TY - JOUR A1 - Kühne, Franziska A1 - Hermann, Myriel A1 - Preisler, Martina A1 - Rohrmoser, Amy A1 - Letsch, Anne A1 - Goerling, Ute T1 - Prognostic Awareness in Advanced Disease BT - A Review Update and Concept Analysis JF - Frontiers in Psychology N2 - Purpose: Although subjective knowledge about the prognosis of an advanced disease is extremely important for coping and treatment planning, the concept of prognostic awareness (PA) remains inconsistently defined. The aims of the scoping review were to synthesize a definition of PA from the most recent literature, describe preconditions, correlates and consequences, and suggest a conceptual model. Methods: By using scoping review methodology, we searched the Web of Science and PubMed databases, and included publications, reviews, meta-analyses or guidelines on all physical diagnoses, as well as publications offering a conceptual or an operational definition of PA. The data were analyzed by means of content analysis techniques. Results: Of the 24 included publications, 21 referred exclusively to cancer, one to patients with hip fractures and two to palliative care in general. The deduced definition of PA comprised the following facets: adequate estimation of chances for recovery, knowledge of limited time to live, adequate estimation of life expectancy, knowledge of therapy goals, and knowledge of the course of the disease. Further content analysis results were mapped graphically and in a detailed table. Conclusion: There appears to be a lack of theoretical embedding of PA that in turn influences the methods used for empirical investigation. Drawing on a clear conceptual definition, longitudinal or experimental studies would be desirable. KW - prognosis KW - cancer KW - oncology KW - palliative care KW - patient-centered care KW - systematic review KW - advanced disease Y1 - 2020 U6 - https://doi.org/10.3389/fpsyg.2021.629050 SN - 1664-1078 VL - 12 PB - Frontiers Research Foundation CY - Lausanne ER - TY - GEN A1 - Kühne, Franziska A1 - Hermann, Myriel A1 - Preisler, Martina A1 - Rohrmoser, Amy A1 - Letsch, Anne A1 - Goerling, Ute T1 - Prognostic Awareness in Advanced Disease BT - A Review Update and Concept Analysis T2 - Postprints der Universität Potsdam : Humanwissenschaftliche Reihe N2 - Purpose: Although subjective knowledge about the prognosis of an advanced disease is extremely important for coping and treatment planning, the concept of prognostic awareness (PA) remains inconsistently defined. The aims of the scoping review were to synthesize a definition of PA from the most recent literature, describe preconditions, correlates and consequences, and suggest a conceptual model. Methods: By using scoping review methodology, we searched the Web of Science and PubMed databases, and included publications, reviews, meta-analyses or guidelines on all physical diagnoses, as well as publications offering a conceptual or an operational definition of PA. The data were analyzed by means of content analysis techniques. Results: Of the 24 included publications, 21 referred exclusively to cancer, one to patients with hip fractures and two to palliative care in general. The deduced definition of PA comprised the following facets: adequate estimation of chances for recovery, knowledge of limited time to live, adequate estimation of life expectancy, knowledge of therapy goals, and knowledge of the course of the disease. Further content analysis results were mapped graphically and in a detailed table. Conclusion: There appears to be a lack of theoretical embedding of PA that in turn influences the methods used for empirical investigation. Drawing on a clear conceptual definition, longitudinal or experimental studies would be desirable. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 745 KW - prognosis KW - advanced disease KW - cancer KW - oncology KW - palliative care KW - patient-centered care KW - systematic review Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-542829 SN - 1866-8364 ER - TY - JOUR A1 - Kühne, Franziska A1 - Fauth, Henriette A1 - Destina Sevde, Ay-Bryson A1 - Visser, Leonie N.C. A1 - Weck, Florian T1 - Communicating the diagnosis of cancer or depression: Results of a randomized controlled online study using video vignettes JF - Cancer Medicine N2 - Background Communicating a diagnosis is highly important, yet complex, especially in the context of cancer and mental disorders. The aim was to explore the communication style of an oncologist vs. psychotherapist in an online study. Methods Patients (N = 136: 65 cancer, 71 depression) were randomly assigned to watch a standardized video vignette with one of two communication styles (empathic vs. unempathic). Outcome measures of affectivity, information recall, communication skills, empathy and trust were applied. Results Regardless of diagnosis, empathic communication was associated with the perception of a significantly more empathic (p < 0.001, η2partial = 0.08) and trustworthy practitioner (p = 0.014, η2partial = 0.04) with better communication skills (p = 0.013, η2partial = 0.05). Cancer patients reported a larger decrease in positive affect (p < 0.001, η2partial = 0.15) and a larger increase in negative affect (p < 0.001, η2partial = 0.14) from pre- to post-video than depressive patients. Highly relevant information was recalled better in both groups (p < 0.001, d = 0.61–1.06). Conclusions The results highlight the importance of empathy while communicating both a diagnosis of cancer and a mental disorder. Further research should focus on the communication of a mental disorder in association with cancer. KW - consultation KW - mental health KW - oncology KW - psycho-oncology KW - skills Y1 - 2021 U6 - https://doi.org/10.1002/cam4.4396 SN - 2045-7634 VL - 10 SP - 9012 EP - 9021 PB - Wiley CY - Hoboken, New Jersey, USA ET - 24 ER - TY - GEN A1 - Kühne, Franziska A1 - Fauth, Henriette A1 - Destina Sevde, Ay-Bryson A1 - Visser, Leonie N.C. A1 - Weck, Florian T1 - Communicating the diagnosis of cancer or depression: Results of a randomized controlled online study using video vignettes T2 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe N2 - Background Communicating a diagnosis is highly important, yet complex, especially in the context of cancer and mental disorders. The aim was to explore the communication style of an oncologist vs. psychotherapist in an online study. Methods Patients (N = 136: 65 cancer, 71 depression) were randomly assigned to watch a standardized video vignette with one of two communication styles (empathic vs. unempathic). Outcome measures of affectivity, information recall, communication skills, empathy and trust were applied. Results Regardless of diagnosis, empathic communication was associated with the perception of a significantly more empathic (p < 0.001, η2partial = 0.08) and trustworthy practitioner (p = 0.014, η2partial = 0.04) with better communication skills (p = 0.013, η2partial = 0.05). Cancer patients reported a larger decrease in positive affect (p < 0.001, η2partial = 0.15) and a larger increase in negative affect (p < 0.001, η2partial = 0.14) from pre- to post-video than depressive patients. Highly relevant information was recalled better in both groups (p < 0.001, d = 0.61–1.06). Conclusions The results highlight the importance of empathy while communicating both a diagnosis of cancer and a mental disorder. Further research should focus on the communication of a mental disorder in association with cancer. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 817 KW - consultation KW - mental health KW - oncology KW - psycho-oncology KW - skills Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-582286 SN - 1866-8364 IS - 817 SP - 9012 EP - 9021 ER - TY - THES A1 - Maier, Corinna T1 - Bayesian data assimilation and reinforcement learning for model-informed precision dosing in oncology T1 - Bayes’sche Datenassimilation und Reinforcement Learning für die modellinformierte Präzisionsdosierung in der Onkologie N2 - While patients are known to respond differently to drug therapies, current clinical practice often still follows a standardized dosage regimen for all patients. For drugs with a narrow range of both effective and safe concentrations, this approach may lead to a high incidence of adverse events or subtherapeutic dosing in the presence of high