Predicting extubation outcomes - a model incorporating heart rate characteristics index

Objective To test the hypothesis that in neonates on mechanical ventilation, heart rate characteristics index (HRCi) can be combined with a clinical model for predicting extubation outcomes in neonates. Study design HRCi and clinical data for all intended intubation-extubation events (episodes) were retrospectively analyzed between June 2014 and January 2015. Each episode started 6 hours pre-extubation or at the time of primary intubation if ventilation duration was shorter than 6 hours (baseline). The episodes ended at 72 hours postextubation for successful extubations or at reintubation for failed extubations. Mean of 6 hourly epoch HRCi-scores (baseline) or fold-changes (postextubation) were analyzed. Results are expressed as medians (IQR) for continuous data and proportions for categorical data. Multivariable logistic regression mixed model was used for statistical analysis. Results Sixty-six infants contributed to 96 episodes (18 failed extubations, 78 successful extubations) in the study. Failed extubations had significantly longer duration of ventilation (65.3 hours, 19.94-158.2 vs 38.4, 16.5-71.3) and more culture positive sepsis (33.3% vs 3.8%) than successful extubations. Baseline HRCi scores (1.68, 1.29-2.45 vs 0.95, 0.54-1.86) and postextubation epoch-1 fold changes (1.25, 0.94-1.55 vs 0.94, 0.82-1.11) were higher in failed extubations compared with successful extubations. Multivariable linear mixed-effects regression was used to create prediction models for success of extubation, using relevant variables. Conclusions The baseline and postextubation HRCi were significantly higher in neonates with extubation failure compared with those who succeeded. Models using HRCi and clinical variables to predict extubation success may add to the confidence of clinicians considering extubation

Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques

One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing(group F), and 21 patients who had successful weaning trials,but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration

Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques

One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing(group F), and 21 patients who had successful weaning trials,but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration.Postprint (published version

Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques

One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing(group F), and 21 patients who had successful weaning trials,but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration