fbpx

ADVERTISEMENT

ADVERTISEMENT

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

Comput Biol Med. 2024 Feb;169:107934. doi: 10.1016/j.compbiomed.2024.107934. Epub 2024 Jan 3.

ABSTRACT

BACKGROUND: In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2.

METHODS: We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm’s performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability.

MEASUREMENTS AND MAIN RESULTS: The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2.

CONCLUSIONS: The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

PMID:38183707 | DOI:10.1016/j.compbiomed.2024.107934

ADVERTISEMENT 

Free CME credits

Both our subscription plans include Free CME/CPD AMA PRA Category 1 credits.

Digital Certificate PDF

On course completion, you will receive a full-sized presentation quality digital certificate.

medtigo Simulation

A dynamic medical simulation platform designed to train healthcare professionals and students to effectively run code situations through an immersive hands-on experience in a live, interactive 3D environment.

medtigo Points

medtigo points is our unique point redemption system created to award users for interacting on our site. These points can be redeemed for special discounts on the medtigo marketplace as well as towards the membership cost itself.
 
  • Registration with medtigo = 10 points
  • 1 visit to medtigo’s website = 1 point
  • Interacting with medtigo posts (through comments/clinical cases etc.) = 5 points
  • Attempting a game = 1 point
  • Community Forum post/reply = 5 points

    *Redemption of points can occur only through the medtigo marketplace, courses, or simulation system. Money will not be credited to your bank account. 10 points = $1.

All Your Certificates in One Place

When you have your licenses, certificates and CMEs in one place, it's easier to track your career growth. You can easily share these with hospitals as well, using your medtigo app.

Our Certificate Courses