JOURNAL CLUB JOURNAL CLUB Russell Payne, MD Einar Bogason, MD Brian Anderson, MD Nicholas Brandmeir, MD Ephraim Church, MD
Journal Club: The Impact of Body Mass Index on Hospital Stay and Complications After Spinal Fusion
Jonathon Cooke, MD* Gareth Davies, MD Namath Hussain, MD Akshal Patel, MD Pratik Rohatgi, MD Emily Sieg, MD Omar Zalatimo, MD Endrit Ziu, MD, PhD Justin Davanzo, MD Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, Pennsylvania *This author is a military service member. This work was prepared as part of his official duties. Title 17, USC, §105 provides that, “Copyright protection under this title is not available for any work of the US Government.” Title 17, USC, §101 defines a US Government work as a work prepared by a military service member or employee of the US Government as part of that person’s official duties. The views expressed in this presentation are those of the author and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, or the US government. Correspondence: Russell Payne, MD, Department of Neurosurgery, Penn State Hershey Medical Center, 500 University Dr, Hershey, PA 17033. E-mail: [email protected] Copyright © 2014 by the Congress of Neurological Surgeons.
SIGNIFICANCE/CONTEXT AND IMPORTANCE OF THE STUDY
M
cClendon et al1 investigate a widely acknowledged and increasingly prevalent health concern, obesity, in a novel context: its effects on the postoperative course of patients undergoing extensive (5 or more levels) spine surgery for deformity. The effect of obesity on surgical morbidity has garnered much interest and is the subject of several recently published articles.2-4 Many neurosurgeons consider that obese patients carry a higher surgical risk by virtue of their comorbid conditions, their body habitus and its effect on the technical aspects of surgery, and the effect of obesity on wound healing. The ability to quantify surgical risk and complications according to body mass index (BMI) would facilitate preoperative risk discussions with patients, help with resource allocation, and could potentially alter the timing of elective procedures— all critical considerations in today’s health care universe.
ORIGINALITY OF THE WORK There is an extensive body of literature looking at obesity and its association with spinal surgery complications and outcomes. This article extends these investigations by focusing on a unique subset of spine patients, namely those undergoing fusion of 5 or more levels for spinal deformity. To our knowledge, this is the first article to look at the effect of body mass index (BMI) on elective spinal surgeries with 5 or more levels of fusion for deformity.
APPROPRIATENESS OF THE STUDY DESIGN OR EXPERIMENTAL APPROACH The authors ask a prognostic question (the association of increased BMI with poor outcomes from 5 or greater levels elective spinal surgery for
NEUROSURGERY
deformity) by using a retrospective cohort study design. Data were generated by review of inpatient and outpatient charts from appropriate patients seen at the authors’ institution between 2007 and 2010. Unfortunately, several aspects of this study design potentially weaken the reliability of the authors’ conclusions. First, the authors did not identify a priori primary or secondary outcome measures or adhere to a consistent definition of their independent variable (which is, variously, BMI category, “ideal vs not ideal,” and “obese vs not obese”). As a result, there is a substantial risk of detecting an association when one really does not exist (ie, a type I error). For example, Table 2 shows that, for each BMI category, 22 different outcomes were assessed. Within each BMI category, therefore, the risk of finding at least 1 spurious association (at the .05 level of statistical significance) is 67.6% (1 2 [1 2 0.05]22). Taking all 5 BMI categories together, the risk approaches 100%. Table 3, which presents even more individual comparisons, conveys an even higher risk of type I error. Second, patients in the various BMI groups differed with respect to a number of prognostically important risk factors. Some of these (for example, smoking) seem not to have been controlled for in the authors’ multivariate analysis, and others were excluded from the analysis because of the authors’ relatively stringent threshold for inclusion (P # .05 in univariate analysis). A less stringent threshold (0.1 or 0.2 is often used in studies of this type) might have been helpful. In addition, some known risk factors in spinal surgery such as diabetes mellitus,5 which the authors mentioned in their opening paragraph, were not included in their analysis. Finally, the abundance of unequally distributed potential known confounders raises the concern that additional important but unknown confounders are also asymmetrically present within the different BMI categories. This problem cannot be addressed by multivariate analysis or any other statistical technique.
