Abstract
Objective
Identify factors associated with chest illness and describe the relationship between chest illness and mortality in chronic spinal cord injury (SCI).
Design
Cross-sectional survey assessing chest illness and a prospective assessment of mortality.
Methods
Between 1994 and 2005, 430 persons with chronic SCI (mean ± SD), 52.0 ± 14.9 years old, and ≥4 years post SCI (20.5 ± 12.5 years) underwent spirometry, completed a health questionnaire, and reported any chest illness resulting in time off work, indoors, or in bed in the preceding 3 years. Deaths through 2007 were identified.
Outcome measures
Logistic regression assessing relationships with chest illness at baseline and Cox regression assessing the relationship between chest illness and mortality.
Results
Chest illness was reported by 139 persons (32.3%). Personal characteristics associated with chest illness were current smoking (odds ratio =2.15; 95% confidence interval =1.25–3.70 per each pack per day increase), chronic obstructive pulmonary disease (COPD) (3.52; 1.79–6.92), and heart disease (2.18; 1.14–4.16). Adjusting for age, subjects reporting previous chest illness had a non-significantly increased hazard ratio (HR) for mortality (1.30; 0.88–1.91). In a multivariable model, independent predictors of mortality were greater age, SCI level and completeness of injury, diabetes, a lower %-predicted forced expiratory volume in 1 second, heart disease, and smoking history. Adjusting for these covariates, the effect of a previous chest illness on mortality was attenuated (HR = 1.15; 0.77–1.73).
Conclusion
In chronic SCI, chest illness in the preceding 3 years was not an independent risk factor for mortality and was not associated with level and completeness of SCI, but was associated with current smoking, physician-diagnosed COPD, and heart disease history.
Keywords: Spinal cord injuries, Respiratory tract infections, Mortality
Introduction
Chest illnesses are an important cause of morbidity in persons with chronic spinal cord injury (SCI).1,2 Injury to the cervical or thoracic regions of the spine results in various degrees of respiratory muscle weakness and paralysis depending on the level and severity of SCI. This leads to reduced pulmonary function and ineffective cough that impairs the clearance of mucus from the airways. As a result, in comparison to the able-bodied, persons with SCI are potentially more susceptible to the effects of respiratory illnesses such as bronchitis and pneumonia, and may have more severe illness and debilitation when afflicted with these disorders. In the years immediately following acute SCI, data from the US Spinal Cord Injury Model Systems registry indicate that diseases of the respiratory system, and in particular pneumonia, are the most common cause of death.3 Persons with higher and more complete injuries are most at risk from dying of respiratory causes.
SCI increases the risk of premature death.4–6 Although many studies have examined short-term mortality following acute SCI, few have focused on factors influencing long-term mortality. We previously examined mortality between 1994 and 2000 in 361 men with chronic SCI at the Veterans Affairs Boston Health Care System (VA Boston) (mean duration of SCI 17.5 years) and found a 47% increase compared to what would have been expected in the general population.7 Adjusting for severity and completeness of SCI, a reduced percent-predicted forced expiratory volume in 1 second (FEV1) was associated with greater mortality risk.7 It has been shown that exacerbations in patients with chronic obstructive pulmonary disease (COPD) are positively related to mortality risk,8 and it is plausible that a similar association exists in persons with SCI.
In this report, we extend our observations regarding mortality through 2007 in 430 participants enrolled in the VA Boston chronic SCI cohort. We hypothesized that a history of chest illness would be associated with level and completeness of injury and that a history of chest illness would be associated with an increased mortality risk.
Methods
Study population and chest illness definition
Between 1994 and 2005, 582 persons at least 1 year post SCI and age 22 years or older were enrolled in a longitudinal study assessing factors associated with respiratory health. Included were persons who did not require ventilator support and who did not have a tracheostomy. Details about this longitudinal cohort have been published elsewhere.9 Briefly, VA Boston is one of 23 Department of Veterans Affairs SCI centers that provide health services to persons with acute and chronic SCI. The cohort included persons previously treated at VA Boston and those recruited by advertisement from the greater Boston area. The analysis excluded persons with other neurological conditions (history of polio, multiple sclerosis, or stroke, n = 27), with lung resection (n = 5), with no known date of injury (n = 1), without a detectable SCI level (n = 16), or who were unable to complete spirometry (n = 10). Study procedures followed the protocol approved by the VA Boston Institutional Review Board and were in accord with the ethical standards of the review board.
