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Marassi C, Socia D, Larie D, An G, Cockrell RC. Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction. Surg Open Sci 2023; 16:77-81. [PMID: 37818461 PMCID: PMC10561114 DOI: 10.1016/j.sopen.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/29/2023] [Accepted: 09/17/2023] [Indexed: 10/12/2023] Open
Abstract
Background Though governed by the same underlying biology, the differential physiology of children causes the temporal evolution from health to a septic/diseased state to follow trajectories that are distinct from adult cases. As pediatric sepsis data sets are less readily available than for adult sepsis, we aim to leverage this shared underlying biology by normalizing pediatric physiological data such that it would be directly comparable to adult data, and then develop machine-learning (ML) based classifiers to predict the onset of sepsis in the pediatric population. We then externally validated the classifiers in an independent adult dataset. Methods Vital signs and laboratory observables were obtained from the Pediatric Intensive Care (PIC) database. These data elements were normalized for age and placed on a continuous scale, termed the Continuous Age-Normalized SOFA (CAN-SOFA) score. The XGBoost algorithm was used to classify pediatric patients that are septic. We tested the trained model using adult data from the MIMIC-IV database. Results On the pediatric population, the sepsis classifier has an accuracy of 0.84 and an F1-Score of 0.867. On the adult population, the sepsis classifier has an accuracy of 0.80 and an F1-score of 0.88; when tested on the adult population, the model showed similar performance degradation ("data drift") as in the pediatric population. Conclusions In this work, we demonstrate that, using a straightforward age-normalization method, EHR's can be generalizable compared (at least in the context of sepsis) between the pediatric and adult populations.
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Affiliation(s)
- Caitlin Marassi
- Department of Surgery, University of Vermont, 89 Beaumont Ave, Given D319, Burlington, VT 05405, United States of America
| | - Damien Socia
- Department of Surgery, University of Vermont, 89 Beaumont Ave, Given D319, Burlington, VT 05405, United States of America
| | - Dale Larie
- Department of Surgery, University of Vermont, 89 Beaumont Ave, Given D319, Burlington, VT 05405, United States of America
| | - Gary An
- Department of Surgery, University of Vermont, 89 Beaumont Ave, Given D319, Burlington, VT 05405, United States of America
| | - R. Chase Cockrell
- Department of Surgery, University of Vermont, 89 Beaumont Ave, Given D319, Burlington, VT 05405, United States of America
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Crum RJ, Johnson SA, Jiang P, Jui JH, Zamora R, Cortes D, Kulkarni M, Prabahar A, Bolin J, Gann E, Elster E, Schobel SA, Larie D, Cockrell C, An G, Brown B, Hauskrecht M, Vodovotz Y, Badylak SF. Transcriptomic, Proteomic, and Morphologic Characterization of Healing in Volumetric Muscle Loss. Tissue Eng Part A 2022; 28:941-957. [PMID: 36039923 DOI: 10.1089/ten.tea.2022.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Skeletal muscle has a robust, inherent ability to regenerate in response to injury from acute to chronic. In severe trauma, however, complete regeneration is not possible, resulting in a permanent loss of skeletal muscle tissue referred to as volumetric muscle loss (VML). There are few consistently reliable therapeutic or surgical options to address VML. A major limitation in investigation of possible therapies is the absence of a well-characterized large animal model. Here, we present results of a comprehensive transcriptomic, proteomic, and morphologic characterization of wound healing following volumetric muscle loss in a novel canine model of VML which we compare to a nine-patient cohort of combat-associated VML. The canine model is translationally relevant as it provides both a regional (spatial) and temporal map of the wound healing processes that occur in human VML. Collectively, these data show the spatiotemporal transcriptomic, proteomic, and morphologic properties of canine VML healing as a framework and model system applicable to future studies investigating novel therapies for human VML.
