CORRESPONDENCE CORRESPONDENCE
TOPIC
Einar T. Bogason, MD
Journal Club: The Epidemiology of Admissions of Nontraumatic Subarachnoid Hemorrhage in the United States
Brian Anderson, MD Nicholas J. Brandmeir, MD Ephraim W. Church, MD Jonathon Cooke, MD* Gareth M. Davies, MD Namath Hussain, MD Akshal S. Patel, MD Russell Payne, MD Pratik Rohatgi, MD Emily Sieg, MD Omar Zalatimo, MD Endrit Ziu, MD, PhD 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 United States government. Correspondence: Einar T. Bogason MD, Department of Neurosurgery, Penn Sate Hershey Medical Center, 500 University Dr, Hershey, PA 17033. E-mail: [email protected] Received, October 3, 2013. Accepted, October 25, 2013. Copyright © 2013 by the Congress of Neurological Surgeons
NEUROSURGERY
SIGNIFICANCE AND IMPORTANCE OF STUDY
investigators interested in SAH and in a multitude of other diagnoses included in the NHDS.
troke is a leading cause of mortality and morbidity in the industrialized world. Subarachnoid hemorrhage (SAH) is an uncommon but extremely virulent cause of stroke with an incidence of 6 to 8 per 100 000 per year.1 This rate appears to vary considerably with geographical location. There have been no large epidemiological studies performed in the United States that have focused on long-term effects, incidence, or outcome measures. The study by Rincon et al (2013) is a retrospective cohort study that addresses this important topic. The authors hypothesize that the incidence of SAH and in-hospital mortality from SAH have not changed significantly in the 30 year time frame spanned by the study. If this hypothesis is true, it suggests the need for more intensive research on the management of patients with SAH. Conversely, as the authors point out, if the frequency, morbidity, or mortality of SAH have changed appreciably over the last 30 years, this finding would have important public health consequences, and would necessitate further study.
APPROPRIATENESS OF STUDY DESIGN
S
ORIGINALITY OF WORK Numerous epidemiological studies of SAH indicate a variable incidence based on geographical location. However, few studies specifically examine the epidemiology of SAH in the United States, and none of these previous studies examine data sets as extensive as the present study. By using a large, easily accessible, extensively characterized, and widely accessed registry with known and very impressive sensitivity and specificity for the accurate diagnosis of SAH (the National Hospital Discharge Survey [NHDS]), the authors are not only able to make highly powered comparisons and strongly supported hypotheses, but also provide a model for other
The population of interest, patients with SAH, was extracted from NHDS data collected between 1979 and 2008. Exposures of interest, including, age, sex, race, and hospital size, as well as multiple outcome measures (organ dysfunction, length of care, and disposition) were also gathered. The NHDS registry comprises data collected from a subset of invited hospitals in the United States who voluntarily agreed to participate. Institutional, federal, military and VA hospitals were excluded from the registry. Approximately 500 hospitals with nearly 350 000 annual discharges are included in the database. Patients with the primary diagnosis of SAH were identified using the ICD9 diagnosis code 430 as the inclusion criteria. The entire cohort was then divided into 6 subsets by date of occurrence (5 year intervals). There are several important limitations inherent in the design of the study. The study is based on data from a national registry, which, despite its large size, is not population-based. Not only are certain hospitals excluded, but 20% of hospitals initially invited to participate in the NHDS registry chose not do so.2 At a minimum, this structuring of the database restricts the external validity (generalizability) of the authors’ conclusions, and if the loss of institutions that declined to participate in the NHDS was nonrandom, an important source of bias will have been introduced. Selection of SAH patients from the registry using an ICD9 code could result in misclassification, most likely non-differential in nature, and could reduce the ability of the authors’ analysis to demonstrate associations when those associations actually exist (type II error). The fact that previous studies have shown a positive predictive value of 94% for SAH case-finding
VOLUME 74 | NUMBER 2 | FEBRUARY 2014 | 227
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited.
