Hospital and Surgeon Variation in Complications and Repeat Surgery Following Incident Lumbar Fusion for Common Degenerative Diagnoses

Martin, Monika; Mirza, Mansha; Franklin, M.P.H. Gary M.; Lurie, Jon D.; MacKenzie, Todd A.; Deyo, M.P.H. Richard A.

doi:10.1111/j.1475-6773.2012.01434.x

Cited by 43 publications

(53 citation statements)

References 31 publications

(28 reference statements)

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“…[7][8][9] There are unique advantages that large databases provide, including large, population-based studies of surgical trends, demographic characteristics, and major complications of common operations. 16 The MedPAR database, for example, captures 100% of Medicare hospital claims and has unique patient identifiers that allow for linkage among files and identification of repeat hospitalizations. 5 Both of these large databases use ICD-9-CM procedure and diagnosis codes to identify and categorize surgeries and hospitalizations.…”

Section: Discussionmentioning

confidence: 99%

The Nationwide Inpatient Sample database does not accurately reflect surgical indications for fusion

Gologorsky

Knightly

John

et al. 2014

SPI

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Object The rates of lumbar spinal fusion operations have increased dramatically over the past 2 decades, and several studies based on administrative databases such as the Nationwide Inpatient Sample (NIS) have suggested that the greatest rise is in the general categories of degenerative disc disease and disc herniation, neither of which is a well-accepted indication for lumbar fusion. The administrative databases classify cases with the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM). The ICD-9-CM discharge codes are not generated by surgeons but rather are assigned by trained hospital medical coders. It is unclear how accurately they capture the surgeon's indication for fusion. The authors sought to compare the ICD-9-CM code(s) assigned by the medical coder to the surgeon's indication based on a review of the medical chart. Methods A retrospective review was undertaken of all lumbar fusions performed at our institution by the department of neurosurgery between 8/1/2011 and 8/31/2013. Based on the authors' review, the indication for fusion for each case was categorized as spondylolisthesis, deformity, tumor, infection, nonpathological fracture, pseudarthrosis, adjacent-level degeneration, stenosis, degenerative disc pathology, or disc herniation. These surgeon diagnoses were compared with the primary ICD-9-CM codes that were submitted to administrative databases. Results There were 178 lumbar fusion operations performed for 170 hospital admissions. There were 44 hospitalizations in which fusion was performed for tumor, infection, or nonpathological fracture; the remaining 126 were for degenerative diagnoses. For these degenerative cases, the primary ICD-9-CM diagnosis matched the surgeon's diagnosis in only 61 of 126 degenerative cases (48.4%). When both the primary and all secondary ICD-9-CM diagnoses were considered, the indication for fusion was identified in 100 of 126 cases (79.4%). Conclusions Characterizing indications for fusion based solely on primary ICD-9-CM codes extracted from large administrative databases does not accurately reflect the surgeon's indication. While these databases may accurately describe national rates of lumbar fusion surgery, the lack of fidelity in the source codes limits their role in accurately identifying indications for surgery. Studying relationships among indications, complications, and outcomes stratified solely by ICD-9-CM codes is not well founded.

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Section: Discussionmentioning

confidence: 99%

The Nationwide Inpatient Sample database does not accurately reflect surgical indications for fusion

Gologorsky

Knightly

John

et al. 2014

SPI

View full text Add to dashboard Cite

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“…21 Health care policy research has become increasingly reliant on large administrative databases (http://www.hcup-us.ahrq.gov/nisoverview.jsp) that use the ICD-9-CM system, such as the NIS, 4-8 because these databases allow researchers to identify, track, and analyze national trends in health care utilization, charges, quality, and outcomes. [2][3][4][5][6][7][8]16,19 Increased attention to code accuracy has occurred both as a result of the application of ICD codes for purposes other than those for which the classifications were originally designed as well as because of their widespread use for making important funding, clinical, and research decisions. 17,21 In the 1980s the prospective payment system using diagnosis-related groups (DRGs) was implemented, and increased scrutiny of diagnostic accuracy began.…”

