Abstract
Objectives
To identify the factors that affect SF-36 mental component summary (MCS) in patients with adult spinal deformity (ASD) at the time of presentation, and to analyse the effect of SF-36 MCS on clinical outcomes in surgically treated patients.
Methods
Prospectively collected data from a multicentric ASD database was analysed for baseline parameters. Then, the same database for surgically treated patients with a minimum of 1-year follow-up was analysed to see the effect of baseline SF-36 MCS on treatment results. A clinically useful SF-36 MCS was determined by ROC Curve analysis.
Results
A total of 229 patients with the baseline parameters were analysed. A strong correlation between SF-36 MCS and SRS-22, ODI, gender, and diagnosis were found (p < 0.05). For the second part of the study, a total of 186 surgically treated patients were analysed. Only for SF-36 PCS, the un-improved cohort based on minimum clinically important differences had significantly lower mean baseline SF-36 MCS (p < 0.001). SF-36 MCS was found to have an odds ratio of 0.914 in improving SF-36 PCS score (unit by unit) (p < 0.001). A cut-off point of 43.97 for SF-36 MCS was found to be predictive of SF-36 PCS (AUC = 0.631; p < 0.001).
Conclusions
The factors effective on the baseline SF-36 MCS in an ASD population are other HRQOL parameters such as SRS-22 and ODI as well as the baseline thoracic kyphosis and gender. This study has also demonstrated that baseline SF-36 MCS does not necessarily have any effect on the treatment results by surgery as assessed by SRS-22 or ODI.
Level of evidence
Level III, prognostic study.
Keywords: Adult spinal deformity, HRQOL, Mental component summary, Outcome, SF-36, Surgery
Introduction
The prevalence of Adult Spinal Deformity (ASD) is high worldwide, and the need for surgical deformity correction and relief of symptoms has subsequently increased. Previous research indicates that in younger subgroup of patients with spine deformity, treatment is directed mainly by the severity of the deformity,1 however in the elderly, the main decisive factors for treatment are pain, poor function and disability2, 3, 4, 5 factors that had been demonstrated to be strongly associated with several radiological parameters.6 ASD surgery has been shown to yield better results compared to conservative treatment in the overall population of patients in terms of Heath Related Quality Of Life (HRQOL) outcomes.7, 8 However, still a significant proportion of operated patients do not improve following surgery regardless of the apparent technical success or failure of the operations.5, 9 What is not yet understood is how various clinical parameters of ASD affect the mental well being of patients before treatment and the potential snowball effect of the impact of baseline mental status of a patient on treatment outcomes after surgery. A recent study suggests that ASD may affect the mental and psychological functioning of patients.10 It is well accepted in others areas of spine that pre-operative psychosocial factors are predictive of outcome and correlates with improvement in QOL after spine surgery.11, 12, 13, 14, 15 Several studies also suggest that psychosocial questionnaires may serve as standardized tools for identifying patients who are more likely to have positive postoperative outcomes and those who are unlikely to derive the expected benefit from surgery.16, 17
SF-36 is a widely used HRQOL tool in spine surgery, commonly broken into two main domains of the Physical Component Summary (PCS) and the Mental Component Summary (MCS). Several publications have demonstrated that SF-36 MCS highly correlates with standard tools used to screen for depressive symptoms.18, 19 Other studies have shown that SF-36 MCS is an effective screening tool for depression20, 21 and that; SF-36 MCS cut-off point of 35 is clinically relevant for identifying depressive symptoms in patients with spine pathology.22 This cut-off point has been found to have a sensitivity of 80% and specificity of 90% for patients with back pain.22
The open questions in regard to the role of the SF-36 MCS in ASD are whether it is strongly affected by other factors (age, other demographic factors, disability) and to what extent it affects the outcome of patients undergoing surgery for ASD. Hence, the specific aims of this study were:
-
1.
To identify the factors that affect SF-36MCS in patients with ASD at the time of presentation, regardless of the severity of deformity, disability and the final treatment decision, and,
-
2.
To analyse, in turn, the effect of SF-36MCS on clinical outcomes in patients treated surgically.
As far as we know this is one of the first report in the literature to directly examine the impact of various HRQOL clinical parameters, on the psychological well being of ASD patients before commencing treatment and the subsequent effect of this, on outcomes after surgery.
