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Horwitz A, McCarthy K, House SL, Beaudoin FL, An X, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Swor RA, Hudak LA, Pascual JL, Seamon MJ, Harris E, Pearson C, Peak DA, Domeier RM, Rathlev NK, Sergot P, Sanchez LD, Bruce SE, Joormann J, Harte SE, Koenen KC, McLean SA, Sen S. Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning. J Anxiety Disord 2024; 104:102876. [PMID: 38723405 PMCID: PMC11215748 DOI: 10.1016/j.janxdis.2024.102876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/09/2024] [Accepted: 04/29/2024] [Indexed: 06/12/2024]
Abstract
There are significant challenges to identifying which individuals require intervention following exposure to trauma, and a need for strategies to identify and provide individuals at risk for developing PTSD with timely interventions. The present study seeks to identify a minimal set of trauma-related symptoms, assessed during the weeks following traumatic exposure, that can accurately predict PTSD. Participants were 2185 adults (Mean age=36.4 years; 64% women; 50% Black) presenting for emergency care following traumatic exposure. Participants received a 'flash survey' with 6-8 varying symptoms (from a pool of 26 trauma symptoms) several times per week for eight weeks following the trauma exposure (each symptom assessed ∼6 times). Features (mean, sd, last, worst, peak-end scores) from the repeatedly assessed symptoms were included as candidate variables in a CART machine learning analysis to develop a pragmatic predictive algorithm. PTSD (PCL-5 ≥38) was present for 669 (31%) participants at the 8-week follow-up. A classification tree with three splits, based on mean scores of nervousness, rehashing, and fatigue, predicted PTSD with an Area Under the Curve of 0.836. Findings suggest feasibility for a 3-item assessment protocol, delivered once per week, following traumatic exposure to assess and potentially facilitate follow-up care for those at risk.
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Affiliation(s)
- Adam Horwitz
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Kaitlyn McCarthy
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Francesca L Beaudoin
- Department of Epidemiology, Brown University, Providence, RI 02930, USA; Department of Emergency Medicine, Brown University, Providence, RI 02930, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30332, USA; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA
| | - Laura T Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA 02478, USA; The Many Brains Project, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Scott L Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA; Department of Psychiatry, McLean Hospital, Belmont, MA 02478, USA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL 32209, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL 32209, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ 08103, USA
| | - Brittany E Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH 43210, USA; Ohio State University College of Nursing, Columbus, OH 43210, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Jose L Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mark J Seamon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erica Harris
- Department of Emergency Medicine, Einstein Medical Center, Philadelphia, PA 19107, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI 48202, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Trinity Health-Ann Arbor, Ypsilanti, MI 48197, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA 01107, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Emergency Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO 63121, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT 06510, USA
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Samuel A McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA; Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27559, USA
| | - Srijan Sen
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
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Hinojosa CA, Liew A, An X, Stevens JS, Basu A, van Rooij SJH, House SL, Beaudoin FL, Zeng D, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Germine LT, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Kurz MC, Swor RA, Hudak LA, Pascual JL, Seamon MJ, Datner EM, Chang AM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, Sergot P, Sanchez LD, Bruce SE, Miller MW, Pietrzak RH, Joormann J, Pizzagalli DA, Sheridan JF, Harte SE, Elliott JM, Kessler RC, Koenen KC, McLean SA, Ressler KJ, Fani N. Associations of alcohol and cannabis use with change in posttraumatic stress disorder and depression symptoms over time in recently trauma-exposed individuals. Psychol Med 2024; 54:338-349. [PMID: 37309917 PMCID: PMC10716364 DOI: 10.1017/s0033291723001642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians. METHODS In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance. RESULTS Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12. CONCLUSIONS Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.
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Affiliation(s)
- Cecilia A. Hinojosa
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Amanda Liew
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jennifer S. Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Archana Basu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Sanne J H. van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Stacey L. House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca L. Beaudoin
- Department of Emergency Medicine & Department of Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas C. Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Sarah D. Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura T. Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- The Many Brains Project, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Scott L. Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - John P. Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Alan B. Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul I. Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Phyllis L. Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Christopher W. Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E. Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, USA
- Ohio State University College of Nursing, Columbus, OH, USA
| | - Michael C. Kurz
- Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, AL, USA
- Department of Surgery, Division of Acute Care Surgery, University of Alabama School of Medicine, Birmingham, AL, USA
- Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert A. Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Lauren A. Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L. Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark J. Seamon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth M. Datner
- Department of Emergency Medicine, Einstein Healthcare Network, Philadelphia, PA, USA
- Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Anna M. Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
| | - David A. Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roland C. Merchant
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Robert M. Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, USA
| | - Niels K. Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Leon D. Sanchez
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Steven E. Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, USA
| | - Mark W. Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Robert H. Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Diego A. Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - John F. Sheridan
- Division of Biosciences, Ohio State University College of Dentistry, Columbus, OH, USA
- Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH, USA
| | - Steven E. Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James M. Elliott
- Kolling Institute, University of Sydney, St Leonards, New South Wales, Australia
- Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, New South Wales, Australia
- Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Karestan C. Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Samuel A. McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry J. Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
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Papini S, Iturralde E, Lu Y, Greene JD, Barreda F, Sterling SA, Liu VX. Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis. Transl Psychiatry 2023; 13:400. [PMID: 38114475 PMCID: PMC10730505 DOI: 10.1038/s41398-023-02699-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023] Open
Abstract
A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.
