Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit

• URL: http://arxiv.org/abs/2303.05504v1
• Date: Thu, 9 Mar 2023 18:55:19 GMT
• Title: Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit
• Authors: Yuanfang Ren (1 and 2), Tyler J. Loftus (1 and 3), Ziyuan Guan (1 and 2), Rayon Uddin (1), Benjamin Shickel (1 and 2), Carolina B. Maciel (4), Katharina Busl (4), Parisa Rashidi (1 and 5), Azra Bihorac (1 and 2), and Tezcan Ozrazgat-Baslanti (1 and 2) ((1) Intelligent Critical Care Center, University of Florida, Gainesville, FL, (2) Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, (3) Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, (4) Department of Neurology, Neurocritical Care Division, College of Medicine, University of Florida, Gainesville, FL, (5) Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL)
• Abstract summary: In the United States, more than 5 million patients are admitted annually to ICUs, with ICU mortality of 10%-29% and costs over $82 billion. This study's objective was to develop automated computable phenotypes for acute brain dysfunction states.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the United States, more than 5 million patients are admitted annually to ICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain dysfunction status, delirium, is often underdiagnosed or undervalued. This study's objective was to develop automated computable phenotypes for acute brain dysfunction states and describe transitions among brain dysfunction states to illustrate the clinical trajectories of ICU patients. We created two single-center, longitudinal EHR datasets for 48,817 adult patients admitted to an ICU at UFH Gainesville (GNV) and Jacksonville (JAX). We developed algorithms to quantify acute brain dysfunction status including coma, delirium, normal, or death at 12-hour intervals of each ICU admission and to identify acute brain dysfunction phenotypes using continuous acute brain dysfunction status and k-means clustering approach. There were 49,770 admissions for 37,835 patients in UFH GNV dataset and 18,472 admissions for 10,982 patients in UFH JAX dataset. In total, 18% of patients had coma as the worst brain dysfunction status; every 12 hours, around 4%-7% would transit to delirium, 22%-25% would recover, 3%-4% would expire, and 67%-68% would remain in a coma in the ICU. Additionally, 7% of patients had delirium as the worst brain dysfunction status; around 6%-7% would transit to coma, 40%-42% would be no delirium, 1% would expire, and 51%-52% would remain delirium in the ICU. There were three phenotypes: persistent coma/delirium, persistently normal, and transition from coma/delirium to normal almost exclusively in first 48 hours after ICU admission. We developed phenotyping scoring algorithms that determined acute brain dysfunction status every 12 hours while admitted to the ICU. This approach may be useful in developing prognostic and decision-support tools to aid patients and clinicians in decision-making on resource use and escalation of care.

Related papers

• Rapid and Accurate Diagnosis of Acute Aortic Syndrome using Non-contrast CT: A Large-scale, Retrospective, Multi-center and AI-based Study [22.886299062772693]
  Chest pain symptoms are highly prevalent in emergency departments (EDs), where acute aortic syndrome (AAS) is a catastrophic cardiovascular emergency with a high fatality rate. Current triage practices in the ED can cause up to half of patients with AAS to have an initially missed diagnosis or be misdiagnosed as having other acute chest pain conditions. We developed an artificial intelligence model (DeepAAS) using non-contrast CT, which is highly accurate for identifying AAS and provides interpretable results.
  arXiv  Detail & Related papers  (2024-06-14T02:15:09Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• A multi-cohort study on prediction of acute brain dysfunction states using selective state space models [12.0129301272171]
  acute brain dysfunction (ABD) is a critical challenge due to its prevalence and severe implications for patient outcomes. Our research attempts to solve these problems by harnessing Electronic Health Records (EHR) data. Existing models solely predict a single state (e.g., either delirium or coma) require at least 24 hours of observation data to make predictions. Our research fills these gaps in the existing literature by dynamically predicting delirium, coma, mortality and fluctuating for 12-hour intervals throughout an ICU stay.
  arXiv  Detail & Related papers  (2024-03-11T22:58:11Z)
• APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model [12.370938858314911]
  The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable. Early detection of deteriorating conditions can result in providing timely interventions and improved survival rates. We propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba) to predict acuity state, transitions, and the need for life-sustaining therapies in real-time in ICU patients.
  arXiv  Detail & Related papers  (2023-11-03T16:52:27Z)
• Clinical Courses of Acute Kidney Injury in Hospitalized Patients: A Multistate Analysis [2.4013793000097103]
  We quantify longitudinal acute kidney injury (AKI) trajectories using multistate models. At seven days following Stage 1 AKI, 69% were resolved to No AKI or discharged. Patients with more frail conditions had lower proportion of transitioning to No AKI or discharge states.
  arXiv  Detail & Related papers  (2023-03-08T19:06:39Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction [53.122045119395594]
  Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results. We propose deep learning for UAD in 3D brain MRI considering additional age information. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
  arXiv  Detail & Related papers  (2022-01-31T09:39:52Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan [49.209225484926634]
  We propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification. Our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
  arXiv  Detail & Related papers  (2020-05-07T12:16:37Z)
• Computable Phenotypes of Patient Acuity in the Intensive Care Unit [0.0]
  The objectives of this study are to develop an electronic phenotype of acuity using automated variable retrieval within the electronic health records. We gathered two single-center, longitudinal electronic health record datasets for 51,372 adult ICU patients admitted to the University of Florida Health.
  arXiv  Detail & Related papers  (2020-04-27T17:36:17Z)
• Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [64.21642241351857]
  We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. We developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports. We also developed a model for multi-organ, multi-disease classification of chest CT volumes.
  arXiv  Detail & Related papers  (2020-02-12T00:59:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.