Narcissistic dimensions and depressive symptoms in patients across mental disorders in cognitive behavioural therapy and in psychoanalytic interactional therapy in Germany: a prospective cohort study.

BACKGROUND: Narcissistic personality traits have been theorised to negatively affect depressive symptoms, therapeutic alliance, and treatment outcome, even in the absence of narcissistic personality disorder. We aimed to examine how the dimensional narcissistic facets of admiration and rivalry affect depressive symptoms across treatment modalities in two transdiagnostic samples. METHODS: We did a naturalistic, observational prospective cohort study in two independent adult samples in Germany: one sample pooled from an inpatient psychiatric clinic and an outpatient treatment service offering cognitive behavioural treatment (CBT), and one sample from an inpatient clinic providing psychoanalytic interactional therapy (PIT). Inpatients treated with CBT had an affective or psychotic disorder. For the other two sites, data from all service users were collected. We examined the effect of core narcissism and its facets admiration and rivalry, measured by Narcissistic Admiration and Rivalry Questionnaire-short version, on depressive symptoms, measured by Beck's Depression Inventory and Patient Health Questionnaire-Depression Scale, at baseline and after treatment in patients treated with CBT and PIT. Primary analyses were regression models, predicting baseline and post-treatment depression severity from core narcissism and its facets. Mediation analysis was done in the outpatient CBT group for the effect of the therapeutic alliance on the association between narcissism and depression severity after treatment. FINDINGS: The sample included 2371 patients (1423 [60·0%] female and 948 [40·0%] male; mean age 33·13 years [SD 13·19; range 18-81), with 517 inpatients and 1052 outpatients in the CBT group, and 802 inpatients in the PIT group. Ethnicity data were not collected. Mean treatment duration was 300 days (SD 319) for CBT and 67 days (SD 26) for PIT. Core narcissism did not predict depression severity before treatment in either group, but narcissistic rivalry was associated with higher depressive symptom load at baseline (ß 2·47 [95% CI 1·78 to 3·12] for CBT and 1·05 [0·54 to 1·55] for PIT) and narcissistic admiration showed the opposite effect (-2·02 [-2·62 to -1·41] for CBT and -0·64 [-1·11 to -0·17] for PIT). Poorer treatment response was predicted by core narcissism (ß 0·79 [0·10 to 1·47]) and narcissistic rivalry (0·89 [0·19 to 1·58]) in CBT, whereas admiration showed no effect. No effect of narcissism on treatment outcome was discernible in PIT. Therapeutic alliance mediated the effect of narcissism on post-treatment depression severity in the outpatient CBT sample. INTERPRETATION: As narcissism affects depression severity before and after treatment with CBT across psychiatric disorders, even in the absence of narcissistic personality disorder, the inclusion of dimensional assessments of narcissism should be considered in future research and clinical routines. The relevance of the therapeutic alliance and therapeutic strategy could be used to guide treatment approaches. FUNDING: IZKF Münster. TRANSLATION: For the German translation of the abstract see Supplementary Materials section.

Symptom monitoring based on digital data collection during inpatient treatment of schizophrenia spectrum disorders - A feasibility study.

Digital acquisition of patients' self-reports on individual risk factors and symptom severity represents a promising, cost-efficient, and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. Yet, studies investigating digital data collection in patients with a schizophrenia spectrum disorder (PSSDs) are scarce. The objective of this study was to explore the feasibility of digitally acquired self-report assessments of risk and symptom profiles at the time of admission into inpatient treatment in an age-representative sample of hospitalized PSSDs. We investigated the required support, the data entry pace, and the subjective user experience. Findings were compared with those of patients with an affective disorder (PADs). Of 82 PSSDs who were eligible for inclusion, 59.8% (n=49) agreed to participate in the study, of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates, and subjective experience ratings did not differ between PSSDs and PADs. Patients reported high satisfaction with the assessment. PSSDs required more support and time for the data entry than PADs. Our results indicate that digital data collection is a feasible and well-received method in PSSDs. Future clinical and research efforts on digitized assessments in psychiatry should include PSSDs and offer support to reduce digital exclusion.

Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study.

BACKGROUND: Empirically driven personalized diagnostic applications and treatment stratification is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which are currently absent in psychiatric clinical routine. OBJECTIVE: Here, we describe the informatics infrastructure implemented at the psychiatric Münster University Hospital, which allows standardized acquisition, transfer, storage, and export of clinical data for future real-time predictive modelling in psychiatric routine. METHODS: We designed and implemented a technical architecture that includes an extension of the electronic health record (EHR) via scalable standardized data collection and data transfer between EHRs and research databases, thus allowing the pooling of EHRs and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses module-driven engineering to generate standardized applications and interfaces. The operational data model was used as the standard. Standardized data were entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, and the standardized transmission, processing, display, and export of data were realized via SMA:T. RESULTS: The technical feasibility of the informatics infrastructure was demonstrated in the course of this study. We created 19 standardized documentation forms with 241 items. For 317 patients, 6451 instances were automatically transferred to the EHR system without errors. Moreover, 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. CONCLUSIONS: In this study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way toward future application of predictive models in psychiatric clinical routine.

Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study.

BACKGROUND: Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. OBJECTIVE: The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. METHODS: We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. RESULTS: Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. CONCLUSIONS: Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.

Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records.

OBJECTIVES: The objective of this study is the conceptual design, implementation and evaluation of a system for generic, standard-compliant data transfer into electronic health records (EHRs). This includes patient data from clinical research and medical care that has been semantically annotated and enhanced with metadata. The implementation is based on the single-source approach. Technical and clinical feasibilities, as well as cost-benefit efficiency, were investigated in everyday clinical practice. METHODS: Münster University Hospital is a tertiary care hospital with 1,457 beds and 10,823 staff who treated 548,110 patients in 2018. Single-source metadata architecture transformation (SMA:T) was implemented as an extension to the EHR system. This architecture uses Model Driven Software Development (MDSD) to generate documentation forms according to the Clinical Data Interchange Standards Consortium (CDISC) operational data model (ODM). Clinical data are stored in ODM format in the EHR system database. Documentation forms are based on Google's Material Design Standard. SMA:T was used at a total of five clinics and one administrative department in the period from March 1, 2018 until March 31, 2019 in everyday clinical practice. RESULTS: The technical and clinical feasibility of SMA:T was demonstrated in the course of the study. Seventeen documentation forms including 373 data items were created with SMA:T. Those were created for 2,484 patients by 283 users in everyday clinical practice. A total of 121 documentation forms were examined retrospectively. The Constructive cost model (COCOMO II) was used to calculate cost and time savings. The form development mean time was reduced by 83.4% from 3,357 to 557 hours. Average costs per form went down from EUR 953 to 158. CONCLUSION: Automated generic transfer of standard-compliant data and metadata into EHRs is technically and clinically feasible, cost efficient, and a useful method to establish comprehensive and semantically annotated clinical documentation. Savings of time and personnel resources are possible.