Derivation and validation of the ABCDMed clinical score to estimate the probability of extubation failure in intensive care units

Objective: To develop and validate a clinical prediction model to estimate the probability of extubation failure (EF) in the intensive care unit (ICU).

Methods: An observational, analytical, prospective cohort study was conducted to derive and validate a clinical prediction model and a risk prediction score for EF. The study was performed in the ICU of the Mayor Méderi University Hospital in Bogotá, Colombia. All consecutive patients older than 18 years who required mechanical ventilation between June 2017 and April 2019 and were extubated were included. The analysis comprised 800 patients. Cases of extubation according to medical advice and on a planned basis were included. The outcome of the study was EF (dependent variable), which was defined as the need for reintubation in the 48 hours following extubation. The characteristics of the patient before being extubated were the variables of interest. The patients were grouped according to the dependent variable (EF). Using multivariate logistic regression, a prediction model was derived and validated using a purposeful selection strategy and a bootstrapping technique, respectively. Subsequently, a risk prediction score was generated for EF.

Results: EF occurred in 71 (8.9%) patients. A model was generated from five variables: A = acid-base status, B = the rapid shallow breathing index, C = the presence of effective cough, D = probability of death and Med. = medical patient status. The Hosmer-Lemeshow (Ĉ) goodness of fit value was 0.465. The discriminative power determined an area under the curve (AUC) of 0.687. Internal validation with the bootstrapping method showed an AUC of 0.695. A risk score was created, which was divided into four groups using multiples of the baseline risk of EF (8.9%). The observed incidences of EF in patients with low, moderate, high and very high risk were 4.1%, 8.1%, 11.5% and 22.7%, respectively. Conclusions: The ABCDMed prediction score allows easy estimation of the risk of EF based on five patient variables available at the bedside.