Dataset Information

Psychiatric Symptom Profiles Predict Functional Impairment.

ABSTRACT: Objective: Mental illness often interferes with daily functioning and an individual's pattern of psychiatric signs and symptoms may predict risk of future disability. Understanding the linkage between psychiatric symptoms and impaired functioning is critical for accurate rehabilitation planning and legal assessment. Here, we investigated the stability of functional impairment measures over 18 months and their association with psychiatric symptoms. Moreover, we developed a clinical self-report measure that allows estimation of functional impairment levels over 18 month observation periods. Methods: Consecutively treated outpatients and daycare patients (N = 155) from several psychiatric units in Switzerland completed the Dissociative Experiences Scale, Somatoform Dissociation Questionnaire, Multidimensional Inventory for Dissociation, Beck Depression Inventory, Brief Symptom Inventory, and WHO Disability Assessment Schedule at baseline, 6, 12, and 18 month follow-up examinations. The association between symptoms functional impairment over time was investigated using longitudinal linear mixed models. Penalized regression was used to identify questionnaire items that best predicted functional impairment. Results: We found high stability in the extent of functional impairment over 18 months. Fear of negative evaluation, fatigue, concentration problems, negative alterations in mood, and dissociative symptoms showed the strongest association with functional impairment measures. The empirically derived scale for functional impairment prediction explained between 0.62 and 0.77 of the variance in disability across various life domains. Conclusion: Given the capability for somatic and mental symptoms associated with social anxiety, depression, and dissociation to predict future disability, these symptoms have strong potential for guiding rehabilitation planning and prognostic evaluation in insurance medicine. The Functional Impairment Prediction Scale may serve as a valuable, empirical-based extension in legal assessments of how work capacity is affected by psychological factors.

SUBMITTER: Tanner J

PROVIDER: S-EPMC6396718 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Psychiatric Symptom Profiles Predict Functional Impairment.

Tanner Joachim J Zeffiro Thomas T Wyss Daniela D Perron Noelle N Rufer Michel M Mueller-Pfeiffer Christoph C

Frontiers in psychiatry 20190213

<b>Objective:</b> Mental illness often interferes with daily functioning and an individual's pattern of psychiatric signs and symptoms may predict risk of future disability. Understanding the linkage between psychiatric symptoms and impaired functioning is critical for accurate rehabilitation planning and legal assessment. Here, we investigated the stability of functional impairment measures over 18 months and their association with psychiatric symptoms. Moreover, we developed a clinical self-re ...[more]

PMID: 30853916

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Project description:This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation. The machine learning techniques rely on the assumptions that (1) the severity of a patient's positive valence symptoms exists on a latent continuous spectrum and (2) all the patient's answers and narratives documented in the psychological evaluation records are informed by the patient's latent severity score along this spectrum. These assumptions motivated our two-step machine learning framework for automatically recognizing psychological symptom severity. In the first step, the latent continuous severity score is inferred from each record; in the second step, the severity score is mapped to one of the four discrete severity levels used in the CEGS/N-GRID challenge. We evaluated three methods for inferring the latent severity score associated with each record: (i) pointwise ridge regression; (ii) pairwise comparison-based classification; and (iii) a hybrid approach combining pointwise regression and the pairwise classifier. The second step was implemented using a tree of cascading support vector machine (SVM) classifiers. While the official evaluation results indicate that all three methods are promising, the hybrid approach not only outperformed the pairwise and pointwise methods, but also produced the second highest performance of all submissions to the CEGS/N-GRID challenge with a normalized MAE score of 84.093% (where higher numbers indicate better performance). These evaluation results enabled us to observe that, for this task, considering pairwise information can produce more accurate severity scores than pointwise regression - an approach widely used in other systems for assigning severity scores. Moreover, our analysis indicates that using a cascading SVM tree outperforms traditional SVM classification methods for the purpose of determining discrete severity levels.

Project description:BackgroundMillions of Americans experience traumatic orthopaedic injuries (TOIs) annually. Post-injury symptoms of acute stress disorder (ASD), anxiety, depression, pain, and sleep disturbance are common. Symptoms often present in clusters. Symptom cluster profiles phenotypically characterize TOI survivors' experiences with clustered symptoms. Expression of brain-derived neurotrophic factor (BDNF) may contribute to the biological underpinnings of symptom cluster profile membership.MethodsWe recruited hospitalized TOI survivors within 72 hours of injury. We measured symptoms of ASD with the Acute Stress Disorder Scale and symptoms of anxiety, depression, pain, and sleep disturbance with Patient-Reported Outcomes Measurement Information System (PROMIS) short forms. We measured serum BDNF concentrations with enzyme-linked immunosorbent assay (ELISA) and identified rs6265 genotypes with TaqMan real-time PCR. We performed latent profile analysis to identify the symptom cluster profiles. We identified the variables associated with symptom cluster profile membership with unadjusted and adjusted multinomial logistic regression.ResultsWe identified 4 symptom cluster profiles characterized by symptom severity that we labelled Physical Symptoms Only, and Mild, Moderate, and Severe Psychological Distress. Age, self-identified Black race, resilience, and serum BDNF concentrations were associated with lower odds, and female sex with higher odds, of being in the Psychological Distress clusters. Clinical characteristics and rs6265 genotypes were not associated with symptom cluster profile membership.ConclusionTOI survivors experience distinct symptom cluster profiles. Sociodemographic characteristics and serum BDNF concentrations, not clinical characteristics, were associated with symptom cluster profile membership. These findings support comprehensive symptom screening and treatment for all TOI survivors and further evaluating BDNF as a biomarker of post-injury symptom burden.

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Psychiatric Symptom Profiles Predict Functional Impairment.

Publications

Psychiatric Symptom Profiles Predict Functional Impairment.

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