The National Inpatient Sample: An Introduction to Researching and Systematically Reviewing Big Data in Neurosurgery

Global Neurosurgery. Mar 2, 2022: S1878-8750(22)00260-1. doi: 10.1016/j.wneu.2022.02.113. Online ahead of print.

ABSTRACT

OBJECTIVE: The National Inpatient Sample—the largest fully paid inpatient database in the United States—is an important instrument for big data analysis of neurosurgical inquiries. However, previous research has determined that many NIS studies are limited by common methodological pitfalls. In this review, we provide the first introduction to the methodological procedures of the NIS in neurosurgical research and review all published neurosurgical studies using the NIS.

METHODS: We designed a protocol for big data research in neurosurgery using NIS, based on the authors’ expertise, NIS documentation, and project input and verification of cost and use. health care. We then used a comprehensive search strategy to identify all neurosurgical studies using the NIS in the PubMed and MEDLINE, Embase, and Web of Science databases from inception through August 2021. The studies were searched. qualitative categorization (years of NIS studied, neurosurgical subspecialty, age group, and thematic focus of study objective) and longitudinal trend analysis.

RESULTS: We identified a canonical four-step protocol for NIS analysis: selection of the study population, definition of additional clinical variables, identification and coding of results, and statistical analysis. Methodological nuances discussed include identification of neurosurgery-specific admissions, handling of missing data, calculation of additional severity and hospital-specific measures, coding of perioperative complications, and application of survey weights. to make estimates at the national level. Inherent database limitations and common pitfalls of NIS studies discussed include lack of disease process-specific variables and data after index admission, inability to calculate some hospital-specific variables after 2011 , performing state-level analyses, confusing hospital fees and costs, and not following proper statistical methodology to perform survey-weighted regression. In a systematic review, we identified 647 neurosurgical studies using the NIS. While almost 60% of studies were published after 2015, less than 10% of studies analyzed NIS data after 2015. The mean study sample size was 507,352 patients (standard deviation = 2,739,900 ). Most studies analyzed cranial procedures (58.1%) and adults (68.1%). The most frequently analyzed topics were trends in surgical outcomes (35.7%) and health policy and economics (17.8%), while disparities between patients (9.4%) and the number of surgeons or hospitals (6.6%) were the least studied.

CONCLUSIONS: We present a standardized methodology for analyzing the NIS, systematically review the state of the neurosurgical NIS literature, suggest potential future directions for neurosurgical big data investigations, and present recommendations for improving the design of future neurosurgical data instruments. .

PMID:35247618 | DO I:10.1016/j.wneu.2022.02.113

Sean N. Ayres