Using a respiratory questionnaire based on a survey from the American Thoracic Society (ATS) and the Division of Lung Diseases (ATS DLD-78),10 participants were asked the following questions upon enrollment into the cohort: “‘During the past year have you had any chest illness that has kept you off work, indoors at home, or in bed?” and “During the past 3 years have you had any chest illness that has kept you off work, indoors at home, or in bed?” Participants who responded “yes” to either question were identified as having a history of chest illness. This approach for querying chest illness in this cohort was previously validated.11 To ensure that the 3-year period occurred entirely during the chronic SCI phase (starting one year post-injury), we excluded 93 persons who were less than four years post-injury at baseline. The remaining 430 persons included in the analyses were at least 4 years post-injury at the baseline study visit.
Health history
The respiratory health questionnaire (ATS DLD-78) with supplemental questions was used to obtain a standardized medical history that included diabetes, physician-diagnosed COPD (emphysema or chronic bronchitis), asthma, hypertension, and heart disease treated in the 10 years before study entry. Any wheeze was defined as wheeze reported with a cold, occasionally apart from colds, or on most days or nights. Chronic cough was defined as cough on most days for 3 consecutive months of the year, and chronic phlegm was defined similarly.
Spirometry
Pulmonary function testing was conducted according to ATS guidelines,12 modified for SCI as previously described.13,14 Briefly, participants were coached to provide three acceptable and reproducible efforts using a 10-1 water-seal spirometer (DSII) in 88% of the sessions or an 8-1 water seal portable spirometer in 2%. Beginning in March 2004 and for 10% of the sessions, a dry-rolling seal CPL spirometer system was used. Equipment was manufactured by Collins Pulmonary Diagnostics (currently nSpire Health, Inc., Longmont, CO, USA). The highest FEV1 and forced vital capacity (FVC) values were used to calculate percent predicted FEV1 and FVC using Hankinson's equations.15
Neurological exam, stature, and weight
At entry, motor level and completeness of injury were assessed based on the American Spinal Injury Association Impairment Scale (AIS) by record review and exam.16,17 Motor incomplete SCI included AIS C (most key muscles below the neurological level grade <3/5) or AIS D (most muscles below the neurological level grade ≥3/5). For analysis, persons were further grouped into cervical complete and AIS C (cervical motor complete (AIS A or B) and cervical AIS C), non-cervical complete and AIS C (thoracic or lower motor complete (AIS A or B) and thoracic or lower AIS C), and all others (AIS D). AIS C were grouped with AIS A and B because of the small numbers of cervical AIS C (N = 14) and non-cervical AIS C (N = 19) participants. We later performed a sensitivity analysis on the effect of combining AIS C with AIS A and B. Stature was obtained by measuring body length from top of the head to the heel with the subject in a supine position. In individuals who declined length measurement or who had severe joint contractures that precluded accurate assessment, stature was self-reported.11 Weight was directly measured at the research visit for most patients (91%), and was otherwise obtained via self report (8%) or medical record (1%).
Mortality
Deaths and dates of death through 31 December 2007 were identified through routine contacts with cohort participants for subsequent studies and through an online search of the Social Security Master Death Index. Additionally, the VA electronic medical record was reviewed for all veterans (71.9% of the cohort).
Statistical analyses
Cross-sectional associations with chest illness in the past 3 years were assessed using logistic regression (PROC LOGISTIC, SAS 9.1, Cary, NC, USA). Predictors of mortality through 2007 were assessed with Cox regression models (PROC PHREG). For both logistic and Cox regression models, variables with significant univariable associations at the 0.1 level were assessed in multivariable models. Percent predicted FEV1 was the pulmonary function variable that showed the best association with chest illness and was thus used in survival modeling. Proportional hazards of the Cox model were assessed for individual covariates using a term for the variable multiplied by the natural log of time. Overall proportional hazard for the model was assessed using the proportionality test statement in PROC PHREG. The P value for linear trend of percent predicted FEV1 in quartiles was obtained by assigning each quartile its median value and then entering the median values as a continuous variable in the multivariable survival model.
