Care Phenotypes in Critical Care

The Social Determinants of Health (SDoH) have long been recognised as significant drivers of health inequalities. Within healthcare settings, large EHR datasets have increasingly enabled the use of machine learning (ML) to explore how patient background and demographic factors mediate and predict clinical outcomes. The intensive care unit (ICU) in particular provides a rich source of data for such research. However, major limitations with current approaches persist, including (i) overreliance on individual demographic labels or measures of difference, (ii) the impracticality of highly intersectional patient groups and (iii) that the underlying accuracy and validity of these demographic constructs is low. The main objective of this study was to take a novel approach, to first understand who within the ICU setting receives sub-standard care and use this to create new, objective labels based on quality of care and outcomes ( Care Phenotypes) when different patients interface with the health system. Using the MIMIC-IV database, we focused on highly protocolised, essential care procedures (turning, mouth care) in mechanically ventilated ICU patients. We performed a series of regression analyses to understand in which patients treatment deviated from these protocols. In a cohort of 8,919 ICU patients undergoing IMV, consistent patterns in sup-optimal protocol adherence for certain groups, notably heavier patients. Compared to equivalently sick peers, for every extra weight decile, a patient can expect a reduction of one percentile in frequency of turning care (0.0760 turning interval percentile per weight percentile, p<0.05). Furthermore, patients who receive fewer turnings should also expect to receive fewer mouth care procedures, in a quantile ratio of 1 to 5 (0.2055 mouth care interval quantile per turning interval quantile, p<0.05). The findings in this initial analysis provide support to the concept of first looking at the actual care delivered to patients to illuminate the relationship between patient demographics and outcomes of interest. The Care Phenotypes approach has the potential to improve fairness evaluations for machine learning in healthcare, support causal inference research and play a larger role in research into healthcare disparities.