Objective: Cancer patients display heterogeneous psychopathology, comprising depressive, anxiety, hostility, and somatic symptoms. Often, clinical pictures evolve over time deteriorating the individual functioning and prognosis. Network models can reveal the relationships between symptoms, thus providing clinical insights. Method: This study examined data of the Brief Symptom Inventory and the Distress Thermometer, from 1108 cancer outpatients. Gaussian Graphical Models were estimated using regularized and non-regularized Bayesian methods. In addition, we used community detection methods to identify the most relevant symptom groupings, and longitudinal network analyses on 515 participants to examine the connections between symptoms over three months. Results: The network models derived from baseline data suggested symptoms clustered into three main complexes (depression/anxiety, hostility, and somatic symptoms). Symptoms related to depression and hostility were highly connected with suicidal and death thoughts. Faintness, weakness, chest pain, and dyspnoea, among somatic symptoms, were more strongly connected with psychopathological features. Longitudinal analyses revealed that sadness, irritability, nervousness, and tension predicted each other. Panic and death thoughts predicted fearfulness and faintness. Conclusions: Somatic symptoms, sadness, irritability, chronic and acute anxiety interact between each other, shaping the heterogeneous clinical picture of distress in cancer. This study, strengthened by robust methods, is the first to employ longitudinal network analyses in cancer patients. Further studies should evaluate whether targeting specific symptoms might prevent the onset of chronic distress and improve clinical outcomes in cancer patients.

The facets of psychopathology in patients with cancer: Cross-sectional and longitudinal network analyses

Murri, Martino Belvederi
Primo
Conceptualization
;
Caruso, Rosangela
Resources
;
Folesani, Federica
Data Curation
;
Nanni, Maria Giulia
Data Curation
;
Grassi, Luigi
Ultimo
Supervision
2023

Abstract

Objective: Cancer patients display heterogeneous psychopathology, comprising depressive, anxiety, hostility, and somatic symptoms. Often, clinical pictures evolve over time deteriorating the individual functioning and prognosis. Network models can reveal the relationships between symptoms, thus providing clinical insights. Method: This study examined data of the Brief Symptom Inventory and the Distress Thermometer, from 1108 cancer outpatients. Gaussian Graphical Models were estimated using regularized and non-regularized Bayesian methods. In addition, we used community detection methods to identify the most relevant symptom groupings, and longitudinal network analyses on 515 participants to examine the connections between symptoms over three months. Results: The network models derived from baseline data suggested symptoms clustered into three main complexes (depression/anxiety, hostility, and somatic symptoms). Symptoms related to depression and hostility were highly connected with suicidal and death thoughts. Faintness, weakness, chest pain, and dyspnoea, among somatic symptoms, were more strongly connected with psychopathological features. Longitudinal analyses revealed that sadness, irritability, nervousness, and tension predicted each other. Panic and death thoughts predicted fearfulness and faintness. Conclusions: Somatic symptoms, sadness, irritability, chronic and acute anxiety interact between each other, shaping the heterogeneous clinical picture of distress in cancer. This study, strengthened by robust methods, is the first to employ longitudinal network analyses in cancer patients. Further studies should evaluate whether targeting specific symptoms might prevent the onset of chronic distress and improve clinical outcomes in cancer patients.
2023
Murri, Martino Belvederi; Caruso, Rosangela; Christensen, Alexander P; Folesani, Federica; Nanni, Maria Giulia; Grassi, Luigi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2517730
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