Using Machine Learning to Predict Which COVID-19 Patients Will Get Worse

New algorithm helps clinicians flag patients who need more care.

2:59 PM

Author | Kelly Malcom

looking in patient room through blines
Getty Images

A patient enters the hospital struggling to breathe— they have COVID-19. Their healthcare team decides to admit them to the hospital. Will they be one of the fortunate ones who steadily improves and are soon discharged? Or will they end up needing mechanical ventilation?

That question may be easier to answer, thanks to a recent study from Michigan Medicine describing an algorithm to predict which patients are likely to quickly deteriorate while hospitalized.

"You can see large variability in how different patients with COVID-19 do, even among close relatives with similar environments and genetic risk," says Nicholas J. Douville, M.D., Ph.D., of the Department of Anesthesiology, one of the study's lead authors. "At the peak of the surge, it was very difficult for clinicians to know how to plan and allocate resources."

Combining data science and their collective experiences caring for COVID-19 patients in the intensive care unit, Douville, Milo Engoren, M.D., and their colleagues explored the potential of predictive machine learning. They looked at a set of patients with COVID-19 hospitalized during the first pandemic surge from March to May 2020 and modeled their clinical course.

The team generated an algorithm with inputs such as a patient's age, whether they had underlying medical conditions and what medications they were on when entering the hospital, as well as variables that changed while hospitalized, including vital signs like blood pressure, heart rate and oxygenation ratio, among others.

Their question: which of these data points helped to best predict which patients would decompensate and require mechanical ventilator or die within 24 hours?

Of the 398 patients in their study, 93 required a ventilator or died within two weeks. The model was able to predict mechanical ventilation most accurately based upon key vital signs, including oxygen saturation ratio (SpO2/FiO2), respiratory rate, heart rate, blood pressure and blood glucose level.

The team assessed the data points of interest at 4, 8, 24 and 48 hour increments, in an attempt to identify the optimal amount of time necessary to predict—and intervene—before a patient deteriorates.

"The closer we were to the event, the higher our ability to predict, which we expected. But we were still able to predict the outcomes with good discrimination at 48 hours, giving providers time to make alterations to the patient's care or to mobilize resources," says Douville.

For instance, the algorithm could quickly identify a patient on a general medical floor who would be a good candidate for transfer to the ICU, before their condition deteriorated to the point where ventilation would be more difficult.

In the long term, Douville and his colleagues hope the algorithm can be integrated into existing clinical decision support tools already used in the ICU. In the short term, the study brings to light patient characteristics that clinicians caring for patients with COVID-19 should keep in the back of their minds. The work also raises new questions about which COVID-19 therapies, such as anti-coagulants or anti-viral drugs, may or may not alter a patient's clinical trajectory.

Says Douville, "While many of our model features are well known to experienced clinicians, the utility of our model is that it performs a more complex calculation than the clinician could perform 'on the back of the envelope' – it also distills the overall risk to an easily interpretable value, which can be used to 'flag' patients in a way so they are not missed."

Paper cited: "Clinically Applicable Approach for Predicting Mechanical Ventilation in Patients with COVID-19," British Journal of Anaesthesia. DOI: 10.1016/j.bja.2020.11.03


More Articles About: Rounds Covid-19 Lungs and Breathing infectious disease
Health Lab word mark overlaying blue cells
Health Lab

Explore a variety of health care news & stories by visiting the Health Lab home page for more articles.

Media Contact Public Relations

Department of Communication at Michigan Medicine

[email protected]

734-764-2220

Stay Informed

Want top health & research news weekly? Sign up for Health Lab’s newsletters today!

Subscribe
Featured News & Stories pink purple cellular microscopic slide
Health Lab
Pathologists find evidence of pre-existing chronic lung disease in people with long COVID
Some symptoms may be caused by damage developed before patients contracted the coronavirus.
drawing of person on black background in white ink laying on a hospital bed
Health Lab
COVID-19 Deaths Really Are Different. But Best Practices for ICU Care Should Still Apply
Do COVID-19 patients really die of COVID? A new study helps put to rest conspiracy theory and shows the importance of evidence-based ICU care for ARDS.
cartoon drawing of experts in white ppe with teal background and screens up talking about COVID
Health Lab
12 Things Science Taught Us About COVID-19 This Past Year
Thanks to healthcare all-stars, the world enters 2021 optimistically with more clues than ever before about defeating the pandemic.
older person getting oxygen reading
Health Lab
Racially Biased Oxygen Readings Could Be Putting Patients at Risk
A new study finds pulse oximeters are less likely to give accurate readings in Black patients vs. white patients, with implications for COVID-19 treatment and beyond.
graphic of person wheeling person in wheelchair
Health Lab
Recovery After Severe COVID Infection Poses Unique Challenges
As more patients are discharged from stressed ICUs, they face multiple problems brought on by the pandemic.
Photo of ECMO machine through small opening of door
Health Lab
When Ventilators Don’t Help COVID-19 Patients, This Might
A life support system called ECMO has helped ARDS patients in past pandemics and other situations; now critical care teams are trying it in COVID-19 patients.