@article{KuehneHermannPreisleretal.2021,
  author    = {K{\"u}hne, Franziska and Hermann, Myriel and Preisler, Martina and Rohrmoser, Amy and Letsch, Anne and Goerling, Ute},
  title     = {Prognostic Awareness in Advanced Disease},
  series = {Frontiers in Psychology},
  volume    = {12},
  journal   = {Frontiers in Psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2021.629050},
  pages     = {6},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@misc{KuehneHermannPreisleretal.2021,
  author    = {K{\"u}hne, Franziska and Hermann, Myriel and Preisler, Martina and Rohrmoser, Amy and Letsch, Anne and Goerling, Ute},
  title     = {Prognostic Awareness in Advanced Disease},
  series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-54282},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-542829},
  pages     = {8},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{KuehneFauthDestinaSevdeetal.2021,
  author    = {K{\"u}hne, Franziska and Fauth, Henriette and Destina Sevde, Ay-Bryson and Visser, Leonie N.C. and Weck, Florian},
  title     = {Communicating the diagnosis of cancer or depression: Results of a randomized controlled online study using video vignettes},
  series = {Cancer Medicine},
  volume    = {10},
  journal   = {Cancer Medicine},
  edition   = {24},
  publisher = {Wiley},
  address   = {Hoboken, New Jersey, USA},
  issn      = {2045-7634},
  doi       = {10.1002/cam4.4396},
  pages     = {9012 -- 9021},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@misc{KuehneFauthDestinaSevdeetal.2021,
  author    = {K{\"u}hne, Franziska and Fauth, Henriette and Destina Sevde, Ay-Bryson and Visser, Leonie N.C. and Weck, Florian},
  title     = {Communicating the diagnosis of cancer or depression: Results of a randomized controlled online study using video vignettes},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  number    = {817},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-58228},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-582286},
  pages     = {9012 -- 9021},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Maier2021,
  author    = {Maier, Corinna},
  title     = {Bayesian data assimilation and reinforcement learning for model-informed precision dosing in oncology},
  doi       = {10.25932/publishup-51587},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-515870},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 138},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}