Material and methods
Factors affecting SF-36 MCS at baseline
Two hundred and twenty nine patients who had been entered in an ASD database on adult spine deformity and had completed SF-36MCS were analysed. The inclusion criteria into the database and hence for this study was as following; age >18 years and scoliosis >20°, or sagittal vertical axis (SVA) >5 cm, or pelvic tilt >25°, or thoracic kyphosis >60°. All patients were enrolled into an institutional review board approved protocol by the respective sites. All patients with baseline parameters were included (before treatment). Baseline demographic data [age, gender, co-morbidities and body mass index (BMI)], HRQOL parameters [The Core Outcome Measures Index (COMI), The Oswestry Disability Index (ODI), Short-Form (SF)-36 Mental Component Summary (MCS), SF-36 Physical Component Summary (PCS) and Scoliosis Research Society-22 questionnaire (SRS-22)], the following radiological features were included in the analysis; Sagittal Vertical Axis (SVA), T2-T12 kyphosis, Coronal balance, Major curve Cobb angle, Lordosis Gap (L Gap),6 Global Tilt (GT)23 and T1 sagittal tilt. Patients were also stratified according to the aetiology of their deformity; idiopathic scoliosis, degenerative scoliosis and others.
Statistical analysis
A multivariate linear regression analysis model was built with SF-36MCS as the dependent variable. Demographic, radiological and HRQOL parameters were independent variables for this model. As a first step, a correlation analysis between the SF-36 MCS and all the independent variables was performed using Pearson's correlation coefficient, point bi-serial correlation and Eta coefficient. The purpose of this step was to define and select the candidate independent variables for multivariate linear regression. Sequential multiple linear regressions were used to explain the variation of SF-36 MCS scores. Each independent variable was regressed by sequentially adding the variables to the model starting first with the variable showing the highest correlation with SF-36 MCS. Decision to remove a variable from the model was based on the direction and magnitude of change in the estimate of the parameter with highest correlation as well as of the resulting adjusted-R2 values (p-value <0.05). Using a stepwise automatic selection technique, a verification of this model was done. All statistical analysis was performed using the SPSS Statistics 21.0 software (IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp).
Impact of SF-36MCS on surgical results
From the same database, one hundred and eighty-six patients (157 women and 29 men) with ASD surgically treated, and with a minimum of one-year follow-up and full documentation were analysed. For this part of the study, the following data was extracted; baseline SF-36 MCS, demographics and HRQOL data (ODI, COMI, SRS-22 and SF-36 PCS) at one-year post surgery. Using the distribution based Minimally Clinically Important Difference (MCID) for each HRQOL parameter calculated from the aforementioned database; patients were dichotomized into two groups of, “un-improved” and “improved” cohorts.24 That is, a patient who had deteriorated in terms of HRQOL or had not reached the MCID for that HRQOL parameter at the 1st year follow-up was categorized as un-improved.
Statistical analysis
The mean values (and SD) of baseline SF-36MCS for each parameter in the “improved” and “un-improved” categories were calculated. Student's t-test was used to compare pre-operative SF-36MCS between improved and un-improved cohorts for each HRQOL outcome measure (p < 0.05).
A univariate binary logistic regression method was then used to analyse the size effect of pre-operative SF-36 MCS on any HRQOL parameter that was statistically significant on Student's t-test. For this model, the pre-operative SF-36 MCS was the independent variable while HRQOL outcome measures were dependent variables (p < 0.05). For the scale (HRQOL) that statistically significant results were obtained, a clinically useful pre-operative SF-36 MCS cut-off point that could predict the likelihood of a patient attaining MCID post surgery was determined by Receiver Operating Characteristic Curve (ROC) analysis (p < 0.05). Statistical analysis was performed using the same the SPSS Statistics 21.0 software (IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp).
Results
Factors affecting SF-36 MCS before treatment
Of the two hundred twenty nine (n = 229) patients analysed in this part of the study, one hundred and eighty one (79.4%) were females and forty-seven (20.6%) were males. Mean age was 49.4 years with a range of 18–85 years. Eighty one (n = 81) patients had co-morbidities, one hundred and forty-eight (n = 148) did not. Ninety-nine patients (43.4%) had idiopathic scoliosis, eighty five (28.5%) had degenerative scoliosis and eighteen patients (7.9%) had other causes of deformity. All patients had complete SF-36 MCS tool, whereas two hundred and five had complete (n = 205) SRS-22, one hundred and ninety eight (n = 198) had complete ODI and two hundred and twenty nine (n = 229) had complete SF-36 PCS (Table 1).
Table 1.
