorance of this phenomenon in statistical analyses would have led to skewed results in which the TBI outcome of smokers would have seemed to be poorer than that of non-smokers. As in univariate analyses, also in multivariate analysis, age at TBI, education, gender and smoking history were nominated as predictor variables for GOS-E. Education was classified into three categories: 0–9 years, 10–12 years and 13 years or more. Age was grouped into 18–25, 26–40 and 41–55 years of age; subjects who were 17 years or 56 years were excluded from the logistic regression analysis. Simple and multiple logistic regression analyses were performed to assess each predictor’s relationship to the outcome, with and without adjustment for the effects of all other predictors. GOS-E was divided into five classes so that the lower severe disability class was fused with the upper severe disability because of the small number of subjects in the lowest GOS-E classes. The unadjusted ORs, CIs and probability values for each predictor, as well as the adjusted ORs, CIs and p-values are presented in Table III. Unadjusted ORs depict each predictor’s effect without adjustment with other predictors, while adjusted ORs depict each predictor’s unique effect after adjustment for all others predictors.
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Brain Inj, 2014; 28(2): 155–160
Table III. Adjusted results of logistic regression analysis categorized by PTA classes.
Predictor
Multiple logistic regression
Simple logistic regression
Comparison
OR (95% CI)
p-Value
OR (95% CI)
41–55, 18–25 26–40, 18–25
1.37 (0.60–3.17) 0.98 (0.39–2.44)
0.30 0.61
1.46 (0.64–3.32) 1.02 (0.14–2.52)
1.61 1.44 1.37 1.08
(0.76–3.42) (0.72–2.88) (0.60–3.17) (0.58–2.00)
0.21 0.29 0.50 0.82
1.65 1.39 1.10 1.11
2.14 (0.90–5.12) 1.95 (0.79–4.77)
0.21 0.41
1.97 (0.88–4.42) 1.81 (0.76–4.27)
1.23 1.44 0.77 0.87
0.61 0.34 0.44 0.70
1.38 1.38 1.00 0.77
4.47 (1.76–11.4) 1.97 (0.84–4.63)
0.003 0.83
4.53 (1.83–11.2) 2.01 (0.87–4.67)
1.30 1.13 0.82 0.51
(0.50–3.38) (0.51–2.49) (0.41–1.68) (0.24–1.11)
0.59 0.76 0.59 0.09
1.39 0.98 1.15 0.57
0.97 (0.34–2.82) 0.54 (0.16–1.77)
0.56 0.26
1.02 (0.36–2.87) 0.54 (0.17–1.73)
1.38 1.40 0.97 1.11
0.62 0.54 0.81 0.84
1.51 1.54 0.87 1.10
(1) PTA mild (n ¼ 157) Age at TBI (years) Education (years) Smoking at time of TBI Gender (2) PTA moderate (n ¼ 132) Age at TBI
0–9, 13 10–12, 13 Non-smoker, smoker male, female 41–55, 18–25 26–40, 18–25
(0.79–3.45) (0.70–2.75) (0.61–1.97) (0.61–2.01)
Education Smoking at time of TBI Gender (3) PTA severe (n ¼ 120) Age at TBI
2–9, 16–30 10–15, 16–30 Non-smoker, smoker male, female 41–55, 18–25 26–40, 18–25
(0.55–2.77) (0.68–3.05) (0.40–1.50) (0.43–1.76)
(0.63–3.02) (0.67–2.85) (0.55–1.84) (0.40–1.47)
Education Smoking at time of TBI Gender (4) PTA very severe (n ¼ 64) Age at TBI
2–9, 16–30 10–15, 16–30 Non-smoker, smoker male, female 41–55, 18–25 26–40, 18–25
(0.56–3.44) (0.48–2.03) (0.60–2.20) (0.28–1.15)
Education Smoking at time of TBI Gender
2–9, 16–30 10–15, 16–30 Non-smoker, smoker male, female
(0.38–4.98) (0.48–4.05) (0.34–2.82) (0.42–2.94)
(0.45–5.16) (0.56–4.20) (0.36–2.11) (0.43–2.81)
p-Value 0.47 0.23 0.63 0.37 0.18 0.34 0.75 0.74 0.24 0.23 0.46 0.61 0.42 0.38 0.99 0.43 0.004 0.002 0.87 0.74 0.48 0.96 0.67 0.12 0.48 0.48 0.23 0.66 0.51 0.40 0.75 0.84
PTA categorized as follows: 524 hours mild, 1–7 days moderate, 1–4 weeks severe and 44 weeks very severe.
The outcome of injury by GOS-E did not differ between smokers and non-smokers in the multivariate model. Age within these age categories (from 18–55 years) did not seem to have any obvious association with TBI outcome in this study. Only in the moderate and severe injury classes by PTA did there seem to be a trend for younger subjects to have better recovery and in only one analysis was this difference statistically significant. Neither education nor gender had significant associations with outcome in this study.
