School of Engineering, Monash University Malaysia, Selangor, Malaysia.
School of Engineering, Monash University Malaysia, Selangor, Malaysia.
Comput Biol Med. 2022 Dec;151(Pt A):106275. doi: 10.1016/j.compbiomed.2022.106275. Epub 2022 Nov 2.
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.
Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (E) 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.
Clinical validation shows all three models captured more than 98% (median) of future E 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 < E (cmHO/L) < 85, suggesting similar predictive capabilities within this clinically relevant E range.
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.
机械通气患者的呼吸力学会随时间、疾病状态和机械通气（MV）治疗而发生显著变化。现有的确定性数据预测方法无法全面描述生物系统的多种异质性来源。本研究提出了两种呼吸力学随机模型，提高了预测准确性和范围，从而提高了 MV 治疗的临床实用性。
使用来自两个临床队列的回顾性患者呼吸弹性（E）数据，分别平均时间间隔为 10 分钟和 30 分钟，开发了两种随机模型（SM2 和 SM3）。使用来自先前研究的一种随机模型（SM1）来进行基准性能评估。将该随机模型应用于 14 名患者的独立回顾性临床队列进行临床验证。通过百分位线和累积分布密度（CDD）曲线的差异评估预测能力的差异。
临床验证表明，所有三种模型都在 5%到 95%的百分位范围内捕获了超过 98%（中位数）的未来 E 数据。随机模型百分位线的比较报告最大平均绝对百分比差异为 5.2%。在 5<E（cmHO/L）<85 的范围内，CDD 曲线的绝对差异小于 0.25，表明在这个临床相关的 E 范围内具有相似的预测能力。
新的随机模型显著提高了预测能力、临床实用性，从而提高了与临床干预同步的可行性。与其他 MV 协议相结合，所开发的随机模型可以潜在地形成决策支持系统的一部分，为 MV 治疗提供有指导、个性化和安全的治疗。
