A Review of Artificial Intelligence and Cardiac Arrest

AI for In-Hospital Cardiac Arrest

A common concern about artificial intelligence (AI) in healthcare is where it will be profitable in addition to improving and/or saving lives. One promising area is the earlier prediction of in-hospital cardiac arrest. [1] The AI software monitoring of vital signs +/- labs can detect changes earlier and can help predict cardiac arrest more accurately and earlier than existing risk models. Finding out a patient is headed towards cardiac arrest earlier can lead to measures to stop and reverse this progression.

The Data for AI Predicting In-Hospital Cardiac Arrest

There is a growing body of data, of varying quality, available for early prediction of cardiac arrest with AI algorithms. Studies show machine learning (ML) and more specifically deep learning (DL) can help predict patients at risk for cardiac arrest earlier than traditional methods. [2] The more recent use of neural networks is leading to the development of better algorithms to predict cardiac arrest earlier versus previous traditional ML algorithms. [3]

Startups in this Space

There are a few startups working in this space. One is Transformative whose AI technology predicts sudden cardiac arrest by analyzing data from patient monitoring devices commonly used in hospitals. Another startup VUNO has an AI-based cardiac arrest prediction software that analyzes vital signs stored in the EHR which then predicts the likelihood of a cardiac arrest occurring within the next 24 hours. Likely startups in this area will continue to publish more around the accuracy of early prediction cardiac arrest AI software and also in regards to the financial benefits of this type of software for hospitals.

Areas of Concern

In healthcare AI there are ongoing issues of training data bias because of skewed data from available patient databases and this is something that will need to be accounted for in algorithms for cardiac arrest as there are differences related to age, gender, and race to account for. Another area of concern is integrating new AI software into existing systems and the associated cost, setup time, and the availability of trained personnel to do this. For hospitals, the amount of money saved from shorter length of stays, fewer patient complications, and decreased need for critical care resources would need to outweigh the cost of integrating new AI software. More emphasis on the cost savings would be helpful as it seems more likely that hospitals will pay the costs to integrate additional software if they’re shown it will be financially beneficial for them.

Future Opportunities

Future opportunities include expanding the capabilities and accuracy of ML algorithms so that the same software can predict cardiac arrest and acute respiratory issues as well. [10,11] In the future this will help better allocate resources, save money on expensive critical care costs, as well as the obvious benefit to the patient. As algorithm accuracy improves, we may even be able to remove overnight vital sign checks for some hospitalized patients, this can help improve patients’ sleep and likely the overall speed of their recovery. [12] Eventually, ML algorithms could include the capability to be all-encompassing and help hospitals improve resource planning and managing capacity for all acute patients, medical and surgical, and related emergency procedures. One of the main benefits of AI in healthcare is in areas where it can help save time and resources with logistics and planning to save money and give staff more time to take care of patients.

References

  1. Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study
  2. Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review
  3. Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning
  4. Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study
  5. Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study
  6. Machine learning for early prediction of in‐hospital cardiac arrest in patients with acute coronary syndromes
  7. Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography
  8. Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data
  9. An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest
  10. Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome
  11. Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model
  12. Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

I’m a cardiologist, consulting and providing advisory services in health tech.