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1. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Author

Xi Zhu, Yoojean Kim, Orren Ravid, Xiaofu He, Benjamin Suarez-Jimenez, Sigal Zilcha-Mano, Amit Lazarov, Seonjoo Lee, Chadi G. Abdallah, Michael Angstadt, Christopher L. Averill, C. Lexi Baird, Lee A. Baugh, Jennifer U. Blackford, Jessica Bomyea, Steven E. Bruce, Richard A. Bryant, Zhihong Cao, Kyle Choi, Josh Cisler, Andrew S. Cotton, Judith K. Daniels, Nicholas D. Davenport, Richard J. Davidson, Michael D. DeBellis, Emily L. Dennis, Maria Densmore, Terri deRoon-Cassini, Seth G. Disner, Wissam El Hage, Amit Etkin, Negar Fani, Kelene A. Fercho, Jacklynn Fitzgerald, Gina L. Forster, Jessie L. Frijling, Elbert Geuze, Atilla Gonenc, Evan M. Gordon, Staci Gruber, Daniel W Grupe, Jeffrey P. Guenette, Courtney C. Haswell, Ryan J. Herringa, Julia Herzog, David Bernd Hofmann, Bobak Hosseini, Anna R. Hudson, Ashley A. Huggins, Jonathan C. Ipser, Neda Jahanshad, Meilin Jia-Richards, Tanja Jovanovic, Milissa L. Kaufman, Mitzy Kennis, Anthony King, Philipp Kinzel, Saskia B.J. Koch, Inga K. Koerte, Sheri M. Koopowitz, Mayuresh S. Korgaonkar, John H. Krystal, Ruth Lanius, Christine L. Larson, Lauren A.M. Lebois, Gen Li, Israel Liberzon, Guang Ming Lu, Yifeng Luo, Vincent A. Magnotta, Antje Manthey, Adi Maron-Katz, Geoffery May, Katie McLaughlin, Sven C. Mueller, Laura Nawijn, Steven M. Nelson, Richard W.J. Neufeld, Jack B Nitschke, Erin M. O'Leary, Bunmi O. Olatunji, Miranda Olff, Matthew Peverill, K. Luan Phan, Rongfeng Qi, Yann Quidé, Ivan Rektor, Kerry Ressler, Pavel Riha, Marisa Ross, Isabelle M. Rosso, Lauren E. Salminen, Kelly Sambrook, Christian Schmahl, Martha E. Shenton, Margaret Sheridan, Chiahao Shih, Maurizio Sicorello, Anika Sierk, Alan N. Simmons, Raluca M. Simons, Jeffrey S. Simons, Scott R. Sponheim, Murray B. Stein, Dan J. Stein, Jennifer S. Stevens, Thomas Straube, Delin Sun, Jean Théberge, Paul M. Thompson, Sophia I. Thomopoulos, Nic J.A. van der Wee, Steven J.A. van der Werff, Theo G.M. van Erp, Sanne J.H. van Rooij, Mirjam van Zuiden, Tim Varkevisser, Dick J. Veltman, Robert R.J.M. Vermeiren, Henrik Walter, Li Wang, Xin Wang, Carissa Weis, Sherry Winternitz, Hong Xie, Ye Zhu, Melanie Wall, Yuval Neria, and Rajendra A. Morey

Subjects
Posttraumatic stress disorder, Multimodal MRI, Machine learning, Deep learning, Classification, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Published
2023
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2. Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups

Author

Ashley N. Clausen, Kelene A. Fercho, Molly Monsour, Seth Disner, Lauren Salminen, Courtney C. Haswell, Emily Clarke Rubright, Amanda A. Watts, M. Nicole Buckley, Adi Maron‐Katz, Anika Sierk, Antje Manthey, Benjamin Suarez‐Jimenez, Bunmi O. Olatunji, Christopher L. Averill, David Hofmann, Dick J. Veltman, Elizabeth A. Olson, Gen Li, Gina L. Forster, Henrik Walter, Jacklynn Fitzgerald, Jean Théberge, Jeffrey S. Simons, Jessica A. Bomyea, Jessie L. Frijling, John H. Krystal, Justin T. Baker, K. Luan Phan, Kerry Ressler, Laura K. M. Han, Laura Nawijn, Lauren A. M. Lebois, Lianne Schmaal, Maria Densmore, Martha E. Shenton, Mirjam vanZuiden, Murray Stein, Negar Fani, Raluca M. Simons, Richard W. J. Neufeld, Ruth Lanius, Sanne vanRooij, Saskia B.J. Koch, Serena Bonomo, Tanja Jovanovic, Terri deRoon‐Cassini, Timothy D. Ely, Vincent A. Magnotta, Xiaofu He, Chadi G. Abdallah, Amit Etkin, Christian Schmahl, Christine Larson, Isabelle M. Rosso, Jennifer Urbano Blackford, Jennifer S. Stevens, Judith K. Daniels, Julia Herzog, Milissa L. Kaufman, Miranda Olff, Richard J. Davidson, Scott R. Sponheim, Sven C. Mueller, Thomas Straube, Xi Zhu, Yuval Neria, Lee A. Baugh, James H. Cole, Paul M. Thompson, and Rajendra A. Morey

Subjects
aging, machine learning, mega‐analysis, neuroimaging, posttraumatic stress disorder, trauma, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA‐PGC PTSD sites underwent T1‐weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel‐wise (brainageR) and region‐of‐interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three‐way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age‐related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.

Published
2022
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3. Critical Care Nurse Burnout, Moral Distress, and Mental Health During the COVID-19 Pandemic: A United States Survey

Author

Jill L Guttormson, Kelly Calkins, Natalie McAndrew, Jacklynn Fitzgerald, Holly Losurdo, and Danielle Loonsfoot

Subjects
Pulmonary and Respiratory Medicine, Mental Health, Critical Care, Surveys and Questionnaires, COVID-19, Humans, Morals, Psychological Distress, Cardiology and Cardiovascular Medicine, Critical Care and Intensive Care Medicine, Burnout, Professional, Pandemics, United States
Abstract

The COVID-19 pandemic has and will continue to have a tremendous influence on intensive care unit (ICU) nurses' mental health.The purpose of this study was to describe the impact of COVID-19 on nurse moral distress, burnout, and mental health.Between October 2020 and January 2021 this descriptive study recruited a national sample of nurses who worked in the ICU during the COVID-19 pandemic through American Association of Critical Care Nurses newsletters and social media.A total of 488 survey responses were received from critical care nurses working in the U.S. during the COVID pandemic. Over two thirds of respondents experienced a shortage of personal protective equipment (PPE). Nurse respondents reported moderate/high levels of moral distress and burnout. Symptoms of moderate to severe depression and anxiety were reported by 44.6% and 31.1% of respondents, respectively. Forty-seven percent of respondents were at risk for having posttraumatic stress disorder (PTSD). Lack of perceived support from administration and shortage of PPE were associated with higher levels of burnout, moral distress, and PTSD symptoms.Respondents had higher anxiety and depression than the general population and higher risk for having PTSD than recent veterans or patients after traumatic injury. High levels of reported burnout and moral distress increase the risk of nurses leaving ICU practice or the profession. This study offers important insights about the mental health of nurses during a global pandemic that can guide the development of customized interventions for ICU nurses related to this health care crisis.

Published
2022
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4. Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

Author

Xi Zhu, Yoojean Kim, Orren Ravid, Xiaofu He, Benjamin Suarez-Jimenez, Sigal Zilcha-Mano, Amit Lazarov, Seonjoo Lee, Chadi G. Abdallah, Michael Angstadt, Christopher L. Averill, C. Lexi Baird, Lee A. Baugh, Jennifer U. Blackford, Jessica Bomyea, Steven E. Bruce, Richard A. Bryant, Zhihong Cao, Kyle Choi, Josh Cisler, Andrew S. Cotton, Judith K. Daniels, Nicholas D. Davenport, Richard J. Davidson, Michael D. DeBellis, Emily L. Dennis, Maria Densmore, Terri deRoon-Cassini, Seth G. Disner, Wissam El Hage, Amit Etkin, Negar Fani, Kelene A. Fercho, Jacklynn Fitzgerald, Gina L. Forster, Jessie L. Frijling, Elbert Geuze, Atilla Gonenc, Evan M. Gordon, Staci Gruber, Daniel W Grupe, Jeffrey P. Guenette, Courtney C. Haswell, Ryan J. Herringa, Julia Herzog, David Bernd Hofmann, Bobak Hosseini, Anna R. Hudson, Ashley A. Huggins, Jonathan C. Ipser, Neda Jahanshad, Meilin Jia-Richards, Tanja Jovanovic, Milissa L. Kaufman, Mitzy Kennis, Anthony King, Philipp Kinzel, Saskia B. J. Koch, Inga K. Koerte, Sheri M. Koopowitz, Mayuresh S. Korgaonkar, John H. Krystal, Ruth Lanius, Christine L. Larson, Lauren A. M. Lebois, Gen Li, Israel Liberzon, Guang Ming Lu, Yifeng Luo, Vincent A. Magnotta, Antje Manthey, Adi Maron-Katz, Geoffery May, Katie McLaughlin, Sven C. Mueller, Laura Nawijn, Steven M. Nelson, Richard W.J. Neufeld, Jack B Nitschke, Erin M. O’Leary, Bunmi O. Olatunji, Miranda Olff, Matthew Peverill, K. Luan Phan, Rongfeng Qi, Yann Quidé, Ivan Rektor, Kerry Ressler, Pavel Riha, Marisa Ross, Isabelle M. Rosso, Lauren E. Salminen, Kelly Sambrook, Christian Schmahl, Martha E. Shenton, Margaret Sheridan, Chiahao Shih, Maurizio Sicorello, Anika Sierk, Alan N. Simmons, Raluca M. Simons, Jeffrey S. Simons, Scott R. Sponheim, Murray B. Stein, Dan J. Stein, Jennifer S. Stevens, Thomas Straube, Delin Sun, Jean Théberge, Paul M. Thompson, Sophia I. Thomopoulos, Nic J.A. van der Wee, Steven J.A. van der Werff, Theo G. M. van Erp, Sanne J. H. van Rooij, Mirjam van Zuiden, Tim Varkevisser, Dick J. Veltman, Robert R.J.M. Vermeiren, Henrik Walter, Li Wang, Xin Wang, Carissa Weis, Sherry Winternitz, Hong Xie, Ye Zhu, Melanie Wall, Yuval Neria, and Rajendra A. Morey

Abstract

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD.Significance⍰Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC).⍰Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation.⍰Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

Published
2022
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5. A mega-analysis of vertex and gyral cortical thickness differences in adults with and without PTSD

Author

Hong Xie, Erin O'Leary, Chia-Hao Shih, Andrew Cotton, John Wall, Tian Chen, Rong Liu, Kevin Xu, Chadi Abdallah, Elpiniki Andrew, C. Lexi Baird, Lee Baugh, Jessica Bomyea, Steven Bruce, Richard Bryant, Kyle Choi, Judith Daniels, Nicholas Davenport, Richard Davidson, Micheal De Bellis, Emily Dennis, Terri deRoon-Cassini, Seth Disner, Negar Fani, Kelene Fercho, Jacklynn Fitzgerald, Gina Forster, Jessie Frijling, Elbert Geuze, Hassan Gomaa, Evan Gordon, Daniel Grupe, Ilan Harpaz-Rotem, Courtney Haswell, Julia Herzog, Davia Hofmann, Micheal Hollifield, Bobak Hosseini, Anna Hudson, Jonathan Ipser, Neda Jahanshad, Tanja Jovanovic, Milissa Kaufman, Anthony King, Saskia koch, Inga Koerte, Sheri-Michelle Koopowitz, Mayuresh Korgaonkar, John Krystal, Christine Larson, Lauren Lebois, Ifat Levy, Gen Li, Mark Logue, Vincent Magnotta, Antje Manthey, Geoffrey May, Katie McLaughlin, Sven Mueller, Laura Nawijn, null Nelson, Yuval Neria, Jack Nitschke, Miranda Olff, Elizabeth Olson, Matthew Peverill, K Luan Phan, Faisal Rashid, Kerry Ressler, Isabelle Rosso, Lauren Salminen, Kelly Sambrook, Freda Scheffler, Christian Schmahl, Martha Shenton, Anika Sierk, Jeffrey Simons, Raluca Simons, Scott Sponheim, Dan Stein, Murray Stein, Jennifer Stevens, Thomas Straube, Benjamin Suarez-Jimenez, Marijo Tamburrino, Sophia Thomopoulos, Nic van der Wee, Steven van der Werff, Theo van Erp, Sanne van Rooij, Mirjam van Zuiden, Tim Varkevisser, Dick Veltman, Robert Vermeiren, Henrik Walter, Li Wang, Xi Zhu, Ye Zhu, Paul Thompson, Xin Wang, Rajendra Morey, and Israel Liberzon

