External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury

Abstract

We propose a new set of clinical variables for a more accurate early prediction of safe decannulation in patients with severe acquired brain injury (ABI), during a post-acute rehabilitation course. Starting from the already validated DecaPreT scale, we tested the accuracy of new logistic regression models where the coefficients of the original predictors were reestimated. Patients with tracheostomy were retrospectively selected from the database of the neurorehabilitation unit at the S. Anna Institute of Crotone, Italy. New potential predictors of decannulation were screened from variables collected on admission during clinical examination, including (a) age at injury, (b) coma recovery scale-revised (CRS-r) scores, and c) length of ICU period. Of 273 patients with ABI (mean age 53.01 years; 34% female; median DecaPreT = 0.61), 61.5% were safely decannulated before discharge. In the validation phase, the linear logistic prediction model, created with the new multivariable predictors, obtained an area under the receiver operating characteristics curve of 0.901. Our model improves the reliability of simple clinical variables detected at the admission of the post-acute phase in predicting decannulation of ABI patients, thus helping clinicians to plan better rehabilitation.

External Validation and Update of the Risk Prediction Model for Denosumab-Induced Hypocalcemia Developed From a Hospital-Based Administrative Database

“…12,13 However, in fact, only a few prediction models have been externally validated among those so far developed. 13-15 In addition, previous external validation studies 16-21 have used medical big data or data from a single facility. In the case of clinical prediction models, which are mainly developed by using patient demographics and baseline characteristics as predictors, the model performance may differ when they are applied in different facilities because of variations in factors such as therapeutic options or patient populations.…”

“…12,13 However, in fact, only a few prediction models have been externally validated among those so far developed. [13][14][15] In addition, previous external validation studies [16][17][18][19][20][21] have used medical big data or data from a single facility. In the case of…”