When it comes to mental health conditions involving psychosis, two people with the same diagnosis may actually have different conditions, and two people with different diagnoses may in fact share a common underlying cause (Joyce & Roiser, 2007). It is difficult to diagnose psychotic spectrum disorders (PSD) accurately in a way that captures the subtle differences between patients, and the different manifestations of the same problem. There is increasing awareness of the pronounced heterogeneity amongst individuals diagnosed with, for example, schizophrenia, but also the striking similarity between genetic risk factors (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013), neuroimaging results and neurobiological features of “different” psychiatric conditions.
The lines between different disorders as we have come to think of them are blurry. Classification schemes like the Diagnostic and Statistical Manual of Mental Disorders (DSM) have been traditionally used to diagnose PSD symptoms (American Psychiatric Association, 2013). The current study (Hanlon et al., 2019) applied the traditional DSM diagnoses and a new classification scheme, based on a continuum of symptoms, to a large sample of PSD patients to see which better predicts the real-world functioning of patients. The study is not an attempt to replace current diagnostic schemes, but to help professionals find additional classifications for disorders based on clinical symptoms and biomarkers.
Could a continuum be a better diagnostic tool for people with psychosis than the traditional distinct classification schemes?
Methods
The study used two different samples of patients diagnosed with schizophrenia (SP), schizoaffective disorder (SA) and bipolar disorder with psychotic features (BP-I), who were compared to healthy controls (HC). The first sample was used to develop their classification scheme, and the second sample to attempt to validate their findings.
The first sample (referred to as “New Mexico, NM”) contained 162 people with PSD and 64 controls (18 to 50 years old). Each person was profiled based on their clinical symptoms, cognitive functioning and real-world functioning through the use of standardised neuropsychological tests. The authors used a series of statistical tests to test whether looking at the severity of an individual’s symptoms would be a good indicator of what their cognitive performance and real-world functioning would look like.
The researchers then used the (pre-existing) second sample (described in Hill et al., 2013), acquired independently of the first, to test whether their findings would hold true in a different data set.
For each subject, clinical symptoms, cognitive capacities and real-world functioning were assessed using a variety of standardised tests. The values were then compared using two different statistical approaches and comparing the results to the distribution that would be obtained by the classic categorisation into SP, SA and BP-I groups. So rather than categorising the patients based on their DSM diagnosis, they were categorised based on the severity of their symptoms, and the researchers wanted to know if the resulting groups would look similar to the original diagnostic groups, or whether a different picture would emerge.
Secondly, a cluster-based approach was employed, which clustered the data based on symptom burden into three different empirically determined groups.
Results
The authors’ expectation that the symptom-based classification would better capture the variance amongst PSD subgroups’ cognitive performance and real-world functioning was partly confirmed. Although the new approach did not always outperform the traditional measure, it overall performed significantly better when it came to capturing variation amongst the individuals diagnosed with PSD.
Although, on average, individuals with schizophrenia show stronger cognitive impairment than patients with BP-I, the present study shows that more fine-grained analysis of a patient’s symptoms serves as a better indicator of cognitive performance than the diagnosis alone.
In the first sample, both positive (delusions, hallucinations) and negative (e.g., lack of affect, impoverished speech, social withdrawal) symptoms (Werbeloff et al., 2015) were good predictors of cognitive variance; better than the traditional DSM diagnosis. Negative symptoms were the most strongly correlated with performance on the different cognitive tests.
Real-world functioning was significantly correlated with both the DSM diagnosis as well as the symptom-burden measures; so the traditional classification scheme performed equally as well as the new continuum approach. However, when singled out as a variable, negative symptoms were better able to predict specific variance amongst real-world functioning than the overall predictions made by DSM and symptom burden.
The results support previous findings showing that severity of symptoms is a better predictor of impairment in social functioning and cognitive performance than DSM diagnosis.
The overall trends could still be detected in the second sample (Bipolar and Schizophrenia Network on Intermediate Phenotypes, B-SNIP). However, in the replication sample, the traditional classification system performed roughly as well as the new system. The replication sample was considerably more variable than the sample collected by the authors, which could explain this discrepancy.