patient variability. Model-informedprecision dosing (MIPD) is a quantitative approach towards dose individualization based on mathematical modeling of dose-response relationships integrating therapeutic drug/biomarker monitoring (TDM) data. MIPD may considerably improve the efficacy and safety of many drug therapies. Current MIPD approaches, however, rely either on pre-calculated dosing tables or on simple point predictions of the therapy outcome. These approaches lack a quantification of uncertainties and the ability to account for effects that are delayed. In addition, the underlying models are not improved while applied to patient data. Therefore, current approaches are not well suited for informed clinical decision-making based on a differentiated understanding of the individually predicted therapy outcome. The objective of this thesis is to develop mathematical approaches for MIPD, which (i) provide efficient fully Bayesian forecasting of the individual therapy outcome including associated uncertainties, (ii) integrate Markov decision processes via reinforcement learning (RL) for a comprehensive decision framework for dose individualization, (iii) allow for continuous learning across patients and hospitals. Cytotoxic anticancer chemotherapy with its major dose-limiting toxicity, neutropenia, serves as a therapeutically relevant application example. For more comprehensive therapy forecasting, we apply Bayesian data assimilation (DA) approaches, integrating patient-specific TDM data into mathematical models of chemotherapy-induced neutropenia that build on prior population analyses. The value of uncertainty quantification is demonstrated as it allows reliable computation of the patient-specific probabilities of relevant clinical quantities, e.g., the neutropenia grade. In view of novel home monitoring devices that increase the amount of TDM data available, the data processing of sequential DA methods proves to be more efficient and facilitates handling of the variability between dosing events. By transferring concepts from DA and RL we develop novel approaches for MIPD. While DA-guided dosing integrates individualized uncertainties into dose selection, RL-guided dosing provides a framework to consider delayed effects of dose selections. The combined DA-RL approach takes into account both aspects simultaneously and thus represents a holistic approach towards MIPD. Additionally, we show that RL can be used to gain insights into important patient characteristics for dose selection. The novel dosing strategies substantially reduce the occurrence of both subtherapeutic and life-threatening neutropenia grades in a simulation study based on a recent clinical study (CEPAC-TDM trial) compared to currently used MIPD approaches. If MIPD is to be implemented in routine clinical practice, a certain model bias with respect to the underlying model is inevitable, as the models are typically based on data from comparably small clinical trials that reflect only to a limited extent the diversity in real-world patient populations. We propose a sequential hierarchical Bayesian inference framework that enables continuous cross-patient learning to learn the underlying model parameters of the target patient population. It is important to note that the approach only requires summary information of the individual patient data to update the model. This separation of the individual inference from population inference enables implementation across different centers of care. The proposed approaches substantially improve current MIPD approaches, taking into account new trends in health care and aspects of practical applicability. They enable progress towards more informed clinical decision-making, ultimately increasing patient benefits beyond the current practice. N2 - Obwohl Patienten sehr unterschiedlich auf medikamentöse Therapien ansprechen, werden in der klinischen Praxis häufig noch standardisierte Dosierungsschemata angewendet. Bei Arzneimitteln mit engen therapeutischen Fenstern zwischen minimal wirksamen und toxischen Konzentrationen kann dieser Ansatz bei hoher interindividueller Variabilität zu häufigem Auftreten von