Artificial intelligence hold promise in the ICU

In a 2018 article entitled “Human Cognitive Limitations”, the University of Utah's Alan Morris estimated the number of variables an intensivist has to take into account for a patient on mechanical ventilation. He came up with 236. A typical ward round for Brijesh Patel, an intensivist at the Royal Brompton hospital in London, involves checking the settings on 20–30 ventilators. It is an enormously time-consuming process. “The question of how to manage the ventilator is at the centre of critical care”, points out Patel. “Yet we are typically working without a deep knowledge of what is happening with patients on a breath-by-breath basis, or from hour to hour. We have to find a better way to do things.” Critical care is a specialty for which artificial intelligence (AI) holds particular promise. After all, the intensive care unit (ICU) is a space in which vast amounts of data are routinely collected, making it ideal for deploying machine-learning techniques. “There are plenty of areas in critical care where it would be extremely helpful to have efficacious, fair, and transparent AI systems”, notes Gary Weissman, assistant professor in pulmonary and critical care medicine at the University of Pennsylvania Perelman School of Medicine. “There are so many possibilities, at every stage of translation”, agrees Patel. “From understanding the trajectory of disease, to understanding when we are not doing things we should be doing.” Weissman cites the example of prognostication. “There is often uncertainty over the short-term and long-term outcomes for the interventions you might be considering in the ICU; a well designed AI system could really inform decision-making”, said Weissman. There is also the potential to hone clinical trials. “There are interventions in the critical care space for which we would love to have large, randomised trials, but barriers to enrolment make it difficult to build up an adequate sample size”, said Weissman. “Good predictive models that tell us which patients are most likely to benefit from the intervention or are at high risk of a poor outcome without an intervention, would help advance our knowledge of what works in the ICU.” Machine-learning techniques have been used to predict patients' length of stay, risk of mortality, and chances of readmission. There have been encouraging results from algorithms designed to diagnose sepsis and for identifying patients who are ready to be discharged from the ICU. A 2019 study demonstrated the feasibility of sustained monitoring of patients in the ICU, using an array of wearable sensors and cameras, to detect delirium. The investigators trained a deep-learning system to distinguish facial expressions and differences in posture between delirious and non-delirious patients. They reckon that hospitals could establish equivalent systems for less than US $300 per ICU room (Weissman cautions that applying AI to facial recognition is controversial, since facial expressions and posture are not objective, inherent aspects of human biology). Patel is involved in a clinical trial examining a device which plugs into the ventilator circuit and monitors pressure levels and flows of oxygen and carbon dioxide in and out of the lungs. “It is about deep physiology”, said Patel. “It is a smarter way to understand the lung and track disease. We are trying to see whether it liberates patients from the ventilator faster.” Along with Aldo Faisal, professor of AI and neuroscience at Imperial College London, Patel co-authored a paper outlining how an AI model was used to identify patients with COVID-19 who would benefit from proning. Applied retrospectively to 633 patients from the first wave of the pandemic in the UK, the model successively predicted disease outcomes as well as the factors that influenced the course of the disease. “The general landscape for AI in the ICU ranges from the early work on sepsis alerts to technology that combines physiological models with intelligence to optimise the parameters on the machines in the unit”, Faisal told The Lancet Respiratory Medicine. “From all the work we have done, it has become increasingly evident that you cannot just look at the admission data; you need to look at the trajectory of the patient on a day by day, hour by hour, and minute by minute basis.” The addition of such information improves the ability of the algorithms to predict deterioration and suggest drugs and dosages. “Different parameters change vastly in importance over time”, said Faisal. “So if you only pay attention to the admission characteristics, you are overlooking the deterioration trajectory of the patient.” In other words, parameters that are relevant on day 1 of a patient's stay in the ICU, in terms of predicting mortality, for example, are no longer as relevant on day 3. “Think of AI as a decision support tool, that can make sense of all these numbers for all these patients”, advises Faisal. “If you have a very diffuse condition, with constantly changing parameters, and it is not entirely clear what to do, AI can show you what to pay attention to, and how things evolve over time.” The UK National Health Service despatches teams from severe acute respiratory failure centres to hospitals up and down the country to advise intensivists on optimising ventilation. “We cannot put Brijesh Patel in every ICU in the world”, points out Faisal. “But we can install AI models that learn from the world's leading experts and apply settings to the ventilator informed by their experience.” Patel envisages a system that sits on a patient's bedside, watching over them with unbroken attention. “Doctors are expensive, and intensive care is a complex situation; there is always a lot to do. But what if we can get to the stage where we have a doctor remotely monitoring several patients, with the nurses and other members of the team at bedside, assisted by AI systems that pick up all the vital data in detail and track patients from one minute to the next?”, he said. Patel does not think AI will supplant clinicians any time soon. But it will augment what they do and homogenise it where needed. It will also allow them to phenotype patients, opening a path to the wide application of precision medicine. Nonetheless, there are a lot of challenges that have yet to be overcome. One major obstacle is the inability of machines within most ICUs to communicate with each other. Different ICUs use different machinery supported by different software. There is marked variation in standards of data collection. Some ICUs still use paper records. Staff might not always have the time to enter information in the requisite format. A more profound concern is the data used to train the systems. “We have seen all kinds of examples from different domains where predictive models have recapitulated existing biases that are encoded in the training datasets, whether by race or gender”, explains Weissman. For example, there is some evidence that Black patients in the USA receive antibiotics later than White patients. If physicians are applying biased practices, such as those observed with antibiotic prescription, this bias will be reflected in the same datasets trawled for patterns by machine-learning systems. In which case, there is a credible risk that predictive models could sustain or even worsen disparities and inequities of care. “We need robust and clear standards for assessing algorithmic equity and there is not yet consensus in the field as to what the standards should be”, said Weissman. Regulators are still figuring out how to address AI. There are knotty questions of whether a device informs or drives clinical decision making. The US Food and Drug Administration might not regulate algorithmic devices that a hospital or health system designs itself and uses in-house. It is not clear whether regulators will be interested or equipped to oversee or audit specific algorithms. Most papers on AI use retrospective datasets to train their model and then test the model on another retrospective dataset. But this does not tell us anything about how the models would fare in real-life conditions. The optimum way to communicate information to the end user has not yet been defined. Weissman argues that the human-machine interface has been overlooked. “Do people want to see dashboards, or get text messages? Do they want to see confidence intervals around prediction estimates, or do they want a simple summary in plain English about what the risk is?”, he wondered. “We do not have enough user-facing research in the predictive modelling world.” It is hardly a trivial issue. If a model is not well integrated into clinical workflows, no matter how good it is, it will not change behaviour. Evaluating AI systems after their introduction to the ICU is vital. “We have been developing protocols where the AI can explain why it is suggesting something in a way that is understandable to non-technical experts, to allay the concerns of sceptical clinicians”, said Faisal. “But we also need to be aware of unthinking acceptance of what AI has to offer.” He added that the extent to which its recommendations are taken up, and the time it takes the clinician to decide, can both be monitored by the AI system, which might allow for self-correcting systems. “We need to be aware of what AI can and cannot do”, concluded Faisal. “It is important we do not hate it or revere it. It is just another member of the team.”