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Affiliation(s)
- Santiago Papini
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
- Department of Psychology, University of Hawai'i at Mānoa, Honolulu, HI, USA.
| | - Esti Iturralde
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Yun Lu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - John D Greene
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Fernando Barreda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stacy A Sterling
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Vincent X Liu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
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Zannas AS, Linnstaedt SD, An X, Stevens JS, Harnett NG, Roeckner AR, Oliver KI, Rubinow DR, Binder EB, Koenen KC, Ressler KJ, McLean SA. Epigenetic aging and PTSD outcomes in the immediate aftermath of trauma. Psychol Med 2023; 53:7170-7179. [PMID: 36951141 DOI: 10.1017/s0033291723000636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
BACKGROUND Psychological trauma exposure and posttraumatic stress disorder (PTSD) have been associated with advanced epigenetic age. However, whether epigenetic aging measured at the time of trauma predicts the subsequent development of PTSD outcomes is unknown. Moreover, the neural substrates underlying posttraumatic outcomes associated with epigenetic aging are unclear. METHODS We examined a multi-ancestry cohort of women and men (n = 289) who presented to the emergency department (ED) after trauma. Blood DNA was collected at ED presentation, and EPIC DNA methylation arrays were used to assess four widely used metrics of epigenetic aging (HorvathAge, HannumAge, PhenoAge, and GrimAge). PTSD symptoms were evaluated longitudinally at the time of ED presentation and over the ensuing 6 months. Structural and functional neuroimaging was performed 2 weeks after trauma. RESULTS After covariate adjustment and correction for multiple comparisons, advanced ED GrimAge predicted increased risk for 6-month probable PTSD diagnosis. Secondary analyses suggested that the prediction of PTSD by GrimAge was driven by worse trajectories for intrusive memories and nightmares. Advanced ED GrimAge was also associated with reduced volume of the whole amygdala and specific amygdala subregions, including the cortico-amygdaloid transition and the cortical and accessory basal nuclei. CONCLUSIONS Our findings shed new light on the relation between biological aging and trauma-related phenotypes, suggesting that GrimAge measured at the time of trauma predicts PTSD trajectories and is associated with relevant brain alterations. Furthering these findings has the potential to enhance early prevention and treatment of posttraumatic psychiatric sequelae.
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Affiliation(s)
- Anthony S Zannas
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Stress Initiative, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xinming An
- Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Nathaniel G Harnett
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA
| | - Alyssa R Roeckner
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Katelyn I Oliver
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - David R Rubinow
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Karestan C Koenen
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA
- Department of Social and Behavioral Sciences, Harvard School of Public Health, Harvard University, Boston, MA, USA
| | - Kerry J Ressler
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA
| | - Samuel A McLean
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Fuchita M, Perkins A, Holler E, Glober N, Lasiter S, Mohanty S, Ortiz D, Gao S, French DD, Boustani M, Zarzaur BL. Utility of the Healthy Aging Brain Care Monitor as a Patient-Reported Symptom Monitoring Tool in Older Injury Survivors. J Surg Res 2023; 290:83-91. [PMID: 37224608 PMCID: PMC10330368 DOI: 10.1016/j.jss.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 03/21/2023] [Accepted: 04/15/2023] [Indexed: 05/26/2023]
Abstract
INTRODUCTION The objective of this study was to evaluate the performance of the Healthy Aging Brain Care Monitor (HABC-M) as a patient-reported outcome tool to measure cognitive, functional, and psychological symptoms among older adults who sustained non-neurologic injuries requiring hospital admission. METHODS We used data from a multicenter randomized controlled trial to evaluate the utility of the HABC-M Self-Report version in older patients recovering from traumatic injuries. A total of 143 patients without cognitive impairment were included in the analysis. Cronbach's alpha was used to measure the internal consistency, and Spearman's rank correlation test was used to evaluate the relationship of the HABC-M with standard measures of cognitive, functional, and psychological outcomes. RESULTS The HABC-M subscales and the total scale showed satisfactory internal consistency (Cronbach's alpha = 0.64 to 0.77). The HABC-M cognitive subscale did not correlate with the Mini-Mental State Examination. The HABC-M functional and psychological subscales correlated with corresponding standard reference measures (|rs| = 0.24-0.59). CONCLUSIONS The HABC-M Self-Report version is a practical alternative to administering multiple surveys to monitor functional and psychological sequelae in older patients recovering from recent non-neurologic injuries. Its clinical application may facilitate personalized, multidisciplinary care coordination among older trauma survivors without cognitive impairment.