VOLUME 75 | NUMBER 5 | NOVEMBER 2014 | 599
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited
PAYNE ET AL
Third, patients in the various BMI categories may not have been managed and evaluated for outcomes of interest in an equivalent manner. For example, in 159 surgeries the authors’ high-risk spine protocol was implemented. Unfortunately, the reader has no way of knowing whether this protocol was applied unequally across BMI categories. Because the authors report that the protocol “has improved communications and outcomes,” it would be important to control for this potentially confounding variable. Similarly, because there was no prespecified algorithm for evaluating patients for outcomes of interest, there is the potential for outcome assessment bias. For example, a “highrisk” (obese or morbidly obese) patient might be watched more closely and evaluated more aggressively for postoperative complications such as cardiac ischemia or venous thromboembolism, or might be kept in the intensive care unit longer simply because of their BMI.
ADEQUACY OF EXPERIMENTAL TECHNIQUES Several aspects of the authors’ investigational technique also raise concerns about the reliability of some conclusions. The data set for this study includes 189 surgeries in 112 patients. Sixty-two of these patients underwent staged procedures (ie, 62 patients underwent 124 surgeries). If the patients who underwent staged operations are systemically different from the patients who underwent single operations (and there is no way for us to assess this possibility), and since these patients are contributing 2 sets of data to the study, the potential for introducing substantial bias into the study results exists. Perhaps limiting this study to first operations would have been prudent. The authors collected outcome data at several time points: patient wound checks, early follow-up, 1-year follow-up, and 2year follow-up. Data from appointments up to and including the first year were very robust (with 180-188 of 189 patients contributing data). Unfortunately, the data at the 2-year end point were sparse, with 44% of patients lost to follow-up. The reason for this large fraction of patients who were lost to follow-up is unknown. At a minimum, this degree of patient dropout makes it harder to demonstrate an association if one truly exists. More concerning, if this loss to follow-up was nonrandom (and again we have no way of knowing this), then there is a substantial risk of introducing bias into this study and detecting an apparent association that does not really exist. Therefore, conclusions based on 2-year outcome data are extremely unreliable. The authors’ experimental procedures also raise 3 important statistical concerns. First, several types of ordinal data (the Oswestry Disability Index [ODI], the Charlson Comorbidity Index, and the American Association of Anesthesiologists classification) were analyzed by using measures and techniques more appropriate to interval data. Median and interquartile distance or median and range (rather than mean and standard deviation) are the most appropriate measures of central tendency and dispersion for ordinal data. More importantly, measures of association and statistical significance more appropriate to
600 | VOLUME 75 | NUMBER 5 | NOVEMBER 2014
interval data (ie, parametric statistical tests) instead of nonparametric tests were applied to these data. Although this concern seems, on its face, to be a minor statistical detail, applying parametric statistics to ordinal data runs the risk of coming to very erroneous conclusions. Although it is true that similar conclusions are sometimes reached when ordinal data are analyzed using either parametric or nonparametric techniques, this is not always the case, and applying parametric statistical techniques to ordinal data is, literally, making up data, even when, as in the case of the ODI, a mathematical transformation (conversion into a percentage score) gives the superficial appearance of interval data. Second, as the authors discuss, “When BMI was placed in the multivariate linear regression analysis for hospital stay as a continuous variable, it was not significant.” As a result, the authors dichotomized BMI (“ideal” vs “not ideal”) when looking at the outcome measure of hospital stay, and used a different dichotomization (“obese” vs “not obese”) when looking at complications at 1 year. In addition to losing information by converting interval data (BMI) into nominal data (eg “ideal” vs “not ideal”), the fact that this manipulation of the independent variable was not prespecified in the authors’ study plan exposes the study results to a substantial risk of type I error.