Number of patients completed the HRQOL questionnaires before treatment and their means.
| SF-36 MCS | SRS-22 | ODI | SF-36 PCS | |
|---|---|---|---|---|
| n | 229 | 205 | 198 | 229 |
| Mean ± Std. Dev. | 41.4 ± 11.8 | 2.9 ± 0.7 | 41.3 ± 22.1 | 36.5 ± 9.1 |
Correlation of baseline SF-36MCS with demographic parameters and HRQOL
There was a strong correlation between baseline SF-36 MCS and baseline SRS-22 and ODI scores with Pearson's correlation coefficients of 0.462, 0,392 respectively, and p value < 0.001 for both. Further, gender (point bi-serial correlation coefficient of 0.150, p value = 0.02) and diagnosis before treatment (Eta coefficient 0.242, p value = 0.010) were found to have significant correlations with baseline SF-36 MCS (Table 2). To evaluate the multi-co linearity between predictor variables for multiple linear regression, correlations between candidate variables were dealt with (Table 3). No multi-co linearity was found amongst independent variables and no variable dimension had more than one variance proportion greater than 0.50.
Table 2.
Correlations between SF-36 MCS and clinical variables.
| Variables | Pearson correlation coefficient | p value |
|---|---|---|
| SRS-22 | 0.462 | <0.001 |
| ODI | −0.391 | <0.001 |
| SF-36 PCS | −0.037 | 0.578 |
| Age | −0.111 | 0.092 |
| BMI | −0.125 | 0.067 |
| Sagittal balance parameters | −0.135 | 0.051 |
| Coronal balance | 0.039 | 0.567 |
| T2-T12 | −0.091 | 0.189 |
| Coronal maximum Cob angle | 0.115 | 0.103 |
| Lgap | 0.033 | 0.636 |
| Global tilt | −0.126 | 0.068 |
| T1 sagittal tilt | −0.032 | 0.654 |
| Major Curve Cobb Angle |
0.115 |
0.103 |
|
Point biserial correlation |
P value |
|
| Gender | 0.150 | 0.02 |
| Comorbidities |
0.060 |
0.39 |
|
Eta coefficient |
P value |
|
| Diagnosis | 0.242 | 0.010 |
| SRS-Schwab Coronal | 0.172 | 0.025 |
| SRS-Schwab Sagittal PI-LL | 0.118 | 0.360 |
| SRS-Schwab Sagittal PT | 0.093 | 0.630 |
| SRS-Schwab Sagittal SVA | 0.106 | 0.420 |
Table 3.
Correlations between candidate clinical variables with statistical significance p < 0.25.
| Variables | SRS22 | ODI | Age | BMI | SVA | T2 T12 | Coronal Maximum Cobb | Global tilt | Major Curve Cobb | Gender | Diagnosis | SRS-Schwab Coronal type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SRS22 | rho | −0.705 | −0.362 | −0.284 | −0.321 | −0.001 | 0.177 | −0.426 | 0.177 | 0.179 | 0.118 | −0.194 | |
| p | <0.001 | <0.001 | <0.001 | <0.001 | 0.984 | 0.016 | <0.001 | 0.016 | 0.010 | 0.091 | 0.006 | ||
| ODI | rho | 0.523 | 0.424 | 0.296 | −0.175 | −0.297 | 0.384 | −0.297 | −0.251 | −0.328 | 0.199 | ||
| p | <0.001 | <0.001 | <0.001 | 0.017 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.005 | |||
| Age | rho | 0.423 | 0.385 | −0.077 | −0.290 | 0.592 | −0.290 | −0.121 | −0.394 | 0.145 | |||
| p | <0.001 | <0.001 | 0.263 | <0.001 | <0.001 | <0.001 | 0.068 | <0.001 | 0.030 | ||||
| BMI | rho | 0.167 | 0.008 | −0.228 | 0.213 | −0.228 | 0.011 | −0.174 | 0.206 | ||||
| p | 0.018 | 0.912 | 0.001 | 0.003 | 0.001 | 0.877 | 0.010 | 0.003 | |||||
| SVA | rho | −0.180 | −0.173 | 0.679 | −0.173 | 0.089 | −0.291 | 0.146 | |||||
| p | 0.011 | 0.016 | <0.001 | 0.016 | 0.199 | <0.001 | 0.034 | ||||||
| T2-T12 | rho | −0.021 | −0.165 | −0.021 | 0.234 | 0.247 | 0.152 | ||||||
| p | 0.775 | 0.020 | 0.775 | 0.001 | <0.001 | 0.027 | |||||||
| Coronal maximum Cobb | rho | −0.240 | 1.000 | 0.023 | −0.014 | −0.616 | |||||||
| p | 0.001 | <0.001 | 0.742 | 0.847 | <0.001 | ||||||||
| Global tilt | rho | −0.240 | −0.105 | −0.318 | 0.141 | ||||||||
| p | 0.001 | 0.132 | <0.001 | 0.042 | |||||||||
| Major Curve Cobb | rho | 0.023 | −0.014 | −0.616 | |||||||||
| p | 0.742 | 0.847 | <0.001 | ||||||||||
| Gender | rho | 0.215 | 0.031 | ||||||||||
| p | 0.001 | 0.648 | |||||||||||
| Diagnosis | rho | 0.011 | |||||||||||
| p | 0.873 |
Regression analysis
Our regression model supported that results were normally distributed and this model met all assumptions as shown in (Fig. 1). R2 is statistically significant F (3,139) = 15.816; (p < 0.001) but only for 0.254 of the study sample. Regression analysis shows that for one unit increase in pre-operative SRS-22, a corresponding increase in the SF-36MCS score by a factor of 0.403 (p < 0.001) is to be expected (ODI was omitted from this analysis because of a strong correlation with it and the SRS-22, a similar trend should apply to the ODI as well). Likewise, one unit (degree) increase in T2-T12 kyphosis decreases SF-36 MCS score by a factor of 0.194 (p = 0.012). Male gender has increased baseline SF-36 MCS by a factor of 0.197 (p = 0.013) compared to females (Table 4).
