Literature DB >> 29474321

Evaluation of ICU Risk Models Adapted for Use as Continuous Markers of Severity of Illness Throughout the ICU Stay.

Omar Badawi1,2,3, Xinggang Liu1, Erkan Hassan1, Pamela J Amelung1, Sunil Swami1.   

Abstract

OBJECTIVES: Evaluate the accuracy of different ICU risk models repurposed as continuous markers of severity of illness.
DESIGN: Nonintervention cohort study.
SETTING: eICU Research Institute ICUs using tele-ICU software calculating continuous ICU Discharge Readiness Scores between January 2013 and March 2016. PATIENTS: Five hundred sixty-one thousand four hundred seventy-eight adult ICU patients with an ICU length of stay between 4 hours and 30 days.
INTERVENTIONS: Not available.
MEASUREMENTS AND MAIN RESULTS: Hourly Acute Physiology and Chronic Health Evaluation IV, Sequential Organ Failure Assessment, and Discharge Readiness Scores were calculated beginning hour 4 of the ICU stay. Primary outcome was the area under the receiver operating characteristic curve for the mean score with ICU mortality. Secondary outcomes included area under the receiver operating characteristic curves for ICU mortality with admission, median, maximum and last scores, and for death within 24 hours. The trajectories of each score were visualized by plotting the hourly averages against time in the ICU, stratified by mortality and length of stay. The area under the receiver operating characteristic curves for mean Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores were 0.90 (0.89-0.90), 0.86 (0.86-0.86), and 0.94 (0.94-0.94), respectively. The area under the receiver operating characteristic curves for hourly Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores predicting 24-hour mortality were 0.81 (0.81-0.81), 0.76 (0.76-0.76), and 0.86 (0.86-0.86). Discharge Readiness Scores had a higher area under the receiver operating characteristic curve than both Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment for each metric. Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment scores increased throughout the first 24 hours in both survivors and nonsurvivors; Discharge Readiness Scores continuously decreased in survivors and temporarily decreased before increasing by hour 36 in nonsurvivors with longer length of stays.
CONCLUSIONS: Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores all have relatively high discrimination for ICU mortality when used continuously; Discharge Readiness Scores tended to have slightly higher area under the receiver operating characteristic curves for each endpoint. These findings validate the use of these models on a population level for continuous risk adjustment in the ICU, although Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment appear slower to respond to improvements in patient status than Discharge Readiness Scores, and Discharge Readiness Scores may reflect physiologic improvement from interventions, potentially underestimating risk.

Entities:  

Mesh:

Substances:

Year:  2018        PMID: 29474321     DOI: 10.1097/CCM.0000000000002904

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  13 in total

Review 1.  Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.

Authors:  Fawaz Al-Mufti; Michael Kim; Vincent Dodson; Tolga Sursal; Christian Bowers; Chad Cole; Corey Scurlock; Christian Becker; Chirag Gandhi; Stephan A Mayer
Journal:  Curr Neurol Neurosci Rep       Date:  2019-11-13       Impact factor: 5.081

2.  Development and Performance of Electronic Pediatric Risk of Mortality and Pediatric Logistic Organ Dysfunction-2 Automated Acuity Scores.

Authors:  Christopher M Horvat; Henry Ogoe; Sajel Kantawala; Alicia K Au; Ericka L Fink; Eric Yablonsky; Patrick M Kochanek; Srinivasan Suresh; Robert S B Clark
Journal:  Pediatr Crit Care Med       Date:  2019-08       Impact factor: 3.624

3.  A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.

Authors:  Kaouter Karboub; Mohamed Tabaa
Journal:  Healthcare (Basel)       Date:  2022-05-24

4.  Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset.

Authors:  Melissa D Aczon; David R Ledbetter; Eugene Laksana; Long V Ho; Randall C Wetzel
Journal:  Pediatr Crit Care Med       Date:  2021-06-01       Impact factor: 3.971

5.  E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database.

Authors:  Nima Safaei; Babak Safaei; Seyedhouman Seyedekrami; Mojtaba Talafidaryani; Arezoo Masoud; Shaodong Wang; Qing Li; Mahdi Moqri
Journal:  PLoS One       Date:  2022-05-05       Impact factor: 3.752

6.  Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.

Authors:  Christopher J McWilliams; Daniel J Lawson; Raul Santos-Rodriguez; Iain D Gilchrist; Alan Champneys; Timothy H Gould; Mathew Jc Thomas; Christopher P Bourdeaux
Journal:  BMJ Open       Date:  2019-03-07       Impact factor: 2.692

7.  DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning.

Authors:  Benjamin Shickel; Tyler J Loftus; Lasith Adhikari; Tezcan Ozrazgat-Baslanti; Azra Bihorac; Parisa Rashidi
Journal:  Sci Rep       Date:  2019-02-12       Impact factor: 4.379

8.  Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs.

Authors:  Junchao Ma; Donald K K Lee; Michael E Perkins; Margaret A Pisani; Edieal Pinker
Journal:  Crit Care Explor       Date:  2019-04-17

9.  Severity Trajectories of Pediatric Inpatients Using the Criticality Index.

Authors:  Eduardo A Trujillo Rivera; Anita K Patel; Qing Zeng-Treitler; James M Chamberlain; James E Bost; Julia A Heneghan; Hiroki Morizono; Murray M Pollack
Journal:  Pediatr Crit Care Med       Date:  2021-01-01       Impact factor: 3.971

10.  Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

Authors:  Ben J Marafino; Miran Park; Jason M Davies; Robert Thombley; Harold S Luft; David C Sing; Dhruv S Kazi; Colette DeJong; W John Boscardin; Mitzi L Dean; R Adams Dudley
Journal:  JAMA Netw Open       Date:  2018-12-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.