BOGASON ET AL
when using the authors’ procedure makes this possibility less likely, however.3 More worrisome is the use of ICD9 codes to collect information on organ dysfunction. According to the authors’ data, the frequency of organ dysfunction increased steadily throughout the study period. However, the intense focus in recent years on accurate billing and coding of complications could represent a confounder that produced an apparent but spurious association between study sub-periods (1979-1983, 1984-1988, etc.) and an increased frequency of organ dysfunction (ie, a type I error). Finally, the 5 disposition categories used by the authors (Death, Discharge to Home, Discharge to Short-Term Facility, Discharge to Long-Term Facility, and Other) lack sufficient precision. Specifically, the nature of the Other category, for which the greatest single between-group change was identified, could completely alter the conclusions of the study. For example, if Other comprises patients transferred to in-hospital palliative care or out-of-hospital hospice care, both of which disposition options have increased dramatically in frequency over the time period of this study, the apparent decline in death rate over time could be completely subverted, as discussed more fully below.
ADEQUACY OF EXPERIMENTAL [STATISTICAL] TECHNIQUES Although the authors very reasonably use a one-way analysis of variance to determine the statistical significance of the falling number of “Days of Care” (“Characteristics of Study Cohort” table), they analyze differences in in-hospital mortality by comparing individual sub-periods using a x2 technique. This method of multiple comparisons increases the likelihood of a type 1 error. Using instead a linear regression model with the subperiods as the independent variable and death as the dependent variable, the association between decline in mortality with consecutive sub-periods is found to be weak and statistically non-significant (R2 = .565, P = .085). In fact, if the same analysis is undertaken after adding “Death” plus “Other” together (which would be appropriate if Other includes primarily patients transferred to palliative in-patient care or out-patient hospice), exactly the opposite correlation (increased Death plus Other over time), albeit once again statistically non-significant, is seen (R2 = .401, P = .177).
SOUNDNESS OF CONCLUSIONS AND INTERPRETATION The authors’ conclusions are well organized. They accurately state that the admission rate has remained stable over the study period and incidence of SAH in their study is comparable to earlier population-based studies. However, as discussed in our analysis, we argue that in-hospital mortality rates and improvement in outcome at larger referral centers compared to smaller hospitals cannot be reliably determined from their data, thus undermining 2 of the authors’ central conclusions.
228 | VOLUME 74 | NUMBER 2 | FEBRUARY 2014
RELEVANCE OF DISCUSSION The authors’ discussion is concise and clearly identifies some of the important limitations of their study. The authors acknowledge the fact that this is a non-population-based study but do not address the questions that arise regarding external validity. They also argue, unconvincingly and by the use of analogy, that because the incidence and some epidemiological findings in their study (for example the increased frequency of SAH in women and non-whites) are consistent with previous studies, their sample is representative of the entire US population. As illustrated above, while in-hospital death rates appear to have decreased significantly during the study period (dropping from 30% to 20% between the first and last sub-periods), the potential confounder represented by the Other category could completely reverse this conclusion. Unfortunately, interrogation of the NHDS database shows that further detail regarding the Other category was not collected. This is a serious flaw in the study conclusions, which is not addressed in the discussion. The authors also cite studies demonstrating a benefit to treatment at high volume centers. However, the most definitive study addressing this question4 showed a stronger correlation between the individual surgeon’s volume and operative mortality than overall hospital volume and mortality. An additional concern when analyzing the relationship between hospital volume and outcome is the potential bias introduced by selective transfer of patients with good prognostic features to larger referral centers. For example, Angileri et al5 assessed transfer rate in telemedicine stroke patients to a tertiary center and argued that poor outcome or terminal patients were often not transferred, leading to possible confounding that would exaggerate the apparent association of poor outcome with smaller hospitals, and good outcomes with larger referral centers. Again, this issue is not discussed by the authors, and could potentially negate one of their primary conclusions and public health-related recommendations. Finally, the change in size of hospitals during the study period is difficult to interpret. There was a decrease in both extra large and small hospitals with a concomitant increase in large hospitals. Thus, when large and extra large hospitals are grouped together there was only a 2% change from small to larger hospitals. The possible effect of larger volume hospitals on outcome cannot be appropriately assessed based on the information collected in this study and the statement in the discussion arguing improved outcomes are seen in larger centers should therefore be treated with caution.
