Breast cancer specialists' views on and use of risk prediction models in clinical practice: a mixed methods approach

Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths: an overview of the 4D PICTURE project

Background

Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients' care paths.

Aim and objectives

The central aim of the 4D PICTURE project is to redesign patients' care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project.

Design methods and analysis

In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states.

Ethics

Through an embedded ethics approach, we will address social and ethical issues.

Discussion

Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.

(Cost-)effectiveness of an individualised risk prediction tool (PERSARC) on patient's knowledge and decisional conflict among soft-tissue sarcomas patients: protocol for a parallel cluster randomised trial (the VALUE-PERSARC study)

INTRODUCTION

Current treatment decision-making in high-grade soft-tissue sarcoma (STS) care is not informed by individualised risks for different treatment options and patients' preferences. Risk prediction tools may provide patients and professionals insight in personalised risks and benefits for different treatment options and thereby potentially increase patients' knowledge and reduce decisional conflict. The VALUE-PERSARC study aims to assess the (cost-)effectiveness of a personalised risk assessment tool (PERSARC) to increase patients' knowledge about risks and benefits of treatment options and to reduce decisional conflict in comparison with usual care in high-grade extremity STS patients.

METHODS

The VALUE-PERSARC study is a parallel cluster randomised control trial that aims to include at least 120 primarily diagnosed high-grade extremity STS patients in 6 Dutch hospitals. Eligible patients (≥18 years) are those without a treatment plan and treated with curative intent. Patients with sarcoma subtypes or treatment options not mentioned in PERSARC are unable to participate. Hospitals will be randomised between usual care (control) or care with the use of PERSARC (intervention). In the intervention condition, PERSARC will be used by STS professionals in multidisciplinary tumour boards to guide treatment advice and in patient consultations, where the oncological/orthopaedic surgeon informs the patient about his/her diagnosis and discusses benefits and harms of all relevant treatment options. The primary outcomes are patients' knowledge about risks and benefits of treatment options and decisional conflict (Decisional Conflict Scale) 1 week after the treatment decision has been made. Secondary outcomes will be evaluated using questionnaires, 1 week and 3, 6 and 12 months after the treatment decision. Data will be analysed following an intention-to-treat approach using a linear mixed model and taking into account clustering of patients within hospitals.

ETHICS AND DISSEMINATION

The Medical Ethical Committee Leiden-Den Haag-Delft (METC-LDD) approved this protocol (NL76563.058.21). The results of this study will be reported in a peer-review journal.

TRIAL REGISTRATION NUMBER

NL9160, NCT05741944.

A nomogram for individually predicting overall survival for elderly patients with early breast cancer: a consecutive cohort study

Background

Elderly patients with breast cancer are highly heterogeneous, and tumor load and comorbidities affect patient prognosis. Prediction models can help clinicians to implement tailored treatment plans for elderly patients with breast cancer. This study aimed to establish a prediction model for breast cancer, including comorbidities and tumor characteristics, in elderly patients with breast cancer.

Methods

All patients were ≥65 years old and admitted to the Peking Union Medical College Hospital. The clinical and pathological characteristics, recurrence, and death were observed. Overall survival (OS) was analyzed using the Kaplan-Meier curve and a prediction model was constructed using Cox proportional hazards model regression. The discriminative ability and calibration of the nomograms for predicting OS were tested using concordance (C)-statistics and calibration plots. Clinical utility was demonstrated using decision curve analysis (DCA).

Results

Based on 2,231 patients, the 5- and 10-year OS was 91.3% and 78.4%, respectively. We constructed an OS prediction nomogram for elderly patients with early breast cancer (PEEBC). The C-index for OS in PEEBC in the training and validation cohorts was 0.798 and 0.793, respectively. Calibration of the nomogram revealed a good predictive capability, as indicated by the calibration plot. DCA demonstrated that our model is clinically useful.

Conclusion

The nomogram accurately predicted the 3-year, 5-year, and 10-year OS in elderly patients with early breast cancer.

Health care professionals' perspectives on shared decision making supported by personalised‐risk‐for‐recurrences‐calculations regarding surveillance after breast cancer

Objective

Breast cancer patients for whom less intensive surveillance is sufficient can be identified based on the risk for locoregional recurrences (LRRs). This study explores health care professionals' (HCPs) perspectives on less intensive surveillance, preferences for shared decision‐making (SDM) about surveillance and perspectives on the use of patients' estimated personal risk for LRRs in decision‐making about surveillance.

