The National Inpatient Sample: A Primer for Neurosurgical Big Data Research and Systematic Review.

OBJECTIVE

The National Inpatient Sample (NIS) (the largest all-payer inpatient database in the United States) is an important instrument for big data analysis of neurosurgical inquiries. However, earlier research has determined that many NIS studies are limited by common methodological pitfalls. In this study, we provide the first primer of NIS methodological procedures in the setting of neurosurgical research and review all reported neurosurgical studies using the NIS.

METHODS

We designed a protocol for neurosurgical big data research using the NIS, based on our subject matter expertise, NIS documentation, and input and verification from the Healthcare Cost and Utilization Project. We subsequently 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 to August 2021. Studies underwent qualitative categorization (years of NIS studied, neurosurgical subspecialty, age group, and thematic focus of study objective) and analysis of longitudinal trends.

RESULTS

We identified a canonical, 4-step protocol for NIS analysis: study population selection; defining additional clinical variables; identification and coding of outcomes; and statistical analysis. Methodological nuances discussed include identifying neurosurgery-specific admissions, addressing missing data, calculating additional severity and hospital-specific metrics, coding perioperative complications, and applying survey weights to make nationwide estimates. Inherent database limitations and common pitfalls of NIS studies discussed include lack of disease process-specific variables and data after the index admission, inability to calculate certain hospital-specific variables after 2011, performing state-level analyses, conflating hospitalization charges and costs, and not following proper statistical methodology for performing survey-weighted regression. In a systematic review, we identified 647 neurosurgical studies using the NIS. Although almost 60% of studies were reported after 2015, <10% of studies analyzed NIS data after 2015. The average sample size of studies was 507,352 patients (standard deviation = 2,739,900). Most studies analyzed cranial procedures (58.1%) and adults (68.1%). The most prevalent topic areas analyzed were surgical outcome trends (35.7%) and health policy and economics (17.8%), whereas patient disparities (9.4%) and surgeon or hospital volume (6.6%) were the least studied.

CONCLUSIONS

We present a standardized methodology to analyze the NIS, systematically review the state of the NIS neurosurgical literature, suggest potential future directions for neurosurgical big data inquiries, and outline recommendations to improve the design of future neurosurgical data instruments.