Impact of Missing Data on Identifying Risk Factors for Postoperative Complications in Hand Surgery

Abstract

Background

The mismanagement of missing data in large clinical databases may lead to inaccurate findings. The purpose of this study was to demonstrate the effects of missing data on hand surgery research findings using an analysis of postoperative morbidity in patients undergoing hospital-based hand surgery.

Methods

The National Surgical Quality Improvement Program database was queried for patients undergoing common hand and upper extremity surgery between 2011 and 2016. Major and minor postoperative complications were identified. Demographics, comorbidity, and preoperative laboratory values were identified, and the percentage missing of each was tabulated. To demonstrate how missing data can alter analysis results, these variables were evaluated for an association with major complications using multivariable regression on 3 separate cohorts: (1) all patients; (2) all patients after exclusion of any patient entry with >10% of missing data; and (3) after removal of any patient entry with any missing data.

Results

Groups 1, 2, and 3 had 48 370, 23 118, and 6280 patients, respectively. There were 14 variables associated with increased odds of major complications in group 1, yet only 10 and 9 variables for groups 2 and 3, respectively. Six variables were associated with increased major complications across all 3 groups, whereas only 1 was associated with decreased odds of major complications across all groups.

Conclusions

Filtering patient cohorts according to the amount of missing patient information affected analyses of predictors for major complications associated with hospital-based hand surgery. These findings highlight the importance of considering and addressing missing data in large database studies.