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Affiliation(s)
- Mikita Fuchita
- Department of Anesthesiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
| | - Anthony Perkins
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana
| | - Emma Holler
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana
| | - Nancy Glober
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana; Indiana University Health Physicians, Indianapolis, Indiana
| | - Sue Lasiter
- School of Nursing and Health Studies, Health Sciences District, University of Missouri, Kansas City, Missouri
| | - Sanjay Mohanty
- Indiana University Health Physicians, Indianapolis, Indiana; Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | - Damaris Ortiz
- Indiana University Health Physicians, Indianapolis, Indiana; Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana; Eskenazi Health, Indianapolis, Indiana
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana
| | - Dustin D French
- Departments of Ophthalmology and Medical Social Sciences, Northwestern University, Chicago, Illinois
| | - Malaz Boustani
- Indiana University Health Physicians, Indianapolis, Indiana; Eskenazi Health, Indianapolis, Indiana; Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana; Center for Health Innovation and Implementation Science, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ben L Zarzaur
- Division of Acute Care and Regional General Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
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Carlson EB, Shieh L, Barlow MR, Palmieri PA, Yen F, Mellman TA, Williams M, Williams MY, Chandran M, Spain DA. Mental health symptoms are comparable in patients hospitalized with acute illness and patients hospitalized with injury. PLoS One 2023; 18:e0286563. [PMID: 37729187 PMCID: PMC10511104 DOI: 10.1371/journal.pone.0286563] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/18/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND High rates of mental health symptoms such as depression, anxiety, and posttraumatic stress disorder (PTSD) have been found in patients hospitalized with traumatic injuries, but little is known about these problems in patients hospitalized with acute illnesses. A similarly high prevalence of mental health problems in patients hospitalized with acute illness would have significant public health implications because acute illness and injury are both common, and mental health problems of depression, anxiety, and PTSD are highly debilitating. METHODS AND FINDINGS In patients admitted after emergency care for Acute Illness (N = 656) or Injury (N = 661) to three hospitals across the United States, symptoms of depression, anxiety, and posttraumatic stress were compared acutely (Acute Stress Disorder) and two months post-admission (PTSD). Patients were ethnically/racially diverse and 54% female. No differences were found between the Acute Illness and Injury groups in levels of any symptoms acutely or two months post-admission. At two months post-admission, at least one symptom type was elevated for 37% of the Acute Illness group and 39% of the Injury group. Within racial/ethnic groups, PTSD symptoms were higher in Black patients with injuries than for Black patients with acute illness. A disproportionate number of Black patients had been assaulted. CONCLUSIONS This study found comparable levels of mental health sequelae in patients hospitalized after emergency care for acute illness as in patients hospitalized after emergency care for injury. Findings of significantly higher symptoms and interpersonal violence injuries in Black patients with injury suggest that there may be important and actionable differences in mental health sequelae across ethnic/racial identities and/or mechanisms of injury or illness. Routine screening for mental health risk for all patients admitted after emergency care could foster preventive care and reduce ethnic/racial disparities in mental health responses to acute illness or injury.
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Affiliation(s)
- Eve B. Carlson
- Dissemination and Training Division, National Center for Posttraumatic Stress Disorder, VA Palo Alto Health Care System, Department of Veterans Affairs, Menlo Park, California, United States of America
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Lisa Shieh
- Department of Medicine, Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - M. Rose Barlow
- Dissemination and Training Division, National Center for Posttraumatic Stress Disorder, VA Palo Alto Health Care System, Department of Veterans Affairs, Menlo Park, California, United States of America
| | - Patrick A. Palmieri
- Traumatic Stress Center, Summa Health, Akron, Ohio, United States of America
| | - Felicia Yen
- Department of Surgery, Stanford University School of Medicine, Stanford, California, United States of America
| | - Thomas A. Mellman
- Georgetown Howard Universities Center for Clinical Translational Research, Washington, DC, United States of America
- Department of Psychiatry and Behavioral Sciences, Howard University College of Medicine, Washington, DC, United States of America
| | - Mallory Williams
- Department of Surgery, Howard University College of Medicine, Washington, DC, United States of America
- Center of Excellence in Trauma and Violence Prevention, Howard University College of Medicine, Washington, DC, United States of America
| | - Michelle Y. Williams
- Department of Medicine, Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Mayuri Chandran
- Department of Medicine, Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - David A. Spain
- Department of Surgery, Stanford University School of Medicine, Stanford, California, United States of America
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7
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Cruz-Gonzalez M, Alegría M, Palmieri PA, Spain DA, Barlow MR, Shieh L, Williams M, Srirangam P, Carlson EB. Racial/ethnic differences in acute and longer-term posttraumatic symptoms following traumatic injury or illness. Psychol Med 2023; 53:5099-5108. [PMID: 35903010 PMCID: PMC9884321 DOI: 10.1017/s0033291722002112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Racial/ethnic differences in mental health outcomes after a traumatic event have been reported. Less is known about factors that explain these differences. We examined whether pre-, peri-, and post-trauma risk factors explained racial/ethnic differences in acute and longer-term posttraumatic stress disorder (PTSD), depression, and anxiety symptoms in patients hospitalized following traumatic injury or illness. METHODS PTSD, depression, and anxiety symptoms were assessed during hospitalization and 2 and 6 months later among 1310 adult patients (6.95% Asian, 14.96% Latinx, 23.66% Black, 4.58% multiracial, and 49.85% White). Individual growth curve models examined racial/ethnic differences in PTSD, depression, and anxiety symptoms at each time point and in their rate of change over time, and whether pre-, peri-, and post-trauma risk factors explained these differences. RESULTS Latinx, Black, and multiracial patients had higher acute PTSD symptoms than White patients, which remained higher 2 and 6 months post-hospitalization for Black and multiracial patients. PTSD symptoms were also found to improve faster among Latinx than White patients. Risk factors accounted for most racial/ethnic differences, although Latinx patients showed lower 6-month PTSD symptoms and Black patients lower acute and 2-month depression and anxiety symptoms after accounting for risk factors. Everyday discrimination, financial stress, past mental health problems, and social constraints were related to these differences. CONCLUSION Racial/ethnic differences in risk factors explained most differences in acute and longer-term PTSD, depression, and anxiety symptoms. Understanding how these risk factors relate to posttraumatic symptoms could help reduce disparities by facilitating early identification of patients at risk for mental health problems.