CLARITY OF WRITING, STRENGTH, AND ORGANIZATION OF PAPER This paper is highly readable and well-organized. The primary hypothesis is clearly stated and the methods and result section focus on that primary hypothesis. The Limitations section is honest and valuable, and the discussion is thorough, but does overlook some important alternative interpretations of the authors’ data.
www.neurosurgery-online.com
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited.
CORRESPONDENCE
ECONOMY OF WORDS The paper is clear, concise, and focused on the purpose and hypothesis stated by the authors in their introduction.
RELEVANCE, ACCURACY AND COMPLETENESS OF BIBLIOGRAPHY An extensive bibliography is provided, but 2 important references (noted above) are omitted: Birkmeyer et al (2003), a meticulous study analyzing the relationship between neurosurgical outcome and hospital volume, and Angilerii et al (2012), which provides a contrary explanation for the apparent improvement in outcomes in referral centers compared to small outlying hospitals.
patients with SAH from smaller to larger hospitals is required based on the limited data available from the NHDS database. In order to more reliably address these critical questions, the authors are now in excellent position, based on their experience with this manuscript, to spearhead the development of a prospective SAH registry that strives to include all neurosurgeons in the United States. One way to accomplish this would be to make participation in such a registry mandatory as part of the credentialing process. In addition to basic demographic data, this prospective registry could include standard quantitative assessments of patient status. Appropriate outcome scores and more detail regarding discharge disposition could be collected as well. In return for participation, the administrators of such a database could make the aggregate data available to any researcher wishing to address questions relevant to the epidemiology and management of SAH in the United States.
NUMBER AND QUALITY OF FIGURES, TABLES, DRAWINGS
Disclosure
The single table provided in the paper was clear and easy to interpret. The 2 figures were also and easy to understand, but as noted above, the P-values reported in the caption for Figure 2 are misleading. Adding figures for the linear regression model would visually help interpret results and show trends across sub-periods.
REFERENCES
FUTURE/NEXT STEPS This study provides a very valuable picture of the incidence of SAH in the United States, suggests a number of testable hypotheses, and perhaps most important of all, has identified a number of shortcomings in the available registry data which limit the reliability of any inferences made from that data. For example, one of the key conclusions of this study is that in-hospital mortality has decreased over the last 30 years. However, as discussed above, this conclusion is flawed. Without further details about disposition locations listed as “other” it is impossible to definitively state that death rates have improved significantly over the years. Adjusted statistical analysis based on this missing information would be valuable. Similar caution in recommending routine transfer of
NEUROSURGERY
This work is not supported by or affiliated with any funding source. The authors have no personal or institutional financial interest in drugs, materials, or devices described in this article.
1. Linn FH, Rinkel GJ, Algra A, van Gijn J. Incidence of subarachnoid hemorrhage: role of region, year, and rate of computed tomography: a meta-analysis. Stroke. 1996;27(4):625-629. 2. Dennison C, Pokras R. Design and operation of the National Hospital Discharge Survey 1988 redesign. Vital Health Stat 1. 2000;39(39):1-42. 3. Tirschwell DL, Longstreth WT Jr. Validating administrative data in stroke research. Stroke. 2002;33(10):2465-2470. 4. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and operative mortality in the United States. N Engl J Med. 2003; 349(22):2117-2127. 5. Angilerii FF, Cardali S, Conti A, Raffa G, Tomasello F. Telemedicine-assisted treatment of patients with intracerebral hemorrhage. Neurosurg Focus. 2012; 32(4):E6.
Acknowledgments The authors gratefully acknowledge the faculty members who provided guidance for this journal club report including Dr Michael J. Glantz, MD, Dr Robert E. Harbaugh, MD, Dr Jonas M. Sheehan, MD, and Dr Scott Simon, MD.
VOLUME 74 | NUMBER 2 | FEBRUARY 2014 | 229
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited.
