injury
A study using machine learning techniques suggests that maintaining an
optimal range of blood pressure control during surgical repair of spinal injuries may improve patient outcomes

Date:
November 16, 2021
Source:
eLife
Summary:
High or low blood pressure in patients during surgery to repair
a spinal cord injury may contribute to poorer outcomes, suggests
a study published in eLife.



FULL STORY ==========================================================================
High or low blood pressure in patients during surgery to repair a spinal
cord injury may contribute to poorer outcomes, suggests a study published
today ineLife.


==========================================================================
The findings suggest that maintaining an optimal blood pressure range
during surgery may help patients recover motor function. They also show
how machine learning techniques may help scientists answer key clinical questions using real-world data.

Current guidelines for treating patients with spinal cord injuries
recommend avoiding low blood pressure, ensuring the adequate flow of
blood to the injury site to allow recovery. But blood pressure that is
too high can cause bleeding in the spinal cord that can add to the damage.

"The precise window for optimal blood pressure management to promote
recovery from acute spinal cord injury is poorly understood," explains
co-first author Abel Torres-Espin, Assistant Professor of Neurological
Surgery at the University of California, San Francisco (UCSF), US. "We
set out to apply machine learning analytics to blood pressure and heart
rate changes in operating rooms. The idea was to test the associations
between these factors during surgery and neurorecovery to determine
treatment thresholds that forecast recovery." Torres-Espin and the interdisciplinary research team analysed blood pressure data from 118
patients who underwent surgery for spinal cord injuries at the Zuckerberg
San Francisco General Hospital and the Santa Clara Valley Medical Center.

"We found that patients with blood pressure that was either too high
or too low during surgery for an acute spinal cord injury had poorer
neuromotor recovery after surgery," says co-first author Jenny Haefeli, Assistant Professor of Neurological Surgery at UCSF Weill Institute for Neurosciences. The team also found that patients had the best chance
at recovery if their mean blood pressure was maintained between 76 mmHg
and 104-117mmHg.

The results suggest that more precise upper and lower blood pressure
targets may help physicians maximise their patients' odds of recovering
after a spinal cord injury, although these findings need to be confirmed
by other studies. But if they are, they could lead to more use of machine learning tools to guide the care of patients with spinal cord injuries.

"Machine learning tools could be used to create real-time models
that help predict the likelihood of a patient's recovery," concludes
senior author Adam Ferguson, Professor of Neurological Surgery
at UCSF School of Medicine. "Such models could also be applied
to forecasting patient outcomes early after a spinal cord injury." ========================================================================== Story Source: Materials provided by eLife. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Abel Torres Espi'n, Jenny Haefeli, Reza Ehsanian, Dolores Torres,
Carlos
A de Almeida, J Russell Huie, Austin Chou, Dmitriy Morozov,
Nicole Sanderson, Benjamin Dirlikov, Catherine G Suen, Jessica
L Nielson, Nikolaos Kyritsis, Debra D Hemmerle, Jason Talbott,
Geoff T Manley, Sanjay S Dhall, William D Whetstone, Jacqueline C
Bresnahan, Michael S Beattie, Stephen L McKenna, Jonathan Z Pan,
Adam Ferguson. Topological network analysis of patient similarity
for precision management of acute blood pressure in spinal cord
injury. eLife, 2021; 10 DOI: 10.7554/ eLife.68015 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211116111328.htm

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