Association Between Body Mass Index, Missing Data, And Mortality Risk Among Critically Ill Patients: The Role Of Missing-Data Imputation

Background: We aimed to investigate the association between body mass index (BNII) 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.Methods: This retrospective study analysed the medical records of adult patients admitted to ICUs in a single tertiary academic hospital. For BM I analysis, data were classified into four groups: underweight (<20 kg/m), normal (20-24.9 kg/m(2)), overweight (25-29.9 kg/m(2)), and obese (030 kg/m(2)).Results: A total of 24,928 patients were examined. Among them, 5,916 (23.7%) patients had missing BNII data at ICU admission, and the missing mechanism was not missing completely at random. In the multivariable Cox regression analysis, the 90-clay mortality risk of underweight patients in the complete-case group increased by 1.49 times compared with that of normal BALI 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 EMI patients (HR: 1.36, 95% confidence interval: 1.24-1.49; P<0.001).Conclusions: We showed that the occurrence of missing BM I 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 BAII data can be an appropriate statistical option to reduce bias.