TOPIC
Einar T. Bogason, MD
Journal Club: The Epidemiology of Admissions of Nontraumatic Subarachnoid Hemorrhage in the United States
Brian Anderson, MD Nicholas J. Brandmeir, MD Ephraim W. Church, MD Jonathon Cooke, MD* Gareth M. Davies, MD Namath Hussain, MD Akshal S. Patel, MD Russell Payne, MD Pratik Rohatgi, MD Emily Sieg, MD Omar Zalatimo, MD Endrit Ziu, MD, PhD 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 United States government. Correspondence: Einar T. Bogason MD, Department of Neurosurgery, Penn Sate Hershey Medical Center, 500 University Dr, Hershey, PA 17033. E-mail: [email protected] Received, October 3, 2013. Accepted, October 25, 2013. Copyright © 2013 by the Congress of Neurological Surgeons
NEUROSURGERY
SIGNIFICANCE AND IMPORTANCE OF STUDY
investigators interested in SAH and in a multitude of other diagnoses included in the NHDS.
troke is a leading cause of mortality and morbidity in the industrialized world. Subarachnoid hemorrhage (SAH) is an uncommon but extremely virulent cause of stroke with an incidence of 6 to 8 per 100 000 per year.1 This rate appears to vary considerably with geographical location. There have been no large epidemiological studies performed in the United States that have focused on long-term effects, incidence, or outcome measures. The study by Rincon et al (2013) is a retrospective cohort study that addresses this important topic. The authors hypothesize that the incidence of SAH and in-hospital mortality from SAH have not changed significantly in the 30 year time frame spanned by the study. If this hypothesis is true, it suggests the need for more intensive research on the management of patients with SAH. Conversely, as the authors point out, if the frequency, morbidity, or mortality of SAH have changed appreciably over the last 30 years, this finding would have important public health consequences, and would necessitate further study.
APPROPRIATENESS OF STUDY DESIGN
S
ORIGINALITY OF WORK Numerous epidemiological studies of SAH indicate a variable incidence based on geographical location. However, few studies specifically examine the epidemiology of SAH in the United States, and none of these previous studies examine data sets as extensive as the present study. By using a large, easily accessible, extensively characterized, and widely accessed registry with known and very impressive sensitivity and specificity for the accurate diagnosis of SAH (the National Hospital Discharge Survey [NHDS]), the authors are not only able to make highly powered comparisons and strongly supported hypotheses, but also provide a model for other
The population of interest, patients with SAH, was extracted from NHDS data collected between 1979 and 2008. Exposures of interest, including, age, sex, race, and hospital size, as well as multiple outcome measures (organ dysfunction, length of care, and disposition) were also gathered. The NHDS registry comprises data collected from a subset of invited hospitals in the United States who voluntarily agreed to participate. Institutional, federal, military and VA hospitals were excluded from the registry. Approximately 500 hospitals with nearly 350 000 annual discharges are included in the database. Patients with the primary diagnosis of SAH were identified using the ICD9 diagnosis code 430 as the inclusion criteria. The entire cohort was then divided into 6 subsets by date of occurrence (5 year intervals). There are several important limitations inherent in the design of the study. The study is based on data from a national registry, which, despite its large size, is not population-based. Not only are certain hospitals excluded, but 20% of hospitals initially invited to participate in the NHDS registry chose not do so.2 At a minimum, this structuring of the database restricts the external validity (generalizability) of the authors’ conclusions, and if the loss of institutions that declined to participate in the NHDS was nonrandom, an important source of bias will have been introduced. Selection of SAH patients from the registry using an ICD9 code could result in misclassification, most likely non-differential in nature, and could reduce the ability of the authors’ analysis to demonstrate associations when those associations actually exist (type II error). The fact that previous studies have shown a positive predictive value of 94% for SAH case-finding
VOLUME 74 | NUMBER 2 | FEBRUARY 2014 | 227
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited.
