1
|
Kamaleswaran R, Sadan O, Kandiah P, Li Q, Coopersmith CM, Buchman TG. Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients. Crit Care Explor 2021; 3:e0570. [PMID: 34984336 PMCID: PMC8718227 DOI: 10.1097/cce.0000000000000570] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
IMPORTANCE Altered heart rate variability has been associated with autonomic dysfunction in a number of disease profiles, in this work we elucidate differences in the biomarker among patients with all-cause sepsis and coronavirus disease 2019. OBJECTIVES To measure heart rate variability metrics in critically ill coronavirus disease 2019 patients with comparison to all-cause critically ill sepsis patients. DESIGN SETTING AND PARTICIPANTS Retrospective analysis of coronavirus disease 2019 patients admitted to an ICU for at least 24 hours at any of Emory Healthcare ICUs between March 2020 and April 2020 up to 5 days of ICU stay. The comparison group was a cohort of all-cause sepsis patients prior to coronavirus disease 2019 pandemic. MAIN OUTCOMES AND MEASURES Continuous waveforms were captured from the patient monitor. The electrocardiogram was then analyzed for each patient over a 300 seconds observational window that was shifted by 30 seconds in each iteration from admission till discharge. A total of 23 heart rate variability metrics were extracted in each iteration. We use the Kruskal-Wallis and Steel-Dwass tests (p < 0.05) for statistical analysis and interpretations of heart rate variability multiple measures. RESULTS A total of 141 critically ill coronavirus disease 2019 patients met inclusion criteria, who were compared with 208 patients with all-cause sepsis. Three nonlinear markers, including the ratio of standard deviation derived from the Poincaré plot, sample entropy, and approximate entropy and four linear features, including mode of beat-to-beat interval, acceleration capacity, deceleration capacity, and the proportion of consecutive RR intervals that differ by more than 50 ms, were all statistically significant (p < 0.05) between the coronavirus disease 2019 and all-cause sepsis cohorts. The three nonlinear features and acceleration capacity, deceleration capacity, and beat-to-beat interval (mode) were statistically significant (p < 0.05) when comparing pairwise analysis among the combinations of survivors and nonsurvivors between the coronavirus disease 2019 and sepsis cohorts. Temporal analysis of the main markers showed low variability across the 5 days of analysis compared with sepsis patients. CONCLUSIONS AND RELEVANCE In this descriptive statistical study, heart rate variability measures were found to be statistically different across critically ill patients infected with severe acute respiratory syndrome coronavirus 2 and distinct from bacterial sepsis.
Collapse
Affiliation(s)
- Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA
| | - Ofer Sadan
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA
- Department of Neurology and Neurosurgery, Division of Neurocritical Care, Emory University School of Medicine, Atlanta, GA
| | - Prem Kandiah
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA
- Department of Neurology and Neurosurgery, Division of Neurocritical Care, Emory University School of Medicine, Atlanta, GA
| | - Qiao Li
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA
| | - Craig M Coopersmith
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA
- Department of Surgery, Emory University School of Medicine, Atlanta, GA
| | - Timothy G Buchman
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA
- Department of Surgery, Emory University School of Medicine, Atlanta, GA
| |
Collapse
|
2
|
Singhal L, Garg Y, Yang P, Tabaie A, Wong AI, Mohammed A, Chinthala L, Kadaria D, Sodhi A, Holder AL, Esper A, Blum JM, Davis RL, Clifford GD, Martin GS, Kamaleswaran R. eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19. PLoS One 2021; 16:e0257056. [PMID: 34559819 PMCID: PMC8462682 DOI: 10.1371/journal.pone.0257056] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/21/2021] [Indexed: 01/08/2023] Open
Abstract
We present an interpretable machine learning algorithm called ‘eARDS’ for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88–0.90), sensitivity of 0.77 (95% CI = 0.75–0.78), specificity 0.85 (95% CI = 085–0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81–0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18–40) (AUROC = 0.93 [0.92–0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81–0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.
Collapse
Affiliation(s)
- Lakshya Singhal
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Yash Garg
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Philip Yang
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Azade Tabaie
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - A. Ian Wong
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Akram Mohammed
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Lokesh Chinthala
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Dipen Kadaria
- Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Amik Sodhi
- Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Andre L. Holder
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Annette Esper
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - James M. Blum
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Department of Anaesthesia, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Robert L. Davis
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Greg S. Martin
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
| |
Collapse
|
3
|
Fartoumi S, Emeriaud G, Roumeliotis N, Brossier D, Sawan M. Computerized Decision Support System for Traumatic Brain Injury Management. J Pediatr Intensive Care 2016; 5:101-107. [PMID: 31110893 DOI: 10.1055/s-0035-1569997] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 10/09/2015] [Indexed: 10/22/2022] Open
Abstract
Mortality and morbidity related to traumatic brain injury (TBI) present a major health care burden. Patients with severe TBI must be managed rapidly and efficiently to minimize secondary brain injury potentially leading to permanent sequelae. This is especially important in young patients, whose brain is still in development, making them particularly susceptible to secondary insults. The complexity of both brain injury pathophysiology and the intensive care unit environment makes the management of these patients challenging, with a risk of delayed response and/or patient instability contributing to worsened outcome. Computerized assistance in TBI appears likely to improve patient management, by helping clinicians quickly analyze and respond to ongoing clinical changes and optimizing patient status by guiding management. Currently, computerized decision support systems (CDSSs) do not feature continuous medical assistance with individualized treatment plans. This review presents new developments in CDSSs specialized in TBI. We also present the framework for future CDSSs needed to improve TBI management in real time, taking into account individual patient characteristics.
Collapse
Affiliation(s)
- Sina Fartoumi
- Polystim Neurotechnology Laboratory, Department of Electrical Engineering, Polytechnique Montreal, Quebec, Canada.,Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - Guillaume Emeriaud
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - Nadia Roumeliotis
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - David Brossier
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Université de Montréal, Quebec, Canada
| | - Mohamad Sawan
- Polystim Neurotechnology Laboratory, Department of Electrical Engineering, Polytechnique Montreal, Quebec, Canada
| |
Collapse
|
4
|
Nizami S, Green JR, McGregor C. Service oriented architecture to support real-time implementation of artifact detection in critical care monitoring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:4925-8. [PMID: 22255443 DOI: 10.1109/iembs.2011.6091221] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The quality of automated real-time critical care monitoring is impacted by the degree of signal artifact present in clinical data. This is further complicated when different clinical rules applied for disease detection require source data at different frequencies and different signal quality. This paper proposes a novel multidimensional framework based on service oriented architecture to support real-time implementation of clinical artifact detection in critical care settings. The framework is instantiated through a Neonatal Intensive Care case study which assesses signal quality of physiological data streams prior to detection of late-onset neonatal sepsis. In this case study requirements and provisions of artifact and clinical event detection are determined for real-time clinical implementation, which forms the second important contribution of this paper.
Collapse
|