Using Routinely Gathered Clinical Data to Develop a Prognostic Online Tool for Decannulation in Subjects With Acquired Brain Injury. - Mortensen, Jesper; Svanborg Kjeldsen, Simon; Honoré, Helene; Roer Pedersen, Asger (Respiratory Care, 2020)
BACKGROUND: Clinicians are often required to provide a qualified guess on the probability of decannulation in estimating patients' rehabilitation potential and relaying information about prognosis to patients and next of kin. The objective of this study was to use routinely gathered clinical data to develop a prognostic model of time to decannulation in subjects with acquired brain injury, for direct implementation in clinical practice. METHODS: Data from a large cohort including 574 tracheostomized subjects admitted for neurorehabilitation were analyzed using discrete time-to-event analysis with logit-link. Within this model, a reference hazard function was modeled using restricted cubic splines, and estimates were presented using odds ratios (95% CIs). RESULTS: A total of 411 subjects (72%) were decannulated within a median of 27 d (interquartile range 16-49) at the rehabilitation hospital. The prognostic model for decannulation included age, diagnosis, days from injury until admission for rehabilitation, swallowing, and overall functional level measured with the Early Functional Abilities score. Among these, the strongest predictors for decannulation were age and a combination of overall functional abilities combined with swallowing ability. CONCLUSIONS: A prognostic model for decannulation was developed using routinely gathered clinical data. Based on the model, an online graphical user interface was applied, in which the probability of decannulation within x days is calculated along with the statistical uncertainty of the probability. Furthermore, a layman's interpretation is provided. The online tool was directly implemented in clinical practice at the rehabilitation hospital, and is available through this link: (http://www.hospitalsenhedmidt.dk/regionshospitalet-hammel/research-unit/Prognosissoftware/).