BOGASON ET AL
when using the authors’ procedure makes this possibility less likely, however.3 More worrisome is the use of ICD9 codes to collect information on organ dysfunction. According to the authors’ data, the frequency of organ dysfunction increased steadily throughout the study period. However, the intense focus in recent years on accurate billing and coding of complications could represent a confounder that produced an apparent but spurious association between study sub-periods (1979-1983, 1984-1988, etc.) and an increased frequency of organ dysfunction (ie, a type I error). Finally, the 5 disposition categories used by the authors (Death, Discharge to Home, Discharge to Short-Term Facility, Discharge to Long-Term Facility, and Other) lack sufficient precision. Specifically, the nature of the Other category, for which the greatest single between-group change was identified, could completely alter the conclusions of the study. For example, if Other comprises patients transferred to in-hospital palliative care or out-of-hospital hospice care, both of which disposition options have increased dramatically in frequency over the time period of this study, the apparent decline in death rate over time could be completely subverted, as discussed more fully below.
ADEQUACY OF EXPERIMENTAL [STATISTICAL] TECHNIQUES Although the authors very reasonably use a one-way analysis of variance to determine the statistical significance of the falling number of “Days of Care” (“Characteristics of Study Cohort” table), they analyze differences in in-hospital mortality by comparing individual sub-periods using a x2 technique. This method of multiple comparisons increases the likelihood of a type 1 error. Using instead a linear regression model with the subperiods as the independent variable and death as the dependent variable, the association between decline in mortality with consecutive sub-periods is found to be weak and statistically non-significant (R2 = .565, P = .085). In fact, if the same analysis is undertaken after adding “Death” plus “Other” together (which would be appropriate if Other includes primarily patients transferred to palliative in-patient care or out-patient hospice), exactly the opposite correlation (increased Death plus Other over time), albeit once again statistically non-significant, is seen (R2 = .401, P = .177).
SOUNDNESS OF CONCLUSIONS AND INTERPRETATION The authors’ conclusions are well organized. They accurately state that the admission rate has remained stable over the study period and incidence of SAH in their study is comparable to earlier population-based studies. However, as discussed in our analysis, we argue that in-hospital mortality rates and improvement in outcome at larger referral centers compared to smaller hospitals cannot be reliably determined from their data, thus undermining 2 of the authors’ central conclusions.
228 | VOLUME 74 | NUMBER 2 | FEBRUARY 2014
RELEVANCE OF DISCUSSION The authors’ discussion is concise and clearly identifies some of the important limitations of their study. The authors acknowledge the fact that this is a non-population-based study but do not address the questions that arise regarding external validity. They also argue, unconvincingly and by the use of analogy, that because the incidence and some epidemiological findings in their study (for example the increased frequency of SAH in women and non-whites) are consistent with previous studies, their sample is representative of the entire US population. As illustrated above, while in-hospital death rates appear to have decreased significantly during the study period (dropping from 30% to 20% between the first and last sub-periods), the potential confounder represented by the Other category could completely reverse this conclusion. Unfortunately, interrogation of the NHDS database shows that further detail regarding the Other category was not collected. This is a serious flaw in the study conclusions, which is not addressed in the discussion. The authors also cite studies demonstrating a benefit to treatment at high volume centers. However, the most definitive study addressing this question4 showed a stronger correlation between the individual surgeon’s volume and operative mortality than overall hospital volume and mortality. An additional concern when analyzing the relationship between hospital volume and outcome is the potential bias introduced by selective transfer of patients with good prognostic features to larger referral centers. For example, Angileri et al5 assessed transfer rate in telemedicine stroke patients to a tertiary center and argued that poor outcome or terminal patients were often not transferred, leading to possible confounding that would exaggerate the apparent association of poor outcome with smaller hospitals, and good outcomes with larger referral centers. Again, this issue is not discussed by the authors, and could potentially negate one of their primary conclusions and public health-related recommendations. Finally, the change in size of hospitals during the study period is difficult to interpret. There was a decrease in both extra large and small hospitals with a concomitant increase in large hospitals. Thus, when large and extra large hospitals are grouped together there was only a 2% change from small to larger hospitals. The possible effect of larger volume hospitals on outcome cannot be appropriately assessed based on the information collected in this study and the statement in the discussion arguing improved outcomes are seen in larger centers should therefore be treated with caution.