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Affiliation(s)
- Mario Cruz-Gonzalez
- Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Margarita Alegría
- Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Patrick A. Palmieri
- Traumatic Stress Center, Department of Psychiatry, Summa Health, Akron, OH, USA
| | - David A. Spain
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - M. Rose Barlow
- National Center for PTSD, Dissemination and Training Division, VA Palo Alto Health Care System, U.S. Department of Veterans Affairs, Menlo Park, CA, USA
| | - Lisa Shieh
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Mallory Williams
- Department of Surgery, Howard University College of Medicine, Washington, DC, USA
- Center of Excellence in Trauma and Violence Prevention, Howard University College of Medicine, Washington, DC, USA
| | | | - Eve B. Carlson
- National Center for PTSD, Dissemination and Training Division, VA Palo Alto Health Care System, U.S. Department of Veterans Affairs, Menlo Park, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
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8
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Puac-Polanco V, Ziobrowski HN, Ross EL, Liu H, Turner B, Cui R, Leung LB, Bossarte RM, Bryant C, Joormann J, Nierenberg AA, Oslin DW, Pigeon WR, Post EP, Zainal NH, Zaslavsky AM, Zubizarreta JR, Luedtke A, Kennedy CJ, Cipriani A, Furukawa TA, Kessler RC. Development of a model to predict antidepressant treatment response for depression among Veterans. Psychol Med 2023; 53:5001-5011. [PMID: 37650342 PMCID: PMC10519376 DOI: 10.1017/s0033291722001982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
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Affiliation(s)
| | | | - Eric L. Ross
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | - Brett Turner
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ruifeng Cui
- VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lucinda B. Leung
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
| | - Corey Bryant
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Andrew A. Nierenberg
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - Edward P. Post
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Nur Hani Zainal
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R. Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris J. Kennedy
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | | | - Toshiaki A. Furukawa
- Department of Health Promotion and Human Behavior, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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9
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Kim R, Lin T, Pang G, Liu Y, Tungate AS, Hendry PL, Kurz MC, Peak DA, Jones J, Rathlev NK, Swor RA, Domeier R, Velilla MA, Lewandowski C, Datner E, Pearson C, Lee D, Mitchell PM, McLean SA, Linnstaedt SD. Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure. Psychol Med 2023; 53:4952-4961. [PMID: 35775366 DOI: 10.1017/s003329172200191x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS. METHODS Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale - Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample). RESULTS Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms. CONCLUSIONS These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.
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Affiliation(s)
- Raphael Kim
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
| | - Tina Lin
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Gehao Pang
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew S Tungate
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Michael C Kurz
- Department of Emergency Medicine, University of Alabama, Birmingham, AL, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey Jones
- Department of Emergency Medicine, Spectrum Health Butterworth Campus, Grand Rapids, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, Baystate State Health System, Springfield, MA, USA
| | - Robert A Swor
- Department of Emergency Medicine, Beaumont Hospital, Royal Oak, MI, USA
| | - Robert Domeier
- Department of Emergency Medicine, St Joseph Mercy Health System, Ann Arbor, MI, USA
| | | | | | - Elizabeth Datner
- Department of Emergency Medicine, Albert Einstein Medical Center, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Detroit Receiving, Detroit, MI, USA
| | - David Lee
- Department of Emergency Medicine, North Shore University Hospital, Manhasset, NY, USA
| | - Patricia M Mitchell
- Department of Emergency Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Samuel A McLean
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
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10
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Branham EM, McLean SA, Deliwala I, Mauck MC, Zhao Y, McKibben LA, Lee A, Spencer AB, Zannas AS, Lechner M, Danza T, Velilla MA, Hendry PL, Pearson C, Peak DA, Jones J, Rathlev NK, Linnstaedt SD. CpG Methylation Levels in HPA Axis Genes Predict Chronic Pain Outcomes Following Trauma Exposure. THE JOURNAL OF PAIN 2023; 24:1127-1141. [PMID: 36906051 PMCID: PMC10330094 DOI: 10.1016/j.jpain.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/21/2023] [Accepted: 03/01/2023] [Indexed: 03/12/2023]
Abstract
Chronic post-traumatic musculoskeletal pain (CPTP) is a common outcome of traumatic stress exposure. Biological factors that influence the development of CPTP are poorly understood, though current evidence indicates that the hypothalamic-pituitary-adrenal (HPA) axis plays a critical role in its development. Little is known about molecular mechanisms underlying this association, including epigenetic mechanisms. Here, we assessed whether peritraumatic DNA methylation levels at 248 5'-C-phosphate-G-3' (CpG) sites in HPA axis genes (FKBP5, NR3C1, CRH, CRHR1, CRHR2, CRHBP, POMC) predict CPTP and whether identified CPTP-associated methylation levels influence expression of those genes. Using participant samples and data collected from trauma survivors enrolled into longitudinal cohort studies (n = 290), we used linear mixed modeling to assess the relationship between peritraumatic blood-based CpG methylation levels and CPTP. A total of 66 (27%) of the 248 CpG sites assessed in these models statistically significantly predicted CPTP, with the three most significantly associated CpG sites originating from the POMC gene region (ie, cg22900229 [β = .124, P < .001], cg16302441 [β = .443, P < .001], cg01926269 [β = .130, P < .001]). Among the genes analyzed, both POMC (z = 2.36, P = .018) and CRHBP (z = 4.89, P < .001) were enriched in CpG sites significantly associated with CPTP. Further, POMC expression was inversely correlated with methylation levels in a CPTP-dependent manner (6-months NRS<4: r = -.59, P < .001; 6-months NRS ≥ 4: r = -.18, P = .2312). Our results suggest that methylation of HPA axis genes including POMC and CRHBP predict risk for and may contribute to vulnerability to CPTP. PERSPECTIVE: Peritraumatic blood levels of CpG methylation sites in HPA axis genes, particularly CpG sites in the POMC gene, predict CPTP development. This data substantially advances our understanding of epigenetic predictors and potential mediators of CPTP, a highly common, morbid, and hard-to-treat form of chronic pain.