Methods

We conducted semi‐structured interviews with 21 HCPs providing follow‐up care for breast cancer patients in seven Dutch teaching hospitals (Santeon hospitals).

Results

HCPs were predominantly positive about less intensive surveillance for women with a low risk for recurrences. They mentioned important prerequisites such as clearly defined surveillance schedules based on risk categories, information provision and communication support for patients and HCPs. Most HCPs supported SDM about surveillance and were positive about using patients' estimated personal risk for LRRs. HCPs specified prerequisites such as clear visualisation and explanation of risk information, attention for fear of cancer recurrence (FCR) and defined surveillance schedules for specific risk groups.

Conclusion

Mentioned prerequisites for less intensive surveillance need to be accounted for. Information needs and existing misconceptions need to be addressed. Outcome information regarding risks for LRRs and FCR can enrich the SDM process about surveillance.

Discussing personalized prognosis in amyotrophic lateral sclerosis: development of a communication guide

BACKGROUND

Personalized ENCALS survival prediction model reliably estimates the personalized prognosis of patients with amyotrophic lateral sclerosis. Concerns were raised on discussing personalized prognosis without causing anxiety and destroying hope. Tailoring communication to patient readiness and patient needs mediates the impact of prognostic disclosure. We developed a communication guide to support physicians in discussing personalized prognosis tailored to individual needs and preferences of people with ALS and their families.

METHODS

A multidisciplinary working group of neurologists, rehabilitation physicians, and healthcare researchers A) identified relevant topics for guidance, B) conducted a systematic review on needs of patients regarding prognostic discussion in life-limiting disease, C) drafted recommendations based on evidence and expert opinion, and refined and finalized these recommendations in consensus rounds, based on feedback of an expert advisory panel (patients, family member, ethicist, and spiritual counsellor).

RESULTS

A) Topics identified for guidance were 1) filling in the ENCALS survival model, and interpreting outcomes and uncertainty, and 2) tailoring discussion to individual needs and preferences of patients (information needs, role and needs of family, severe cognitive impairment or frontotemporal dementia, and non-western patients). B) 17 studies were included in the systematic review. C) Consensus procedures on drafted recommendations focused on selection of outcomes, uncertainty about estimated survival, culturally sensitive communication, and lack of decisional capacity. Recommendations for discussing the prognosis include the following: discuss prognosis based on the prognostic groups and their median survival, or, if more precise information is desired, on the interquartile range of the survival probability. Investigate needs and preferences of the patients and their families for prognostic disclosure, regardless of cultural background. If the patient does not want to know their prognosis, with patient permission discuss the prognosis with their family. If the patient is judged to lack decisional capacity, ask the family if they want to discuss the prognosis. Tailor prognostic disclosure step by step, discuss it in terms of time range, and emphasize uncertainty of individual survival time.

CONCLUSION

This communication guide supports physicians in tailoring discussion of personalized prognosis to the individual needs and preferences of people with ALS and their families.

How to optimize the design and implementation of risk prediction tools: focus group with patients with IgA nephropathy

Background

IgA nephropathy (IgAN) is a common type of chronic immune-mediated kidney disease with variable risk of progression to end-stage kidney disease. Risk stratification helps clinicians weight the potential risks and benefits of immunosuppressive therapy for individual patients, and can inform patient-centred communication. No prior research examined barriers of risk predication tools (RPT) specific to IgAN. The purpose of this study was to explore determinants (facilitators, barriers) of RPT use from the patient perspective.

Methods

We conducted a single focus group with English-speaking adults aged 18 or older with biopsy-proven IgAN. We asked about how they would use an IgAN RPT, and how to improve its design and implementation. We analyzed the transcript using constant comparison to inductively derive themes, and complied with qualitative research reporting criteria.

