BackgroundAlthough the Brief Psychiatric Rating Scale (BPRS) is widely used for evaluating patients with schizophrenia, it has limited value in estimating the clinical weight of individual symptoms. The aim of this study was 4-fold: 1) to investigate the relationship of the BPRS to the Clinical Global Impression-Schizophrenia Scale (CGI-SCH), 2) to express this relationship in mathematical form, 3) to seek significant symptoms, and 4) to consider a possible modified BPRS subscale.MethodsWe evaluated 150 schizophrenia patients using the BPRS and the CGI-SCH, then examined the scatter plot distribution of the two scales and expressed it in a mathematical equation. Next, backward stepwise regression was performed to select BPRS items that were highly associated with the CGI-SCH. Multivariate regression was conducted to allocate marks to individual items, proportional to their respective magnitude. We assessed the influence of modifications to the BPRS in terms of Pearson's r correlation coefficient and r-squared to evaluate the relationship between the two scales. Utilizing symptom weighting, we assumed a possible BPRS subscale.ResultsBy plotting the scores for the two scales, a logarithmic curve was obtained. By performing a logarithmic transformation of the BPRS total score, the curve was modified to a linear distribution, described by [CGI-SCH] = 7.1497 × log10[18-item BPRS] - 6.7705 (p < 0.001). Pearson's r for the relationship between the scales was 0.7926 and r-squared was 0.7560 (both p < 0.001). Applying backward stepwise regression using small sets of items, eight symptoms were positively correlated with the CGI-SCH (p < 0.005) and the subset gave Pearson's r of 0.8185 and r-squared of 0.7198. Further selection at the multivariate regression yielded Pearson's r of 0.8315 and r-squared of 0.7036. Then, modification of point allocation provided Pearson's r of 0.8339 and r-squared of 0.7036 (all these p < 0.001). A possible modified BPRS subscale, "the modified seven-item BPRS", was designed.ConclusionsLimited within our data, a logarithmic relationship was assumed between the two scales, and not only individual items of the BPRS but also their weightings were considered important for a linear relationship and improvement of the BPRS for evaluating schizophrenia.
BackgroundWhile many branches of natural science have embraced group theory reaping enormous advantages for their respective fields, clinical medicine lacks to date such applications. Here we intend to explain a prototypal model based on the postulates of groups that could have potential in categorizing clinical states.MethodAs an example, we begin by modifying the original ‘Brief Psychiatric Rating Scale’ (BPRS), the most frequently used standards for evaluating the psychopathology of patients with schizophrenia. We consider a presumptively idealized (virtually standardized) BPRS (denoted BPRS-I) with assessments ranging from ‘0’ to ‘6’ to simplify our discussion. Next, we introduce the modulo group Z7 containing elements {0,1,2,…,6} defined by composition rule, ‘modulo 7 addition’, denoted by *. Each element corresponds to a score resulting from grading a symptom under the BPRS-I assessment. By grading all symptoms associated with the illness, a Cartesian product, denoted Aj, constitutes a summary of a patient assessment. By considering operations denoted A(j→k) that change state Aj into state Ak, a group M (that itself contains Aj and Ak as elements) is also considered. Furthermore, composition of these operations obey modulo 7 arithmetic (i.e., addition, multiplication, and division). We demonstrate the application with a simple example in the form of a series of states (A4 = A1*A(1→2)*A(2→3)*A(3→4)) to illustrate this result.ResultsThe psychiatric disease states are defined as 18-fold Cartesian products of Z7, i.e., Z7×18 = Z7×…×Z7 (18 times). We can construct set G ≡ {a(m)i| m = 1,2,3,…(the patient’s history of the i-th symptom)} and M ≡ {Am | Am ∈ Z7×18 (the set of all possible assessments of a patient)} simplistically, at least, in terms of modulo 7 addition that satisfies the group postulates.ConclusionsDespite the large limitations of our methodology, there are grounds not only within psychiatry but also within other medical fields to consider more generalized notions based on groups (if not rings and fields). These might enable through some graduated expression a systematization of medical states and of medical procedures in a manner more aligned with other branches of natural science.
BackgroundAlthough a score of less than 7 for the 17-item Hamilton Depression Rating Scale (HAM-D17) has been widely adopted to define remission of depression, a full recovery from depression is closely related to the patient’s quality of life as well. Accordingly, we re-evaluated this definition of remission using HAM-D17 in comparison with the corresponding score for health-related quality of life (HRQOL) measured by the SF-36.MethodsUsing the data for depressive patients reported by GlaxoSmithKline K.K. (Study No. BRL29060A/863) in a post-marketing observational study of paroxetine, with a sample size of n = 722, multivariate logistic regression was performed with the HAM-D17 score as a dependent variable and with each of the eight domain scores of HRQOL (from the SF-36) transformed into a binominal form according to the national standard value for Japan. Then, area under curve of receiver operating characteristic analyses were conducted. Based on the obtained results, a multivariate analysis was performed using the HAM-D17 score in a binomial form with HAM-D17 as a dependent variable and with each of the eight HRQOL domain scores (SF-36) as binominalized independent variables.ResultsA cutoff value for the HAM-D17 score of 5 provided the maximum ROC-AUC at “0.864.” The significantly associated scores of the eight HRQOL domains (SF-36) were identified for the HAM-D17 cutoff values of ≥5 and ≤4. The scores for physical functioning (odds ratio, 0.473), bodily pain (0.557), vitality (0.379), social functioning (0.540), role-emotion (0.265), and mental health (0.467) had a significant negative association with the HAM-D17 score (p < 0.05), and HRQOL domain scores for HAM-D17 ≥ 5 were significantly lower compared with those for HAM-D17 ≤ 4.ConclusionsA cutoff value for HAM-D17 of less than or equal to 4 was the best candidate for indicating remission of depression when the recovery of HRQOL is considered. Restoration of social function and performance should be considered equally important in assessing the adequacy of treatment for patients with depression.
The accuracy of non-psychiatrist assessments for psychiatric problems in cancer patients differs by presumed diagnosis. Oncologists should consider unrecognized delirium in cancer inpatients who appear depressed or demented.
BackgroundIn a previous report, we suggested a prototypal model to describe patient states in a graded vector-like format based on the modulo groups via the psychiatric rating scale. In this article, using other simple examples, we provide additional suggestions to clarify how other clinical data can be treated practically in line with our proposed model.MethodsAs illustrations of the wider applicability, we treat four cases commensurate with modulo arithmetic: 1) prescription doses of three medicines (lithium carbonate, mirtazapine, and nitrazepam), 2) changes in laboratory data (blood concentrations of lithium carbonate, white blood cells, percutaneous oxygen saturation and systolic blood pressure), 3) the tumor node metastasis (TNM) classification of malignant tumors applied for esophageal tumors, and 4) the coding schemes of the International Classification of Diseases (ICD) for selected diseases or laboratory data. For each case, we present simple examples in the form of product of states to illustrate these results.Results1) Medications and their changes can be represented as elements of a modulo group; e.g., group S = {Sj | Sj ∈ Z13×Z4×Z3} can represent the set of all possible prescription combinations of three specified medicines. Likewise, 2) clinical values can also be expressed as a modulo group; e.g., group T = {Tj | Tj ∈ Z600×Z50000×Z100×Z300} representing the set of all possible data based on any number of clinical values and their differences. Also, 3) the TNM classification for malignant tumors can be treated within a single modulo group C = {Cj | Cj ∈ Z8×Z4×Z2×Z2}, the set of all composable disease states graded in terms of tumor expansion. Finally, 4) ICD coding schemes provide several examples treatable as a modulo group D = {Dj | Dj ∈ Z7×Z7× …×Z7 (an n-fold product)}, constituting the set of all possible severities of diseases states and laboratory data within provided tuples.ConclusionsDespite the limited scope of our methodology, there are grounds where other clinical quantities (prescription of medicine, laboratory data, TNM classification of malignant tumors, and ICD coding schemes) can be also treatable with the same group-theory approach as was suggested for psychiatric disease states in our previous report.
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