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Affiliation(s)
- Erica M Branham
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina; Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, North Carolina
| | - Samuel A McLean
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina; Department of Emergency Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Ishani Deliwala
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
| | - Matthew C Mauck
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
| | - Ying Zhao
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
| | - Lauren A McKibben
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
| | - Aaron Lee
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
| | - Alex B Spencer
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
| | - Anthony S Zannas
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina; Department of Genetics, University of North Carolina, Chapel Hill, North Carolina; Carolina Stress Initiative, University of North Carolina, Chapel Hill, North Carolina
| | - Megan Lechner
- Forensic Nursing Program, Memorial Health System, Colorado Springs, Colorado
| | - Teresa Danza
- Forensic Nursing Program, Albuquerque SANE Collaborative, Albuquerque, New Mexico
| | | | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, Florida
| | - Claire Pearson
- Department of Emergency Medicine, Detroit Receiving, Detroit, Michigan
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jeffrey Jones
- Department of Emergency Medicine, Spectrum Health Butterworth Campus, Grand Rapids, Michigan
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Chan Medical School Baystate, Springfield, Massachusetts
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, North Carolina; Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina; Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, North Carolina.
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11
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Karstoft KI, Eskelund K, Gradus JL, Andersen SB, Nissen LR. Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models. J Psychiatr Res 2023; 163:109-117. [PMID: 37209616 DOI: 10.1016/j.jpsychires.2023.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 04/20/2023] [Accepted: 05/01/2023] [Indexed: 05/22/2023]
Abstract
Military personnel deployed to war zones are at increased risk of mental health problems such as posttraumatic stress disorder (PTSD) or depression. Early pre- or post-deployment identification of those at highest risk of such problems is crucial to target intervention to those in need. However, sufficiently accurate models predicting objectively assessed mental health outcomes have not been put forward. In a sample consisting of all Danish military personnel who deployed to war zones for the first (N = 27,594), second (N = 11,083) and third (N = 5,161) time between 1992 and 2013, we apply neural networks to predict psychiatric diagnoses or use of psychotropic medicine in the years following deployment. Models are based on pre-deployment registry data alone or on pre-deployment registry data in combination with post-deployment questionnaire data on deployment experiences or early post-deployment reactions. Further, we identified the most central predictors of importance for the first, second, and third deployment. Models based on pre-deployment registry data alone had lower accuracy (AUCs ranging from 0.61 (third deployment) to 0.67 (first deployment)) than models including pre- and post-deployment data (AUCs ranging from 0.70 (third deployment) to 0.74 (first deployment)). Age at deployment, deployment year and previous physical trauma were important across deployments. Post-deployment predictors varied across deployments but included deployment exposures as well as early post-deployment symptoms. The results suggest that neural network models combining pre- and early post-deployment data can be utilized for screening tools that identify individuals at risk of severe mental health problems in the years following military deployment.
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Affiliation(s)
- Karen-Inge Karstoft
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark; Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark.
| | - Kasper Eskelund
- Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark; Center for Applied Audiology Research, Oticon, Kongebakken 9, 2765, Smørum, Denmark.
| | - Jaimie L Gradus
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA; Department of Psychiatry, Psychiatry, Boston University School of Medicine, Boston, MA, USA.
| | - Søren B Andersen
- Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark.
| | - Lars R Nissen
- Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark.
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12
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Papini S, Norman SB, Campbell-Sills L, Sun X, He F, Kessler RC, Ursano RJ, Jain S, Stein MB. Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment. JAMA Netw Open 2023; 6:e2321273. [PMID: 37389870 PMCID: PMC10314304 DOI: 10.1001/jamanetworkopen.2023.21273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/16/2023] [Indexed: 07/01/2023] Open
Abstract
Importance Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. Objective To develop and validate a machine learning (ML) model to predict postdeployment PTSD. Design, Setting, and Participants This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Main Outcomes and Measures Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. Results This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. Conclusions and Relevance In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.
