Association between body mass index, missing data, and mortality risk among critically ill patients: the role of missing-data imputation.

Abstract

We aimed to investigate the association between body mass index (BMI) at the time of intensive care unit (ICU) admission and 90-day mortality risk in complete-case datasets that ignore missing data as well as in datasets with multiple imputation for missing data. This retrospective study analysed the medical records of adult patients admitted to ICUs in a single tertiary academic hospital. For BMI analysis, data were classified into four groups: underweight (<20 kg/m2 ), normal (20-24.9 kg/m2 ), overweight (25-29.9 kg/m2 ), and obese (≥30 kg/m2 ). A total of 24,928 patients were examined. Among them, 5,916 (23.7%) patients had missing BMI data at ICU admission, and the missing mechanism was not missing completely at random. In the multivariable Cox regression analysis, the 90-day mortality risk of underweight patients in the complete-case group increased by 1.49 times compared with that of normal BMI patients with a hazard ratio (HR) of 1.49 (95% confidence interval: 1.34-1.66; P<0.001), whereas the 90-day mortality risk of underweight patients in the multiple imputation group increased by 1.36 times compared with that of normal BMI patients (HR: 1.36, 95% confidence interval: 1.24-1.49; P<0.001). We showed that the occurrence of missing BMI data at ICU admission could affect the prediction of 90-day mortality in critically ill patients. Particularly, missing BMI data had the potential to slightly overestimate the 90-day mortality of underweight patients. Therefore, multiple imputation for missing BMI data can be an appropriate statistical option to reduce bias.