Severity classification and influencing variables of the Postintensive Care Syndrome.

OBJECTIVE

The study aims to characterise Postintensive Care Syndrome by classifying the severity of the disease and identifying the variables of influence in two highly complex intensive care units for adults in Colombia.

METHODS

A descriptive, cross-sectional, prospective study was carried out to characterise survivors of critical illness using the Healthy Aging Brain Care -Monitor in a sample of 135 patients. Postintensive Care Syndrome severity was classified using Gaussian Mixture Models for clustering, and the most influencing variables were identified through ordinal logistic regression.

RESULTS

Clustering based on Gaussian Mixture Models allowed the classification of Postintensive Care Syndrome severity into mild, moderate, and severe classes, with an Akaike Information Criterion of 308 and an area under the curve of 0.80, which indicates a good fit; Thus, the mild class was characterised by a score on the HABC-M Total scale ≤9; the moderate class for a HABC-M Total score ≥10 and ≤42 and the severe class for a HABC-M Total score ≥43. Regarding the most influencing variables, the probability of belonging to the moderate or severe classes was related to male sex (91%), APACHE II score (22.5%), age (13%), intensive care units days of stay (10.6%), the use of sedation, analgesia and neuromuscular blockers.

CONCLUSION

Intensive care units survivors were characterised using the Healthy Aging Brain Care-Monitor scale, which made it possible to classify Postintensive Care Syndrome through Gaussian Mixture Models clustering into mild, moderate, and severe and to identify variables that had the major influence on the presentation of Postintensive Care Syndrome.