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Affiliation(s)
- Raphael John Crum
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, 450 Technology Dr., Suite 300, Pittsburgh, Pennsylvania, United States, 15219;
| | - Scott A Johnson
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, 450 Technology Dr, Suite 300, Pittsburgh, Pennsylvania, United States, 15219;
| | - Peng Jiang
- Cleveland State University, Center for Gene Regulation in Health and Disease, Cleveland, Ohio, United States.,Cleveland State University, Center for Applied Data Analysis and Modeling (ADAM), Cleveland, Ohio, United States.,Cleveland State University, Department of Biological, Geological, and Environmental Sciences (BGES), Cleveland, Ohio, United States;
| | - Jayati H Jui
- University of Pittsburgh, Department of Computer Science, Pittsburgh, Pennsylvania, United States;
| | - Ruben Zamora
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Surgery, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Center for Inflammation and Regeneration Modeling, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Center for Systems Immunology, Pittsburgh, Pennsylvania, United States;
| | - Devin Cortes
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Bioengineering, Pittsburgh, Pennsylvania, United States;
| | - Mangesh Kulkarni
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Bioengineering, Pittsburgh, Pennsylvania, United States;
| | - Archana Prabahar
- Cleveland State University, Center for Gene Regulation in Health and Disease, Cleveland, Ohio, United States;
| | - Jennifer Bolin
- Morgridge Institute for Research, Madison, Wisconsin, United States;
| | - Eric Gann
- Uniformed Services University of the Health Sciences, Surgery, Bethesda, Maryland, United States.,Uniformed Services University of the Health Sciences, Surgical Critical Care Initiative, Department of Surgery, Bethesda, Maryland, United States.,Henry M Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, Maryland, United States;
| | - Eric Elster
- Uniformed Services University of the Health Sciences, Surgery, Bethesda, Maryland, United States.,Henry M Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, Maryland, United States.,Uniformed Services University of the Health Sciences, Surgical Critical Care Initiative, Department of Surgery, Bethesda, Maryland, United States.,Walter Reed Army Medical Center, Bethesda, Maryland, United States;
| | - Seth A Schobel
- Uniformed Services University of the Health Sciences, Surgery, Bethesda, Maryland, United States.,Henry M Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, Maryland, United States.,Uniformed Services University of the Health Sciences, Surgical Critical Care Initiative, Department of Surgery, Bethesda, Maryland, United States;
| | - Dale Larie
- University of Vermont, Department of Surgery, Burlington, Vermont, United States;
| | - Chase Cockrell
- University of Vermont, Department of Surgery, Burlington, Vermont, United States;
| | - Gary An
- University of Vermont, Department of Surgery, Burlington, Vermont, United States;
| | - Bryan Brown
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Bioengineering, Pittsburgh, Pennsylvania, United States;
| | - Milos Hauskrecht
- University of Pittsburgh, Department of Computer Science, Pittsburgh, Pennsylvania, United States;
| | - Yoram Vodovotz
- University of Pittsburgh, Surgery, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Surgery, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Center for Inflammation and Regeneration Modeling, Pittsburgh, Pennsylvania, United States.,University of Pittsburgh, Center for Systems Immunology, Pittsburgh, Pennsylvania, United States;
| | - Stephen F Badylak
- University of Pittsburgh, McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, United States;
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Tulipani LJ, Meyer B, Larie D, Solomon AJ, McGinnis RS. Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis. Gait Posture 2020; 80:361-366. [PMID: 32615409 PMCID: PMC7413823 DOI: 10.1016/j.gaitpost.2020.06.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND Approximately half of the 2.3 million people with multiple sclerosis (PwMS) will fall in any three-month period. Currently clinicians rely on self-report measures or simple functional assessments, administered at discrete time points, to assess fall risk. Wearable inertial sensors are a promising technology for increasing the sensitivity of clinical assessments to accurately predict fall risk, but current accelerometer-based approaches are limited. RESEARCH QUESTION Will metrics derived from wearable accelerometers during a 30-second chair stand test (30CST) correlate with clinical measures of disease severity, balance confidence and fatigue in PwMS, and can these metrics be used to accurately discriminate fallers from non-fallers? METHODS Thirty-eight PwMS (21 fallers) completed self-report outcome measures then performed the 30CST while triaxial acceleration data were collected from inertial sensors adhered to the thigh and chest. Accelerometer metrics were derived for the sit-to-stand and stand-to-sit transitions and relationships with clinical metrics were assessed. Finally, the metrics were used to develop a logistic regression model to classify fall status. RESULTS Accelerometer-derived metrics were significantly associated with multiple clinical metrics that capture disease severity, balance confidence and fatigue. Performance of a logistic regression for classifying fall status was enhanced by including accelerometer features (accuracy 74%, AUC 0.78) compared to the standard of care (accuracy 68%, AUC 0.74) or patient reported outcomes (accuracy 71%, AUC 0.75). SIGNIFICANCE Accelerometer derived metrics were associated with clinically relevant measures of disease severity, fatigue and balance confidence during a balance challenging task. Inertial sensors could feasibly be utilized to enhance the accuracy of functional assessments to identify fall risk in PwMS. Simplicity of these accelerometer-based metrics could facilitate deployment for community-based monitoring.
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Affiliation(s)
- Lindsey J. Tulipani
- M-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT
| | - Brett Meyer
- M-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT
| | - Dale Larie
- M-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT
| | - Andrew J. Solomon
- Department of Neurological Sciences, University of Vermont, Burlington, VT
| | - Ryan S. McGinnis
- M-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT;,Corresponding Author: Dr. Ryan S. McGinnis (), Department of Electrical and Biomedical Engineering, 33 Colchester Avenue, Burlington, VT 05405
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