A dynamic risk score for early prediction of cardiogenic shock using
machine learning
- URL: http://arxiv.org/abs/2303.12888v2
- Date: Tue, 28 Mar 2023 12:08:42 GMT
- Title: A dynamic risk score for early prediction of cardiogenic shock using
machine learning
- Authors: Yuxuan Hu, Albert Lui, Mark Goldstein, Mukund Sudarshan, Andrea
Tinsay, Cindy Tsui, Samuel Maidman, John Medamana, Neil Jethani, Aahlad Puli,
Vuthy Nguy, Yindalon Aphinyanaphongs, Nicholas Kiefer, Nathaniel Smilowitz,
James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz,
Samuel Bernard, Rajesh Ranganath
- Abstract summary: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US.
Early recognition of cardiogenic shock is critical.
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU to predict onset of cardiogenic shock.
- Score: 15.597400667978913
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Myocardial infarction and heart failure are major cardiovascular diseases
that affect millions of people in the US. The morbidity and mortality are
highest among patients who develop cardiogenic shock. Early recognition of
cardiogenic shock is critical. Prompt implementation of treatment measures can
prevent the deleterious spiral of ischemia, low blood pressure, and reduced
cardiac output due to cardiogenic shock. However, early identification of
cardiogenic shock has been challenging due to human providers' inability to
process the enormous amount of data in the cardiac intensive care unit (ICU)
and lack of an effective risk stratification tool. We developed a deep
learning-based risk stratification tool, called CShock, for patients admitted
into the cardiac ICU with acute decompensated heart failure and/or myocardial
infarction to predict onset of cardiogenic shock. To develop and validate
CShock, we annotated cardiac ICU datasets with physician adjudicated outcomes.
CShock achieved an area under the receiver operator characteristic curve
(AUROC) of 0.820, which substantially outperformed CardShock (AUROC 0.519), a
well-established risk score for cardiogenic shock prognosis. CShock was
externally validated in an independent patient cohort and achieved an AUROC of
0.800, demonstrating its generalizability in other cardiac ICUs.
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