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Affiliation(s)
- Santiago Papini
- Department of Psychiatry, University of California, San Diego, La Jolla
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Sonya B. Norman
- Department of Psychiatry, University of California, San Diego, La Jolla
- National Center for PTSD, White River Junction, Vermont
- Veterans Affairs Center of Excellence for Stress and Mental Health, San Diego, California
| | | | - Xiaoying Sun
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Feng He
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Robert J. Ursano
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Sonia Jain
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Murray B. Stein
- Department of Psychiatry, University of California, San Diego, La Jolla
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
- Psychiatry Service, Veterans Affairs San Diego Healthcare System, San Diego, California
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13
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Ziobrowski HN, Cui R, Ross EL, Liu H, Puac-Polanco V, Turner B, Leung LB, Bossarte RM, Bryant C, Pigeon WR, Oslin DW, Post EP, Zaslavsky AM, Zubizarreta JR, Nierenberg AA, Luedtke A, Kennedy CJ, Kessler RC. Development of a model to predict psychotherapy response for depression among Veterans. Psychol Med 2023; 53:3591-3600. [PMID: 35144713 PMCID: PMC9365879 DOI: 10.1017/s0033291722000228] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
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Affiliation(s)
| | - Ruifeng Cui
- VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Eric L. Ross
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | | | - Brett Turner
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lucinda B. Leung
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
| | - Corey Bryant
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward P. Post
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R. Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Andrew A. Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris J. Kennedy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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14
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Im JE, Park S, Kim YJ, Yoon SA, Lee JH. Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network. Sci Rep 2023; 13:6213. [PMID: 37069174 PMCID: PMC10106895 DOI: 10.1038/s41598-023-33353-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 04/12/2023] [Indexed: 04/19/2023] Open
Abstract
Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze four time-series data collected at 1-h intervals and 19 variables including demographic, physiological and laboratory parameters. Evaluating the dataset of 128 neonates with respiratory distress who underwent non-invasive ventilation, our model achieved an area under the curve of 0.917, sensitivity of 85.2%, and specificity of 89.2%. These findings demonstrate promising results for the multimodal model in predicting neonatal intubation within 3 h.
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Affiliation(s)
- Jueng-Eun Im
- Biomedical Engineering, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Seung Park
- Biomedical Engineering, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Yoo-Jin Kim
- Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Chungdae-ro 1, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Shin Ae Yoon
- Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Chungdae-ro 1, Seowon-gu, Cheongju, 28644, Republic of Korea.
| | - Ji Hyuk Lee
- Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Chungdae-ro 1, Seowon-gu, Cheongju, 28644, Republic of Korea
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15
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Schultebraucks K, Stevens JS, Michopoulos V, Maples-Keller J, Lyu J, Smith RN, Rothbaum BO, Ressler KJ, Galatzer-Levy IR, Powers A. Development and validation of a brief screener for posttraumatic stress disorder risk in emergency medical settings. Gen Hosp Psychiatry 2023; 81:46-50. [PMID: 36764261 PMCID: PMC10866012 DOI: 10.1016/j.genhosppsych.2023.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 01/30/2023]
Abstract
OBJECTIVE Predicting risk of posttraumatic stress disorder (PTSD) in the acute care setting is challenging given the pace and acute care demands in the emergency department (ED) and the infeasibility of using time-consuming assessments. Currently, no accurate brief screening for long-term PTSD risk is routinely used in the ED. One instrument widely used in the ED is the 27-item Immediate Stress Reaction Checklist (ISRC). The aim of this study was to develop a short screener using a machine learning approach and to investigate whether accurate PTSD prediction in the ED can be achieved with substantially fewer items than the IRSC. METHOD This prospective longitudinal cohort study examined the development and validation of a brief screening instrument in two independent samples, a model development sample (N = 253) and an external validation sample (N = 93). We used a feature selection algorithm to identify a minimal subset of features of the ISRC and tested this subset in a predictive model to investigate if we can accurately predict long-term PTSD outcomes. RESULTS We were able to identify a reduced subset of 5 highly predictive features of the ISRC in the model development sample (AUC = 0.80), and we were able to validate those findings in the external validation sample (AUC = 0.84) to discriminate non-remitting vs. resilient trajectories. CONCLUSION This study developed and validated a brief 5-item screener in the ED setting, which may help to improve the diagnostic process of PTSD in the acute care setting and help ED clinicians plan follow-up care when patients are still in contact with the healthcare system. This could reduce the burden on patients and decrease the risk of chronic PTSD.
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Affiliation(s)
- K Schultebraucks
- Department of Psychiatry, NYU Grossman School of Medicine, New York, USA; Department of Population Health, NYU Grossman School of Medicine, New York, USA.
| | - J S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA; Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans' Affairs Health Care System, Atlanta, GA, USA
| | - V Michopoulos
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - J Maples-Keller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - J Lyu
- Department of Biostatistics, Columbia University, Mailman School of Public Health, New York, NY, USA
| | - R N Smith
- Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA; Department of Behavioral, Social and Health Education Sciences, Emory University School of Public Health, Atlanta, GA, USA
| | - B O Rothbaum
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - K J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; McLean Hospital, Belmont, MA, USA
| | - I R Galatzer-Levy
- Department of Psychiatry, NYU Grossman School of Medicine, New York, USA
| | - A Powers
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
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16
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Jones CW, An X, Ji Y, Liu M, Zeng D, House SL, Beaudoin FL, Stevens JS, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Germine LT, Bollen KA, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Punches BE, Lyons MS, Kurz MC, Swor RA, McGrath ME, Hudak LA, Pascual JL, Seamon MJ, Datner EM, Harris E, Chang AM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, O'Neil BJ, Sergot P, Sanchez LD, Bruce SE, Miller MW, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Sheridan JF, Smoller JW, Harte SE, Elliott JM, Koenen KC, Ressler KJ, Kessler RC, McLean SA. Derivation and Validation of a Brief Emergency Department-Based Prediction Tool for Posttraumatic Stress After Motor Vehicle Collision. Ann Emerg Med 2023; 81:249-261. [PMID: 36328855 PMCID: PMC11181458 DOI: 10.1016/j.annemergmed.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 07/28/2022] [Accepted: 08/04/2022] [Indexed: 11/05/2022]
Abstract
STUDY OBJECTIVE To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department (ED) patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision. METHODS Derivation (n=1,282, 19 ED sites) and validation (n=282, 11 separate ED sites) data were obtained from adults prospectively enrolled in the Advancing Understanding of RecOvery afteR traumA study who were discharged from the ED after motor vehicle collision-related trauma. The primary outcome was substantial posttraumatic stress symptoms at 3 months (Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders-5 ≥38). Logistic regression derivation models were evaluated for discriminative ability using the area under the curve and the accuracy of predicted risk probabilities (Brier score). Candidate posttraumatic stress predictors assessed in these models (n=265) spanned a range of sociodemographic, baseline health, peritraumatic, and mechanistic domains. The final model selection was based on performance and ease of administration. RESULTS Significant 3-month posttraumatic stress symptoms were common in the derivation (27%) and validation (26%) cohort. The area under the curve and Brier score of the final 8-question tool were 0.82 and 0.14 in the derivation cohort and 0.76 and 0.17 in the validation cohort. CONCLUSION This simple 8-question tool demonstrates promise to risk-stratify individuals with substantial posttraumatic stress symptoms who are discharged to home after a motor vehicle collision. Both external validation of this instrument, and work to further develop more accurate tools, are needed. Such tools might benefit public health by enabling the conduct of preventive intervention trials and assisting the growing number of EDs that provide services to trauma survivors aimed at promoting psychological recovery.
