Background: The nitrogen balance estimates a protein net difference. However, since it has a number of limitations, it is important to consider the trajectory of the nitrogen balance in the clinical course of critically ill patients.
Objectives: We herein exploratively classified the nitrogen balance trajectory using a machine learning method.
Method: This is a post hoc analysis of a single-center prospective study for the patients admitted to our Emergency and Critical Center ICU. The nitrogen balance was evaluated with 24-h urine collection from ICU days 1-10 with 9 points. K-means clustering was performed to classify the nitrogen balance trajectory. We also evaluated factors associated with uncovered clusters.
Results: Seventy-six eligible patients were included in the present study. After clustering, the nitrogen balance trajectory was classified into 4 classes. Class 1 was trajected as a negative balance over 10 days (24 patients). Class 2 had a positive conversion on day 3 or 4 (8 patients). Class 3 had a positive conversion on day 8 or 9 (28 patients). Class 4 initially had a positive balance and then converted to a negative balance (16 patients). Sepsis complication and steroid use were associated with negative nitrogen balance trajectory. Class 2 was associated with lower length of hospital stay and femoral muscle volume loss, however, frequently had frailty and sarcopenia on admission. Active nutrition therapy intention was not correlated with positive trajectory.
Conclusions: The nitrogen balance trajectory in critically ill patients may be classified into 4 classes for clinical practice. Among patients emergently admitted to the ICU, the positive conversion of the nitrogen balance might be delayed over 10 days.
Keywords: Critical care; K-means; Machine learning; Nitrogen balance; Protein.
© 2023 The Author(s). Published by S. Karger AG, Basel.