Using cluster analysis of anxiety‐depression to identify subgroups of prostate cancer patients for targeted treatment planning

Sharpley, Christopher F.; Bitsika, Vicki; Warren, A.; Christie, David

doi:10.1002/pon.4391

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Such approaches may to promote social connectedness and reduce depression among men with prostate cancer ( Cormie, Galvão, et al, 2015 ; Cormie, Turner, Kaczmarek, Drake, & Chambers, 2015 ). Building on these insights and recommendations by Sharpley et al (2017) , future work might also focus on prevention and treatments of depression among specific subgroups, with formal evaluation of prostate cancer psychosocial programs inclusive of end-users’ depressive symptoms and scores over time.…”

Section: Discussionmentioning

confidence: 99%

Depression and Prostate Cancer: Examining Comorbidity and Male-Specific Symptoms

et al. 2018

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Depression in men with prostate cancer is a significant and complex issue that can challenge clinicians’ diagnostic efforts. The objective of the current study was to evaluate prototypic and male-specific depression symptoms and suicidal ideation in men with a diagnosis of prostate cancer relative to those with and without comorbidity. The Patient Health Questionnaire-9 (PHQ-9) and Male Depression Risk Scale-22 (MDRS-22) were completed online along with demographic and background variables by 100 men with a diagnosis of prostate cancer (n = 54 prostatectomy, n = 33 receiving active treatment). Hierarchical logistic regression was used to examine recent (past 2 weeks) suicide ideation. Over one-third of the sample (38%) reported a comorbidity, and this group had significantly higher total depression scores on the PHQ-9 (Cohen’s d = 0.65), MDRS-22 emotion suppression (d = 0.35), and drug use subscales (d = 0.38) compared to respondents without comorbidity. A total of 14% reported recent suicidal ideation, of which 71.4% of cases were identified by the PHQ-9 “moderate” cut-off, and 85.7% of cases were identified by the MDRS-22 “elevated” cut-off. After control variables, MDRS-22 subscales accounted for 45.1% of variance in recent suicidal ideation. While limited by the exclusive use of self-report data, findings point to the potential benefits of evaluating male-specific symptoms as part of depression and suicide risk screening in men with prostate cancer and the need to be mindful of the heightened risk for depression among men with prostate cancer who have comorbidity.

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Section: Discussionmentioning

confidence: 99%

Depression and Prostate Cancer: Examining Comorbidity and Male-Specific Symptoms

et al. 2018

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“…Cluster analysis is a statistical technique that identifies subgroups in wider multidimensional or heterogeneous data, which application to multifaceted diseases, such as major depression, could help dissect disease heterogeneity, advancing diagnostic criteria, and improving treatment plans [34][35][36] .…”

Section: Introductionmentioning

confidence: 99%

Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study

et al. 2020

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A cross-diagnostic, post-hoc analysis of the BRIDGE-II-MIX study was performed to investigate how unipolar and bipolar patients suffering from an acute major depressive episode (MDE) cluster according to severity and duration. Duration of index episode, Clinical Global Impression-Bipolar Version-Depression (CGI-BP-D) and Global Assessment of Functioning (GAF) were used as clustering variables. MANOVA and post-hoc ANOVAs examined between-group differences in clustering variables. A stepwise backward regression model explored the relationship with the 56 clinical-demographic variables available. Agglomerative hierarchical clustering with two clusters was shown as the best fit and separated the study population (n = 2314) into 65.73% (Cluster 1 (C1)) and 34.26% (Cluster 2 (C2)). MANOVA showed a significant main effect for cluster group (p < 0.001) but ANOVA revealed that significant betweengroup differences were restricted to CGI-BP-D (p < 0.001) and GAF (p < 0.001), showing greater severity in C2. Psychotic features and a minimum of three DSM-5 criteria for mixed features (DSM-5-3C) had the strongest association with C2, that with greater disease burden, while non-mixed depression in bipolar disorder (BD) type II had negative association. Mixed affect defined as DSM-5-3C associates with greater acute severity and overall impairment, independently of the diagnosis of bipolar or unipolar depression. In this study a pure, non-mixed depression in BD type II significantly associates with lesser burden of clinical and functional severity. The lack of association for less restrictive, researchedbased definitions of mixed features underlines DSM-5-3C specificity. If confirmed in further prospective studies, these findings would warrant major revisions of treatment algorithms for both unipolar and bipolar depression.

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“…In psychoneurological filed, many studies have clustered pattern of symptoms with a psychoneurological symptom cluster intensity score because they have showed high heterogeneity which lead diagnosis and therapeutic failures [13, 14]. Clustering analysis is a method to define subgroups of individuals with high heterogeneity to explore clinical phenotypes in patients with various diseases [15].…”

Section: Introductionmentioning

confidence: 99%

Four Subgroups of Blood Stasis Syndrome Are Identified by Manifestation Cluster Analysis in Males

You

Kang

Kim

et al. 2019

Evidence-Based Complementary and Alternative Medicine

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Blood stasis syndrome (BSS) is an important pathological condition in traditional East Asian medicine and is associated with ischemic heart disease, cerebral vascular accident, diabetes mellitus, chronic renal failure, severe traumatic injury, and dysmenorrhea. However, previous studies have been unable to reveal the clinical and biological characteristics or biological markers of BSS. We hypothesized that the heterogeneity among the manifestations of BSS or non-BSS could interfere with an analysis to describe the characteristics of BSS. In this study, male participants based on the severity of BSS-associated symptoms and signs were clustered and classified into four subgroups: BSS subgroups (1), (2), (3), and (4). Non-BSS core subgroup was redefined using manifestation cluster analysis. Biological characteristics of subgroups BSS(1) and BSS(2) belong to the range of the non-BSS core subgroup (1), whereas that of subgroups BSS(3) and BSS(4) are characterized by different biological parameters such as systemic inflammatory conditions and elevated D-dimer level. Our results suggested that patients in subgroups of BSS(3) and BSS(4) are more likely to be exposed in an inflammatory state than other BSS subgroups. We found the heterogeneity among the manifestations which could mask the characteristics of BSS and identified the clinical and biological profiles of the four BSS subgroups through comparisons of the redefined non-BSS and BSS subgroups. This finding could provide accurate diagnostic criteria and new approaches for BSS treatments in different subgroups.

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Using cluster analysis of anxiety‐depression to identify subgroups of prostate cancer patients for targeted treatment planning

Cited by 3 publications

References 22 publications

Depression and Prostate Cancer: Examining Comorbidity and Male-Specific Symptoms

Depression and Prostate Cancer: Examining Comorbidity and Male-Specific Symptoms

Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study

Four Subgroups of Blood Stasis Syndrome Are Identified by Manifestation Cluster Analysis in Males

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