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Qiu J, Zimmet AN, Bell TD, Gadrey S, Brandberg J, Maldonado S, Zimmet AM, Ratcliffe S, Chernyavskiy P, Moorman JR, Clermont G, Henry TR, Nguyen NR, Moore CC. Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients. Clin Infect Dis 2024; 78:1011-1021. [PMID: 37889515 DOI: 10.1093/cid/ciad662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/12/2023] [Accepted: 10/25/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Identification of bloodstream infection (BSI) in transplant recipients may be difficult due to immunosuppression. Accordingly, we aimed to compare responses to BSI in critically ill transplant and non-transplant recipients and to modify systemic inflammatory response syndrome (SIRS) criteria for transplant recipients. METHODS We analyzed univariate risks and developed multivariable models of BSI with 27 clinical variables from adult intensive care unit (ICU) patients at the University of Virginia (UVA) and at the University of Pittsburgh (Pitt). We used Bayesian inference to adjust SIRS criteria for transplant recipients. RESULTS We analyzed 38.7 million hourly measurements from 41 725 patients at UVA, including 1897 transplant recipients with 193 episodes of BSI and 53 608 patients at Pitt, including 1614 transplant recipients with 768 episodes of BSI. The univariate responses to BSI were comparable in transplant and non-transplant recipients. The area under the receiver operating characteristic curve (AUC) was 0.82 (95% confidence interval [CI], .80-.83) for the model using all UVA patient data and 0.80 (95% CI, .76-.83) when using only transplant recipient data. The UVA all-patient model had an AUC of 0.77 (95% CI, .76-.79) in non-transplant recipients and 0.75 (95% CI, .71-.79) in transplant recipients at Pitt. The relative importance of the 27 predictors was similar in transplant and non-transplant models. An upper temperature of 37.5°C in SIRS criteria improved reclassification performance in transplant recipients. CONCLUSIONS Critically ill transplant and non-transplant recipients had similar responses to BSI. An upper temperature of 37.5°C in SIRS criteria improved BSI screening in transplant recipients.
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Affiliation(s)
- Jiaxing Qiu
- Department of Medicine, Division of Cardiovascular Diseases, Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Alex N Zimmet
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Taison D Bell
- Department of Medicine, Division of Pulmonary and Critical Care Medicine and Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Shrirang Gadrey
- Department of Medicine, Division of Hospital Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Jackson Brandberg
- Department of Medicine, Division of Cardiovascular Diseases, Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Samuel Maldonado
- Department of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Harvard University School of Medicine, Boston, Massachusetts, USA
| | - Amanda M Zimmet
- Department of Medicine, Division of Cardiovascular Diseases, Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Sarah Ratcliffe
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Pavel Chernyavskiy
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - J Randall Moorman
- Department of Medicine, Division of Cardiovascular Diseases, Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Teague R Henry
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - N Rich Nguyen
- Department of Computer Science, University of Virginia School of Engineering, Charlottesville, Virginia, USA
| | - Christopher C Moore
- Department of Medicine, Division of Infectious Diseases and International Health, Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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Monfredi OJ, Moore CC, Sullivan BA, Keim-Malpass J, Fairchild KD, Loftus TJ, Bihorac A, Krahn KN, Dubrawski A, Lake DE, Moorman JR, Clermont G. Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration. J Electrocardiol 2023; 76:35-38. [PMID: 36434848 PMCID: PMC10061545 DOI: 10.1016/j.jelectrocard.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 11/24/2022]
Abstract
The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.
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Affiliation(s)
- Oliver J Monfredi
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Christopher C Moore
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Brynne A Sullivan
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, University of Virginia, United States of America; School of Nursing, University of Virginia, United States of America
| | - Karen D Fairchild
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Tyler J Loftus
- Department of Surgery, University of Florida, United States of America
| | - Azra Bihorac
- Department of Medicine, University of Florida, United States of America
| | - Katherine N Krahn
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Artur Dubrawski
- Robotics Institute, Carnegie Mellon University, United States of America
| | - Douglas E Lake
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
| | - Gilles Clermont
- Department of Critical Care, University of Pittsburgh, United States of America
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Spaeder MC, Moorman JR, Moorman LP, Adu-Darko MA, Keim-Malpass J, Lake DE, Clark MT. Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study. Front Pediatr 2022; 10:1016269. [PMID: 36440325 PMCID: PMC9682496 DOI: 10.3389/fped.2022.1016269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/26/2022] [Indexed: 11/09/2022] Open
Abstract
Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction.
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Affiliation(s)
- Michael C. Spaeder
- Department of Pediatrics, Division of Pediatric Critical Care, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - J. Randall Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Liza P. Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States
| | - Michelle A. Adu-Darko
- Department of Pediatrics, Division of Pediatric Critical Care, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Acute and Specialty Care, School of Nursing, University of Virginia, Charlottesville, VA, United States
| | - Douglas E. Lake
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Matthew T. Clark
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States
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