SOUNDNESS OF CONCLUSIONS AND INTERPRETATIONS To their credit, the authors identify many of the study limitations outlined in the preceding paragraphs. Although their data suggest an association between BMI and ODI, and between certain BMI categories and specific postoperative outcomes (ideal vs not ideal weight and hospital stay; obese vs not obese and complications at 1 year), the authors’ study design (retrospective cohort) and additional threats to reliability suggest that the authors’ results should be considered hypothesis generating rather than conclusive. In the end, neurosurgeons must use the data available to them to make the best treatment decisions for their patients. The data from McClendon et al1 are currently the best data available to help direct therapeutic decisions in this group of patients, but the authors should emphasize that additional investigation is necessary, and, in the absence of compelling evidence, a commensurately larger role must be given to physician judgment and experience and to patient preference.
RELEVANCE OF DISCUSSION The authors do an excellent job of summarizing the current literature regarding the effect of obesity on spinal surgery, and they present their conclusions clearly and concisely.
CLARITY OF WRITING, STRENGTH, AND ORGANIZATION OF THE ARTICLE This article was well organized and clearly written.
www.neurosurgery-online.com
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited
JOURNAL CLUB
ECONOMY OF WORDS The content of the article was presented economically and effectively.
the risks of poor outcomes. In addition, prospective intervention trials evaluating the benefit of preoperative weight loss in the context of elective spine surgery should be considered.
RELEVANCE, ACCURACY, AND COMPLETENESS OF BIBLIOGRAPHY
Disclosure
The authors do an excellent job of reviewing the relevant literature with regard to the association between increased BMI and outcomes from spinal surgery. Because the authors also discuss a potential association between underweight individuals and surgical outcomes, inclusion of studies addressing this issue (for example, Tarrant, et al6) would have been useful.
REFERENCES
NUMBER AND QUALITY OF FIGURES, TABLES, AND ILLUSTRATIONS The tables in this article were easy to interpret and contributed substantially to the reader’s understanding.
FUTURE/NEXT STEPS McClendon et al1 have drawn appropriate attention to the potential risks of extensive spine surgery for deformity in patients with increased BMI. Obesity is a profound health concern in the United States, and one that will remain an important consideration for neurosurgeons for the foreseeable future. Future studies should focus on a more precise and reliable estimation of
NEUROSURGERY
The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
1. McClendon J Jr, Smith TR, Thompson SE, et al. The impact of body mass index on hospital stay and complications after spinal fusion. Neurosurgery. 2014;74(1):42-50. 2. Marquez-Lara A, Nandyala SV, Sankaranarayanan S, Noureldin M, Singh K. Body mass index as a predictor of complications and mortality after lumbar spine surgery. Spine (Phila Pa 1976). 2014;39(10):798-804. 3. Seicean A, Alan N, Seicean S, et al. Impact of increased body mass index on outcomes of elective spinal surgery. Spine (Phila Pa 1976). 2014 Aug 15;39(18): 1520-30. doi: 10.1097/BRS.0000000000000435. 4. Buerba RA, Fu MC, Gruskay JA, Long WD, Grauer JN. Obese Class III patients at significantly greater risk of multiple complications after lumbar surgery: an analysis of 10,387 patients in the ACS NSQIP database. Spine J. 2014 Sep 1;14(9):2008-18. doi: 10.1016/j.spinee.2013.11.047. Epub 2013 Dec 6. 5. Appaduray SP, Lo P. Effects of diabetes and smoking on lumbar spinal surgery outcomes. J Clin Neurosci. 2013;20(12):1713-1717. 6. Tarrant RC, Lynch S, Sheeran P, et al. Low body mass index in adolescent idiopathic scoliosis: relationship with pre- and postsurgical factors. Spine (Phila Pa 1976). 2014;39(2):140-148.
Acknowledgments The authors gratefully acknowledge the faculty members who provided guidance for this journal club report including Michael J. Glantz, MD, Robert E. Harbaugh, MD, Jonas M. Sheehan, MD, and Scott Simon, MD.
