From Big Data to Artificial Intelligence: Harnessing Data Routinely Collected in the Process of Care*

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Excerpt

Frieden (1) provides us with the most compelling reason to leverage data that is routinely collected in the process of care: “For much, and perhaps most of medical practice, RCT-based data are lacking and no RCT is being planned or is likely to be completed to provide evidence for action. … [It] leaves practitioners with large information gaps for most conditions and increases reliance on clinical lore.” With over 90% of care providers in the United States now using an electronic health record (EHR) system, health data are being collected at a scale (exabytes), resolution (up to 500 Hz), and levels of heterogeneity, which are historically unprecedented (2). The sheer magnitude of such data can leverage population data and facilitate the application of advanced algorithmic techniques which were previously not feasible due to small sample sizes (e.g., for deep learning). Indeed, recent investigations have reported impressive performances using algorithms to automate the diagnosis of skin cancers (3) and diabetic retinopathy (4). Critically ill patients are an ideal population for clinical database investigations because while the data from ICUs are extensive, the value of many treatments and interventions remains largely unproven, and high-quality studies supporting or discouraging specific practices are relatively sparse (5). The data-rich ICU environment provides a potential area for uses of artificial intelligence (AI), a highly data-dependent entity (6).
In this issue of Critical Care Medicine, Sottile et al (7) expand the scope of big data and machine learning (a current application of AI) to the realm of ventilator dyssynchrony (VD) in ventilated patients with and at risk for acute respiratory distress syndrome. In this small, single-center prospective analysis, the authors explored the association between VD, delivered tidal volumes, and level of sedation in 62 patients meeting inclusion criteria. After analysis of 4.26 million breaths, the majority of which occurred in a proprietary mode delivered by a specific ventilator, the authors observed that compared with synchronous breaths, high tidal volume breaths (defined as > 10/kg) were significantly more likely to be delivered with double triggered or flow-limited breaths. Nonsynchronous breaths were observed in 34% of observed breaths. The rate of nonsynchronous breaths was decreased with deep sedation; however, only neuromuscular blockade led to complete elimination.
The results described by Sottile et al (7) must be interpreted with caution and analyzed in the context of the study design. This preliminary hypothesis-generating study used a novel algorithm to detect VD; however, the generalizability and clinical implications of these findings are unknown. The clinical impact of VD as well as the impact of infrequent high tidal volume breaths in the era of low tidal volume ventilation is not well known. Some authors have suggested that infrequent high-volume breaths may even be protective by promoting sustained alveolar recruitment (8, 9). Furthermore, the impact of ventilator mode, sedation level, and rate and type of VD needs to be examined. A large majority of the breaths analyzed were in adaptive pressure ventilation/controlled mandatory ventilation, one particular and proprietary mode of ventilation, so that extrapolation of these findings to other modes and models of ventilator are unknown. Other studies have demonstrated that altering ventilator mode and settings is a superior option to increased sedation in the reduction of VD (10, 11).
The authors noted (in the supplementary materials in [7]) that a nonuniform patient recruitment process occurred during the study because of unavoidable logistical issues. A careful examination of how this could have affected the makeup of the patient cohort was performed in order to address concerns that bias might have been introduced by feeding the model with data from a select group of patients for training.
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