Is a Diagnosis of Schizophrenia Spectrum Disorder Associated With Increased Mortality in Patients With COVID-19?

Abstract Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

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Type and Timing of Childhood Maltreatment and Severity of Shutdown Dissociation in Patients with Schizophrenia Spectrum Disorder

PLoS ONE ◽

10.1371/journal.pone.0127151 ◽

2015 ◽

Vol 10 (5) ◽

pp. e0127151 ◽

Cited By ~ 20

Author(s):

Inga Schalinski ◽

Martin H. Teicher

Keyword(s):

Childhood Maltreatment ◽

Spectrum Disorder ◽

Schizophrenia Spectrum Disorder ◽

Schizophrenia Spectrum

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Traumatic Stress Disorders and Risk of Subsequent Schizophrenia Spectrum Disorder or Bipolar Disorder: A Nationwide Cohort Study

Schizophrenia Bulletin ◽

10.1093/schbul/sbw082 ◽

2016 ◽

Vol 43 (1) ◽

pp. 180-186 ◽

Cited By ~ 23

Author(s):

Niels Okkels ◽

Betina Trabjerg ◽

Mikkel Arendt ◽

Carsten Bøcker Pedersen

Keyword(s):

Bipolar Disorder ◽

Cohort Study ◽

Traumatic Stress ◽

Spectrum Disorder ◽

Stress Disorders ◽

Schizophrenia Spectrum Disorder ◽

Schizophrenia Spectrum ◽

Traumatic Stress Disorders

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Identification of Risk Factors to Predict the Occurrences of Relapses in Individuals with Schizophrenia Spectrum Disorder in Iran

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18020546 ◽

2021 ◽

Vol 18 (2) ◽

pp. 546

Author(s):

Omran Davarinejad ◽

Tahereh Mohammadi Majd ◽

Farzaneh Golmohammadi ◽

Payam Mohammadi ◽

Farnaz Radmehr ◽

...

Keyword(s):

Risk Factors ◽

Suicide Attempts ◽

Disease Onset ◽

Sudden Onset ◽

Spectrum Disorder ◽

Therapeutic Interventions ◽

Schizophrenia Spectrum Disorder ◽

Schizophrenia Spectrum ◽

History Of ◽

Log Normal

Schizophrenia Spectrum Disorder (SSD) is a chronic psychiatric disorder with a modest treatment outcome. In addition, relapses are commonplace. Here, we sought to identify factors that predict relapse latency and frequency. To this end, we retrospectively analyzed data for individuals with SSD. Medical records of 401 individuals with SSD were analyzed (mean age: 25.51 years; 63.6% males) covering a five-year period. Univariate and multivariate Penalized Likelihood Models with Shared Log-Normal Frailty were used to determine the correlation between discharge time and relapse and to identify risk factors. A total of 683 relapses were observed in males, and 422 relapses in females. The Relapse Hazard Ratio (RHR) decreased with age (RHR = 0.99, CI: (0.98–0.998)) and with participants’ adherence to pharmacological treatment (HR = 0.71, CI: 0.58–0.86). In contrast, RHR increased with a history of suicide attempts (HR = 1.32, CI: 1.09–1.60), and a gradual compared to a sudden onset of disease (HR = 1.45, CI: 1.02–2.05). Gender was not predictive. Data indicate that preventive and therapeutic interventions may be particularly important for individuals who are younger at disease onset, have a history of suicide attempts, have experienced a gradual onset of disease, and have difficulties adhering to medication.

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