VOLUME 75 | NUMBER 5 | NOVEMBER 2014 | 601
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited
Journal Club: The Impact of Body Mass Index on Hospital Stay and Complications After Spinal Fusion
Jonathon Cooke, MD* Gareth Davies, MD Namath Hussain, MD Akshal Patel, MD Pratik Rohatgi, MD Emily Sieg, MD Omar Zalatimo, MD Endrit Ziu, MD, PhD Justin Davanzo, MD Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, Pennsylvania *This author is a military service member. This work was prepared as part of his official duties. Title 17, USC, §105 provides that, “Copyright protection under this title is not available for any work of the US Government.” Title 17, USC, §101 defines a US Government work as a work prepared by a military service member or employee of the US Government as part of that person’s official duties. The views expressed in this presentation are those of the author and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, or the US government. Correspondence: Russell Payne, MD, Department of Neurosurgery, Penn State Hershey Medical Center, 500 University Dr, Hershey, PA 17033. E-mail: [email protected] Copyright © 2014 by the Congress of Neurological Surgeons.
SIGNIFICANCE/CONTEXT AND IMPORTANCE OF THE STUDY
M
cClendon et al1 investigate a widely acknowledged and increasingly prevalent health concern, obesity, in a novel context: its effects on the postoperative course of patients undergoing extensive (5 or more levels) spine surgery for deformity. The effect of obesity on surgical morbidity has garnered much interest and is the subject of several recently published articles.2-4 Many neurosurgeons consider that obese patients carry a higher surgical risk by virtue of their comorbid conditions, their body habitus and its effect on the technical aspects of surgery, and the effect of obesity on wound healing. The ability to quantify surgical risk and complications according to body mass index (BMI) would facilitate preoperative risk discussions with patients, help with resource allocation, and could potentially alter the timing of elective procedures— all critical considerations in today’s health care universe.
ORIGINALITY OF THE WORK There is an extensive body of literature looking at obesity and its association with spinal surgery complications and outcomes. This article extends these investigations by focusing on a unique subset of spine patients, namely those undergoing fusion of 5 or more levels for spinal deformity. To our knowledge, this is the first article to look at the effect of body mass index (BMI) on elective spinal surgeries with 5 or more levels of fusion for deformity.
APPROPRIATENESS OF THE STUDY DESIGN OR EXPERIMENTAL APPROACH The authors ask a prognostic question (the association of increased BMI with poor outcomes from 5 or greater levels elective spinal surgery for
NEUROSURGERY
deformity) by using a retrospective cohort study design. Data were generated by review of inpatient and outpatient charts from appropriate patients seen at the authors’ institution between 2007 and 2010. Unfortunately, several aspects of this study design potentially weaken the reliability of the authors’ conclusions. First, the authors did not identify a priori primary or secondary outcome measures or adhere to a consistent definition of their independent variable (which is, variously, BMI category, “ideal vs not ideal,” and “obese vs not obese”). As a result, there is a substantial risk of detecting an association when one really does not exist (ie, a type I error). For example, Table 2 shows that, for each BMI category, 22 different outcomes were assessed. Within each BMI category, therefore, the risk of finding at least 1 spurious association (at the .05 level of statistical significance) is 67.6% (1 2 [1 2 0.05]22). Taking all 5 BMI categories together, the risk approaches 100%. Table 3, which presents even more individual comparisons, conveys an even higher risk of type I error. Second, patients in the various BMI groups differed with respect to a number of prognostically important risk factors. Some of these (for example, smoking) seem not to have been controlled for in the authors’ multivariate analysis, and others were excluded from the analysis because of the authors’ relatively stringent threshold for inclusion (P # .05 in univariate analysis). A less stringent threshold (0.1 or 0.2 is often used in studies of this type) might have been helpful. In addition, some known risk factors in spinal surgery such as diabetes mellitus,5 which the authors mentioned in their opening paragraph, were not included in their analysis. Finally, the abundance of unequally distributed potential known confounders raises the concern that additional important but unknown confounders are also asymmetrically present within the different BMI categories. This problem cannot be addressed by multivariate analysis or any other statistical technique.