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Affiliation(s)
- Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ
| | - Xinming An
- Department of Anesthesiology, Department of Psychiatry, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Yinyao Ji
- Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Mochuan Liu
- Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO
| | - Francesca L Beaudoin
- Department of Emergency Medicine and Department of Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, RI
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA
| | - Thomas C Neylan
- Department of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine and Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI
| | - Sarah D Linnstaedt
- Department of Anesthesiology, Department of Psychiatry, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Laura T Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA; The Many Brains Project, Belmont, MA; Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience and Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Scott L Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA; Department of Psychiatry, Harvard Medical School, Boston, MA; Department of Psychiatry, McLean Hospital, Belmont, MA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Phyllis L Hendry
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL; Department of Emergency Medicine, University of Cincinnati College of Medicine, and College of Nursing, University of Cincinnati, Cincinnati, OH
| | - Brittany E Punches
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL
| | - Michael S Lyons
- College of Nursing, University of Cincinnati, Cincinnati, OH
| | - Michael C Kurz
- Department of Emergency Medicine, Division of Acute Care Surgery, Department of Surgery, University of Alabama School of Medicine, and Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, MA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA; Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Pennsylvania, PA
| | - Jose L Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Pennsylvania, PA; Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA
| | - Mark J Seamon
- Division of Traumatology, Department of Surgery, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Pennsylvania, PA
| | - Elizabeth M Datner
- Department of Emergency Medicine, Einstein Healthcare Network, and the Sidney Kimmel Medical College, Thomas Jefferson University, Pennsylvania, PA
| | | | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Pennsylvania, PA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St John Hospital, Detroit, MI
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School, University of Texas Health, Houston, TX
| | - Leon D Sanchez
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA; Department of Emergency Medicine, Harvard Medical School, Boston, MA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St Louis, St Louis, MO
| | - Mark W Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, and Department of Psychiatry, Boston University School of Medicine, Boston, MA; Clinical Neurosciences Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT
| | | | - Jutta Joormann
- Department of Psychology, Yale School of Medicine, New Haven, CT
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, MA; Division of Depression and Anxiety, McLean Hospital, Belmont, MA
| | - John F Sheridan
- Department of Biosciences, and the Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH
| | - Jordan W Smoller
- Department of Psychiatry, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Steven E Harte
- Department of Anesthesiology, and Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI
| | - James M Elliott
- Kolling Institute of Medical Research, University of Sydney, St Leonards, and Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, New South Wales, Australia, and Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T H Chan School of Public Health, Harvard University, Boston, MA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA; Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA
| | - Samuel A McLean
- Departments of Emergency Medicine and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC.
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17
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Li Y, Li N, Zhang L, Liu Y, Zhang T, Li D, Bai D, Liu X, Li L. Predicting PTSD symptoms in firefighters using a fear-potentiated startle paradigm and machine learning. J Affect Disord 2022; 319:294-299. [PMID: 36162659 DOI: 10.1016/j.jad.2022.09.094] [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: 01/26/2022] [Revised: 05/19/2022] [Accepted: 09/20/2022] [Indexed: 10/14/2022]
Abstract
This study develops a fear-potentiated startle paradigm (FPS) and a machine learning approach to accurately predict PTSD symptoms using electrogram data. A three-phase fear-potentiated startle paradigm was designed to assess the conditioning, generalization, and extinction of fear. Electrooculogram and electrocardiogram signals were collected during the FPS. A total of 1107 Chinese firefighters participated in the study. The Chinese version PCL-C was administered to all subjects. A cutoff of 38 or higher is used to indicate PTSD symptoms. Electrogram features were extracted and selected to build a machine learning model to classify individuals. The machine learning model was 5-fold cross validated. The importance of the selected features was calculated. Classification performance metrics were evaluated for the machine learning model. The machine learning model can identify firefighters with a PCL-C score of 38 or above with sensitivity and specificity both above 0.85 when 5-fold cross validated on a 1107-person sample. The area under the receiver operating characteristic curve of the model is 0.93. Features related to fear generalization are found to be the most important. The proposed fear-potentiated startle paradigm and machine learning approach can accurately predict PTSD symptoms in Chinese firefighters, which can improve the screening and diagnosis of PTSD.
