Spanish validation of the Negative Symptom Assessment-16 (NSA-16) in patients with schizophrenia

Clinician-Reported Negative Symptom Scales: A Systematic Review of Measurement Properties

BACKGROUND

Negative symptoms of schizophrenia are correlated with reduction of normal function and lower quality of life. They were newly defined by the NIMH-MATRICS Consensus in 2005, dividing the rating tools to assess them into first-generation scales, developed before the Consensus, and second-generation scales, based on the recently introduced definitions.

METHODS

The COnsensus-based Standards for the selection of health Measurement Instrument (COSMIN) guidelines for systematic reviews were used to evaluate the quality of psychometric data of the first-generation scales that cover the 5 negative symptom domains of the NIMHS Consensus: the Scale for the Assessment of Negative Symptoms (SANS), the High Royds Evaluation of Negativity Scale (HEN), and the Negative Symptom Assessment-16 (NSA-16).

RESULTS

The search strategy resulted in the inclusion of a total of 13 articles, 7 for the SANS, 4 for the NSA-16, and 2 for the HEN. For the SANS and the NSA-16, the overall results of the scales' measurement properties are mostly insufficient or indeterminate. The quality of evidence for the HEN is poor, due to a small number of validation studies/included patients.

CONCLUSIONS

After applying the COSMIN guidelines, we do not recommend the usage of these first-generation scales to rate negative symptoms. At the minimum they require further validation.

Optimal cutoff scores of the Chinese version of 15-item negative symptom assessment that indicate prominent negative symptoms of schizophrenia

OBJECTIVE

The Chinese version of 15-item negative symptom assessment (NSA-15) is an instrument with a three-factor structure specifically validated for assessing negative symptoms of schizophrenia. To provide a reference for future practical applications in the recognition of schizophrenia patients with negative symptoms, this study aimed to determine an appropriate NSA-15 cutoff score regarding negative symptoms to identify prominent negative symptoms (PNS).

METHODS

A total of 199 participants with schizophrenia were recruited and divided into the PNS group (n = 79) and non-PNS group (n = 120) according to scale for assessment of negative symptoms (SANS) scores. Receiver-operating characteristic (ROC) curve analysis was used to determine the optimal NSA-15 cutoff score for identifying PNS.

RESULTS

The optimal cutoff NSA-15 score for identifying PNS was 40. Communication, emotion and motivation factors in the NSA-15 had cutoffs of 13, 6, and 16, respectively. The communication factor score had slightly better discrimination than scores on the other two factors. The discriminant ability of the global rating of the NSA-15 was not as good as that of the NSA-15 total score (area under the curve (AUC): 0.873 vs. 0.944).

CONCLUSION

The optimal NSA-15 cutoff scores for identifying PNS in schizophrenia were determined in this study. The NSA-15 provides a convenient and easy-to-use assessment for identifying patients with PNS in Chinese clinical situations. The communication factor of the NSA-15 also has excellent discrimination.

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

EPA guidance on assessment of negative symptoms in schizophrenia

Background

During the last decades, a renewed interest for negative symptoms (NS) was brought about by the increased awareness that they interfere severely with real-life functioning, particularly when they are primary and persistent.

Methods

In this guidance paper, we provide a systematic review of the evidence and elaborate several recommendations for the conceptualization and assessment of NS in clinical trials and practice.

Results

Expert consensus and systematic reviews have provided guidance for the optimal assessment of primary and persistent negative symptoms; second-generation rating scales, which provide a better assessment of the experiential domains, are available; however, NS are still poorly assessed both in research and clinical settings.

This European Psychiatric Association (EPA) guidance recommends the use of persistent negative symptoms (PNS) construct in the context of clinical trials and highlights the need for further efforts to make the definition of PNS consistent across studies in order to exclude as much as possible secondary negative symptoms. We also encourage clinicians to use second-generation scales, at least to complement first-generation ones.

The EPA guidance further recommends the evidence-based exclusion of several items included in first-generation scales from any NS summary or factor score to improve NS measurement in research and clinical settings. Self-rated instruments are suggested to further complement observer-rated scales in NS assessment.

Several recommendations are provided for the identification of secondary negative symptoms in clinical settings.

Conclusions

The dissemination of this guidance paper may promote the development of national guidelines on negative symptom assessment and ultimately improve the care of people with schizophrenia.

Early versus late stage schizophrenia. What markers make the difference?

OBJECTIVES

To identify the psychopathological, cognitive, functional, physical health and inflammatory markers that differentiate between early-stage schizophrenia (ESSCH) and late-stage schizophrenia (LSSCH).

METHODS

Cross-sectional, naturalistic study of 104 patients with SCH. The sample was divided in two groups: 35 ESSCH (≤7 years' duration of illness) and 69 LSSCH (>10 years' duration of illness).

STATISTICAL ANALYSIS

chi-square test and Student's t-test and ANCOVA (or Quade test) controlling for age, sex, BMI and number of cigarettes/day. Finally, a binomial logistic regression was made.

RESULTS

ESSCH show greater negative symptom severity (t = 2.465, p = 0.015), lower levels of IκBα (F = 7.644, p = 0.007), were more frequently classified as normal weight (40% vs 18.8%, p = 0.032) compared with LSSCH. The binomial logistic regression model included age (B = 0.127, p = 0.001) and IκBα (B = 0.025, p = 0.002) and accounted for 38.9% of the variance (model df =7, chi-square =41.841, p < 0.0001).

CONCLUSIONS

Age and IκBα are the unique markers that differentiate between ESSCH patients whose duration of illness is less than 7 years and LSSCH patients. These results support the hypothesis of toxicity of episodes and highlight the importance of preventing new episodes.