Fig. 1.
Distribution plot demonstrating all assumptions are met for this regression analysis model.
Table 4.
Multiple linear regression analysis measuring influence of clinical parameters on pre-operative SF-36 MCS.
| Model | Standardized coefficients | t | p |
|---|---|---|---|
| Constant | 4.518 | <0.001 | |
| SRS-22 | 0.403 | 5.344 | <0.001 |
| T2-T12 | −0.194 | −2.557 | 0.012 |
| Gender | 0.197 | 2.522 | 0.013 |
Impact of SF-36MCS on surgical results
A total of one hundred and eighty-six (n = 186) patients who had complete SF-36MCS data and one-year follow-up were analysed in this part of the study. One hundred and fifty seven patients were females (n = 157) and twenty-nine were males (n = 29). There was no statistically significant difference in mean pre-operative SF-36MCS between the surgically “improved” and “un-improved” patients for ODI (p = 0.487). Similarly, there was no statistically significant difference in mean pre-operative SF-36MCS between surgically “improved” and “un-improved” patients for COMI and SRS-22 (p values = 0.881 and 0.415, respectively). On the other hand for SF-36 PCS, this difference was found to be significant; the “un-improved” cohort has a significantly lower mean pre-operative SF-36MCS compared to the “improved” cohort (p = 0.025) (Table 5).
Table 5.
Pre-operative SF-36 MCS of improved group and un-improved group of patients as determined by MCID for ODI, COMI, SF-36 PCS and SRS-22 by t-test.
| Baseline SF36MCS |
p | ||
|---|---|---|---|
| Mean (std.dev.) | |||
| ODI | Un-improved | 38.89 (8.25) | 0.487 |
| Improved | 41.45 (11.95) | ||
| COMI | Un-improved | 42.09 (8.34) | 0.881 |
| Improved | 41.22 (11.46) | ||
| SF-36 PCS | Un-improved | 39.35 (11.14) | 0.025 |
| Improved | 43.44 (12.07) | ||
| SRS-22 | Un-improved | 42.17 (11.12) | 0.415 |
| Improved | 40.65 (12.13) | ||
Univariate binary regression analysis
Since SF-36 PCS was the only HRQOL parameter significant on the t-test, it was the only one included in regression analysis. It was hence found that one unit increase of baseline SF-36 MCS increases the probability of improvement by SF-36 PCS by a factor of 0.914 (p < 0.001). The optimum cut-off for pre-operative SF-36 MCS that provides distinct differentiation between the un-improved and improved by SF-36 PCS was found to be 43.97 (AUC = 0.604; p = 0020), i.e., those patients with SF-36MCS values lower than 43.97 have a significantly lower chance of improvement in SF-36 PCS by surgery.
Discussion
This study aimed to analyse the factors affecting the baseline SF-36 MCS values of patients with adult spinal deformity and whether SF-36 MCS in turn affects the clinical outcomes (in terms of HRQOL parameters) following surgery. Our results suggest that factors highly correlating with baseline SF-36 MCS are baseline SRS-22 and ODI, the magnitude of thoracic kyphosis and gender. In addition this study further demonstrated that the common intuitive belief that those patients with lower MCS scores might not do as well as their counterparts with higher holds only for SF-36 PCS, with a cut-off value for MCS of 43.97.