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Affiliation(s)
- Yuanhui Li
- Adai Technology (Beijing) Co., Ltd., Beijing, China
| | - Nan Li
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Liqun Zhang
- Adai Technology (Beijing) Co., Ltd., Beijing, China
| | - Yanru Liu
- Adai Technology (Beijing) Co., Ltd., Beijing, China
| | | | - Dai Li
- Adai Technology (Beijing) Co., Ltd., Beijing, China
| | | | - Xiang Liu
- Department of Industrial Engineering, Tsinghua University, Beijing, China.
| | - Lingjiang Li
- Mental Health Institute, the Second Xiangya Hospital, Central South University, Changsha, China.
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18
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Short NA, van Rooij SJH, Murty VP, Stevens JS, An X, Ji Y, McLean SA, House SL, Beaudoin FL, Zeng D, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Bollen KA, Rauch SL, Haran JP, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Swor RA, McGrath ME, Hudak LA, Pascual JL, Seamon MJ, Datner EM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, O'Neil BJ, Sergot P, Sanchez LD, Bruce SE, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Sheridan JF, Smoller JW, Harte SE, Elliott JM, Kessler RC, Koenen KC, Jovanovic T. Anxiety sensitivity as a transdiagnostic risk factor for trajectories of adverse posttraumatic neuropsychiatric sequelae in the AURORA study. J Psychiatr Res 2022; 156:45-54. [PMID: 36242943 PMCID: PMC10960961 DOI: 10.1016/j.jpsychires.2022.09.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/16/2022] [Accepted: 09/16/2022] [Indexed: 01/20/2023]
Abstract
Anxiety sensitivity, or fear of anxious arousal, is cross-sectionally associated with a wide array of adverse posttraumatic neuropsychiatric sequelae, including symptoms of posttraumatic stress disorder, depression, anxiety, sleep disturbance, pain, and somatization. The current study utilizes a large-scale, multi-site, prospective study of trauma survivors presenting to emergency departments. Hypotheses tested whether elevated anxiety sensitivity in the immediate posttrauma period is associated with more severe and persistent trajectories of common adverse posttraumatic neuropsychiatric sequelae in the eight weeks posttrauma. Participants from the AURORA study (n = 2,269 recruited from 23 emergency departments) completed self-report assessments over eight weeks posttrauma. Associations between heightened anxiety sensitivity and more severe and/or persistent trajectories of trauma-related symptoms identified by growth mixture modeling were analyzed. Anxiety sensitivity assessed two weeks posttrauma was associated with severe and/or persistent posttraumatic stress, depression, anxiety, sleep disturbance, pain, and somatic symptoms in the eight weeks posttrauma. Effect sizes were in the small to medium range in multivariate models accounting for various demographic, trauma-related, pre-trauma mental health-related, and personality-related factors. Anxiety sensitivity may be a useful transdiagnostic risk factor in the immediate posttraumatic period identifying individuals at risk for the development of adverse posttraumatic neuropsychiatric sequelae. Further, considering anxiety sensitivity is malleable via brief intervention, it could be a useful secondary prevention target. Future research should continue to evaluate associations between anxiety sensitivity and trauma-related pathology.
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Affiliation(s)
- Nicole A Short
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA; Department of Psychology, University of Nevada, Las Vegas, NV, 89154, USA.
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, 30329, USA
| | - Vishnu P Murty
- Department of Psychology, Temple University, Philadelphia, PA, 19121, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, 30329, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA
| | - Yinyao Ji
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA
| | - Samuel A McLean
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA; Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Francesca L Beaudoin
- Department of Epidemiology, The Brown University School of Public Health, Providence, RI, 02930, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, 27559, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, 30332, USA; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA
| | - Laura T Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA; The Many Brains Project, Belmont, MA, 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience & Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27559, USA
| | - Scott L Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA; Department of Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, 01655, USA
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, 32209, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, 32209, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, 08103, USA
| | - Brittany E Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, 43210, USA; Ohio State University College of Nursing, Columbus, OH, 43210, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, 48309, USA
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, MA, 02118, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, 30329, USA
| | - Jose L Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mark J Seamon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth M Datner
- Department of Emergency Medicine, Einstein Healthcare Network, Philadelphia, PA, 19141, USA; Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, 48202, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, 48197, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, 01107, USA
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI, 48202, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX, 77030, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Department of Emergency Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, 63121, USA
| | - Robert H Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, 06516, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, 06510, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Diego A Pizzagalli
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, 02478, USA
| | - John F Sheridan
- Division of Biosciences, Ohio State University College of Dentistry, Columbus, OH, 43210, USA; Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH, 43211, USA
| | - Jordan W Smoller
- Department of Psychiatry, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02142, USA
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA; Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - James M Elliott
- Kolling Institute, University of Sydney, St Leonards, New South Wales, 2065, Australia; Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, New South Wales, 2006, Australia; Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60208, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, 02115, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, 48202, USA
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19
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Ćosić K, Popović S, Šarlija M, Kesedžić I, Gambiraža M, Dropuljić B, Mijić I, Henigsberg N, Jovanovic T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Front Psychol 2021; 12:782866. [PMID: 35027902 PMCID: PMC8751545 DOI: 10.3389/fpsyg.2021.782866] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/02/2021] [Indexed: 12/30/2022] Open
Abstract
The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.
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Affiliation(s)
- Krešimir Ćosić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Siniša Popović
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Marko Šarlija
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Ivan Kesedžić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Mate Gambiraža
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Branimir Dropuljić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Igor Mijić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Neven Henigsberg
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
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