Results

The 5 participants were Caucasian men who varied in age from 35 to 55. The glomerular filtration rate ranged from 29 to 71 mL/min/1.73m², and proteinuria ranged from 0.36 to 1.41 g/d. Participants identified both benefits and harms of the risk score. They said physicians should first ask patients for permission to use it. To make it more useful, participants offered suggestions to enhance RTP design: visual display, information on how to interpret the risk score, risk categories, health implications, modifiable risk factors, multiple scenarios, and comparison with similar patients. They offered additional suggestions to enhance RPT implementation: it should not replace patient-provider discussion, it should be accompanied by self-management education so that patients can take an active role in their health. Participants appreciated information from members of the multidisciplinary team in addition to physicians. Participants also said that physicians should monitor patient emotions or concerns on an ongoing basis.

Conclusions

Patients with IgAN identified numerous ways to enhance the design and use of an RPT. Others could use this information to design and implement RPTs for patients with other conditions, but should employ user-centred design to develop RPTs that address patient preferences.

Conceptualising, evaluating and communicating uncertainty in forensic science: Identifying commonly used tools through an interdisciplinary configurative review

This study provides a set of tools for conceptualising, evaluating and communicating uncertainty in forensic science. Given that the concept of uncertainty is one that transcends disciplinary boundaries, an interdisciplinary configurative review was carried out incorporating the disciplines of medicine, environmental science and economics, in order to identify common themes which could have valuable applications to the discipline of forensic science. Critical Interpretive Synthesis was used to develop sub-synthetic and synthetic constructs which interpreted and synthesised the underlying evidence and codes. This study provides three toolkits, one each for conceptualisation, evaluation and communication. The study identified an underlying theme concerning the obstacles that would need to be overcome for the effective application of these toolkits and achieving effective conceptualisation, evaluation and communication of uncertainty in forensic science to lay-stakeholders. These toolkits offer a starting point for developing the conversation for achieving greater transparency in the communication of uncertainty. They also have the potential to offer stakeholders enhanced understanding of the nuances and limitations of forensic science evidence and enable more transparent evaluation and scrutiny of the reliability, relevance and probative value of forensic materials in a crime reconstruction.

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

Treatment decision-making for patients with incurable non-small cell lung cancer (NSCLC) is complex due to the rapidly increasing number of treatments and discovery of new biomarkers. Decision support systems (DSS) could assist thoracic oncologists (TO) weighing of the pros and cons of treatments in order to arrive at an evidence-based and personalized treatment advice. Our aim is to inventory (1) TO's needs with regard to DSS in the treatment of incurable (stage IIIB/IV) NSCLC patients, and (2) preferences regarding the development of future tools in this field. We disseminated an online inventory questionnaire among all members of the Section of Oncology within the Society of Physicians in Chest Medicine and Tuberculosis. Telephone interviews were conducted to better contextualize the findings from the questionnaire. In total, 58 TO completed the questionnaire and expressed a need for new DSS. They reported that it is important for tools to include genetic and immune markers, to be sufficiently validated, regularly updated, and time-efficient. Also, future DSS should incorporate multiple treatment options, integrate estimates of toxicity, quality of life and cost-effectiveness of treatments, enhance communication between caregivers and patients, and use IT solutions for a clear interface and continuous updating of tools. With this inventory among Dutch TO, we summarized the need for new DSS to aid treatment decision-making for patients with incurable NSCLC. To meet the expressed needs, substantial additional efforts will be required by DSS developers, above already existing tools.

A Value of Information Analysis of Research on the 21-Gene Assay for Breast Cancer Management

OBJECTIVES

The 21-gene assay Oncotype DX (21-GA) shows promise as a guide in deciding when to initiate adjuvant chemotherapy in women with hormone receptor-positive early-stage breast cancer. Nevertheless, its routine use remains controversial, owing to insufficient evidence of its clinical utility and cost-effectiveness. Accordingly, we aim to quantify the value of conducting further research to reduce decision uncertainty in the use of the 21-GA.

METHODS

Using value of information methods, we first generated probability distributions of survival and costs for decision making with and without the 21-GA alongside traditional risk prediction. These served as the input to a comparison of 3 alternative study designs: a retrospective observational study to update risk classification from the 21-GA, a prospective observational study to estimate prevalence of chemotherapy use, and a randomized controlled trial (RCT) of the 21-GA predictive value.

