Empirical phenotyping in coupled patient+care systems: Generating low-dimensional categories for hypothesis-driven investigation of mechanically-ventilated patients.

Background: Analyzing patient data under current mechanical ventilation (MV) management processes is essential to develop hypotheses about improvements and to understand MV consequences over time. However, progress is complicated by the complexity of lung-ventilator system (LVS) interactions, patient-care and patient-ventilator heterogeneity, and a lack of classification schemes for observable behavior. Method: Ventilator waveform data arise from patient-ventilator interactions within the LVS while care processes manage both patient and ventilator settings. This study develops a computational pipeline that segments these joint waveform data and care settings timeseries to phenotype the data generating process. The modular method supports many methodological choices for representing waveform data and unsupervised clustering. Results: Applied to 35 ARDS patients including 8 with COVID-19, typcially 8[6.8] (median[IQR]) phenotypes capture 97[3.1]% of data using naive similarity assumptions on waveform and MV settings data. Individual phenotypes organized around ventilator mode, PEEP, and tidal volume with additional segmentation reflecting waveform behaviors. Few (< 10% of) phenotype changes tie to ventilator settings, indicating considerable dynamics in LVS behaviors. Evaluation of phenotype heterogeneity reveals LVS dynamics that cannot be discretized into sub-phenotypes without additional data or alternate assumptions. Suitably normalized individual phenotypes may be aggregated into coherent groupings suitable for analysis of cohort data. Conclusions: The pipeline is generalizable although empirical output is data- and algorithm-dependent. Further, output phenotypes compactly discretize the data for longitudinal analysis and may be optimized to resolve features of interest for specific applications.