Bipolar Disorder, Schizoaffective Disorder, and Schizophrenia Overlap: A New Comorbidity Index
ABSTRACT Growing evidence of an etiologic overlap between schizophrenia, schizoaffective disorder, and bipolar disorder has become increasingly difficult to disregard. We investigated the magnitude of the overlap between the clinical diagnoses of bipolar affective disorder, schizoaffective disorder, and schizophrenia over a 35-year period based on the entire Danish population.
We established a register-based prospective cohort study of more than 2.5 million persons born in Denmark after 1954. Risks for the 3 psychiatric disorders were estimated by survival analysis using the Aalen-Johansen method. Cohort members were followed from 1970 to 2006. We introduced a new comorbidity index measuring the magnitude of the overlap between the 3 disorders.
Overall, 12,734 patients were admitted with schizophrenia, 4,205 with bipolar disorder, and 1,881 with schizoaffective disorder. A female bipolar patient's risk of also being admitted with a schizoaffective disorder by the age of 45 years was approximately 103 times higher than that of a woman at the same age in the general population. Thus, we defined the comorbidity index between schizoaffective disorder and bipolar disorder at age 45 years to be 103. At age 45 years, the index between schizophrenia and schizoaffective disorder was 80 and between schizophrenia and bipolar disorder was 20. Similar large comorbidity indexes were found for men.
A large comorbidity index between schizophrenia and schizoaffective disorder was found, as well as a large index between bipolar disorder and schizoaffective disorder. But, more surprisingly, it was clear that a substantial comorbidity index between bipolar disorder and schizophrenia was present. This study supports the existence of an overlap between bipolar disorder and schizophrenia and thus challenges the strict categorical approach used in both DSM-IV and ICD-10 classification systems.
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ABSTRACT: Background. Persons with severe mental illness (SMI) have excess mortality, which may partly be explained by their high prevalence of diabetes. Method. We compared the overall and cause-specific mortality in persons with SMI and diabetes with that of the general Danish population between 1997 and 2009 by linking data from Danish national registries. Results. The cohort counted 4734703 persons, and during follow-up 651080 persons died of whom 1083 persons had SMI and diabetes. Compared with the background population, the overall mortality rate ratios (MRRs) for persons with SMI and diabetes were 4.14 [95% confidence interval (CI) 3.81-4.51] for men and 3.13 (95% CI 2.88-3.40) for women. The cause-specific MRRs for persons with SMI and diabetes were lowest for malignant neoplasms (women: MRR=1.98, 95% CI 1.64-2.39; men: MRR=2.08, 95% CI 1.69-2.56) and highest for unnatural causes of death (women: MRR=12.31, 95% CI 6.80-22.28; men: MRR=7.89, 95% CI 5.51-11.29). The cumulative risks of death within 7 years of diabetes diagnosis for persons with SMI and diabetes were 15.0% (95% CI 12.4-17.6%) for those younger than 50 years, 30.7% (95% CI 27.8-33.4%) for those aged 50-69 years, and 63.8% (95% CI 58.9-68.2%) for those aged 70 years or older. Among persons suffering from both diseases, 33.4% of natural deaths were attributed to diabetes and 14% of natural deaths were attributed to the interaction between diabetes and SMI. Conclusions. Long-term mortality is high for persons with SMI and diabetes. This calls for effective intervention from a coordinated and collaborating healthcare system.Psychological Medicine 03/2014; 44(14):1-11. DOI:10.1017/S0033291714000634 · 5.43 Impact Factor
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ABSTRACT: The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype diagnoses was calculated against diagnoses from direct semistructured interviews of 190 patients by trained clinicians blind to EHR diagnosis. The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR-classified control subject received a diagnosis of bipolar disorder on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based classifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.American Journal of Psychiatry 04/2015; 172(4):363-72. DOI:10.1176/appi.ajp.2014.14030423 · 13.56 Impact Factor
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ABSTRACT: Schizophrenia, schizoaffective and bipolar disorders share some common symptoms. However, the biomarkers underlying those disorders remain unclear. In fact, there is still controversy about the schizoaffective disorder with respect to its validity of independent category and its relationship with schizophrenia and bipolar disorders. In this paper, based on brain functional networks extracted from resting-state fMRI using a recently proposed group information guided ICA (GIG-ICA) method, we explore the biomarkers for discriminating healthy controls, schizophrenia patients, bipolar patients, and patients with two symptom defined subsets of schizoaffective disorder, and then investigate the relationship between different groups. The results demonstrate that the discriminating regions mainly including frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insular and supramarginal cortices perform well in distinguishing the different diagnostic groups. The results also suggest that schizoaffective disorder may be an independent disorder, although its subtype characterized by depressive episodes shares more similarity with schizophrenia.