Missing data treatments matter: an analysis of multiple imputation for anterior cervical discectomy and fusion procedures

Publication date: Available online 9 April 2018
Source:The Spine Journal
Author(s): Nathaniel T. Ondeck, Michael C. Fu, Laura A. Skrip, Ryan P. McLynn, Jonathan J. Cui, Bryce A. Basques, Todd J. Albert, Jonathan N. Grauer
BACKGROUND CONTEXTThe presence of missing data is a limitation of large datasets, including the National Surgical Quality Improvement Program (NSQIP). In addressing this issue, most studies utilize complete case analysis, which excludes cases with missing data, thus potentially introducing selection bias. Multiple imputation, a statistically rigorous approach that approximates missing data and preserves sample size, may be an improvement over complete case analysis.PURPOSETo evaluate the impact of using multiple imputation in comparison to complete case analysis for assessing the associations between preoperative laboratory values and adverse outcomes following anterior cervical discectomy and fusion (ACDF) procedures.STUDY DESIGN/SETTINGRetrospective review of prospectively collected dataPATIENT SAMPLEPatients undergoing one-level ACDF were identified in NSQIP 2012-2015.OUTCOME MEASURESPerioperative adverse outcome variables assessed included the occurrence of any adverse event, severe adverse events, and hospital readmission.METHODSMissing preoperative albumin and hematocrit values were handled using complete case analysis and multiple imputation. These preoperative laboratory levels were then tested for associations with 30-day postoperative outcomes using logistic regression.RESULTSA total of 11,999 patients were included. Of this cohort, 63.5% of patients were missing preoperative albumin and 9.9% were missing preoperative hematocrit.When utilizing complete case analysis, only 4,311 patients were studied. The removed patients were significantly younger, healthier, of a common BMI and male. Logistic regression analysis failed to identify either preoperative hypoalbuminemia or preoperative anemia as significantly associated with adverse outcomes.When employing multiple imputation, all 11,999 patients were included. Preoperative hypoalbuminemia was significantly associated with the occurrence of any adverse event and severe adverse events. Preoperative anemia was significantly associated with the occurrence of any adverse event, severe adverse events, and hospital readmission.CONCLUSIONMultiple imputation is a rigorous statistical procedure that is being increasingly used to address missing values in large datasets. Utilizing this technique for ACDF avoided the loss of cases that may have affected the representativeness and power of the study and led to different results than complete case analysis. Multiple imputation should be considered for future spine studies.



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