CLARITY OF WRITING, STRENGTH, AND ORGANIZATION OF PAPER This paper is highly readable and well-organized. The primary hypothesis is clearly stated and the methods and result section focus on that primary hypothesis. The Limitations section is honest and valuable, and the discussion is thorough, but does overlook some important alternative interpretations of the authors’ data.
www.neurosurgery-online.com
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited.
CORRESPONDENCE
ECONOMY OF WORDS The paper is clear, concise, and focused on the purpose and hypothesis stated by the authors in their introduction.
RELEVANCE, ACCURACY AND COMPLETENESS OF BIBLIOGRAPHY An extensive bibliography is provided, but 2 important references (noted above) are omitted: Birkmeyer et al (2003), a meticulous study analyzing the relationship between neurosurgical outcome and hospital volume, and Angilerii et al (2012), which provides a contrary explanation for the apparent improvement in outcomes in referral centers compared to small outlying hospitals.
patients with SAH from smaller to larger hospitals is required based on the limited data available from the NHDS database. In order to more reliably address these critical questions, the authors are now in excellent position, based on their experience with this manuscript, to spearhead the development of a prospective SAH registry that strives to include all neurosurgeons in the United States. One way to accomplish this would be to make participation in such a registry mandatory as part of the credentialing process. In addition to basic demographic data, this prospective registry could include standard quantitative assessments of patient status. Appropriate outcome scores and more detail regarding discharge disposition could be collected as well. In return for participation, the administrators of such a database could make the aggregate data available to any researcher wishing to address questions relevant to the epidemiology and management of SAH in the United States.
NUMBER AND QUALITY OF FIGURES, TABLES, DRAWINGS
Disclosure
The single table provided in the paper was clear and easy to interpret. The 2 figures were also and easy to understand, but as noted above, the P-values reported in the caption for Figure 2 are misleading. Adding figures for the linear regression model would visually help interpret results and show trends across sub-periods.
REFERENCES
FUTURE/NEXT STEPS This study provides a very valuable picture of the incidence of SAH in the United States, suggests a number of testable hypotheses, and perhaps most important of all, has identified a number of shortcomings in the available registry data which limit the reliability of any inferences made from that data. For example, one of the key conclusions of this study is that in-hospital mortality has decreased over the last 30 years. However, as discussed above, this conclusion is flawed. Without further details about disposition locations listed as “other” it is impossible to definitively state that death rates have improved significantly over the years. Adjusted statistical analysis based on this missing information would be valuable. Similar caution in recommending routine transfer of
NEUROSURGERY
This work is not supported by or affiliated with any funding source. The authors have no personal or institutional financial interest in drugs, materials, or devices described in this article.
1. Linn FH, Rinkel GJ, Algra A, van Gijn J. Incidence of subarachnoid hemorrhage: role of region, year, and rate of computed tomography: a meta-analysis. Stroke. 1996;27(4):625-629. 2. Dennison C, Pokras R. Design and operation of the National Hospital Discharge Survey 1988 redesign. Vital Health Stat 1. 2000;39(39):1-42. 3. Tirschwell DL, Longstreth WT Jr. Validating administrative data in stroke research. Stroke. 2002;33(10):2465-2470. 4. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and operative mortality in the United States. N Engl J Med. 2003; 349(22):2117-2127. 5. Angilerii FF, Cardali S, Conti A, Raffa G, Tomasello F. Telemedicine-assisted treatment of patients with intracerebral hemorrhage. Neurosurg Focus. 2012; 32(4):E6.
Acknowledgments The authors gratefully acknowledge the faculty members who provided guidance for this journal club report including Dr Michael J. Glantz, MD, Dr Robert E. Harbaugh, MD, Dr Jonas M. Sheehan, MD, and Dr Scott Simon, MD.
VOLUME 74 | NUMBER 2 | FEBRUARY 2014 | 229
Copyright © Congress of Neurological Surgeons. Unauthorized reproduction of this article is prohibited.
