A network analysis approach to the needs of oncological patients

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Background: In recent years, an ever greater importance is given to the needs of cancer patients which could impact medical treatments, adherence and compliance, and patients’ quality of life (Teo et al. 2019; NCCN, 2015). However, recognizing and addressing the needs of patients may not be enough. Indeed, on one hand, needs that clinicians might consider as 'important' could play a marginal role, and – on the other hand – needs that clinicians may perceive as 'unimportant' may be central for patients. An innovative approach – psychometric network analysis (PNA; Epskamp, 2017) – was used to assess the network among needs ( nodes). Notably, the more a central node (need) is modified (addressed), the more a cascade change will occur in all the other nodes (needs). This study aimed to evaluate: (A) the structure of relationships among needs ( edges) of cancer patients, (B) which needs ( nodes) are the most relevant ( central), and (C) play a key role in the network. Methods: Patients ( n = 511; mean age = 65.95, SD = 12.72; 280 males) were enrolled at the Oncology Day Hospital at the “Presidio Ospedaliero” of Saronno, ASST Valle Olona, Italy. Patients were tested with the Need Evaluation Questionnaire (NEQ) which is composed of 23 items that investigate as many needs – divided into 4 areas: (1) information about diagnosis/prognosis, (2) information about exams and treatment, (3) communicative, and (4) relational needs. Results: Preliminary analysis revealed that all of the items were informative (SDitem < 2.5SDall_itemsSD) and there was no redundancy between items (redundancy index < 0.25). An Ising model (5,000 nonparametric bootstraps) with LASSO regularized nodewise logistic regression was performed. PNA showed a high accuracy: CS-coefficient = 0.56. On one hand, PNA showed that item#2 ( “I need more information about my future condition”; z = 2.039), item#17 ( “I need to speak with a psychologist”; z = 1.209), and item#13 ( “I need to be reassured more by the doctors”; z = 1.201) were the strongest (central) nodes. On the other hand, item#14 ( “ I need the hospital to provide better services”; z = -2.261) was the weakest node in the network. Conclusions: These findings show useful implications for clinical practice. Clinical interventions should address the needs showing the strongest connections in the network. These central nodes can influence all the other connected needs, thus representing important needs to be targeted by clinicians – allowing to tailoring more targeted and efficient therapeutic approaches to meet patients’ needs, with beneficial effects for medical treatments and quality of life.

Effectiveness of brief-focused cognitive behavioral therapy in the reduction of cancer-related distress and emotive problems: A comparative study

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Background: In recent years, there was an increase of cognitive-behavioral therapy (CBT) oriented brief-focused psychological intervention protocols for the reduction of cancer-related emotive difficulties that oncological patients have to face almost every day and that could have a negative impact on medical treatment (Di Matteo, Lepper & Croghan, 2006; NCCN, 2015). Thus, this study aimed to determine the effectiveness of a 10-session brief CBT (B-CBT-P) compared with a control group (CG). Methods: Participants ( n = 67; mean age = 63.11, SD = 12.1, 35 female) enrolled at the Oncology Day Hospital at the “Presidio Ospedaliero” of Saronno, ASST Valle Olona, Italy who undertook (B-CBT-P: n = 34) or non-undertook (CG: n = 33) a psychotherapy intervention – with one of two different psychologists (authors MM and MI) to avoid potential biases. Participants were tested with the Distress Thermometer and the Problem List (DT&PL) at the baseline (T1; overall McDonald’s omega = .90), at the end of the 10-sessions B-CBT-P (T2; overall McDonald’s omega = .89). Results: A multilevel-multivariate repeated measure regression analysis was performed – controlling for age, type and localization of tumor, and therapist. In line with psychotherapy protocol, results showed no statistical significant changes for ‘practical problems’ ( p = 0.0497 ns), ‘spiritual problems’ ( p = 0.321 ns), and ‘physical problems’ ( p = 0.206). Results revealed a statistical significance reduction of ‘relationships problems’ ( p = 0.015; pooled Cohen’s d = 0.341), ‘emotive problems’ ( p = 0.012; pooled Cohen’s d = 0.349), and the distress thermometer ( p = 0.008; pooled Cohen’s d = 0.373). Conclusions: Considering that emotive and relational problems, as well as distress, occurs frequently among oncological patients, this study is into an important area. Results suggest that B-CBT-P is statistically and clinically effective to improve the emotive and relational sphere of oncological patients. However, the small effect sizes found suggest that these short protocols may not be sufficient to bring important changes in the patient's life. This study opens up to the idea of improving these protocols so that the cancer patient can improve his quality of life – even with a few psychotherapy sessions.