Some of these findings are somewhat predictable whereas some are particularly interesting in several ways. Firstly, knowing that most HRQOL parameters correlate with each other, it may not be surprising to see that the baseline SF-36 MCS has a very strong correlation with both these tests. As our disability and/or dissatisfaction due to ASD increases, we are expected to acquire a lower mental status. The interesting point here though might have been the distinction between the SRS-22 and ODI that has been shown to exist in previous studies,8 but had not appeared in this study. This may be explained by the heterogeneity of the patient group that had been analysed in this study; those younger patients predominantly affected by their appearances (better reflected by SRS-22), are balanced by the older ones who are not necessarily disturbed by their appearances but rather the disability caused by their deformities (better reflected by ODI). Hence, a breakdown of the patient population in a larger study in the future may identify the specific effects of these two different HRQOL parameters on SF-36 MCS separately.
The authors of this study tended to interpret the effect of thoracic kyphosis angle on SF-36 MCS potentially as one related to appearance. It is particularly interesting that kyphosis and kyphosis only, appears to be important rather than the magnitude of the major coronal curve and/or coronal or sagittal balance parameters that may also affect not only appearance but disability as well. In this respect, SF-36 MCS appears to be significantly different from other HRQOL parameters for which the most important governing factor had been shown to be sagittal balance and the aetiology of the deformity.25 This being said, we may also consider that extreme cases of thoracic kyphosis may also be associated with a higher degree of pain and/or respiratory problems, both of which being factors increasing disability. Hence, the importance of thoracic kyphosis in determining the baseline SF-36 MCS may be multi-factorial, affected by appearance as well as disability of patients.
The effect of gender on the other hand is one that has been shown by other studies as well.25, 26, 27, 28 This is probably due to the different coping mechanisms of genders with the problems associated with ASD, be it appearance or pain and disability or both. Research consistently demonstrates that men and women differ in their responses to pain and there are multiple psychosocial processes that contribute to this difference.29 While men tend to use more behavioural distraction and problem-focused strategies to cope with pain, women are more likely to cope with emotion-oriented behaviours and to seek social support.28, 30 There is also an association between gender and quality of life. Research shows that females have poorer quality of life than males.31 It is possible to explain this by various mediators such as differences in perceived social support,32 catastrophizing33 and self-efficacy.34 Perceived social support is somehow measured by the MCS and as stated before it is a crucial coping mechanism for women. Therefore it is not surprising to see that females reported less emotional well-being (lower MCS scores) than men in this study. Similarly, catastrophizing seems to be mediating gender differences in pain experience and hence self-reported quality of life. Research has shown that women engage in catastrophic thinking more than men, especially in terms of rumination and helplessness.35 Therefore, it is possible that women focus more on their deformity/pain and exaggerate it more than men. And finally, self-efficacy, one's belief that one can successfully perform a behaviour to achieve a goal, could explain the lower emotional QOL in women. Evidence has indicated that women generally demonstrate lower self-efficacy than men, and a lower degree of self-efficacy is associated with higher levels of pain. So, in the present study it is possible that various psychosocial mechanisms have contributed to the lower emotional QOL scores observed in women.
The importance of this finding therefore does not necessarily lie in the fact that it has been found significant, but rather in its potential effect on treatment decisions in patients with ASD. It may important for the surgeons and/or other decision makers to understand that although females appear more prone to be affected by ASD mentally, this does not necessarily mean that surgery (or any other treatment modality) will be less effective in improving their HRQOL, as has been shown by other studies.8 This finding therefore is the materialisation of a natural phenomenon in this specific subject and not necessarily anything else.
As has been summarized above, the potential effects of the mental status of patients have been scrutinised for many pain and disability related spinal conditions.11, 12, 13, 14, 15, 16, 17 Based on this background, the finding that baseline SF-36 MCS does not necessarily affect the outcome at the first year after surgery for both SRS-22 and ODI appears surprising. The only significantly affected factor was found to be the SF-36 PCS, for which a SF-36 MCS cut-off point of 43.97 could be identified. These findings may be explained in several ways. One would be the face value explanation, as indirectly discussed on the matter of gender above, the fact that a patient with ASD should not necessarily mean that the surgical (treatment) outcomes in this patient would be less satisfactory. This line of thought follows the argument that ASD is a very complex blend of several problems (deformity itself, pain, spinal stenosis, disability) that may individually be affected by the baseline mental status of those involved in terms of treatment outcomes but not so when they blend with each other. The relative insensitivity of the disease specific HRQOL measures (SRS-22 specific for deformity and ODI and COMI specific for back related disability) compared to the relative sensitivity of a generic HRQOL measure (SF-36PCS) may support this explanation. Another (fairly obvious) explanation of the relative sensitivity of the treatment outcomes by SF-36 PCS may be that this measure and SF-36 MCS originate and are calculated from the same set of questions and are therefore expected to be very highly correlated and/or affected by each other in any case. What might have been interesting to analyse would be to perform a similar study on patients who had not been treated surgically (or, not at all) to see whether the outcomes by SF-36 PCS would be affected by the SF-36 MCS. This may be elaborated by future studies.
