Let's talk about sex! Why should healthcare professionals address sexual distress in breast cancer patients and survivors?

Purpose: Sexual distress impacts the quality of life (QoL) of breast cancer patients but is often overlooked in standard care pathways. This study evaluated the prevalence and factors of sexual distress among Dutch breast cancer patients, compared them to the general population, and explored how sexual distress is discussed in clinical settings from the perspectives of patients and healthcare professionals (HCPs). Methods: Questionnaires containing the Female Sexual Distress Scale (FSDS) and demographic variables were distributed to women with breast cancer. The effect of breast cancer on sexual distress was assessed with a Mann-Whitney U test. Multivariable linear regression was used to analyze variables associated with FSDS. The Sexuality Attitudes and Beliefs Survey (SABS) was sent to HCPs. Results: Breast cancer patients reported significantly higher sexual distress compared to a Dutch non-breast cancer cohort, respectively 16.38 (SD 11.81) and 23.35 (SD 11.39). Factors associated with higher sexual distress were psychological comorbidities, the body image scale, and being diagnosed &gt;10 years ago. Sexual distress was not discussed as often as patients needed. Barriers to addressing sexual distress were time constraints, HCPs’ confidence in their ability to address sexual distress, and uncertainty about who is responsible for initiation. Conclusions: Breast cancer patients showed significantly higher sexual distress compared to the Dutch population. However, it was not frequently addressed in the consultation room. While some barriers have been identified, this study highlights the importance of further exploring obstacles to integrating discussions about sexual distress into routine care to improve QoL of breast cancer patients.</p

Health-related Quality of Life using the EQ-5D-5L:normative utility scores in a Dutch female population

PURPOSE: Normative utility scores represent the health related quality of life of the general population, are of utmost importance in cost-effectiveness studies and should reflect relevant sexes and age groups. The aim of this study was to estimate EQ-5D-5L normative utility scores in a population of Dutch females, stratified by age, and to compare these scores to those of female populations of three other countries. METHODS: Dutch women completed the EQ-5D-5L online between January and July 2020. Mean normative utilities were computed using the Dutch EQ-5D-5L value set, stratified by age, tested for differences using the Kruskall–Wallis test, and compared to normative utility scores of female populations elsewhere. Additionally, to support the use of the Dutch EQ-5D-5L data in other settings, normative utility scores were also calculated by applying the value sets of Germany, United Kingdom and USA. RESULTS: Data of 9037 women were analyzed and the weighted mean utility score was 0.911 (SD 0.155, 95% CI 0.908–0.914). The mean normative utility scores differed between age groups, showing lower scores in older females. Compared to other normative utility scores of female populations, Dutch mean utilities were consistently higher except for age groups 18–24 and 25–34. With the three country-specific value sets, new age-specific mean normative utility scores were provided. CONCLUSION: This study provides mean normative utility scores of a large cohort of Dutch females per age group, which were found to be lower in older age groups. Utility scores calculated with three other value sets were made available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11136-022-03271-3

Internal Responsiveness of EQ-5D-5L and EORTC QLQ-C30 in Dutch Breast Cancer Patients during the First Year Post-Surgery: A Longitudinal Cohort Study

The EuroQoL 5-Dimension 5-Level questionnaire (EQ-5D-5L) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (EORTC QLQ-C30) are commonly used Patient-Reported Outcome Measures (PROMs) for breast cancer. This study assesses and compares the internal responsiveness of the EQ-5D-5L and EORTC QLQ-C30 in Dutch breast cancer patients during the first year post-surgery. Women diagnosed with breast cancer who completed the EQ-5D-5L and EORTC QLQ-C30 pre-operatively (T0), 6 months (T6), and 12 months post-surgery (T12) were included. Mean differences of the EQ-5D-5L and EORTC QLQ-C30 between baseline and 6 months (delta 1) and between baseline and 12 months post-surgery (delta 2) were calculated and compared against the respective minimal clinically important differences (MCIDs) of 0.08 and 5. Internal responsiveness was assessed using effect sizes (ES) and standardized response means (SRM) for both deltas. In total, 333 breast cancer patients were included. Delta 1 and delta 2 for the EQ-5D-5L index and most scales of the EORTC QLQ-C30 were below the MCID. The internal responsiveness for both PROMs was small (ES and SRM &lt; 0.5), with greater internal responsiveness for delta 1 compared to delta 2. The EQ-5D-5L index showed greater internal responsiveness than the EORTC QLQ-C30 Global Quality of Life scale and summary score. These findings are valuable for the interpretation of both PROMs in Dutch breast cancer research and clinical care.</p

Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial

As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long-term physical, sexual, and psychosocial outcomes is very important in treatment decision-making. Patient-reported outcomes (PROs) can help facilitate this shared decision-making. Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, we are striving to develop an algorithm using machine learning techniques to predict PROs in breast cancer patients treated with breast surgery. This short communication describes the bottlenecks in our attempt to predict PROs

Internal Responsiveness of EQ-5D-5L and EORTC QLQ-C30 in Dutch Breast Cancer Patients during the First Year Post-Surgery:A Longitudinal Cohort Study

The EuroQoL 5-Dimension 5-Level questionnaire (EQ-5D-5L) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (EORTC QLQ-C30) are commonly used Patient-Reported Outcome Measures (PROMs) for breast cancer. This study assesses and compares the internal responsiveness of the EQ-5D-5L and EORTC QLQ-C30 in Dutch breast cancer patients during the first year post-surgery. Women diagnosed with breast cancer who completed the EQ-5D-5L and EORTC QLQ-C30 pre-operatively (T0), 6 months (T6), and 12 months post-surgery (T12) were included. Mean differences of the EQ-5D-5L and EORTC QLQ-C30 between baseline and 6 months (delta 1) and between baseline and 12 months post-surgery (delta 2) were calculated and compared against the respective minimal clinically important differences (MCIDs) of 0.08 and 5. Internal responsiveness was assessed using effect sizes (ES) and standardized response means (SRM) for both deltas. In total, 333 breast cancer patients were included. Delta 1 and delta 2 for the EQ-5D-5L index and most scales of the EORTC QLQ-C30 were below the MCID. The internal responsiveness for both PROMs was small (ES and SRM &lt; 0.5), with greater internal responsiveness for delta 1 compared to delta 2. The EQ-5D-5L index showed greater internal responsiveness than the EORTC QLQ-C30 Global Quality of Life scale and summary score. These findings are valuable for the interpretation of both PROMs in Dutch breast cancer research and clinical care.</p

Health utility values of breast cancer treatments and the impact of varying quality of life assumptions on cost-effectiveness

In breast cancer research, utility assumptions are outdated and inconsistent which may affect the results of quality adjusted life year (QALY) calculations and thereby cost-effectiveness analyses (CEAs). Four hundred sixty four female patients with breast cancer treated at Erasmus MC, the Netherlands, completed EQ-5D-5L questionnaires from diagnosis throughout their treatment. Average utilities were calculated stratified by age and treatment. These utilities were applied in CEAs analysing 920 breast cancer screening policies differing in eligible ages and screening interval simulated by the MISCAN-Breast microsimulation model, using a willingness-to-pay threshold of €20,000. The CEAs included varying sets on normative, breast cancer treatment and screening and follow-up utilities. Efficiency frontiers were compared to assess the impact of the utility sets. The calculated average patient utilities were reduced at breast cancer diagnosis and 6 months after surgery and increased toward normative utilities 12 months after surgery. When using normative utility values of 1 in CEAs, QALYs were overestimated compared to using average gender and age-specific values. Only small differences in QALYs gained were seen when varying treatment utilities in CEAs. The CEAs varying screening and follow-up utilities showed only small changes in QALYs gained and the efficiency frontier. Throughout all variations in utility sets, the optimal strategy remained robust; biennial for ages 40-76 years and occasionally biennial 40-74 years. In sum, we recommend to use gender and age stratified normative utilities in CEAs, and patient-based breast cancer utilities stratified by age and treatment or disease stage. Furthermore, despite varying utilities, the optimal screening scenario seems very robust.</p

A preliminary prediction model for potentially guiding patient choices between breast conserving surgery and mastectomy in early breast cancer patients; a Dutch experience

