Yew, Christopher Shuen Ang and Yeong, Shiong Chiew and Wang, Xin and Mat Nor, Mohd Basri and E. Cove, Matthew and Chase, Geoffrey
(2022)
Predicting mechanically ventilated patients future respiratory system elastance – A stochastic modelling approach.
Computers in Biology and Medicine, 151 (106275).
pp. 1-14.
ISSN 0010-4825
E-ISSN 0010-4825X
Abstract
Background and objective: Respiratory mechanics of mechanically ventilated patients evolve significantly with
time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods
fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research
presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering
improved clinical utility in MV treatment.
Methods: Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance
(Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were
clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive
ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves.
Results: Clinical validation shows all three models captured more than 98% (median) of future Ers data within the
5th – 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of
5 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers range.
Conclusion: The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for
synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can
potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.
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