Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study

Abstract

Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction.

Keywords: child; infant; intensive care units; intubation; machine learning; pediatric; respiratory failure.

Figure 1

Characteristics of the study population. (A) Histogram of admissions for each patient type as a function of age. (B) Rate of unplanned intubation for each patient type as a function of age. (C) Mortality rate for each patient type with (red) and without (black) unplanned intubation. Error bars are 95% confidence interval. (D) Median length of stay in days for each patient type with (red) and without (black) unplanned intubation. Error bars show the IQR.

Figure 2

Empirical relative risk for cardiac surgery (left), medical (center), and non-cardiac surgery (right) patients as a function of age and mean respiratory rate from continuous monitoring data. On each panel, younger patients are to the left, and slower respiratory rates are to the bottom. Each colored tile estimates the relative risk of unplanned intubation for each decile of age and respiratory rate based on the surrounding quintile. Deciles of respiratory rate and age remain the same across patient type panels. Higher relative risk is redder while lower is bluer.

Figure 3

Risk profiles for exemplary features in the random forest model. These are marginal risk profiles that average out dependence on other features, such as the dependence of heart rate on age. The (natural) log odds of unplanned intubation in the next 12 h (ordinate) is shown as a function of the value of each measured variable (abscissa) holding all other features at their median values. Lower blood pressure, for example, is associated with increased risk for unplanned intubation independent of changes in any other feature.

Figure 4

Area under the receiver operating characteristic (AUC) for predicting unplanned intubation based on chart review. For comparison, the AUC is also shown for combinations of models built to detect a computable phenotype and either using all features or excluding continuous monitoring data. Each AUC is based on 5-fold cross-validation with confidence intervals based on 200 bootstrap runs resampled by admission.

Figure 5

Average time series of model outputs leading up to the time of unplanned intubation. Results are cross-validated predictions from the model based on all patients and are shown for all patients as well as patients of each patient type. Open circles indicate risk estimates are significantly higher than risk estimates for the same patients 8 h prior based on a Wilcoxon signed rank test.