Background & aims: A new Body Mass Index (BMI) formula has been developed for a better approximation of under and overweight. The aim of this study was to investigate the predictive value of this newly proposed BMI formula for postoperative complications in elective colorectal cancer surgery compared with the conventional BMI formula.
Methods: A digital database of patients undergoing elective colorectal cancer surgery was prospectively maintained in three centers and retrospectively analyzed. Data consisted of patient characteristics, surgical procedure, length of hospital stay (LOS), postoperative complications, mortality, reoperation and readmission. The BMI was calculated using both the conventional and new BMI formula. Patients were divided into four groups (BMI <20, 20-25, 25-30, ≥30 kg/m(2)).
Results: A total of 1614 patients were included. There was no significant difference in mean BMI between males and females using the conventional BMI formula (26.0 versus 26.2, p = 0.347), whereas a trend was observed using the new BMI formula (26.3 versus 25.6, p = 0.071). The proportion of overweight (BMI ≥25) male patients was significantly higher compared with the proportion of overweight female patients using the conventional formula (58.9% versus 51.0%, p = 0.021), whereas a non-significant difference was observed using the new formula (51.7% versus 53.4%, p = 0.515). Neither the conventional nor the new BMI were associated with postoperative complications and LOS. Higher age, higher ASA classification, male gender, and conventional surgery were independent predictors of the occurrence of postoperative complications. A longer LOS was also independently predicted by higher age, higher ASA classification and conventional surgery.
Conclusions: This study showed no superiority of the new BMI formula in predicting postoperative complications after colorectal cancer surgery. Confirmation of the results in a larger cohort is desirable.
Keywords: Body Mass Index; Cancer; Colorectal surgery; Postoperative complications; Predictive value.
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