Abstract

A number of studies of posttraumatic stress disorder (PTSD) report thinner cerebral cortical gyri using gyrus-based analysis or thinner foci within the gyri using vertex-based analysis. However, the locations of these findings are inconsistent across studies, and the spatial transformations required during vertex-based analysis may affect the focal findings. A mega-analysis using a large number of subjects from multiple PTSD studies could potentially identify more reproducible cortical thickness abnormalities. Investigating both the vertex and gyral thicknesses simultaneously may verify the vertex-based focal findings using gyral data without imposing any spatial transformation. Here we aggregated data from 24 international laboratories using ENIGMA standardized procedures for 949 adult PTSD patients and 1493 controls without PTSD (age 18 to 65 years). We examined whether gyral and vertex cortical thickness are (a) different between subjects with PTSD and controls and (b) associated with PTSD symptom severity in trauma-exposed subjects. Regions with overlapping thinner cortical gyri and thinner vertex clusters were located in frontal, temporal, parietal, and occipital cortices. Thinner right lateral orbitofrontal and right lingual gyri and concomitantly thinner vertex clusters in the anterior portions of both gyri were associated with PTSD symptom severity. Convergent findings in these locations suggest focally thinner cortex in these gyri, which may be involved in altered processing and regulation of emotion and sensory inputs underlying posttraumatic stress symptoms.

Published
2022
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6. Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Author

Delin Sun, Gopalkumar Rakesh, Emily K. Clarke-Rubright, Courtney C. Haswell, Mark W. Logue, Erin N. O’Leary, Andrew S. Cotton, Hong Xie, Emily L. Dennis, Neda Jahanshad, Lauren E. Salminen, Sophia I. Thomopoulos, Faisal M. Rashid, Christopher R.K. Ching, Saskia B.J. Koch, Jessie L. Frijling, Laura Nawijn, Mirjam van Zuiden, Xi Zhu, Benjamin Suarez-Jimenez, Anika Sierk, Henrik Walter, Antje Manthey, Jennifer S. Stevens, Negar Fani, Sanne J.H. van Rooij, Murray B. Stein, Jessica Bomyea, Inga Koerte, Kyle Choi, Steven J.A. van der Werff, Robert R.J.M. Vermeiren, Julia I. Herzog, Lauren A.M. Lebois, Justin T. Baker, Kerry J. Ressler, Elizabeth A. Olson, Thomas Straube, Mayuresh S. Korgaonkar, Elpiniki Andrew, Ye Zhu, Gen Li, Jonathan Ipser, Anna R. Hudson, Matthew Peverill, Kelly Sambrook, Evan Gordon, Lee A. Baugh, Gina Forster, Raluca M. Simons, Jeffrey S. Simons, Vincent A. Magnotta, Adi Maron-Katz, Stefan du Plessis, Seth G. Disner, Nicholas D. Davenport, Dan Grupe, Jack B. Nitschke, Terri A. deRoon-Cassini, Jacklynn Fitzgerald, John H. Krystal, Ifat Levy, Miranda Olff, Dick J. Veltman, Li Wang, Yuval Neria, Michael D. De Bellis, Tanja Jovanovic, Judith K. Daniels, Martha E. Shenton, Nic J.A. van de Wee, Christian Schmahl, Milissa L. Kaufman, Isabelle M. Rosso, Scott R. Sponheim, David Bernd Hofmann, Richard A. Bryant, Kelene A. Fercho, Dan J. Stein, Sven C. Mueller, K. Luan Phan, Katie A. McLaughlin, Richard J. Davidson, Christine Larson, Geoffrey May, Steven M. Nelson, Chadi G. Abdallah, Hassaan Gomaa, Amit Etkin, Soraya Seedat, Ilan Harpaz-Rotem, Israel Liberzon, Xin Wang, Paul M. Thompson, Rajendra A. Morey, Adult Psychiatry, APH - Mental Health, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Global Health, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Neurology, Pediatric surgery, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, and APH - Personalized Medicine