Results
Participant characteristics
The majority of participants were male (93.0%) and white (92.8%) with age of 52.0 ± 14.9 (mean ± standard deviation) years and 20.5 ± 12.5 years since SCI at study entry. In 382 participants (88.8%) SCI was due to trauma and 115 (26.7%) participants had cervical complete and AIS C, 189 (44.0%) had non-cervical complete and AIS C, and 126 (29.3%) were AIS D.
Prior chest illness
Chest illness in the 3 years prior to study entry was reported by 139 participants (32.3%) and was significantly associated with current smoking (Tables 1 and 2). After controlling for smoking, prior chest illness was significantly associated with female sex, doctor-diagnosed COPD, asthma, obstructive lung disease, heart disease requiring treatment in the past 10 years, chronic phlegm, and any wheeze (Table 2). There were suggestive effects of percent predicted FEV1 and percent predicted FVC, but did not reach statistical significance. Current smoking (odds ratio (OR) = 2.15 per pack per day; 95% confidence interval (CI) = 1.25–3.70) and COPD (OR = 3.52; 95% CI = 1.79–6.92) were independently significant in a multivariable model (Table 3). After controlling for smoking status and COPD, heart disease requiring treatment in the past 10 years was the only other variable significantly associated with prior chest illness (Table 3). Any wheeze was strongly associated with prior chest illness when included in a multivariable model excluding smoking history (OR = 3.59; 95% CI = 2.25–5.73; Table 3). Adding pulmonary function variables to the multivariable chest illness models did not improve model fit.
Table 1 .
Descriptive statistics of 430 persons with chronic SCI with and without reported chest illness in the 3 years before study entry*
| Characteristic | Prior chest illness N = 139 (32.3) | No prior chest illness N = 291 (67.7) |
|---|---|---|
| Deceased | 42 (30.2) | 69 (23.7) |
| Female | 14 (10.1) | 16 (5.5) |
| Nonwhite | 8 (5.8) | 23 (7.9) |
| Age (years) | 52.8 (14.8) | 51.6 (14.9) |
| Job status | ||
| Employed/student | 47 (33.8) | 102 (35.1) |
| Unemployed/retired | 92 (66.2) | 189 (64.9) |
| Years post-injury | 21.6 (12.1) | 20.0 (12.7) |
| Level of injury | ||
| Cervical complete and AIS C | 42 (30.2) | 73 (25.1) |
| Non-cervical complete and AIS C | 52 (37.4) | 137 (47.1) |
| AIS D | 45 (32.4) | 81 (27.8) |
| Body mass index continuous | 27.3 (6.7) | 26.6 (5.3) |
| Underweight (<18.5) | 6 (4.3) | 8 (2.8) |
| Normal (≥18.5 to <25.0) | 53 (38.1) | 113 (38.8) |
| Overweight (≥25.0 to <30.0) | 38 (27.3) | 98 (33.7) |
| Obese (≥30.0) | 42 (30.2) | 72 (24.7) |
| Smoking status | ||
| Never smoker | 37 (26.6) | 109 (37.5) |
| Ex-smoker | 60 (43.2) | 124 (42.6) |
| Quit >3 years | 53 (38.1) | 112 (38.5) |
| Quit ≤3 years | 7 (5.0) | 12 (4.1) |
| Current smoker | 42 (30.2) | 58 (19.9) |
| Current packs per day | 0.91 (0.60) | 0.71 (0.46) |
| COPD | 27 (19.4) | 16 (5.5) |
| Asthma | 25 (18.0) | 22 (7.6) |
| Obstructive lung disease (asthma or COPD) | 40 (28.8) | 33 (11.3) |
| Hypertension | 47 (33.8) | 96 (33.0) |
| Diabetes | 18 (13.9) | 32 (11.0) |
| Heart disease treated in past 10 years | 24 (17.3) | 23 (7.9) |
| Chronic cough | 34 (24.5) | 45 (15.5) |
| Chronic phlegm | 42 (30.2) | 48 (16.5) |
| Any wheeze | 105 (75.5) | 122 (41.9) |
| FEV1/FVC | 0.772 (0.099) | 0.784 (0.099) |
| FEV1/FVC ≤0.7 | 25 (18.0) | 49 (16.8) |
| % Predicted FEV1 | 72.9 (20.0) | 76.6 (20.2) |
| % Predicted FVC | 73.5 (20.2) | 76.0 (18.8) |
*For categorical variables the number (percent) and mean (standard deviation) for continuous variables are displayed.