VOLUME 75 | NUMBER 5 | NOVEMBER 2014 | 599
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited
PAYNE ET AL
Third, patients in the various BMI categories may not have been managed and evaluated for outcomes of interest in an equivalent manner. For example, in 159 surgeries the authors’ high-risk spine protocol was implemented. Unfortunately, the reader has no way of knowing whether this protocol was applied unequally across BMI categories. Because the authors report that the protocol “has improved communications and outcomes,” it would be important to control for this potentially confounding variable. Similarly, because there was no prespecified algorithm for evaluating patients for outcomes of interest, there is the potential for outcome assessment bias. For example, a “highrisk” (obese or morbidly obese) patient might be watched more closely and evaluated more aggressively for postoperative complications such as cardiac ischemia or venous thromboembolism, or might be kept in the intensive care unit longer simply because of their BMI.
ADEQUACY OF EXPERIMENTAL TECHNIQUES Several aspects of the authors’ investigational technique also raise concerns about the reliability of some conclusions. The data set for this study includes 189 surgeries in 112 patients. Sixty-two of these patients underwent staged procedures (ie, 62 patients underwent 124 surgeries). If the patients who underwent staged operations are systemically different from the patients who underwent single operations (and there is no way for us to assess this possibility), and since these patients are contributing 2 sets of data to the study, the potential for introducing substantial bias into the study results exists. Perhaps limiting this study to first operations would have been prudent. The authors collected outcome data at several time points: patient wound checks, early follow-up, 1-year follow-up, and 2year follow-up. Data from appointments up to and including the first year were very robust (with 180-188 of 189 patients contributing data). Unfortunately, the data at the 2-year end point were sparse, with 44% of patients lost to follow-up. The reason for this large fraction of patients who were lost to follow-up is unknown. At a minimum, this degree of patient dropout makes it harder to demonstrate an association if one truly exists. More concerning, if this loss to follow-up was nonrandom (and again we have no way of knowing this), then there is a substantial risk of introducing bias into this study and detecting an apparent association that does not really exist. Therefore, conclusions based on 2-year outcome data are extremely unreliable. The authors’ experimental procedures also raise 3 important statistical concerns. First, several types of ordinal data (the Oswestry Disability Index [ODI], the Charlson Comorbidity Index, and the American Association of Anesthesiologists classification) were analyzed by using measures and techniques more appropriate to interval data. Median and interquartile distance or median and range (rather than mean and standard deviation) are the most appropriate measures of central tendency and dispersion for ordinal data. More importantly, measures of association and statistical significance more appropriate to
600 | VOLUME 75 | NUMBER 5 | NOVEMBER 2014
interval data (ie, parametric statistical tests) instead of nonparametric tests were applied to these data. Although this concern seems, on its face, to be a minor statistical detail, applying parametric statistics to ordinal data runs the risk of coming to very erroneous conclusions. Although it is true that similar conclusions are sometimes reached when ordinal data are analyzed using either parametric or nonparametric techniques, this is not always the case, and applying parametric statistical techniques to ordinal data is, literally, making up data, even when, as in the case of the ODI, a mathematical transformation (conversion into a percentage score) gives the superficial appearance of interval data. Second, as the authors discuss, “When BMI was placed in the multivariate linear regression analysis for hospital stay as a continuous variable, it was not significant.” As a result, the authors dichotomized BMI (“ideal” vs “not ideal”) when looking at the outcome measure of hospital stay, and used a different dichotomization (“obese” vs “not obese”) when looking at complications at 1 year. In addition to losing information by converting interval data (BMI) into nominal data (eg “ideal” vs “not ideal”), the fact that this manipulation of the independent variable was not prespecified in the authors’ study plan exposes the study results to a substantial risk of type I error.