This study has several minor shortcomings. One is our inability to include a large number of patients from the database due to the incompleteness of their SF-36MCS data. The present patient number allows for a fairly high statistical power but apparently, inclusion of a larger number of patients would have made this study even stronger. Secondly, one year followed up had been chosen arbitrarily as it has been thought that one year may be the time that the treatment effects (on baseline parameters) may be at their peak, but it may not be so and the effects of baseline HRQOL parameters may still ensue at longer follow-up periods. Thus future work should also include similar analyses at longer follow-up points. And thirdly, as discussed above, the lack of a control group (of another type of treatment and/or no treatment) needs to be considered as a shortcoming as well. This study has provided information on the effect of SF-36 MCS on treatment results for surgical ASD patients but we still do not have this particular information for non-surgical patients.
Conclusion
This study has demonstrated that the factors effective on the baseline SF-36 MCS in an ASD population are other HRQOL parameters such as SRS-22 and ODI as well as the baseline thoracic kyphosis and gender. There is parallelism between the HRQOL parameters whereas those patients with higher thoracic kyphosis and females have lower SF-36 MCS values. On the other hand, this study has also demonstrated that baseline SF-36 MCS does not necessarily have any effect on the treatment results by surgery as assessed by SRS-22 or ODI. Only outcomes by a non disease specific test, the SF-36 PCS at the first year was found to be significantly affected by baseline SF-36 MCS, a finding that may be explained by the complexity of ASD as a medical problem.
Conflicts of interest
The authors of this manuscript declare the conflicts of interests disclosed below, which are not directly or indirectly related to the results and discussions provided in this manuscript.
ESSG is a clinical research group funded by a research grant from DePuy-Synthes.
Disclosures
Tiro Mmopelwa: Grants/research support: Medtronic.
Selim Ayhan: None
Selcen Yuksel: None
Vugar Nabiyev: None
Asli Niyazi: None.
Ferran Pellise: Grants/research support: Depuy Synthes, K2M.
Consultant: Depuy Synthes.
Ahmet Alanay: Grants/research support: Depuy Synthes.
Consultant: Depuy Synthes, Stryker, Medtronic.
Francisco JS Perez-Grueso Grants/research support: Depuy Synthes.
Consultant: Depuy Synthes Spine.
Frank Kleinstuck: Grants/research support: Depuy Synthes.
Ibrahim Obeid: Grants/research support: Depuy Synthes.
Consultant: Depuy Synthes, Medtronic, Alphatec.
Emre Acaroglu: Grants/Research Support: DePuy Synthes, Medtronic, Stryker Spine.
Speaker's Bureau: AO Spine, Medtronic, Stryker Spine, Zimmer Biomet.
Advisory Board or Panel: AO Spine.
Stock/Shareholder: IncredX (self-managed).
Acknowledgement
ESSG is a clinical research group funded by a research grant from DePuy-Synthes.
Footnotes
Peer review under responsibility of Turkish Association of Orthopaedics and Traumatology.