Table 2 .
Logistic regression models of self-reported chest illness in the past 3 years (N = 430)
| Variable | OR† | aOR‡ | 95% CI‡ | P value‡ |
|---|---|---|---|---|
| Female | 1.93 | 2.21 | 1.04–4.71 | 0.040 |
| Nonwhite | 0.71 | 0.80 | 0.34–1.84 | 0.591 |
| Age | 1.01 | 1.01 | 0.99–1.02 | 0.281 |
| Job status | ||||
| Employed/student | 0.95 | 1.04 | 0.67–1.60 | 0.869 |
| Unemployed/retired | Ref | |||
| Years post injury | 1.01 | 1.01 | 1.00–1.03 | 0.127 |
| Level of injury | ||||
| Cervical complete and AIS C | 1.04 | 1.17 | 0.68–2.01 | 0.896 |
| Non-cervical complete and AIS C | 0.68 | 0.76 | 0.46–1.24 | 0.265 |
| AIS D | Ref | |||
| BMI | ||||
| Underweight (<18.5) | 1.60 | 1.68 | 0.55–5.13 | 0.368 |
| Normal (≥18.5 to <25.0) | Ref | |||
| Overweight (≥25.0 to <30.0) | 0.83 | 0.85 | 0.51–1.40 | 0.143 |
| Obese (≥30.0) | 1.24 | 1.24 | 0.74–2.06 | 0.737 |
| Smoking status | ||||
| Never smoker | Ref | |||
| Ex-smoker | 1.38 | – | 0.87–2.17 | 0.167 |
| Current smoker packs per day | 2.35* | – | 1.39–3.97 | 0.002 |
| COPD | 4.14* | 3.81 | 1.95–7.41 | <0.001 |
| Asthma | 2.68* | 2.78 | 1.50–5.18 | 0.001 |
| Obstructive lung disease | 3.16* | 3.03 | 1.79–5.11 | <0.001 |
| Hypertension | 1.04 | 1.09 | 0.70–1.68 | 0.710 |
| Diabetes | 1.20 | 1.20 | 0.64–2.23 | 0.577 |
| Heart disease in past 10 years | 2.43* | 2.45 | 1.31–4.57 | 0.005 |
| Chronic cough | 1.77* | 1.45 | 0.86–2.46 | 0.166 |
| Chronic phlegm | 2.19* | 1.87 | 1.13–3.07 | 0.014 |
| Any wheeze | 4.28* | 3.95 | 2.49–6.25 | <0.001 |
| FEV1/FVC | 0.29 | 0.67 | 0.08–5.92 | 0.719 |
| FEV1/FVC ≤0.7 | 1.08 | 0.92 | 0.53–1.60 | 0.773 |
| % Predicted FEV1 | 0.99 | 0.99 | 0.98–1.00 | 0.088 |
| % Predicted FEV1 in quartiles | 0.212 | |||
| Quartile 1 – (28–61%) | 1.34 | 1.32 | 0.74–2.34 | |
| Quartile 2 – (61–78%) | 1.30 | 1.29 | 0.72–2.29 | |
| Quartile 3 – (78–90%) | 0.77 | 0.75 | 0.41–1.37 | |
| Quartile 4 – (90–132%) | Ref | |||
| % Predicted FVC | 0.99 | 0.99 | 0.98–1.00 | 0.105 |
| % Predicted FVC in quartiles | 0.162 | |||
| Quartile 1 – (25–61%) | 1.27 | 1.44 | 0.81–2.55 | |
| Quartile 2 – (61–78%) | 0.97 | 1.02 | 0.57–1.82 | |
| Quartile 3 – (78–89%) | 0.64 | 0.73 | 0.40–1.34 | |
| Quartile 4 – (89–124%) | Ref |
†Crude odds ratio.
‡Odds ratio adjusted for smoking status (never smoker, ex-smoker, current smoker packs per day).
*P < 0.05 for the crude odds ratio.
Ref, reference group.
Table 3 .
Multivariable logistic regression models of self-reported chest illness in the past 3 years (N = 430)
| Variable | Odds ratio | 95% Confidence interval | P value |
|---|---|---|---|
| Model 1 | |||
| Smoking status | |||
| Never smoker | Ref | ||
| Ex-smoker | 1.21 | 0.76–1.94 | 0.422 |
| Current smoker packs per day | 2.15 | 1.25–3.70 | 0.006 |
| COPD | 3.52 | 1.79–6.92 | <0.001 |
| Heart disease in past 10 years | 2.18 | 1.14–4.16 | 0.018 |
| Model 2 | |||
| Any wheeze | 3.59 | 2.25–5.73 | <0.001 |
| COPD | 2.40 | 1.20–4.79 | 0.013 |
| Heart disease in past 10 years | 1.87 | 0.97–3.61 | 0.062 |
Ref, reference group.
Survival
There were 111 deaths (25.8%) (Table 1). Length of time in the study for each individual was calculated from date of initial clinic visit to date of death or 31 December 2007, whichever occurred first. Participants were followed for mean 8.08 ± 3.77 years, ranging from 0.04 to 13.82 years. Year of entry into the cohort was not significantly related to mortality. In an age-adjusted multivariable Cox regression model, cervical complete and AIS C (compared to all AIS D), non-cervical complete and AIS C (compared to all AIS D), current smoking packs per day (compared to never smokers), recent cessation of smoking (≤3 years) (compared to never smokers), diabetes, heart disease requiring treatment in the past 10 years, and lower percent predicted FEV1 were each significantly associated with an increased hazard of death (Table 4). Adjusting for only age, subjects reporting chest illness in the past 3 years at study entry had a higher hazard for death than those who did not report chest illness (hazard ratio (HR) = 1.30; 95% CI = 0.88–1.91; data not shown), but this relationship was not statistically significant and was further attenuated in the multivariable model (HR = 1.15; 95% CI = 0.77–1.73; Table 4). We present a multivariable model with percent predicted FEV1 in quartiles as it better satisfies the proportional hazards assumption and is easier to interpret clinically compared to a continuous measure of percent predicted FEV1. The multivariable model satisfies the Cox regression proportional hazards assumption overall and for each individual variable.
Table 4 .
Multivariable Cox regression model assessing mortality (N = 430)
| Variable | Hazard ratio | 95% confidence interval | P value |
|---|---|---|---|
| Age | 1.07 | 1.06–1.09 | <0.001 |
| Level of injury | |||
| Cervical complete and AIS C | 2.15 | 1.18–3.93 | 0.013 |
| Non-cervical complete and AIS C | 1.88 | 1.14–3.09 | 0.013 |
| AIS D | Ref | ||
| Never smoker | Ref | ||
| Quit >3 years | 1.11 | 0.72–1.71 | 0.634 |
| Quit ≤3 years | 3.37 | 1.44–7.90 | 0.005 |
| Current smoker packs per day | 1.80 | 1.20–2.71 | 0.004 |
| Diabetes | 1.75 | 1.06–2.91 | 0.030 |
| Heart disease in past 10 years | 2.18 | 1.29–3.67 | 0.004 |
| % Predicted FEV1 | |||
| Quartile 1 – (25–61%) | 3.57 | 1.91–6.67 | <0.001 |
| Quartile 2 – (61–78%) | 2.37 | 1.25–4.48 | 0.008 |
| Quartile 3 – (78–89%) | 2.21 | 1.15–4.28 | 0.018 |
| Quartile 4 – (89–124%) | Ref | ||
| P value for trend | <0.001 | ||
| Chest illness in past 3 years | 1.15 | 0.77–1.73 | 0.491 |
Ref, reference group.
We performed a sensitivity analysis to assess the influence of our SCI level and severity categorization on the results and whether the association between history of chest illness and mortality was modified by level and severity of SCI (data not shown). Removing n = 33 AIS C from the multivariable model did not change the HR for the relationship between chest illness and mortality (HR = 1.14; 95% CI = 0.73–1.76). Additionally, there was no association between chest illness and mortality among only motor completes (HR = 1.31; 95% CI = 0.78–2.20), or when further restricting to cervical motor completes (HR = 1.02; 95% CI = 0.40–2.55), adjusting for the same covariates as the multivariable survival model in Table 4.
Discussion
In 430 persons at least 4 years post SCI, self-reported history of chest illness in the past 3 years was most strongly associated with current smoking, doctor diagnosed COPD, and prior heart disease. However, a history of chest illness assessed at study entry was not an independent predictor of mortality assessed over a mean of 8.1 years. This suggests that while a chest illness history may be related to morbidity, it does not have a substantial independent effect on the long-term mortality of persons with chronic SCI who have survived at least 4 years.
A prior study found that acute respiratory illness in SCI patients increased the risk of 60-day mortality.18 The fact that our findings differ further supports the concept that the risks for short- and long-term mortality in SCI patients are not the same. Previous studies that have used outpatient visits for acute respiratory infections as a measure of chest illness may not be directly comparable to our self-reported measure and may represent more serious illnesses.18,19 Additionally, our results differ from findings in patients with COPD where associations between exacerbations and mortality have been reported.8
Level and completeness of SCI was not related to chest illness in the 3 years prior to study entry, a finding similar to the study by Stolzmann et al.11 where chest illness risk in persons in the same cohort was studied prospectively. However, the cross-sectional predictors of chest illness history in the presented multivariable model differ slightly from those in the prospective analysis.11 Namely, after controlling for smoking and COPD, any wheeze and a lower percent predicted FEV1 were associated with prospective chest illness, but they were not independently associated with chest illness in our cross-sectional analysis. Current smoking and any wheeze were each strongly associated with prior chest illness when modeled separately, and in a single predictor model there was a suggestive effect of a reduced percent predicted FEV1 on chest illness (P = 0.088). Compared to this study, the previous prospective study included follow-up for chest illness over a longer time period (an average of 5.1 years as compared to a 3-year recall) and accounted for recurrent chest illness within an individual, and so more chest illnesses were reported (n = 247). Therefore, it is likely that greater statistical power in the prospective study permitted inclusion of additional predictor variables compared to the current cross-sectional study. Regardless, both this retrospective study and previous prospective study demonstrate the importance of factors other than level and severity of SCI in determining chest illness risk.11
In our previous report based on 36 deaths in 348 participants through 2000,7 SCI level and completeness was not a significant predictor of mortality. However, in the current analysis, in a larger cohort with more deaths, level and severity of SCI was a risk factor for mortality even when controlling for the other main predictors of mortality. Our results confirm the findings of prior studies of long-term mortality in chronic SCI, including increased risks of mortality due to level and severity of injury.4,5,20–22 In addition, our findings are similar to studies in the general population showing that heart disease,23,24 diabetes,24,25 reduced pulmonary function,26–28 and cigarette smoking29 are determinants of mortality. This study demonstrates and confirms the contribution of reduced pulmonary function, smoking, heart disease, and diabetes to greater mortality in chronic SCI independent of level and severity of injury. The risk of mortality in current and recent smokers, persons with diabetes, or persons with heart disease was similar to the risk of mortality due to cervical and non-cervical complete or AIS C status (as compared to AIS D).
A history of chest illness resulting in time off work, indoors at home, or in bed was not an independent predictor of mortality, but chest illness was associated with smoking history, heart disease, and reduced pulmonary function, and these factors were related to an increased risk of mortality. Therefore, the occurrence of chest illness may identify persons in poor health who are at a greater risk of mortality due to other clinical factors but is not predictive of mortality itself.
Study limitations
A limitation of this study is that the main variable of interest, chest illness that kept one off work, at home indoors, or in bed in the past 3 years, was queried by self-report. However, the events reported reflect significant illnesses likely to prompt recall, and we also previously validated this question with medical records.11 In addition, the results of this study are applicable to patients with chronic SCI who survived at least 4 years and who do not require tracheostomy or mechanical ventilation, and are not generalizable to the entire SCI population. Strengths of this study include a large cohort of persons with chronic SCI, the use of a standardized health questionnaire to assess multiple personal risk factors for chest illness, and the prospective assessment of mortality.