SOUNDNESS OF CONCLUSIONS AND INTERPRETATIONS To their credit, the authors identify many of the study limitations outlined in the preceding paragraphs. Although their data suggest an association between BMI and ODI, and between certain BMI categories and specific postoperative outcomes (ideal vs not ideal weight and hospital stay; obese vs not obese and complications at 1 year), the authors’ study design (retrospective cohort) and additional threats to reliability suggest that the authors’ results should be considered hypothesis generating rather than conclusive. In the end, neurosurgeons must use the data available to them to make the best treatment decisions for their patients. The data from McClendon et al1 are currently the best data available to help direct therapeutic decisions in this group of patients, but the authors should emphasize that additional investigation is necessary, and, in the absence of compelling evidence, a commensurately larger role must be given to physician judgment and experience and to patient preference.
RELEVANCE OF DISCUSSION The authors do an excellent job of summarizing the current literature regarding the effect of obesity on spinal surgery, and they present their conclusions clearly and concisely.
CLARITY OF WRITING, STRENGTH, AND ORGANIZATION OF THE ARTICLE This article was well organized and clearly written.
www.neurosurgery-online.com
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited
JOURNAL CLUB
ECONOMY OF WORDS The content of the article was presented economically and effectively.
the risks of poor outcomes. In addition, prospective intervention trials evaluating the benefit of preoperative weight loss in the context of elective spine surgery should be considered.
RELEVANCE, ACCURACY, AND COMPLETENESS OF BIBLIOGRAPHY
Disclosure
The authors do an excellent job of reviewing the relevant literature with regard to the association between increased BMI and outcomes from spinal surgery. Because the authors also discuss a potential association between underweight individuals and surgical outcomes, inclusion of studies addressing this issue (for example, Tarrant, et al6) would have been useful.
REFERENCES
NUMBER AND QUALITY OF FIGURES, TABLES, AND ILLUSTRATIONS The tables in this article were easy to interpret and contributed substantially to the reader’s understanding.
FUTURE/NEXT STEPS McClendon et al1 have drawn appropriate attention to the potential risks of extensive spine surgery for deformity in patients with increased BMI. Obesity is a profound health concern in the United States, and one that will remain an important consideration for neurosurgeons for the foreseeable future. Future studies should focus on a more precise and reliable estimation of
NEUROSURGERY
The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
1. McClendon J Jr, Smith TR, Thompson SE, et al. The impact of body mass index on hospital stay and complications after spinal fusion. Neurosurgery. 2014;74(1):42-50. 2. Marquez-Lara A, Nandyala SV, Sankaranarayanan S, Noureldin M, Singh K. Body mass index as a predictor of complications and mortality after lumbar spine surgery. Spine (Phila Pa 1976). 2014;39(10):798-804. 3. Seicean A, Alan N, Seicean S, et al. Impact of increased body mass index on outcomes of elective spinal surgery. Spine (Phila Pa 1976). 2014 Aug 15;39(18): 1520-30. doi: 10.1097/BRS.0000000000000435. 4. Buerba RA, Fu MC, Gruskay JA, Long WD, Grauer JN. Obese Class III patients at significantly greater risk of multiple complications after lumbar surgery: an analysis of 10,387 patients in the ACS NSQIP database. Spine J. 2014 Sep 1;14(9):2008-18. doi: 10.1016/j.spinee.2013.11.047. Epub 2013 Dec 6. 5. Appaduray SP, Lo P. Effects of diabetes and smoking on lumbar spinal surgery outcomes. J Clin Neurosci. 2013;20(12):1713-1717. 6. Tarrant RC, Lynch S, Sheeran P, et al. Low body mass index in adolescent idiopathic scoliosis: relationship with pre- and postsurgical factors. Spine (Phila Pa 1976). 2014;39(2):140-148.
Acknowledgments The authors gratefully acknowledge the faculty members who provided guidance for this journal club report including Michael J. Glantz, MD, Robert E. Harbaugh, MD, Jonas M. Sheehan, MD, and Scott Simon, MD.
VOLUME 75 | NUMBER 5 | NOVEMBER 2014 | 601
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited
