Family system dynamics and type I diabetic glycemic variability : a vector-auto-regressive model

Statistical approaches rooted in econometric methodology, so farforeign to psychosomatic medicine, have provided dynamic psycho-somatic models on brittle diabetes in yielding to Meissner’s (2006) and others’ call for a more integrated view on mind-body relationships with no a priori cause and effect assignments to interacting variables. The conception and portrayal of such models is the focus of this work, in addition to the clinical findings they provide on the case study otherwise primarily serving as an example for their application. Over 120 days, this structured diary time series case study explored the mutual interactions of individual affect states in a classic three person family with its type 1 diabetic adolescent’s daily blood glucose variability and vice versa. Glycemic variability was measured through daily standard deviations of blood glucose recordings (at least three per day). For the same period of time, affect states were captured individually utilizing the self-assessment manikin (Lang, 1980; Bradley & Lang, 1994) on affective valence (positive – negative), arousal (high – low), and dominance (sense of being absent – sense of being present). Auto- and cross-correlating the stationary absolute (level) values of the mutuallyinteracting parallel time series data sets through standard vector autoregression (VAR, Lütkepohl, 2005; Lütkepohl & Krätzig, 2004) and a newly conceived Optimized Multivariate Lag Order Selection Process (Winker, 1995, 2000; Savin & Winker, 2013)allowed for the formulation of threepredominantly consistent models. In the two standard VAR models Cholesky Impulse Response Analysis was applied at a 95 per cent confidence level, cumulatively evidencing for an adolescent being happy, calm, and experiencing high dominance to exhibit less glycemic variability andhence diabetic derailment. A non-dominating mother and a happy father also seemed to reduce glycemic variability. Random external shocks to the two VAR models increasing glycemic variability affected onlythe adolescent and her father: In one model, the male parent exhibited high dominance; in the other, hecalmed down while his daughter turned sad. All effects lasted for lessthan four full days. In the third model based on the Optimized Multivariate Lag Selection Process, more specific temporal relations between affect states and the biological marker of glycemic variability were isolated at statistical significance: Low glycemic variability and therefore good diabetic control correlated with high glycemic variability four days earlier, an excited mother three days earlier, a calm mother seven days earlier, a non-dominating mother four days earlier, a happy father both five and six days earlier, an excited father both three and seven days earlier, and a non-dominating father both two and five days earlier. Low glycemic variability also correlated with a happy child six days later, a calm mother three days later, and a non-dominating father one day later. Graphical representations were proposed for all three models – the intention being a demonstration of avenues for clinically oriented presentations of arguably rather abstract quantitative findings. Additionally, a multiply regressive approach to the data with interval-valued variables and a qualitative case vignette were presented, to complement these highly quantitative models. Extant literatureon brittle diabetes in children andadolescents and the family dynamics complementing it was reviewed in light of all findings. The recurring correlation between maternal dominance and poor glycemic control was recognized. In addition, the prospects and contingencies arising from applying econometric theory to psychosomatic questionswere discussed throughout this thesis. The value and limitations of qualitative and quantitative research on brittle diabetes in general, as well as pertaining to this study, received particular attention.Der Psychosomatischen Medizin bisher fremde statistische Anwendungen der Ökonometrie konnten, unter Berücksichtigung der Forderung Meissners (2006) und anderen nach einer ganzheitlicheren Betrachtungsweise der Körper-Seele-Beziehung, ohne a priori Festlegung von Ursache-Wirkungs-Beziehungen zwischen interagierenden Variablen, dynamische psycho-somatische Modelle zum Brittle Diabetes liefern. Die Konzeption und Darstellung solcher Modelle am Fallbeispiel ist Fokus dieser Arbeit – zusätzlich zu den klinischen Ergebnisse der Fallstudie an sich. Im Rahmen einer Einzelfall-Zeitreihenanalyse, basierend auf strukturierten Tagebuchaufzeichnungen über 120 Tage, wurden die wechselseitigen Interaktionen zwischen Affekten der Mitglieder einer klassischen dreiköpfigen Familie und der Blutzuckervariabilität des Typ-I diabetischen Jugendlichen exploriert. Die Blutzuckervariabilität wurde mittels der täglichen Standardabweichung der Blutzuckermessungen (mindestens drei pro Tag) verfolgt. Für den gleichen Zeitraum wurden die Affektzustände für jedes Familienmitglied mittels self-assessment manikin (Lang, 1980; Bradley & Lang, 1994) im Sinne von Valenz (positiv – negativ), Erregung (hoch – niedrig) und Dominanz (Gefühl der Abwesenheit – Gefühl der Präsenz) erhoben. Korrelationen und Autokorrelationen zwischen den stationären Werten der wechselseitig interagierenden parallel erhobenen Zeitreihen konnten mittels Vektorautoregression (VAR, Lütkepohl, 2005; Lütkepohl & Krätzig, 2004) und dem Optimized Multivariate Lag Order Selection Process (Winker, 1995, 2000; Savin & Winker, 2013) in Form von drei weitgehend übereinstimmenden Modellen dargestellt werden. Durch die zwei Standard-VAR-Modelle konnte mittels Cholesky Impulsantwortfolgen mit einem 95 Prozent Konfidenzintervall insgesamt gezeigt werden, dass Gefühle von Glück, innerer Ruhe und hoher Dominanz bei der Jugendlichen mit weniger Blutzuckervariabilität und daher weniger diabetischen Entgleisungen verbunden waren. Eine nicht-dominante Mutter und ein glücklicher Vater schienen ebenfalls die Blutzuckervariabilität zu reduzieren. Zufällige extern verursachte Anhebungen der Blutzuckervariabilität zeigten in beiden VAR-Modellen nur auf die Jugendliche und ihren Vater Einfluss: In einem Modell zeigte der Vater erhöhte Dominanz, in dem anderen innere Ruhe und seine Tochter Traurigkeit. Alle Effekte konnten für weniger als vier Tage nachgewiesen werden. In dem dritten, auf den Optimized Multivariate Lag Selection Process basierenden Modell, konnten spezifischere zeitliche Relationen zwischen den Affektzuständen und dem biologischen Marker der Blutzuckervariabilität mit statistischer Signifikanz nachgewiesen werden: Niedrige Blutzuckervariabilität und daher eine gute diabetische Stoffwechselkontrolle korrelierte mit hoher Blutzuckervariabilität vier Tage vorher, einer erregten Mutter drei Tage vorher, einer ruhigen Mutter sieben Tage vorher, einer nicht-dominanten Mutter vier Tage zuvor, einem glücklichen Vater sowohl fünf als auch sechs Tage zuvor, einem erregten Vater drei und sieben Tage zuvor und einem nicht-dominanten Vater zwei und fünf Tage vorher. Niedrige Blutzuckervariabilität korrelierte auch mit einer glücklichen Jugendlichen sechs Tage später, einer ruhigen Mutter drei Tage danach und einem nicht-dominanten Vater am nächsten Tag. Für alle drei Modelle wurden graphische Darstellungen vorgeschlagen – mit dem Ziel Wege für eine klinisch orientierte Präsentation der eher abstrakten quantitativen Ergebnisse zu finden. Außerdem wurde eine statistische Bearbeitung mittels Multipler Regression mit Intervallvariablen, sowie eine qualitative Fallvignette vorgestellt, um die höchst quantitativen Modelle zu ergänzen. Literatur zum Brittle Diabetes in Kindern und Jugendlichen und den damit einhergehenden Familiendynamiken wurden unter dem Gesichtspunkt der Ergebnisse besprochen. Die wiederkehrende Korrelation zwischen mütterlicher Dominanz und schlechter Blutzuckereinstellung wurde erwähnt. Weiterhin wurden die Chancen und Risiken der Anwendung ökonometrischer Theorie auf psychosomatische Fragestellungen innerhalb der gesamten Arbeit diskutiert. Dabei wurde besonderes Augenmerk auf den Wert und die Grenzen qualitativer und quantitativer Darstellungen beim Brittle Diabetes allgemein als auch in Bezug auf diese Studie gelegt

High Risk, High Dose?—Pharmacotherapeutic Prescription Patterns of Offender and Non-Offender Patients with Schizophrenia Spectrum Disorder

Compared to acute or community settings, forensic psychiatric settings, in general, have been reported to make greater use of antipsychotic polypharmacy and/or high dose pharmacotherapy, including overdosing. However, there is a scarcity of research specifically on offender patients with schizophrenia spectrum disorders (SSD), although they make up a large proportion of forensic psychiatric patients. Our study, therefore, aimed at evaluating prescription patterns in offender patients compared to non-offender patients with SSD. After initial statistical analysis with null-hypothesis significance testing, we evaluated the interplay of the significant variables and ranked them in accordance with their predictive power through application of supervised machine learning algorithms. While offender patients received higher doses of antipsychotics, non-offender patients were more likely to receive polypharmacologic treatment as well as additional antidepressants and benzodiazepines. To the authors’ knowledge, this is the first study to evaluate a homogenous group of offender patients with SSD in comparison to non-offender controls regarding patterns of antipsychotic and other psychopharmacologic prescription patterns. Keywords: schizophrenia spectrum disorders; antipsychotics; polypharmacy; overdosing; offender patients; forensic psychiatry; benzodiazepines; antidepressan

Data-based identification of knowledge transfer needs in global production networks

Manufacturing companies’ value chains are increasingly distributed globally, which presents companies with the challenge of coordinating complex production networks. In general, these production networks grew historically rather than having been continuously planned, leading to heterogeneous production structures with many tangible and intangible flows to be coordinated. Thereby, many authors claim that the knowledge flow is one of the most important flows and the source of competitive advantage. However, today’s managers face major challenges in transferring production knowledge, especially across globally distributed production sites. The first obstacle to a successful knowledge transfer is to identify what kind of knowledge should be transferred between whom and at what time. This process can take months of information collection and evaluation and is often too time-consuming and costly. Thus, this paper presents an approach to automatically identify at what point knowledge should be transferred. In order to achieve this, the company's raw data is being used to identify which employees work on similar production processes and how these processes perform. Therefore, production processes, which can be compared with each other, need to be formed, even though these processes may be performed at different production sites. Still, not every defined cluster of production processes necessarily requires the initiation of knowledge transfer since performing a knowledge transfer always entails considerable effort and some processes might already be aligned with each other. Consequently, in a next step it is analyzed how these comparable production processes differ from each other by taking into account their performances by means of feedback data. As a result, trigger points for knowledge transfer initiation can be determined

Exploring substance use as rule‐violating behaviour during inpatient treatment of offender patients with schizophrenia

Background: Rule-violating behaviour in the form of substance misuse has been studied primarily within the context of prison settings, but not in forensic psychiatric settings. Aims: Our aim was to explore factors that are associated with substance misuse during hospitalisation in patients among those patients in a Swiss forensic psychiatric inpatient unit who were suffering from a disorder along the schizophrenia spectrum. Methods: From a database of demographic, clinical and offending data on all residents at any time between 1982 and 2016 in the forensic psychiatric hospital in Zurich, 364 cases fulfilled diagnostic criteria for schizophrenia or a schizophrenia-like illness and formed our sample. Any confirmed use of alcohol or illicit substances during admission (yes/no) was the dependent variable. Its relationship to all 507 other variables was explored by machine learning. To counteract overfitting, data were divided into training and validation set. The best model from the training set was tested on the validation set. Results: Substance use as a secure hospital inpatient was unusual (15, 14%). Prior substance use disorder accounted for so much of the variance (AUC 0.92) that it was noted but excluded from further models. In the resulting model of best fit, variables related to rule breaking, younger age overall and at onset of schizophrenia and nature of offending behaviour, substance misuse as a minor and having records of complications in prior psychiatric treatment were associated with substance misuse during hospitalisation, as was length of inpatient treatment. In the initial model the AUC was 0.92. Even after removal of substance use disorder from the final model, performance indicators were meaningful with a balanced accuracy of 67.95, an AUC of 0.735, a sensitivity of 81.48% and a specificity of 57.58%. Conclusions: Substance misuse in secure forensic psychiatric hospitals is unusual but worthy of clinical and research consideration because of its association with other rule violations and longer hospitalisation. More knowledge is needed about effective interventions and rehabilitation for this group. Keywords: rule-violations; schizophrenia; substance misus

Towards identifying cancer patients at risk to miss out on psycho-oncological treatment via machine learning

Objective: In routine oncological treatment settings, psychological distress, including mental disorders, is overlooked in 30% to 50% of patients. High workload and a constant need to optimise time and costs require a quick and easy method to identify patients likely to miss out on psychological support. Methods: Using machine learning, factors associated with no consultation with a clinical psychologist or psychiatrist were identified between 2011 and 2019 in 7,318 oncological patients in a large cancer treatment centre. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting. Results: Patients were least likely to receive psycho-oncological (i.e., psychiatric/psychotherapeutic) treatment when they were not formally screened for distress, had inpatient treatment for less than 28 days, had no psychiatric diagnosis, were aged 65 or older, had skin cancer or were not being discussed in a tumour board. The final validated model was optimised to maximise sensitivity at 85.9% and achieved an area under the curve (AUC) of 0.75, a balanced accuracy of 68.5% and specificity of 51.2%. Conclusion: Beyond conventional screening tools, results might contribute to identify patients at risk to be neglected in terms of referral to psycho-oncology within routine oncological care. Keywords: cancer; machine learning; mental disorders; psycho-oncology; psychological support

Different needs in patients with schizophrenia spectrum disorders who behave aggressively towards others depend on gender: a latent class analysis approach

Background There is limited research with inconsistent findings on differences between female and male offender patients with a schizophrenia spectrum disorder (SSD), who behave aggressively towards others. This study aimed to analyse inhomogeneities in the dataset and to explore, if gender can account for those. Methods Latent class analysis was used to analyse a mixed forensic dataset consisting of 31 female and 329 male offender patients with SSD, who were accused or convicted of a criminal offence and were admitted to forensic psychiatric inpatient treatment between 1982 and 2016 in Switzerland. Results Two homogenous subgroups were identified among SSD symptoms and offence characteristics in forensic SSD patients that can be attributed to gender. Despite an overall less severe criminal and medical history, the female-dominated class was more likely to receive longer prison terms, similarly high antipsychotic dosages, and was less likely to benefit from inpatient treatment. Earlier findings were confirmed and extended in terms of socio-demographic variables, diseases and criminal history, comorbidities (including substance use), the types of offences committed in the past and as index offence, accountability assumed in court, punishment adjudicated, antipsychotic treatment received, and the development of symptoms during psychiatric inpatient treatment. Conclusions Female offender patients with schizophrenia might need a more tailored approach in prevention, assessment and treatment to diminish tendencies of inequity shown in this study

Reading Wishes from the Lips: Cancer Patients’ Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment

Background: Psycho-oncological support (PO) is an effective measure to reduce distress and improve the quality of life in patients with cancer. Currently, there are only a few studies investigating the (expressed) wish for PO. The aim of this study was to evaluate the number of patients who request PO and to identify predictors for the wish for PO. Methods: Data from 3063 cancer patients who had been diagnosed and treated at a Comprehensive Cancer Center between 2011 and 2019 were analyzed retrospectively. Potential predictors for the wish for PO were identified using logistic regression. As a novelty, a Back Propagation Neural Network (BPNN) was applied to establish a prediction model for the wish for PO. Results: In total, 1752 patients (57.19%) had a distress score above the cut-off and 14.59% expressed the wish for PO. Patients’ requests for pastoral care (OR = 13.1) and social services support (OR = 5.4) were the strongest predictors of the wish for PO. Patients of the female sex or who had a current psychiatric diagnosis, opioid treatment and malignant neoplasms of the skin and the hematopoietic system also predicted the wish for PO, while malignant neoplasms of digestive organs and older age negatively predicted the wish for PO. These nine significant predictors were used as input variables for the BPNN model. BPNN computations indicated that a three-layer network with eight neurons in the hidden layer is the most precise prediction model. Discussion: Our results suggest that the identification of predictors for the wish for PO might foster PO referrals and help cancer patients reduce barriers to expressing their wish for PO. Furthermore, the final BPNN prediction model demonstrates a high level of discrimination and might be easily implemented in the hospital information system

Severe mental illness in cancer is associated with disparities in psycho-oncological support

Patients with both cancer and a severe mental illness (SMI) have a higher risk of advanced stage cancer at diagnosis and poorer survival in comparison to individuals with cancer alone. The present study explores if similar disparities exist in terms of psycho-oncological support. Latent class analysis (LCA) was used to group 10,945 patients with any type of cancer, of which 72 (0.7%) had been diagnosed with a SMI (ICD10-codes F20-F22, F24, F25, F28-F31, F32.3, F33.3), and 1056 (9.6%) with another mental disorder. Subgrouping was based on presence of SMI, other mental illnesses, stage of cancer at its first detection, screening for distress and receipt of information on psycho-oncology, consultation with a psychotherapist and/or psychiatrist, prescription of different psychotropic medication, and use of a patient care attendant. Five subgroups were identified. Patients with SMI were most likely to suffer from further mental comorbidities, to be prescribed antipsychotics, antidepressants, or mood stabilizers, and be in need of a patient care attendant. In comparison to patients without SMI, the larger one of 2 subgroups of patients with SMI had a low probability to be screened for distress and informed about psycho-oncological support services. A smaller subgroup of patients with SMI was probable to be diagnosed with an advanced stage of cancer. In subgroups without patients with mental disorders, screening for distress and offering psycho-oncological support seemed to be economized unless benzodiazepines or opioids were prescribed. Contrary to published evidence, distress screening and offering psycho-oncological support is neglected in patients with SMI unless an advanced stage of cancer is being diagnosed

Predictive Factors Associated with Declining Psycho-Oncological Support in Patients with Cancer

(1) Background: International cancer treatment guidelines recommend low-threshold psycho-oncological support based on nurses’ routine distress screening (e.g., via the distress thermometer and problem list). This study aims to explore factors which are associated with declining psycho-oncological support in order to increase nurses’ efficiency in screening patients for psycho-oncological support needs. (2) Methods: Using machine learning, routinely recorded clinical data from 4064 patients was analyzed for predictors of patients declining psycho-oncological support. Cross validation and nested resampling were used to guard against model overfitting. (3) Results: The developed model detects patients who decline psycho-oncological support with a sensitivity of 89% (area under the cure of 79%, accuracy of 68.5%). Overall, older patients, patients with a lower score on the distress thermometer, fewer comorbidities, few physical problems, and those who do not feel sad, afraid, or worried refused psycho-oncological support. (4) Conclusions: Thus, current screening procedures seem worthy to be part of daily nursing routines in oncology, but nurses may need more time and training to rule out misconceptions of patients on psycho-oncological support

Clinically Significant Distress and Physical Problems Detected on a Distress Thermometer are Associated With Survival Among Lung Cancer Patients

Objective: The distress thermometer (DT) is a well-established screening tool to detect clinically significant distress in cancer patients. It is often administered in combination with the problem list (PL), differentiating further between various (e.g., physical and emotional) sources of distress. The present study aimed to extend previous research on the association between distress and overall survival. Further exploratory analysis aimed to evaluate the predictive value of the PL for overall survival. Methods: Patients (n=323) with newly diagnosed lung cancer were recruited from a large cancer center. Patients were split into two groups, those with (DT score ≥5) and those without significant distress. Overall survival time was illustrated by a Kaplan Meier curve and compared with a log rank test. Univariable Cox proportional hazard models were built to control the association of distress with overall survival for age, gender, disease stage,comorbidity and their interaction terms. A multiple linear regression was used to investigate the association of the items from the problem list with survival time. Results: Patients with significant distress had a shorter survival time compared to patients without significant distress (25 vs. 43 months). Regression analysis revealed more problems with both "bathing and dressing" and "eating", as well as absence of "diarrhea" and increased "nervousness" to negatively impact overall survival time. Conclusion: Our results show that estimation of the survival function using cancer-related distress is possible. However, when using Cox regression, distress shows no significant value for survival as a predictor. Moreover, our study did not reveal an interaction effect between disease stage, comorbidity, and distress. Overall, results suggest that physical and emotional problems that arise from lung cancer may be useful to identify patients at risk for poor prognosis. Keywords: cancer-related distress; distress thermometer; lung-cancer; problem list; psycho-oncology; survival