Conclusion
Factors not directly related to SCI are the most important correlates of chest illness reported over 3 years. SCI level and completeness of injury, reduced pulmonary function, heart disease history, smoking, and diabetes are predictors of mortality, while history of chest illness is not significantly associated with long-term mortality in chronic SCI.
Disclaimer statements
Contributors VAD has made substantial contributions to the design, analysis, statistical support, and writing of this manuscript. KLS has made substantial contributions to the design, analysis, and writing of this manuscript. DRG has made substantial contributions to the statistical support and writing of this manuscript. RB has made substantial contributions to the design and writing of this manuscript. CGT has made substantial contributions to the design and writing of this manuscript. LRM has made substantial contributions to the design and writing of this manuscript. EG has made substantial contributions to the design, analysis, and writing of this manuscript.
Conflicts of interest None.
Ethics approval This study has been approved by the VA Boston Healthcare System Institutional Review Board.
Funding The work was supported by NIH/NICHD RO1 HD42141, VA Rehabilitation Research and Development Merit Review Grant B6618R (Dr. Garshick), the Massachusetts Veterans Epidemiology Research and Information Center, Department of Veterans Affairs, Cooperative Studies Program, and the Department of Education, National Institute on Disability and Rehabilitation Research Grant H133N110010 (Dr. Morse).
References
- 1.Cardenas DD, Hoffman JM, Kirshblum S, McKinley W. Etiology and incidence of rehospitalization after traumatic spinal cord injury: a multicenter analysis. Arch Phys Med Rehabil 2004;85(11):1757–63. [DOI] [PubMed] [Google Scholar]
- 2.Dryden DM, Saunders LD, Rowe BH, May LA, Yiannakoulias N, Svenson LW, et al. . Utilization of health services following spinal cord injury: a 6-year follow-up study. Spinal Cord 2004;42(9):513–25. [DOI] [PubMed] [Google Scholar]
- 3.DeVivo MJ, Stover SL. Long term survival and causes of death. In: Stover SL, DeLisa JAWG, (eds.) Spinal cord injury. Clinical outcomes from the model systems. Gaithersburg, MD: Aspen Publishers Inc, 1995. p. 289–316. [Google Scholar]
- 4.DeVivo MJ, Krause JS, Lammertse DP. Recent trends in mortality and causes of death among persons with spinal cord injury. Arch Phys Med Rehabil 1999;80(11):1411–9. [DOI] [PubMed] [Google Scholar]
- 5.Mesard L, Carmody A, Mannarino E, Ruge D. Survival after spinal cord trauma. Arch Neurol 1978;35(2):78–83. [DOI] [PubMed] [Google Scholar]
- 6.Whiteneck GG, Charlifue SW, Frankel HL, Fraser MH, Gardner BP, Gerhart KA, et al. . Mortality, morbidity, and psychological outcomes of persons spinal cord injured more than 20 years ago. Paraplegia 1992;30(9):617–30. [DOI] [PubMed] [Google Scholar]
- 7.Garshick E, Kelley A, Cohen SA, Garrison A, Tun CG, Gagnon D, et al. . A prospective assessment of mortality in chronic spinal cord injury. Spinal Cord 2005;43(7):408–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Soler-Cataluna JJ, Martinez-Garcia MA, Roman Sanchez P, Salcedo E, Navarro M, Ochando R. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax 2005;60(11):925–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Jain NB, Brown R, Tun CG, Gagnon D, Garshick E. Determinants of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1/FVC in chronic spinal cord injury. Arch Phys Med Rehabil 2006;87(10):1327–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ferris BG. Epidemiology Standardization Project (American Thoracic Society). Am Rev Respir Dis 1978;118(6 Pt. 2):1–120. [PubMed] [Google Scholar]
