Given the rapid proliferation of trajectory-based approaches to study clinical consequences to stress and potentially traumatic events (PTEs), there is a need to evaluate emerging findings. This ...review examined convergence/divergences across 54 studies in the nature and prevalence of response trajectories, and determined potential sources of bias to improve future research. Of the 67 cases that emerged from the 54 studies, the most consistently observed trajectories following PTEs were resilience (observed in: n = 63 cases), recovery (n = 49), chronic (n = 47), and delayed onset (n = 22). The resilience trajectory was the modal response across studies (average of 65.7% across populations, 95% CI 0.616, 0.698), followed in prevalence by recovery (20.8% 0.162, 0.258), chronicity (10.6%, 0.086, 0.127), and delayed onset (8.9% 0.053, 0.133). Sources of heterogeneity in estimates primarily resulted from substantive population differences rather than bias, which was observed when prospective data is lacking. Overall, prototypical trajectories have been identified across independent studies in relatively consistent proportions, with resilience being the modal response to adversity. Thus, trajectory models robustly identify clinically relevant patterns of response to potential trauma, and are important for studying determinants, consequences, and modifiers of course following potential trauma.
•A review of n = 54 studies demonstrates that resilience is the modal response to major life stressors and potential trauma.•Resilience, recovery, chronicity, and delayed onset were consistently identified adjustment outcome trajectories.•Pattern stability across contextual factors indicates that the trajectories are likely phenotypic human stress responses.•Trait and state factors associated with trajectory membership have implications for risk identification and interventions.•Trajectory models provide a robust methodology to study clinically relevant responses to stress and potential trauma.
Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate ...maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high‐dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.
Resumen
Spanish s by Asociación Chilena de Estrés Traumático (ACET)
Aprendizaje de Máquinas para la Predicción del Estrés Postraumático y Resiliencia después del Trauma: Una Visión General de los Conceptos Básicos y Avances Recientes
APRENDIZAJE DE MAQUINAS Y ESTRÉS POSTRAUMÁTICO
Las respuestas al estrés postraumático se caracterizan por una heterogeneidad en el aspecto clínico y etiología. Esta heterogeneidad afecta la capacidad del campo para caracterizar, predecir y remediar respuestas desadaptativas al trauma. Los enfoques de aprendizaje maquinas (AM) son cada vez más utilizados para superar este problema fundamental en la caracterización, predicción y selección de tratamiento a través de las ramas de la medicina que han luchado con realidades clínicas similares de heterogeneidad en la etiología y resultados, como la oncología. En este artículo, revisamos y evaluamos los enfoques y las aplicaciones de AM utilizados en las áreas de estrés postraumático, patología del estrés, e investigación en resiliencia y presenta información didáctica y ejemplos para ayudar a investigadores interesados en la relevancia del AM para su propia investigación. Los estudios examinados ejemplifican el alto potencial de los enfoques de AM para construir modelos predictivos y de diagnóstico precisos de estrés postraumático y riesgo de estrés patológico basados en diversas fuentes de Información disponible. El uso de enfoques de AM para integrar datos multidimensionales demuestran ganancias sustanciales en la predicción del riesgo, incluso cuando las fuentes de datos son las mismas que las utilizadas en los modelos predictivos tradicionales. Esta área de investigación se beneficiará enormemente de la colaboración y el intercambio de datos entre los investigadores del trastorno de estrés postraumático, la patología del estrés y resiliencia.
抽象
Traditional and Simplified Chinese s by the Asian Society for Traumatic Stress Studies (AsianSTSS)
簡體及繁體中文撮要由亞洲創傷心理研究學會翻譯
Machine Learning for Prediction of Posttraumatic Stress and Resilience following Trauma: An Overview of Basic Concepts and Recent Advances
Traditional Chinese
標題: 以機器學習法來預測個人受創後的創傷後壓力和恢復力:對基本概念和近期發展的回顧
撮要: 創傷後壓力反應可根據臨床表現和病原學的異質性來找出。這種異質性會影響業界對創傷後適應不良反應的特徵定義、預測和治療。在病原學和治療情況面對相似的臨床異質性的各種醫科, 如腫瘤學, 越來越多人採用機器學習法(ML)來解決特徵定義、預測和治療選擇的根本問題。本研究檢視和評核覆蓋創傷後壓力、壓力病理學和恢復力的研究中ML的方法和應用, 以及為有興趣使用ML的研究員提供教學資訊和例子。檢視的研究都展示了ML有很大機會可基於多元的資訊來源, 就創傷後壓力和壓力病理學風險建立準確的預測和診斷模型。即使數據來源與傳統預測模型所使用的一樣, 以ML來綜合高因次的數據在風險預測方面有顯著功效。研究創傷後壓力症、壓力病理學和恢復力的研究員如果合作並分享數據, 將大大有助這類研究發展。
Simplified Chinese
标题: 以机器学习法来预测个人受创后的创伤后压力和恢复力:对基本概念和近期发展的回顾
撮要: 创伤后压力反应可根据临床表现和病原学的异质性来找出。这种异质性会影响业界对创伤后适应不良反应的特征定义、预测和治疗。在病原学和治疗情况面对相似的临床异质性的各种医科, 如肿瘤学, 越来越多人采用机器学习法(ML)来解决特征定义、预测和治疗选择的根本问题。本研究检视和评核覆盖创伤后压力、压力病理学和恢复力的研究中ML的方法和应用, 以及为有兴趣使用ML的研究员提供教学信息和例子。检视的研究都展示了ML有很大机会可基于多元的信息来源, 就创伤后压力和压力病理学风险建立准确的预测和诊断模型。即使数据来源与传统预测模型所使用的一样, 以ML来综合高因次的数据在风险预测方面有显著功效。研究创伤后压力症、压力病理学和恢复力的研究员如果合作并分享数据, 将大大有助这类研究发展。
Traumatic injuries affect millions of patients each year, and resulting post-traumatic stress disorder (PTSD) significantly contributes to subsequent impairment.
