A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor

Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event 1 . These patients are at substantial psychiatric risk, with approximately 10–20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (P...

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Vydané v:Nature medicine Ročník 26; číslo 7; s. 1084 - 1088
Hlavní autori: Schultebraucks, Katharina, Shalev, Arieh Y., Michopoulos, Vasiliki, Grudzen, Corita R., Shin, Soo-Min, Stevens, Jennifer S., Maples-Keller, Jessica L., Jovanovic, Tanja, Bonanno, George A., Rothbaum, Barbara O., Marmar, Charles R., Nemeroff, Charles B., Ressler, Kerry J., Galatzer-Levy, Isaac R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Nature Publishing Group US 01.07.2020
Nature Publishing Group
Predmet:
631/114/1305
631/114/2415
692/499
692/53/2422
692/53/2423
Adolescent
Adult
Aged
Algorithms
Anxiety
Biomedical and Life Sciences
Biomedicine
Cancer Research
Care and treatment
Complications and side effects
Diagnosis
Electronic health records
Electronic medical records
Emergency medical care
Emergency medical services
Emergency Service, Hospital - standards
Female
Health services
Hospitalization
Humans
Infectious Diseases
Learning algorithms
Letter
Machine learning
Male
Medical records
Mental disorders
Metabolic Diseases
Middle Aged
Molecular Medicine
Neurosciences
Patients
Post traumatic stress disorder
Prognosis
Psychological stress
Psychopathology
Risk
Risk Assessment
Risk Factors
Signs and symptoms
Stress Disorders, Post-Traumatic - diagnosis
Stress Disorders, Post-Traumatic - etiology
Stress Disorders, Post-Traumatic - pathology
Stress Disorders, Post-Traumatic - psychology
Trauma
Wounds and injuries
Wounds and Injuries - complications
Wounds and Injuries - diagnosis
Wounds and Injuries - physiopathology
Wounds and Injuries - psychology
Young Adult
United States
ISSN:1078-8956, 1546-170X, 1546-170X
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Abstract Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event 1 . These patients are at substantial psychiatric risk, with approximately 10–20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD) 2 – 4 . At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma 5 . Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment 6 – 9 to mitigate subsequent psychopathology in high-risk populations 10 , 11 . This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient’s immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care. A machine-learning algorithm using electronic medical records and self-reported measures of stress at admission to the emergency department due to trauma can predict the risk and long-term trajectories of post-traumatic stress disorder in two independent cohorts.
AbstractList Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event.sup.1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD).sup.2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma.sup.5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment.sup.6-9 to mitigate subsequent psychopathology in high-risk populations.sup.10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment6-9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment6-9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with approximately 10–20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2–4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment6–9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient’s immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.A machine-learning algorithm using electronic medical records and self-reported measures of stress at admission to the emergency department due to trauma can predict the risk and long-term trajectories of post-traumatic stress disorder in two independent cohorts.
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event 1 . These patients are at substantial psychiatric risk, with approximately 10–20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD) 2 – 4 . At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma 5 . Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment 6 – 9 to mitigate subsequent psychopathology in high-risk populations 10 , 11 . This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient’s immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care. A machine-learning algorithm using electronic medical records and self-reported measures of stress at admission to the emergency department due to trauma can predict the risk and long-term trajectories of post-traumatic stress disorder in two independent cohorts.
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event . These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD) . At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma . Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment to mitigate subsequent psychopathology in high-risk populations . This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event.sup.1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD).sup.2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma.sup.5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment.sup.6-9 to mitigate subsequent psychopathology in high-risk populations.sup.10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care. A machine-learning algorithm using electronic medical records and self-reported measures of stress at admission to the emergency department due to trauma can predict the risk and long-term trajectories of post-traumatic stress disorder in two independent cohorts.
Audience Academic
Author Shin, Soo-Min
Marmar, Charles R.
Ressler, Kerry J.
Jovanovic, Tanja
Rothbaum, Barbara O.
Nemeroff, Charles B.
Schultebraucks, Katharina
Maples-Keller, Jessica L.
Michopoulos, Vasiliki
Grudzen, Corita R.
Bonanno, George A.
Galatzer-Levy, Isaac R.
Shalev, Arieh Y.
Stevens, Jennifer S.
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  email: ks3796@cumc.columbia.edu
  organization: Department of Psychiatry, New York University Grossman School of Medicine, Vagelos School of Physicians and Surgeons, Department of Emergency Medicine, Columbia University Irving Medical Center, Data Science Institute, Columbia University
– sequence: 2
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  surname: Shalev
  fullname: Shalev, Arieh Y.
  organization: Department of Psychiatry, New York University Grossman School of Medicine
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  givenname: Vasiliki
  surname: Michopoulos
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  organization: Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Yerkes National Primate Research Center
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  organization: Department of Psychiatry and Behavioral Neurosciences, Wayne State University
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32632194$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright The Author(s), under exclusive licence to Springer Nature America, Inc. 2020
COPYRIGHT 2020 Nature Publishing Group
The Author(s), under exclusive licence to Springer Nature America, Inc. 2020.
Copyright_xml – notice: The Author(s), under exclusive licence to Springer Nature America, Inc. 2020
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– reference: 32632192 - Nat Med. 2020 Jul;26(7):1012-1013
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Snippet Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event 1 . These patients are at substantial...
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event . These patients are at substantial...
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event.sup.1. These patients are at substantial...
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial...
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SubjectTerms 631/114/1305
631/114/2415
692/499
692/53/2422
692/53/2423
Adolescent
Adult
Aged
Algorithms
Anxiety
Biomedical and Life Sciences
Biomedicine
Cancer Research
Care and treatment
Complications and side effects
Diagnosis
Electronic health records
Electronic medical records
Emergency medical care
Emergency medical services
Emergency Service, Hospital - standards
Female
Health services
Hospitalization
Humans
Infectious Diseases
Learning algorithms
Letter
Machine learning
Male
Medical records
Mental disorders
Metabolic Diseases
Middle Aged
Molecular Medicine
Neurosciences
Patients
Post traumatic stress disorder
Prognosis
Psychological stress
Psychopathology
Risk
Risk Assessment
Risk Factors
Signs and symptoms
Stress Disorders, Post-Traumatic - diagnosis
Stress Disorders, Post-Traumatic - etiology
Stress Disorders, Post-Traumatic - pathology
Stress Disorders, Post-Traumatic - psychology
Trauma
Wounds and injuries
Wounds and Injuries - complications
Wounds and Injuries - diagnosis
Wounds and Injuries - physiopathology
Wounds and Injuries - psychology
Young Adult
Title A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor
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