Machine Learning Poised To Revolutionise Clinical Trials And Improve Patient Outcomes

In a recently published study, Effects of Individualized Oxygenation Targets on Mortality in Critically Ill Adults: A Machine Learning Analysis, researchers from Aotearoa New Zealand collaborating with scientists in the United States have unlocked a new frontier in medicine using advanced machine learning techniques.

Traditionally, randomised clinical trials determine the average effect of treatments on patient outcomes. Such clinical trials have revolutionised medicine by providing high-quality evidence on the effect of treatments on patient outcomes; however, one key problem with these trials is that they assume every patient responds to treatment in the same way.

In their recent study, a collaborative research team used machine learning to generate individualised predictions about the effect of higher or lower amounts of oxygen on mortality in critically ill adults receiving life support in the intensive care unit (ICU).

Specifically, they used the data from one study, the Pragmatic Investigation of Optimal Oxygen Targets (PILOT) trial, to generate a model using machine learning, and then they tested the model using data from another trial, the Intensive Care Unit Randomized Trial Comparing Two Approaches to Oxygen Therapy (ICU-ROX) trial.

Their results were remarkable. One of the study's senior investigators, Professor Paul Young, Deputy Director and Intensive Care Medicine lead at the Medical Research Institute of New Zealand (MRINZ), explains, “When we applied the machine-learning model we found that the individual responses to higher or lower levels of oxygen varied dramatically. At one extreme, the model predicted a 27.2% absolute decrease in the risk of death by using a lower oxygen target, and at the other extreme, the model predicted a 34.4% absolute decrease in the risk of death by using a higher oxygen target."

“Overall, if patients in the ICU-ROX trial had received the oxygen therapy regimen recommended by the machine learning model, the mortality rate would have been 6.4 percentage points lower,” says Professor Young.

Dr Alex Psirides, Chair, Critical Care Advisory Group to Te Whatu Ora, states, “Around 40% of the 24,000 patients admitted to New Zealand ICUs each year receive life support. If these findings are confirmed, they would be expected to save the lives of around 600 New Zealanders every year.”

In the accompanying editorial, Dr Derek Angus, the Deputy Editor of the Journal of the American Medical Association, commented, “If the results are true and generalisable, then the consequences are staggering.”

“If one could instantly assign every patient into their appropriate group of predicted benefit or harm and assign their oxygen target accordingly, the intervention would theoretically yield the greatest single improvement in lives saved from critical illness in the history of the field,” states Dr Angus.

Professor Richard Beasley, MRINZ Director, notes that the implications of this research extend well beyond oxygen therapy for patients in the ICU, stating, “This technique of using machine learning to predict individualised treatment responses for patients using data from clinical trials is likely the greatest advance in the generation of medical evidence in decades.”

Professor Beasley says, “It is difficult to overstate the degree to which research of this kind could change medicine.”

“For decades, the practice of medicine has experienced the tension of choosing between care that is personalised but not evidence-based and care that is evidence-based but not personalised. Paul Young and his collaborators have shown machine learning methods can predict individualised treatment effects allowing care that is both evidence-based and personalised,” says Professor Beasley.

This study’s innovative use of machine learning in determining individualised oxygenation targets marks a significant step forward in healthcare. With the potential to bridge the gap between evidence-based and personalised care, it could offer significantly improved patient outcomes. This approach represents a transformative shift in medical research and practice.

STUDY LINK
https://jamanetwork.com/journals/jama/article-abstract/2816677

KEY POINTS AT A GLANCE

1. The study Effects of Individualized Oxygenation Targets on Mortality in Critically Ill Adults: A Machine Learning Analysis represents a ground-breaking collaboration between medical researchers from New Zealand and the United States, showcasing an innovative approach to personalized medicine using state-of-the-art machine learning techniques.
2. Traditional randomised clinical trials excel at assessing treatment effects on average but struggle to address individual variations, posing a long-standing challenge in medical practice. This challenge leaves clinicians uncertain about whether the findings of a clinical trial apply to the individual that they are treating.
3. Machine learning algorithms can play a pivotal role by enabling precise predictions of oxygenation targets for critically ill adults in intensive care units (ICUs). These predictions reveal significant variations in patient responses to different oxygen levels, potentially leading to substantial reductions in mortality rates.
4. The study, led in New Zealand by Professor Paul Young, Deputy Director and Intensive Care Medicine programme lead at the Medical Research Institute of New Zealand (MRINZ), highlights the transformative potential of machine learning in healthcare. This research directly impacts treatment optimisation and personalised care, tailoring interventions based on patient-specific characteristics.
5. Integration of machine learning into clinical practice offers significant opportunity to bridge the gap between evidence-based medicine and personalised care. This paradigm shift has the potential to revolutionise medical research and health care delivery, ensuring treatments are customised to individual patient needs while maintaining a foundation in robust scientific evidence.

BIOS

Medical Research Institute of New Zealand
Rangahautia Te Ora

The Medical Research Institute of New Zealand (MRINZ) is Aotearoa New Zealand’s leading independent medical research institute. MRINZ research is guided by a simple philosophy: it must challenge dogma, increase knowledge, and have the potential to improve clinical practice and outcomes, both in Aotearoa New Zealand, and internationally. Committed to contributing toward a more equitable society that celebrates Te Ao Māori and upholds Te Tiriti o Waitangi, MRINZ’s research teams are dedicated to investigating important public health problems, delivering high quality evidence on which to improve the management of disease and patient care.

Professor Paul Young, MRINZ Deputy Director, Intensive Care Medicine Programme lead

Paul Young’s primary research interest is in the design and conduct of large-scale multicentre randomised clinical trials in the field of Intensive Care Medicine. An active member of the Australian and New Zealand Intensive Care Society Clinical Trials Group (ANZICS CTG), Paul is a leading member of the international intensive care research community. Alongside his role at the MRINZ, Paul is the Medical Director of the Wakefield Hospital ICU and co-clinical leader of the Intensive Care Research Unit at Wellington Hospital. Paul is Clinical Associate Professor, Department of Critical Care, at the University of Melbourne, an Adjunct Professor at the Australian and New Zealand Intensive Care Research Centre, Monash University, and the Associate Editor for Critical Care and Resuscitation, the highest impact journal in the field of Intensive Care Medicine outside the US and Europe.

Involved in research collaborations with colleagues worldwide, Paul has published over 250 peer-reviewed journal articles, including numerous high impact publications in the New England Journal of Medicine, the Lancet, and the Journal of the American Medical Association. You can follow Paul’s clinical trial research on Twitter @DogICUma.
 

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