PHOENIX — Using admission data, a machine learning model detected patients with acute coronary syndrome at high-risk for cardiogenic shock 1.8 hours before the onset of hypotension, according to data presented at the CHEST Annual Meeting.
“For the clinician at the bedside, the practical value [of the model] is earlier situational awareness,” Sadhu Reddy, MD, internal medicine resident at Northwestern Medicine McHenry Hospital, told Healio. “Embedded in an acute coronary syndrome (ACS) care pathway, a model like this could surface a risk estimate at the moment of admission — before the blood

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