Toxizitäten oder subtherapeutischen Konzentrationen führen. Die modellinformierte Präzisionsdosierung (MIPD) ist ein quantitativer Ansatz zur Dosisindividualisierung, der auf der mathematischen Modellierung von Dosis-Wirkungs-Beziehungen beruht und Daten aus dem therapeutischen Drug/Biomarker-Monitoring (TDM) einbezieht. Die derzeitigen MIPD-Ansätze verwenden entweder Dosierungstabellen oder einfache Punkt-Vorhersagen des Therapieverlaufs. Diesen Ansätzen fehlt eine Quantifizierung der Unsicherheiten, verzögerte Effekte werden nicht berücksichtigt und die zugrunde liegenden Modelle werden im Laufe der Anwendung nicht verbessert. Daher sind die derzeitigen Ansätze nicht ideal für eine fundierte klinische Entscheidungsfindung auf Grundlage eines differenzierten Verständnisses des individuell vorhergesagten Therapieverlaufs. Das Ziel dieser Arbeit ist es, mathematische Ansätze für das MIPD zu entwickeln, die (i) eine effiziente, vollständig Bayes’sche Vorhersage des individuellen Therapieverlaufs einschließlich der damit verbundenen Unsicherheiten ermöglichen, (ii) Markov-Entscheidungsprozesse mittels Reinforcement Learning (RL) in einen umfassenden Entscheidungsrahmen zur Dosisindividualisierung integrieren, und (iii) ein kontinuierliches Lernen zwischen Patienten erlauben. Die antineoplastische Chemotherapie mit ihrer wichtigen dosislimitierenden Toxizität, der Neutropenie, dient als therapeutisch relevantes Anwendungsbeispiel. Für eine umfassendere Therapievorhersage wenden wir Bayes’sche Datenassimilationsansätze (DA) an, um TDM-Daten in mathematische Modelle der Chemotherapie-induzierten Neutropenie zu integrieren. Wir zeigen, dass die Quantifizierung von Unsicherheiten einen großen Mehrwert bietet, da sie eine zuverlässige Berechnung der Wahrscheinlichkeiten relevanter klinischer Größen, z.B. des Neutropeniegrades, ermöglicht. Im Hinblick auf neue Home-Monitoring-Geräte, die die Anzahl der verfügbaren TDM-Daten erhöhen, erweisen sich sequenzielle DA-Methoden als effizienter und erleichtern den Umgang mit der Unsicherheit zwischen Dosierungsereignissen. Basierend auf Konzepten aus DA und RL, entwickeln wir neue Ansätze für MIPD. Während die DA-geleitete Dosierung individualisierte Unsicherheiten in die Dosisauswahl integriert, berücksichtigt die RL-geleitete Dosierung verzögerte Effekte der Dosisauswahl. Der kombinierte DA-RL-Ansatz vereint beide Aspekte und stellt somit einen ganzheitlichen Ansatz für MIPD dar. Zusätzlich zeigen wir, dass RL Informationen über die für die Dosisauswahl relevanten Patientencharakteristika liefert. Der Vergleich zu derzeit verwendeten MIPD Ansätzen in einer auf einer klinischen Studie (CEPAC-TDM-Studie) basierenden Simulationsstudie zeigt, dass die entwickelten Dosierungsstrategien das Auftreten subtherapeutischer Konzentrationen sowie lebensbedrohlicher Neutropenien drastisch reduzieren. Wird MIPD in der klinischen Routine eingesetzt, ist eine gewisse Modellverzerrung unvermeidlich. Die Modelle basieren in der Regel auf Daten aus vergleichsweise kleinen klinischen Studien, die die Heterogenität realer Patientenpopulationen nur begrenzt widerspiegeln. Wir schlagen einen sequenziellen hierarchischen Bayes’schen Inferenzrahmen vor, der ein kontinuierliches patientenübergreifendes Lernen ermöglicht, um die zugrunde liegenden Modellparameter der Ziel-Patientenpopulation zu erlernen. Zur Aktualisierung des Modells erfordert dieser Ansatz lediglich zusammenfassende Informationen der individuellen Patientendaten, was eine Umsetzung über verschiedene Versorgungszentren hinweg erlaubt. Die vorgeschlagenen Ansätze verbessern die derzeitigen MIPD-Ansätze erheblich, wobei neue Trends in der Gesundheitsversorgung und Aspekte der praktischen Anwendbarkeit berücksichtigt werden. Damit stellen sie einen Fortschritt in Richtung einer fundierteren klinischen Entscheidungsfindung dar. KW - data assimilation KW - Datenassimilation KW - reinforcement learning KW - model-informed precision dosing KW - pharmacometrics KW - oncology KW - modellinformierte Präzisionsdosierung KW - Onkologie KW - Pharmakometrie KW - Reinforcement Learning Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-515870 ER -