Cavernous Malformation Surgery in the United States: Validation of a Novel International Classification of Disease, 10th Edition, Clinical Modification Code Search Algorithm and Volume-Driven Surgical Outcomes

Authors

Kavelin Rumalla, Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA; Department of Biomedical and Health Informatics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, USA.Follow
Visish M. Srinivasan, Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA.Follow
Monica Gaddis, Department of Biomedical and Health Informatics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, USA.
Svetlana Kvint, Department of Neurosurgery, Hospital of the University of Pennsylvania, Penn Medicine, Philadelphia, Pennsylvania, USA.
Akash J. Patel, Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA; Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston, Texas, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas, USA.
Peter Kan, Department of Neurosurgery, University of Texas Medical Branch, Galveston, Texas, USA.
Michael T. Lawton, Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA.Follow
Jan-Karl Burkhardt, Department of Neurosurgery, Hospital of the University of Pennsylvania, Penn Medicine, Philadelphia, Pennsylvania, USA. Electronic address: jan.burkhardt@pennmedicine.upenn.edu.

Document Type

Article

Abstract

OBJECTIVE: The surgical decision-making process for cavernous malformation (CM) must weigh the risks of surgery against the burden of patient symptoms/hemorrhage and anticipated natural history. Here, we sought to internally validate an International Classification of Disease (ICD)-10 search algorithm for CM surgery to use to analyze a nationwide administrative database. METHODS: Institutional records were accessed to test the validity of a novel ICD-10 search algorithm for CM surgery. The algorithm identified patients with positive predictive value (92%), specificity (100%), and sensitivity of 55%. The algorithm was applied to extract our target population from the Nationwide Readmissions Database. Univariate and multivariable analyses were used to identify factors influencing patient outcomes. RESULTS: We identified 1235 operations for supratentorial (87%) or infratentorial (13%) CM surgery from the Nationwide Readmissions Database (2016-2017). The overall rate of adverse disposition and 30-day readmission were 19.7% and 7.5%, respectively. The rate of adverse disposition was significantly higher for infratentorial (vs. supratentorial cases) (34.3% vs. 17.6%, P = 0.001) and brainstem (vs. cerebellar) cases (55% vs. 28%, P = 0.03). Hospital case-volume percentile was associated with decreasing rates of adverse disposition (1-74th: 22%, 75th: 16%, 90th: 13%, 95th: 7%). Treatment at HVCs was also associated with shorter average length of stay (4.6 vs. 7.3 days, P < 0.001) without significant changes to average cost of hospitalization (P = 0.60). CONCLUSIONS: Our ICD-10 coding algorithm reliably identifies CM surgery with minimal false positives. Outcomes were influenced by patient age, clinical presentation, location of CM, and experience of institution. Centralization of care may improve outcomes and warrants further investigation.

Medical Subject Headings

Adolescent; Adult; Child; Child, Preschool; Data Collection (methods); Databases, Factual; Female; Hemangioma, Cavernous, Central Nervous System (surgery); Hospitals, High-Volume (statistics & numerical data); Hospitals, Low-Volume (statistics & numerical data); Humans; Infant; International Classification of Diseases; Male; Middle Aged; Neurosurgical Procedures; Treatment Outcome; Young Adult

Publication Date

3-1-2021

Publication Title

World neurosurgery

E-ISSN

1878-8769

Volume

150

First Page

e66

Last Page

e73

PubMed ID

33640531

Digital Object Identifier (DOI)

10.1016/j.wneu.2021.02.081

Recommended Citation

Rumalla, Kavelin; Srinivasan, Visish M.; Gaddis, Monica; Kvint, Svetlana; Patel, Akash J.; Kan, Peter; Lawton, Michael T.; and Burkhardt, Jan-Karl, "Cavernous Malformation Surgery in the United States: Validation of a Novel International Classification of Disease, 10th Edition, Clinical Modification Code Search Algorithm and Volume-Driven Surgical Outcomes" (2021). Neurosurgery. 1419.
https://scholar.barrowneuro.org/neurosurgery/1419