Prediction of High Airway Pressure Using a Non-Linear Autoregressive Model of Pulmonary Mechanics

Biomed Eng Online. 2017 Nov 2;16(1):126. doi: 10.1186/s12938-017-0415-y.

Abstract

Background: For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI.

Methods and results: Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH2O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH2O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II).

Conclusions: Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.

Keywords: Autoregressive models; Biomedical systems; Pulmonary modelling.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Biomechanical Phenomena
  • Humans
  • Lung / physiopathology*
  • Middle Aged
  • Models, Statistical*
  • Nonlinear Dynamics*
  • Pressure*
  • Respiration
  • Respiration, Artificial
  • Respiratory Distress Syndrome, Adult / physiopathology
  • Respiratory Distress Syndrome, Adult / therapy
  • Young Adult