To map the distinctive long-term ...trajectories of PTSD responses over 6 years by using latent growth mixture modelling.
Randomly selected injury patients (n = 1084) admitted to four hospitals around Australia were assessed in hospital, and at 3, 12, 24 and 72 months. Lifetime psychiatric history and current PTSD severity and funxctioning were assessed.
Five trajectories of PTSD response were noted across the 6 years: (a) chronic (4%), (b) recovery (6%), (c) worsening/recovery (8%), (d) worsening (10%) and (e) resilient (73%). A poorer trajectory was predicted by female gender, recent life stressors, presence of mild traumatic brain injury and admission to intensive care unit.
These findings demonstrate the long-term PTSD effects that can occur following traumatic injury. The different trajectories highlight that monitoring a subset of patients over time is probably a more accurate means of identifying PTSD rather than relying on factors that can be assessed during hospital admission.
In an attempt to capture the variety of symptoms that emerge following traumatic stress, the revision of posttraumatic stress disorder (PTSD) criteria in the 5th edition of the Diagnostic and ...Statistical Manual of Mental Disorders (DSM-5) has expanded to include additional symptom presentations. One consequence of this expansion is that it increases the amorphous nature of the classification. Using a binomial equation to elucidate possible symptom combinations, we demonstrate that the DSM-IV criteria listed for PTSD have a high level of symptom profile heterogeneity (79,794 combinations); the changes result in an eightfold expansion in the DSM-5, to 636,120 combinations. In this article, we use the example of PTSD to discuss the limitations of DSM-based diagnostic entities for classification in research by elucidating inherent flaws that are either specific artifacts from the history of the DSM or intrinsic to the underlying logic of the DSM's method of classification. We discuss new directions in research that can provide better information regarding both clinical and nonclinical behavioral heterogeneity in response to potentially traumatic and common stressful life events. These empirical alternatives to an a priori classification system hold promise for answering questions about why diversity occurs in response to Stressors.
Psychiatric diagnoses are currently distinguished based on sets of specific symptoms. However, genetic and clinical analyses find similarities across a wide variety of diagnoses, suggesting that a ...common neurobiological substrate may exist across mental illness.
To conduct a meta-analysis of structural neuroimaging studies across multiple psychiatric diagnoses, followed by parallel analyses of 3 large-scale healthy participant data sets to help interpret structural findings in the meta-analysis.
PubMed was searched to identify voxel-based morphometry studies through July 2012 comparing psychiatric patients to healthy control individuals for the meta-analysis. The 3 parallel healthy participant data sets included resting-state functional magnetic resonance imaging, a database of activation foci across thousands of neuroimaging experiments, and a data set with structural imaging and cognitive task performance data.
Studies were included in the meta-analysis if they reported voxel-based morphometry differences between patients with an Axis I diagnosis and control individuals in stereotactic coordinates across the whole brain, did not present predominantly in childhood, and had at least 10 studies contributing to that diagnosis (or across closely related diagnoses). The meta-analysis was conducted on peak voxel coordinates using an activation likelihood estimation approach.
We tested for areas of common gray matter volume increase or decrease across Axis I diagnoses, as well as areas differing between diagnoses. Follow-up analyses on other healthy participant data sets tested connectivity related to regions arising from the meta-analysis and the relationship of gray matter volume to cognition.
