Editorial. The use of big data for improving understanding of the natural history of neurosurgical disease

Abstract

nderstanding the natural history of a condition is essential in determining appropriate patient management. Unfortunately, current understanding of the natural history for many conditions is quite poor. The impact of natural history studies and their importance is significant; since 1956, 15 of the 100 most-cited articles in neurosurgery journals are natural history studies. 1 These studies have aided neurosurgeons in determining which treatment methodologies are best applied to certain patient populations with unruptured intracranial aneurysms, 2 vestibular schwannomas, 3 and dural arteriovenous fistulas. 4 There has been an exponential generation of healthcarerelated data in the past decade and, accordingly, increased usage of big data repositories in neurosurgical research. 5,6 These databases store vast quantities of de-identified patient information that are readily available for querying. Examples of such repositories include, but are not limited to, the Healthcare Cost and Utilization Project Kids' Inpatient Database (HCUP-KID), National Inpatient Sample, and American College of Surgeons National Surgical Quality Improvement Program. 6,7 Big data studies are an increasingly important tool for identifying predictive factors associated with predetermined outcomes; 6 however, the limitations of these databases are important to consider. Incomplete or missing data, the validity of diagnosis codes, and false-positive associations are concerns to be addressed in order to yield meaningful results in big data studies.Large database reviews allow researchers the ability to examine both the treated and untreated natural histories of diseases. Each database is different. Some databases are created for the purpose of collecting clinical information, while others are administrative in nature or are insurance registries that have been adapted for use by clinicians. The aim of the database influences which patient populations are represented. Certain databases aim to be representative of state or national populations, whereas others aim to represent patients with a specific disease or treatment. 6 As