Subjects
Adult, Aged, 80 and over, Adolescent, Depression, Cognitive Neuroscience, Surface area, Neuroimaging, PTSD, Middle Aged, Brain network, Magnetic Resonance Imaging, Cortical thickness, Structural covariance, Stress Disorders, Post-Traumatic, Young Adult, Case-Control Studies, Connectome, Humans, Radiology, Nuclear Medicine and imaging, Neurology (clinical), Child, Biological Psychiatry, Aged
Abstract

Background: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). Methods: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2–85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis–Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2–148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks. Results: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. Conclusions: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

Published
2022
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10. Author response for 'Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups'

Author

null Ashley N. Clausen, null Kelene A. Fercho, null Molly Monsour, null Seth Disner, null Lauren Salminen, null Courtney C. Haswell, null Emily Clarke Rubright, null Amanda A. Watts, null M. Nicole Buckley, null Adi Maron‐Katz, null Anika Sierk, null Antje Manthey, null Benjamin Suarez‐Jimenez, null Bunmi O. Olatunji, null Christopher L. Averill, null David Hofmann, null Dick J. Veltman, null Elizabeth A. Olson, null Gen Li, null Gina L. Forster, null Henrik Walter, null Jacklynn Fitzgerald, null Jean Théberge, null Jeffrey S. Simons, null Jessica A. Bomyea, null Jessie L. Frijling, null John H. Krystal, null Justin T. Baker, null K. Luan Phan, null Kerry Ressler, null Laura K. M. Han, null Laura Nawijn, null Lauren A. M. Lebois, null Lianne Schmaal, null Maria Densmore, null Martha E. Shenton, null Mirjam Zuiden, null Murray Stein, null Negar Fani, null Raluca M. Simons, null Richard W. J. Neufeld, null Ruth Lanius, null Sanne Rooij, null Saskia B.J. Koch, null Serena Bonomo, null Tanja Jovanovic, null Terri deRoon‐Cassini, null Timothy D. Ely, null Vincent A. Magnotta, null Xiaofu He, null Chadi G. Abdallah, null Amit Etkin, null Christian Schmahl, null Christine Larson, null Isabelle M. Rosso, null Jennifer Urbano Blackford, null Jennifer S. Stevens, null Judith K. Daniels, null Julia Herzog, null Milissa L. Kaufman, null Miranda Olff, null Richard J. Davidson, null Scott R. Sponheim, null Sven C. Mueller, null Thomas Straube, null Xi Zhu, null Yuval Neria, null Lee A. Baugh, null James H. Cole, null Paul M. Thompson, and null Rajendra A. Morey

Published
2021
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14. Critical Care Nurses' Experiences During the COVID-19 Pandemic: A US National Survey.

Author

Guttormson JL, Calkins K, McAndrew N, Fitzgerald J, Losurdo H, and Loonsfoot D

Subjects
Critical Care, Humans, Pandemics, SARS-CoV-2, COVID-19, Nurses
Abstract

Background: Given critical care nurses' high prepandemic levels of moral distress and burnout, the COVID-19 pandemic will most likely have a tremendous influence on intensive care unit (ICU) nurses' mental health and continuation in the ICU workforce., Objective: To describe the experiences of ICU nurses during the COVID-19 pandemic in the United States., Methods: Nurses who worked in ICUs in the United States during the COVID-19 pandemic were recruited to complete a survey from October 2020 through early January 2021 through social media and the American Association of Critical-Care Nurses. Three open-ended questions focused on the experiences of ICU nurses during the pandemic., Results: Of 498 nurses who completed the survey, 285 answered the open-ended questions. Nurses reported stress related to a lack of evidence-based treatment, poor patient prognosis, and lack of family presence in the ICU. Nurses perceived inadequate leadership support and inequity within the health care team. Lack of consistent community support to slow the spread of COVID-19 or recognition that COVID-19 was real increased nurses' feelings of isolation. Nurses reported physical and emotional symptoms including exhaustion, anxiety, sleeplessness, and moral distress. Fear of contracting COVID-19 or of infecting family and friends was also prevalent., Conclusions: Intensive care unit nurses in the United States experienced unprecedented and immense burden during the COVID-19 pandemic. Understanding these experiences provides insights into areas that must be addressed to build and sustain an ICU nurse workforce. Studies are needed to further describe nurses' experiences during the COVID-19 pandemic and identify effective resources that support ICU nurse well-being., (©2022 American Association of Critical-Care Nurses.)

Published
2022
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