References
- 1.Bess S., Boachie-Adjei O., Burton D. Pain and disability determine treatment modality for older patients with adult scoliosis, while deformity guides treatment for younger patients. Spine. 2009;34(20):2186–2190. doi: 10.1097/BRS.0b013e3181b05146. [DOI] [PubMed] [Google Scholar]
- 2.Glassman S.D., Schwab F.J., Bridwell K.H., Ondra S.L., Berven S., Lenke L.G. The selection of operative versus nonoperative treatment in patients with adult scoliosis. Spine. 2007;32(1):93–97. doi: 10.1097/01.brs.0000251022.18847.77. [DOI] [PubMed] [Google Scholar]
- 3.Glassman S.D., Berven S., Bridwell K., Horton W., Dimar J.R. Correlation of radiographic parameters and clinical symptoms in adult scoliosis. Spine. 2005;30(6):682–688. doi: 10.1097/01.brs.0000155425.04536.f7. [DOI] [PubMed] [Google Scholar]
- 4.Acaroglu R.E., Dede O., Pellise F. Adult spinal deformity: a very heterogeneous population of patients with different needs. Acta Orthop Traumatol Turc. 2016;50(1):57–62. doi: 10.3944/AOTT.2016.14.0421. [DOI] [PubMed] [Google Scholar]
- 5.Li G., Passias P., Kozanek M. Adult scoliosis in patients over sixty-five years of age: outcomes of operative versus nonoperative treatment at a minimum two-year follow-up. Spine (Phila Pa 1976) 2009;34(20):2165–2170. doi: 10.1097/BRS.0b013e3181b3ff0c. [DOI] [PubMed] [Google Scholar]
- 6.Pellise F., Domingo-Sabat M., Alanay A. Impact of radiographic parameters on HRQOL in adult spinal deformity. The “Lordosis Gap” better than lumbar lordosis ? Eur Spine J. 2012;21(3):S269–S337. [Google Scholar]
- 7.Smith J.S., Lafage V., Shaffrey C.I. Outcomes of operative and nonoperative treatment for adult spinal deformity: a prospective, multicenter, propensity-matched cohort assessment with minimum 2-year follow-up. Neurosurgery. 2016;78(6):851–861. doi: 10.1227/NEU.0000000000001116. [DOI] [PubMed] [Google Scholar]
- 8.Acaroglu E., Yavuz A.C., Guler U.O. A decision analysis to identify the ideal treatment for adult spinal deformity: is surgery better than non-surgical treatment in improving health-related quality of life and decreasing the disease burden? Eur Spine J. 2016 Aug;25(8):2390–2400. doi: 10.1007/s00586-016-4413-8. [DOI] [PubMed] [Google Scholar]
- 9.Hofstetter C.P., Hofer A.S., Wang M.Y. Economic impact of minimally invasive lumbar surgery. World J Orthop. 2015;6(2):190–201. doi: 10.5312/wjo.v6.i2.190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Pellise F., Vila-Casademunt A., Ferrer M. Impact on health related quality of life of adult spinal deformity (ASD) compared with other chronic conditions. Eur Spine J. 2015;24(1):3–11. doi: 10.1007/s00586-014-3542-1. [DOI] [PubMed] [Google Scholar]
- 11.Miller J.A., Derakhshan A., Lubelski D. The impact of preoperative depression on quality of life outcomes after lumbar surgery. Spine J. 2015;15(1):58–64. doi: 10.1016/j.spinee.2014.06.020. [DOI] [PubMed] [Google Scholar]
- 12.Trief P.M., Grant W., Fredrickson B. A prospective study of psychological predictors of lumbar surgery outcome. Spine (Phila Pa 1976) 2000;25(20):2616–2621. doi: 10.1097/00007632-200010150-00012. [DOI] [PubMed] [Google Scholar]
- 13.Greenough C.G., Taylor L.J., Fraser R.D. Anterior lumbar fusion. A comparison of noncompensation patients with compensation patients. Clin Orthop Relat Res. 1994;300:30–37. [PubMed] [Google Scholar]
- 14.Greenough C.G., Taylor L.J., Fraser R.D. Anterior lumbar fusion: results, assessment techniques and prognostic factors. Eur Spine J. 1994;3(4):225–230. doi: 10.1007/BF02221598. [DOI] [PubMed] [Google Scholar]
- 15.Van Susante J., Van de Schaaf D., Pavlov P. Psychological distress deteriorates the subjective outcome of lumbosacral fusion. A prospective study. Acta Orthop Belg. 1998;64(4):371–377. [PubMed] [Google Scholar]
- 16.Celestin J., Edwards R.R., Jamison R.N. Pretreatment psychosocial variables as predictors of outcomes following lumbar surgery and spinal cord stimulation: a systematic review and literature synthesis. Pain Med. 2009;10(4):639–653. doi: 10.1111/j.1526-4637.2009.00632.x. [DOI] [PubMed] [Google Scholar]