Based on the voxel-based morphometry meta-analysis of 193 studies comprising 15 892 individuals across 6 diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety), we found that gray matter loss converged across diagnoses in 3 regions: the dorsal anterior cingulate, right insula, and left insula. By contrast, there were few diagnosis-specific effects, distinguishing only schizophrenia and depression from other diagnoses. In the parallel follow-up analyses of the 3 independent healthy participant data sets, we found that the common gray matter loss regions formed a tightly interconnected network during tasks and at resting and that lower gray matter in this network was associated with poor executive functioning.
We identified a concordance across psychiatric diagnoses in terms of integrity of an anterior insula/dorsal anterior cingulate-based network, which may relate to executive function deficits observed across diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology, despite likely diverse etiologies, which is currently not an explicit component of psychiatric nosology.
Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to ...improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).
N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally.
Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82).
Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.
Background
Posttraumatic stress disorder (PTSD) is associated with high rates of psychiatric comorbidity, most notably substance use disorders, major depression, and other anxiety disorders. However, ...little is known about how these disorders cluster together among people with PTSD, if disorder clusters have distinct etiologies in terms of trauma type, and if they confer greater burden over and above PTSD alone.
Method
Utilizing Latent Class Analysis, we tested for discrete patterns of lifetime comorbidity with PTSD following trauma exposure (n = 409). Diagnoses were based on the Structured Clinical Interview for DSM‐IV (SCID). Next, we examined if gender, trauma type, symptom frequency, severity, and interference with everyday life were associated with the latent classes.
Results
Three patterns of lifetime comorbidity with PTSD emerged: a class characterized by predominantly comorbid mood and anxiety disorders; a class characterized by predominantly comorbid mood, anxiety, and substance dependence; and a relatively pure low‐comorbidity PTSD class. Individuals in both high comorbid classes had nearly two and a half times the rates of suicidal ideation, endorsed more PTSD symptom severity, and demonstrated a greater likelihood of intimate partner abuse compared to the low comorbidity class. Men were most likely to fall into the substance dependent class.
Conclusion
PTSD comorbidity clusters into a small number of common patterns. These patterns may represent an important area of study, as they confer distinct differences in risk and possibly etiology. Implications for research and treatment are discussed.
The course of depression in relation to myocardial infarction (MI), commonly known as heart attack, and the consequences for mortality are not well characterized. Further, optimism may predict both ...the effects of MI on depression as well as mortality secondary to MI. In the current study, we utilized a large population-based prospective sample of older adults (N = 2,147) to identify heterogeneous trajectories of depression from 6 years prior to their first-reported MI to 4 years after. Findings indicated that individuals were at significantly increased risk for mortality when depression emerged after their first-reported MI, compared with resilient individuals who had no significant post-MI elevation in depression symptomatology. Individuals with chronic depression and those demonstrating pre-event depression followed by recovery after MI were not at increased risk. Further, optimism, measured before MI, prospectively differentiated all depressed individuals from participants who were resilient.
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.
Uncovering heterogeneities in the progression of early PTSD symptoms can improve our understanding of the disorder's pathogenesis and prophylaxis.
To describe discrete symptom trajectories and ...examine their relevance for preventive interventions.
Latent Growth Mixture Modeling (LGMM) of data from a randomized controlled study of early treatment. LGMM identifies latent longitudinal trajectories by exploring discrete mixture distributions underlying observable data.
Hadassah Hospital unselectively receives trauma survivors from Jerusalem and vicinity.
Adult survivors of potentially traumatic events consecutively admitted to the hospital's emergency department (ED) were assessed ten days and one-, five-, nine- and fifteen months after ED admission. Participants with data at ten days and at least two additional assessments (n = 957) were included; 125 received cognitive behavioral therapy (CBT) between one and nine months.
We used LGMM to identify latent parameters of symptom progression and tested the effect of CBT on these parameters. CBT consisted of 12 weekly sessions of either cognitive therapy (n = 41) or prolonged exposure (PE, n = 49), starting 29.8±5.7 days after ED admission, or delayed PE (n = 35) starting at 151.8±42.4 days. CBT effectively reduced PTSD symptoms in the entire sample.
Latent trajectories of PTSD symptoms; effects of CBT on these trajectories.
THREE TRAJECTORIES WERE IDENTIFIED: Rapid Remitting (rapid decrease in symptoms from 1- to 5-months; 56% of the sample), Slow Remitting (progressive decrease in symptoms over 15 months; 27%) and Non-Remitting (persistently elevated symptoms; 17%). CBT accelerated the recovery of the Slow Remitting class but did not affect the other classes.
The early course of PTSD symptoms is characterized by distinct and diverging response patterns that are centrally relevant to understanding the disorder and preventing its occurrence. Studies of the pathogenesis of PTSD may benefit from using clustered symptom trajectories as their dependent variables.
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