Purpose: To guide early stage breast cancer patients to choose between breast conserving surgery (BCS) and mastectomy (MST) considering the predicted cosmetic result and quality of life (QoL). Methods: A decision model was built to compare QoL after BCS and MST. Treatment could result in BCS with good cosmesis, BCS with poor cosmesis, MST only, and MST with breast reconstruction. QoL for these treatment outcomes were obtained from a previous study and the literature and translated into EuroQoL-5D derived utilities. Chance of good cosmesis after BCS was predicted based on tumor location and tumor/breast volume ratio. The decision model determined whether the expected QoL was superior after BCS or MST based on chance of good cosmesis. Results: The mean utility for the treatments such as BCS with good cosmesis, BCS with poor cosmesis, MST only, and MST with breast reconstruction were 0.908, 0.843, 0.859, and 0.876, respectively. BCS resulted in superior QoL compared to MST in patients with a chance of good cosmesis above 36%. This 36% threshold is reached in case the tumor is located in the upper lateral, lower lateral, upper medial, lower medial, and central quadrant of the breast with a tumor/breast volume ratio below 21.6, 4.1, 15.1, 3.2, and 14.7, respectively. Conclusions: BCS results in superior QoL in patients with tumors in the upper breast quadrants or centrally and a tumor/breast volume ratio below 15. MST results in superior QoL in patients with tumors in the lower breast quadrants and a tumor/breast volume ratio above 4

Health utility values of breast cancer treatments and the impact of varying quality of life assumptions on cost-effectiveness

In breast cancer research, utility assumptions are outdated and inconsistent which may affect the results of quality adjusted life year (QALY) calculations and thereby cost-effectiveness analyses (CEAs). Four hundred sixty four female patients with breast cancer treated at Erasmus MC, the Netherlands, completed EQ-5D-5L questionnaires from diagnosis throughout their treatment. Average utilities were calculated stratified by age and treatment. These utilities were applied in CEAs analysing 920 breast cancer screening policies differing in eligible ages and screening interval simulated by the MISCAN-Breast microsimulation model, using a willingness-to-pay threshold of €20,000. The CEAs included varying sets on normative, breast cancer treatment and screening and follow-up utilities. Efficiency frontiers were compared to assess the impact of the utility sets. The calculated average patient utilities were reduced at breast cancer diagnosis and 6 months after surgery and increased toward normative utilities 12 months after surgery. When using normative utility values of 1 in CEAs, QALYs were overestimated compared to using average gender and age-specific values. Only small differences in QALYs gained were seen when varying treatment utilities in CEAs. The CEAs varying screening and follow-up utilities showed only small changes in QALYs gained and the efficiency frontier. Throughout all variations in utility sets, the optimal strategy remained robust; biennial for ages 40-76 years and occasionally biennial 40-74 years. In sum, we recommend to use gender and age stratified normative utilities in CEAs, and patient-based breast cancer utilities stratified by age and treatment or disease stage. Furthermore, despite varying utilities, the optimal screening scenario seems very robust.</p

From Multiple Quality Indicators of Breast Cancer Care Toward Hospital Variation of a Summary Measure

Objectives: To improve quality in breast cancer care, large numbers of quality indicators are collected per hospital, but benchmarking remains complex. We aimed to assess the validity of indicators, develop a textbook outcome summary measure, and compare case-mix adjusted hospital performance. Methods: From a nationwide population-based registry, all 79 690 nonmetastatic breast cancer patients surgically treated between 2011 and 2016 in 91 hospitals in The Netherlands were included. Twenty-one indicators were calculated and their construct validity tested by Spearman's rho. Between-hospital variation was expressed by interquartile range (IQR), and all valid indicators were included in the summary measure. Standardized scores (observed/expected based on case mix) were calculated as above (>100) or below (<100) expected. The textbook outcome was presented as a continuous and all-or-none score. Results: The size of between-hospital variation varied between indicators. Sixteen (76%) of 21 quality indicators showed construct validity, and 13 were included in the summary measure after excluding redundant indicators that showed collinearity with others owing to strong construct validity. The median all-or-none textbook outcome score was 49% (IQR 42%-54%) before and 49% (IQR 48%-51%) after case-mix adjustment. From the total of 91 hospitals, 3 hospitals were positive (3%) and 9 (10%) were negative outlier