Background: The mini-mental state examination (MMSE) was adapted by individual countries according to their languages and cultures, though it has not been systematically compared. The objective of this study was to compare the linguistic and cultural variations of the MMSE used in various Asian countries. With this, we can analyze the strengths and weaknesses of the MMSE and consider using a common version in future international clinical studies in Asia. Methods: We collected the MMSEs used in 11 Asian nations. After translating those into English, we compared them to understand the differences in the questionnaires with regard to cultural aspects. Results: Many items may be applicable or comparable with a little modification, for Asian countries. However, attention and calculation and repetition may be incomparable. There were some differences in the contents and the ways to administer. Conclusions: The lack of consideration of the cultural differences and their influences on the interpretation of the same cognitive test makes cross-cultural studies difficult. Some items of MMSE tasks need readjusting for, if any, multi-national studies. This study might serve as a first step in the development of a standardized cross-cultural cognitive instrument, especially in Asia.
Psychiatric practice routinely uses semistructured and/or unstructured free text to record the behavior and mental state of patients. Many of these data are unstructured, lack standardization, and are difficult to use for analysis. Thus, it is difficult to quantitatively analyze a patient's illness trajectory over time and his or her responsiveness to treatment, and it is also difficult to compare different patients quantitatively. In this article, experts in the field of psychiatry, along with machine learning models, have collaboratively transformed patient data available in status assessments generated by physicians into binary vector representations. Data from patients with mental health disorders collected within a real-world clinical setting from one of the largest behavioral electronic health record (EHR) systems in the United States have been used for generating these representations. The binary vector representation of these health records is shown to be useful in various clinical tasks, such as disease phenotyping, characterizing the suicidality of patients, and inferring diagnoses. To summarize, this approach can transform semistructured free-text summaries of patients' status assessments into a structured, quantifiable format, which enriches the data that reside within EHR systems. This allows for effective intra-and interpatient quantifications and comparisons, which are much needed in the field of mental health. With the aid of these binary representations, patients' mental states can be systematically tracked over time, as can their responses to medications at the individual and population levels.
IntroductionCurrently approved treatments for schizophrenia (antipsychotics) have demonstrated effectiveness for treating positive symptoms; however, these agents are largely ineffective in treating other domains. Negative symptoms, including avolition, alogia, blunted affect, and asociality, are difficult to treat, and often persist despite adequate control of positive symptoms. Additionally, some patients experience “predominant” (moderate-to-severe negative symptoms that have greater relative severity than co-occurring positive symptoms) or “prominent” (severity of negative symptoms [moderate-to-severe] without any reference to positive symptoms) negative symptoms. These symptoms are known to have great impact on patient social functioning and quality of life, and are associated with poorer clinical course and outcomes for patients. Here, we examined inpatient healthcare resource utilization in patients with schizophrenia experiencing predominantly negative symptoms (PNS).MethodsDe-identified data were extracted from electronic health records in the NeuroBlu Database across 25 US mental healthcare providers. Positive and negative symptom data were derived from free-text records using natural language processing. PNS was defined as the presence of three or more negative symptoms and three or fewer positive symptoms at first clinical contact following schizophrenia diagnosis. Groups were balanced for baseline demographic and clinical characteristics by minimizing the generalized Mahalanobis distance and compared using chi-square and t-tests. Treatment patterns were visualized using Sankey diagrams.ResultsA total of 4444 patients with schizophrenia were identified and 8% were classified as PNS. A balanced cohort of 720 patients (50% PNS) was generated. Patients with PNS were more likely to be hospitalized in the 12 months following diagnosis (PNS: 76%, non-PNS: 60%, χ2: 22.5, p < 0.001) and were switched to a second-line antipsychotic after a shorter first-line treatment duration. The most frequently prescribed antipsychotics differed between groups (PNS: risperidone, aripiprazole, haloperidol; non-PNS: risperidone, olanzapine, other atypical).DiscussionThis study demonstrates that negative symptoms in schizophrenia may be associated with worse illness course and higher healthcare resource utilization. There remains a need for new treatment options for patients with persistent, prominent, or predominant negative symptoms which specifically improve this historically hard-to-treat and assess symptom domain.FundingSunovion Pharmaceuticals, Inc.
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