- 17.Pakarinen M., Vanhanen S., Sinikallio S. Depressive burden is associated with a poorer surgical outcome among lumbar spinal stenosis patients: a 5-year follow-up study. Spine J. 2014;14(10):2392–2396. doi: 10.1016/j.spinee.2014.01.047. [DOI] [PubMed] [Google Scholar]
- 18.Kristjansdottir J., Olsson G.I., Sundelin C., Naessen T. Could SF-36 be used as a screening instrument for depression in a Swedish youth population? Scand J Caring Sci. 2011;25(2):262–268. doi: 10.1111/j.1471-6712.2010.00821.x. [DOI] [PubMed] [Google Scholar]
- 19.Elliott T.E., Renier C.M., Palcher J.A. Chronic pain, depression, and quality of life: correlations and predictive value of the SF-36. Pain Med. 2003;4(4):331–339. doi: 10.1111/j.1526-4637.2003.03040.x. [DOI] [PubMed] [Google Scholar]
- 20.Qualitymetric I. 2000. The SF-36 Health Survey: A Summary of Responsiveness to Clinical Interventions. Lincoln, RI. [Google Scholar]
- 21.Longworth L., Rowen D. Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value Health. 2013;16(1):202–210. doi: 10.1016/j.jval.2012.10.010. [DOI] [PubMed] [Google Scholar]
- 22.Walsh T.L., Homa K., Hanscom B., Lurie J., Sepulveda M.G., Abdu W. Screening for depressive symptoms in patients with chronic spinal pain using the SF-36 Health Survey. Spine J. 2006;6(3):316–320. doi: 10.1016/j.spinee.2005.11.004. [DOI] [PubMed] [Google Scholar]
- 23.Obeid I., Boissiere L., Yilgor C. Global tilt: a single parameter incorporating spinal and pelvic sagittal parameters and least affected by patient positioning. Eur Spine J. 2016 Nov;25(11):3644–3649. doi: 10.1007/s00586-016-4649-3. [DOI] [PubMed] [Google Scholar]
- 24.Yuksel S., Ayhan S., Domingo-Sabat M. SRS 52nd Annual Meeting & Course. 2017. Minimum Detectable Change (MDC) and Minimum Clinically Important Difference (MCID) of Health Related Quality of Life Parameters in Adult Spinal Deformity. Philadelphia, PA. [Google Scholar]
- 25.Acaroglu E., Guler U.O., Olgun Z.D. Multiple regression analysis of factors affecting health-related quality of life in adult spinal deformity. Spine Deform. 2015;3:360–366. doi: 10.1016/j.jspd.2014.11.004. [DOI] [PubMed] [Google Scholar]
- 26.Pochon L., Kleinstuck F.S., Porchet F., Mannion A.F. Influence of gender on patient-oriented outcomes in spine surgery. Eur Spine J. 2016;25(1):235–246. doi: 10.1007/s00586-015-4062-3. [DOI] [PubMed] [Google Scholar]
- 27.Kothari P., Lee N.J., Leven D.M. Impact of gender on 30-day complications after adult spinal deformity surgery. Spine (Phila Pa 1976) 2016;41(14):1133–1138. doi: 10.1097/BRS.0000000000001499. [DOI] [PubMed] [Google Scholar]
- 28.Unruh A.M., Ritchie J., Merskey H. Does gender affect appraisal of pain and pain coping strategies? Clin J Pain. 1999;15(1):31–40. doi: 10.1097/00002508-199903000-00006. [DOI] [PubMed] [Google Scholar]
- 29.Bartley E.J., Fillingim R.B. Sex differences in pain: a brief review of clinical and experimental findings. Br J Anaesth. 2013;111(1):52–58. doi: 10.1093/bja/aet127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Keogh E., Eccleston C. Sex differences in adolescent chronic pain and pain-related coping. Pain. 2006;123(3):275–284. doi: 10.1016/j.pain.2006.03.004. [DOI] [PubMed] [Google Scholar]
- 31.da Rocha N.S., Schuch F.B., Fleck M.P. Gender differences in perception of quality of life in adults with and without chronic health conditions: the role of depressive symptoms. J Health Psychol. 2014;19(6):721–729. doi: 10.1177/1359105313478644. [DOI] [PubMed] [Google Scholar]
- 32.Emery C.F., Frid D.J., Engebretson T.O. Gender differences in quality of life among cardiac patients. Psychosom Med. 2004;66(2):190–197. doi: 10.1097/01.psy.0000116775.98593.f4. [DOI] [PubMed] [Google Scholar]
- 33.Sullivan M.J.L., Tripp D.A., Santor D. Gender differences in pain and pain behaviour: the role of catastrophizing. Cognit Ther Res. 2000;24(1):121–134. [Google Scholar]
- 34.Jackson T., Iezzi T., Gunderson J., Nagasaka T., Fritch A. Gender differences in pain perception: the mediating role of self-efficacy beliefs. Sex Roles. 2002;47(11):561–568. [Google Scholar]
- 35.Sullivan M.J.L., Bishop S.R., Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess. 1995;7(4):524–532. [Google Scholar]