A continuum of symptoms based on severity was a better predictor of cognitive and social functioning than traditional diagnoses.
Conclusions
- The study suggests that to explain variation amongst PSD patients in how well they function in the real world, we can use a classification scheme based on the severity of a patient’s specific symptoms, rather than a traditional diagnostic strategy.
- Negative symptoms were the best predictor of cognitive and social functioning.
- Further analyses with well-controlled data sets (avoiding confounding variation between participants) are necessary to further develop additional diagnostic tools.
Strengths and limitations
The study used traditional neuropsychological tests to assess cognitive and real-world functioning profiles. These tests have been criticised by the National Advisory Mental Health Council Workgroup for their lack of specificity. The authors decided to rely on them nonetheless for their high degree of standardisation. This is a limitation, shared by all work relying on neuropsychological assessments and is not a limitation specific to this study.
The authors further note that schizophrenia patients (the subgroup that exhibited the highest degree of cognitive impairment) were taking the highest amount of medication. They suggest future work relying on unmedicated or early-onset PSD individuals to avoid this problem.
There were significant differences between the genders, but the authors did not discuss this further. Considering psychotic disorders are known to exhibit significant gender differences (Grossman et al., 2008), it is surprising that the authors did not dedicate more space to this phenomenon. Real-world functioning is particularly known to be subject to gender differences (Usall et al., 2002). Could their diagnostic strategy benefit from separating their data based on gender from the start?
We know that real-world functioning is subject to gender differences in people with severe mental illness, but gender differences were not explored in this study.
Implications for practice
Although the findings are not sufficiently conclusive to translate directly into practice, the developments do look promising. There is hope that in the future researchers will be able to pin down the biomarkers that best predict both cognitive performance and real-world functioning. Through this, mental health professionals will be able to enrich the current diagnostic scheme and reach more specific, tailored diagnoses in the future.
A more fine-tuned diagnostic tool for psychosis can be crucial to finding appropriate treatment strategies and greatly improving the quality of life of people with psychosis. We have come very far in a short time from treating all neurologically atypical people under the umbrella term of “madness” to an already quite sophisticated understanding of different mental illnesses, but there is still a long way to go. Studies like this help advance our understanding of the still only roughly defined concept of “psychosis”.
The current results are not yet sufficiently conclusive, but add to the evidence-base for a continuum approach to the diagnosis of psychotic disorders.
Statement of interests
No conflict of interest.
Links
Primary paper
Hanlon FM, Yeo RA, Shaff NA. et al (2019) A symptom-based continuum of psychosis explains cognitive and real-world functional deficits better than traditional diagnoses. Sch Res 2019 208 344–352. [Abstract]
Other references
Grossman LS, Harrow M, Rosen C, Faull R, & Strauss GP (2008) Sex differences in schizophrenia and other psychotic disorders: a 20-year longitudinal study of psychosis and recovery. Comprehensive psychiatry 2008 49(6) 523-529.
Hill SK, Reilly JL, Keefe RSE, et al (2013) Neuropsychological Impairments in Schizophrenia and Psychotic Bipolar Disorder: Findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Study. AJP 2013 170(11) 275-1284.
Joyce, EM, & Roiser JP. (2007) Cognitive heterogeneity in schizophrenia. Current opinion in psychiatry 2007 20(3) 268.
Usall J, Haro JM, Ochoa S, Marquez M, Araya S, & NEDES group (2002) Influence of gender on social outcome in schizophrenia. Acta Psychiatrica Scandinavica 2002 106(5) 337-342. [Abstract]
Werbeloff N, Dohrenwend BP, Yoffe R. et al (2015) The association between negative symptoms, psychotic experiences and later schizophrenia: a population-based longitudinal study. PLoS ONE 2015 10(3) e0119852.
American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders. BMC Med 2013 17 133-137.
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013) Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. The Lancet 2013 381(9875) 1371-1379. [Abstract]
National Advisory Mental Health Council Workgroup on Tasks and Measures for Research Domain Criteria (2016) Behavioral Assessment Methods for RDoC Constructs. 2016 1-167.
Photo credits
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