Discussion The aim of this study was to search for a possible association between smoking history and outcome of TBI, assessed with GOS-E. No association was found between smoking and outcome from TBI. A clear difficulty in this study was that smoking is so intimately associated with many known or suggested predictors of TBI outcome (age, education) [38] that this might easily lead to distorted results. The aim was to take this into account by using multiple logistic regression analyses. There is evidence for both negative [39] and positive [40] effects of smoking on brain function, but this study did not find support for either of these in subjects with TBI.
One might expect that smokers would have poorer outcome from TBI because of the known associations between smoking, alcohol consumption and low level of education [41–43]. On the other hand, smokers are on the average younger and younger patients have generally better outcomes. Accordingly, existing weak or moderate influences of smoking might be hidden because of these strong associations to known outcome predictive factors. For unknown reasons, in this study population, the younger subjects had more severe injuries and this was also the group that smoked most. Without recognition of this fact, it would have seemed that the smoker’s outcome is poorer. On the other hand, taking this into account by analysing the various PTA classes separately reduced the statistical power. Known associations of gender, education and age with smoking, and taking these into account in the analyses, further weakens the statistical power of the study. Some of the weaknesses of this study have already been discussed above. Although most subjects with TBI-related hospitalization from the referral area were included in the initial study material, there is surely an over-representation of subjects who have remained symptomatic after TBI. It is also possible that the most severely injured are under-represented because of their lower capability to answer the questionnaire.
DOI: 10.3109/02699052.2013.860468
Cognitive problems may also have had some influence on the reliability of the answers. Also the long-time gap between the survey and TBI in some subjects may have influenced the reliability of the results. However, it seems unlikely that these issues would have influenced the associations between smoking and outcome. It was also considered unlikely that previous TBIs would affect the eventual effect of smoking on recovery, but, if the frequency of earlier TBIs would for some reason significantly differ in smokers and non-smokers, this could have affected the results. In conclusion, these results cannot as such be generalized to all subjects with TBI and there remains a possibility that smoking could increase the likelihood of a good or full recovery, especially from milder injuries. A negative influence on recovery would probably have been revealed by this study, if it existed.
Acknowledgements We express our warm thanks to Hans Helenius, MSc, Department of Biostatistics, University of Turku, for thorough guidance in statistical matters.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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Appendix: Smoking effect on outcome from TBI Questionnaire Code
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œœœ
We kindly ask you to answer the questions below by choosing the closest accurate option—only one answer to each question. (1) How have you recovered from your traumatic brain injury? perfectly, I am as before I am almost as before, practically no lingering symptoms I have slight problems in daily functions I have clear problems in daily functions I have remarkable difficulties in daily functions I need continuous aid in all daily functions
œ œ œ œ œ œ
(2) Have you ever smoked (occasional episodes doesn’t count)? never (move to question 6) before injury ___ years but stopped smoking the year____ I have started smoking after the injury (move to question 5) I smoked before the injury and do still smoke I smoked before the injury but have stopped smoking after that
œ œ œ œ œ
(3) If you did smoke before the injury, did the effect of smoking change after the injury? no change œ effect of smoking changed to a more negative œ effect of smoking changed to a more positive œ I don’t remember œ (4) If you smoked before the injury, did you smoke during the hospital care? I didn’t get hospital care œ No, I didn’t œ I started smoking immediately when I could raise from bed œ not until the last days at care œ I don’t remember œ (5) If you did smoke before the injury, how much was your average daily tobacco consumption? about _________ packets per day œ I don’t remember œ
(7)
Did the effect of alcohol change after the injury? I haven’t used any alcohol before or after the injury I don’t know because I haven’t used alcohol after the injury no change my tolerance to alcohol became lower my tolerance to alcohol became higher I don’t remember
œ œ œ œ œ œ
Has your alcohol consumption changed after the injury? No I haven’t used alcohol after the injury I have reduced alcohol consumption after the injury I have increased alcohol consumption after the injury
œ œ œ œ
(8)
Have you used or tried AChEI (Exelon/Reminyl/Aricept) medication to treat the symptoms of your traumatic brain injury (so called Alzheimer’s medication)? never œ I don’t remember œ I have tried, but do not use any longer œ Yes, I use them currently œ
(9)
If you have tried AChEI medication, what was the effect of this medication? I don’t remember œ no benefit, no adverse effects œ no benefit, only adverse effects œ little benefit, no adverse effects œ little benefit, but also adverse effects œ lots of benefit, no adverse effects œ lots of benefit, but also adverse effects œ
(10) How many years you have studied in total? _____________ years
Thank you for your participation to this study!
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