Section: Discussionmentioning

confidence: 99%

“…[6][7][8]16 Two of the largest include the Medicare Provider Analysis and Review (MedPAR) database and the Nationwide Inpatient Sample ([NIS] http://www.hcup-us. ahrq.gov/nisoverview.jsp).…”

mentioning

confidence: 99%

Improving discharge data fidelity for use in large administrative databases

Gologorsky

Knightly

Lü

et al. 2014

FOC

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Object Large administrative databases have assumed a major role in population-based studies examining health care delivery. Lumbar fusion surgeries specifically have been scrutinized for rising rates coupled with ill-defined indications for fusion such as stenosis and spondylosis. Administrative databases classify cases with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The ICD-9-CM discharge codes are not designated by surgeons, but rather are assigned by trained hospital medical coders. It is unclear how accurately they capture the surgeon's indication for fusion. The authors first sought to compare the ICD-9-CM code(s) assigned by the medical coder according to the surgeon's indication based on a review of the medical chart, and then to elucidate barriers to data fidelity. Methods A retrospective review was undertaken of all lumbar fusions performed in the Department of Neurosurgery at the authors' institution between August 1, 2011, and August 31, 2013. Based on this review, the indication for fusion in each case was categorized as follows: spondylolisthesis, deformity, tumor, infection, nonpathological fracture, pseudarthrosis, adjacent-level degeneration, stenosis, degenerative disc disease, or disc herniation. These surgeon diagnoses were compared with the primary ICD-9-CM codes that were generated by the medical coders and submitted to administrative databases. A follow-up interview with the hospital's coders and coding manager was undertaken to review causes of error and suggestions for future improvement in data fidelity. Results There were 178 lumbar fusion operations performed in the course of 170 hospital admissions. There were 44 hospitalizations in which fusion was performed for tumor, infection, or nonpathological fracture. Of these, the primary diagnosis matched the surgical indication for fusion in 98% of cases. The remaining 126 hospitalizations were for degenerative diseases, and of these, the primary ICD-9-CM diagnosis matched the surgeon's diagnosis in only 61 (48%) of 126 cases of degenerative disease. When both the primary and all secondary ICD-9-CM diagnoses were considered, the indication for fusion was identified in 100 (79%) of 126 cases. Still, in 21% of hospitalizations, the coder did not identify the surgical diagnosis, which was in fact present in the chart. There are many different causes of coding inaccuracy and data corruption. They include factors related to the quality of documentation by the physicians, coder training and experience, and ICD code ambiguity. Conclusions Researchers, policymakers, payers, and physicians should note these limitations when reviewing studies in which hospital claims data are used. Advanced domain-specific coder training, increased attention to detail and utilization of ICD-9-CM diagnoses by the surgeon, and improved direction from the surgeon to the coder may augment data fidelity and minimize coding errors. By understanding sources of error, users of these large databases can evaluate their limitations and make more useful decisions based on them.

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“…To the extent that the contextual factors are actionable or alterable, evidence-based recommendations for changes in the delivery and organization of health care may be generated. For example, Martin et al [21] used data from more than 6000 patients in a state database of nonfederal hospitals that included characteristics of the patients, surgeons, and hospitals to investigate 90-day reoperation and complication rates after lumbar fusion surgery. While complication and reoperation rates varied between hospitals, a substantial amount of the variability was attributable to differences in surgeons' practices even after accounting for patient-level factors (eg, age, comorbidities, diagnosis, severity) [21].…”

Section: Contextual Factors: Capture Variance To Explain Variancementioning

confidence: 99%

“…For example, Martin et al [21] used data from more than 6000 patients in a state database of nonfederal hospitals that included characteristics of the patients, surgeons, and hospitals to investigate 90-day reoperation and complication rates after lumbar fusion surgery. While complication and reoperation rates varied between hospitals, a substantial amount of the variability was attributable to differences in surgeons' practices even after accounting for patient-level factors (eg, age, comorbidities, diagnosis, severity) [21]. This type of multilevel analysis provides results that may identify potential changes in service delivery to decrease complications and improve outcome.…”

Section: Contextual Factors: Capture Variance To Explain Variancementioning

confidence: 99%