- 11.Stolzmann KL, Gagnon DR, Brown R, Tun CG, Garshick E. Risk factors for chest illness in chronic spinal cord injury: a prospective study. Am J Phys Med Rehabil 2010;89(7):576–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. . Standardisation of spirometry. Eur Respir J 2005;26(2):319–38. [DOI] [PubMed] [Google Scholar]
- 13.Ashba J, Garshick E, Tun CG, Lieberman SL, Polakoff DF, Blanchard JD, et al. . Spirometry – acceptability and reproducibility in spinal cord injured subjects. J Am Paraplegia Soc 1993;16(4):197–203. [DOI] [PubMed] [Google Scholar]
- 14.Kelley A, Garshick E, Gross ER, Lieberman SL, Tun CG, Brown R. Spirometry testing standards in spinal cord injury. Chest 2003;123(3):725–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med 1999;159(1):179–87. [DOI] [PubMed] [Google Scholar]
- 16.Marino RJ, Barros T, Biering-Sorenson F, Burns SP, Donovan WH, Graves DE, et al. . International standards for neurological classification of spinal cord injury. J Spinal Cord Injury Med 2003;26(Supp. 1):550–6. [DOI] [PubMed] [Google Scholar]
- 17.Kirshblum SC, Waring W, Biering-Sorensen F, Burns SP, Johansen M, Schmidt-Read M, et al. . Reference for the 2011 revision of the International Standards for Neurological Classification of Spinal Cord Injury. J Spinal Cord Med 2011;34(6):547–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Weaver FM, Smith B, Evans CT, Kurichi JE, Patel N, Kapur VK, et al. . Outcomes of outpatient visits for acute respiratory illness in veterans with spinal cord injuries and disorders. Am J Phys Med Rehabil 2006;85(9):718–26. [DOI] [PubMed] [Google Scholar]
- 19.Smith BM, Evans CT, Kurichi JE, Weaver FM, Patel N, Burns SP. Acute respiratory tract infection visits of veterans with spinal cord injuries and disorders: rates, trends, and risk factors. J Spinal Cord Med 2007;30(4):355–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Frankel HL, Coll JR, Charlifue SW, Whiteneck GG, Gardner BP, Jamous MA, et al. . Long-term survival in spinal cord injury: a fifty year investigation. Spinal Cord 1998;36(4):266–74. [DOI] [PubMed] [Google Scholar]
- 21.Strauss DJ, DeVivo MJ, Paculdo DR, Shavelle RM. Trends in life expectancy after spinal cord injury. Arch Phys Med Rehabil 2006;87(8):1079–85. [DOI] [PubMed] [Google Scholar]
- 22.Yeo JD, Walsh J, Rutkowski S, Soden R, Craven M, Middleton J. Mortality following spinal cord injury. Spinal Cord 1998;36(5):329–36. [DOI] [PubMed] [Google Scholar]
- 23.Heron M, Hoyert D, Murphy S, Xu J, Kochanek K, Tejada-Vera B. Deaths: final data for 2006. National vital statistics reports [57] Hyattsville, MD: National Center for Health Statistics; 2009. [PubMed] [Google Scholar]
- 24.Malik S, Wong ND, Franklin SS, Kamath TV, L'Italien GJ, Pio JR, et al. . Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in United States adults. Circulation 2004;110(10):1245–50. [DOI] [PubMed] [Google Scholar]
- 25.Bertoni AG, Kirk JK, Goff DC Jr., Wagenknecht LE. Excess mortality related to diabetes mellitus in elderly Medicare beneficiaries. Ann Epidemiol 2004;14(5):362–7. [DOI] [PubMed] [Google Scholar]
- 26.Knuiman MW, James AL, Divitini ML, Ryan G, Bartholomew HC, Musk AW. Lung function, respiratory symptoms, and mortality: results from the Busselton Health Study. Ann Epidemiol 1999;9(5):297–306. [DOI] [PubMed] [Google Scholar]
- 27.Schunemann HJ, Dorn J, Grant BJ, Winkelstein W Jr., Trevisan M. Pulmonary function is a long-term predictor of mortality in the general population: 29-year follow-up of the Buffalo Health Study. Chest 2000;118(3):656–64. [DOI] [PubMed] [Google Scholar]
- 28.Sorlie PD, Kannel WB, O'Connor G. Mortality associated with respiratory function and symptoms in advanced age. The Framingham Study. Am Rev Respir Dis 1989;140(2):379–84. [DOI] [PubMed] [Google Scholar]
- 29.US Department of Health and Human Services. The Health Benefits of Smoking Cessation. A report of the Surgeon General 1990. [DOI] [PubMed]