Effectiveness of psychological distress reduction with cognitive behavioral therapy for oncological patients: A one-year follow-up study

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Background: Distress has a negative impact on medical treatment (Di Matteo, Lepper & Croghan, 2006) and it is considered one of the most important indexes of psychological suffering in oncological patients (NCCN, 2015). Thus, the purpose of this study was to determine the long term effectiveness of brief Cognitive Behavioral Therapy for patients with cancer (CBT-C) compared with a control group (CG) of oncological patients without any psychotherapy intervention - at one year after a chemotherapy treatment. Methods: Participants ( n = 80; mean age = 63.3, SD = 13.4; 54 female) enrolled at the Oncology Day Hospital at the “Presidio Ospedaliero” of Saronno, ASST Valle Olona, Italy who undertook (CBT-C: n = 40) or non-undertook (CG: n = 40) a psychotherapy intervention. Individual psychotherapy sessions strictly followed the IPOS guidelines (Watson & Kissane, 2017). Participants were tested with the Psychological distress Inventory (PDI) at the baseline (T1; Cronbach α = .88) at the end of the chemotherapy treatment (T2; Cronbach α = .87), at the end of the psychotherapy intervention (T3; Cronbach α = .88), 6-month follow-up (T4; Cronbach α = .85), and 1-year follow-up (T5; Cronbach α = .84). Results: Multilevel growth curve modeling – controlling for age, number of sessions, type and localization of tumor – showed a sharper reduction of distress for CBT-C participants that continue after posttreatment until 1-year follow-up ( p < .001); whereas for CG participants it reduced more gradually from pretreatment to 1-year follow-up ( p < .001). The results revealed a significant difference between the linear slopes for each treatment condition ( p < .001). The overall Hedges’ g comparing the two groups for distress reduction between pretreatment and 1-year follow-up was 2.14 (p < .001) in favor of CBT-C. Conclusions: Given that psychological distress occurs frequently among oncological patients this study is into an important area of study. Results suggest that CBT-C is statistically and clinically effective in treating psychological distress 1 year after the chemotherapy treatment. These findings revealed a kind of long-term effectiveness psychological intervention able both to reduce psychological suffering and improve a better quality of life in oncological settings.

Profiling cancer-related distress and problems: A latent class analysis approach

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Background: Psychological distresses, emotional troubles, social difficulties as well as both physical and practical issues are all among oppressive problems that oncological patients have to face almost every day. On one hand, these problems seem to have a strong impact on quality of life; and to the other hand, these issues seem to be related to specific cancer-related variables ( e.g.: type and localization of the tumor). Thus, the aim of the study was to use a Latent Class Analysis (LCA) approach to profile a latent structure accounting for the covariance between psychological distress and everyday problems due to cancer. A one-factor model with three classes was hypothesized, which comprised distress and cancer-related problems as indicators and age, type of medical treatment as well as type and localization of tumor as external variables that moderate the latent structure. Methods: Patients ( N = 264, 62.3% female, mean age = 65.3, SD = 12.4) were enrolled at the Oncology Day Hospital, “Presidio Ospedaliero” of Saronno, ASST Valle Olona, Italy. Using the standardized Distress Thermometer and Problem List (TD&PL; NCCN, 2015) patients were tested for: (A) distress; (B) practical problems (Cronbach’s α = .63); (C) social difficulties (Cronbach’s α = .65); (D) emotional issues (Cronbach’s α = .86) and (E) physical problems (Cronbach’s α = .77). Results: First of all, a CFA was performed to test the original factorial structure of the DT&PL. The original five factor solution was supported by adequate fit indices: RMSEA = .063; CFI = .924. Then, the LCA (10000 bootstrap resampling) shows the goodness-of-model fit [χ2 = 10.01; p = .11; LRχ2 = 9.30; p = .09] and the goodness-of-classification quality [Entropy = .80 ( > .7); Average-Latent-Class-Assignment-Probability: .975, .900, and .902 for Class1, Class2, and Class3, respectively]. The LCA identified a latent variable with three classes (VLMR = 193.38; p < .001; Class1 = .49%; Class2 = .23%, and Class3 = .28%). In addition, interactions with the latent variable were found for age ( β = .12; p = .039), type of medical treatment ( β = .29; p = .009), as well as type and localization of tumor ( β = .20; p = .025). Conclusions: These results provided a better understand of psychological distress and cancer-related issues: each class represents a specific “profile” – moderated by age, type of medical treatment, and type and localization of tumor. These results recommend paying more attention to the specific profile expressed by the patient suggesting new ways to improve their quality of life.