RESULTS

We found that current evidence strongly supports the use of the 21-GA in intermediate- and high-risk women. Further research should focus on low-risk women, among whom the cost-effectiveness findings remained equivocal. For this population, we identified a high value of reducing uncertainty in the 21-GA use for all proposed research studies. The RCT had the greatest potential to efficiently reduce the likelihood of choosing a suboptimal strategy, providing a value between $162 million and $1.1 billion at willingness-to-pay thresholds of $150 000 to $200 000/quality-adjusted life years.

CONCLUSION

Future research to inform 21-GA decision making is of high value. The RCT of the 21-GA predictive value has the greatest potential to efficiently reduce decision uncertainty around 21-GA use in women with low-risk early-stage breast cancer.

Clinical prediction rules: A systematic review of healthcare provider opinions and preferences

OBJECTIVE

The act of predicting clinical endpoints and patient trajectories based on past and current states is on the precipice of a technological revolution. This systematic review summarises the available evidence describing healthcare provider opinions and preferences with respect to the use of clinical prediction rules. The primary goal of this work is to inform the design and implementation of future systems, and secondarily to identify gaps for the development of clinician education programs.

METHODS

Five databases were systematically searched in May 2016 for studies collecting empirical opinions of healthcare providers regarding clinical prediction rule usage. Reference lists were scanned for additional eligible materials and an update search was made in August 2017. Data was extracted on high-level study features, before in-depth thematic analysis was performed.

RESULTS

45 articles were identified from 9 countries. Most studies utilised surveys (28) or interviews (14). Fewer employed focus groups (9) or formal usability testing (4). Three high-level themes were identified, which form the basis of healthcare provider opinions of clinical prediction rules and their implementation - utility, credibility and usability.

CONCLUSIONS

Some of the objections and preferences stated by healthcare providers are inherent to the nature of the clinical problem addressed, which may or may not be within the designer's capacity to change; however, others (in particular - actionability, validation, integration and provision of high quality education materials) should be considered by prediction rule designers and implementation teams, in order to increase user acceptance and improve uptake of these tools. We summarise these findings across the clinical prediction rule lifecycle and pose questions for the rule developers, in order to produce tools that are more likely to successfully translate into clinical practice.

Prognostic & predictive factors for planning adjuvant chemotherapy of early-stage breast cancer

Breast cancer is a heterogeneous disease and may present with different clinical and biological characteristics. At present, breast cancer is divided into molecular subgroups besides its histopathological classification. Decision for adjuvant chemotherapy is made based on not only histopathological characteristics but also molecular and genomic characteristics using indices, guidelines and calculators in early-stage breast cancer. Making a treatment plan through all these prognostic and predictive methods according to risk categories aims at preventing unnecessary or useless treatments. In this review, an attempt to make a general assessment of prognostic and predictive methods is made which may be used for planning individualized therapy and also the comments of the guidelines used by the oncologists worldwide on these methods.

Cost-Effectiveness Analyses of the 21-Gene Assay in Breast Cancer: Systematic Review and Critical Appraisal

Purpose Prior studies examining cost effectiveness of the 21-gene assay (Oncotype DX [ODX]) for women with hormone receptor-positive, early-stage breast cancer have yielded disparate results. We aimed to explore why these analyses may have yielded different conclusions. Methods We conducted a systematic literature review of cost-effectiveness analyses (CEAs) of ODX. We examined the extent to which the structure of CEA modeling, the assumptions of the models, and the selection of input parameters influenced cost-effectiveness estimates. We also explored the prevalence of industry funding and whether industry funding was associated with study designs favoring ODX. Results We identified 27 analyses, 15 of which received industry funding. In 18 studies, the clinical characteristics (eg, tumor size and grade) commonly used to make chemotherapy decisions were not incorporated into simulation modeling; thus, these studies would favor ODX being cost effective and might not reflect clinical practice. Most studies ignored the heterogeneous effect of ODX on chemotherapy use; only five studies assumed that ODX would increase chemotherapy use for clinically low-risk patients but decrease chemotherapy use for clinically high-risk patients. No study used population-based joint distributions of ODX recurrence score and tumor characteristics, and 12 studies inappropriately assumed that chemotherapy would increase distant recurrence for the low recurrence score group; both approaches overestimated the benefits of ODX. Industry-funded studies tended to favor ODX; all five studies that reported ODX as being cost saving were industry funded. In contrast, two studies that reported an incremental cost-effectiveness ratio > $50,000 per quality-adjusted life-year were not funded by industry. Conclusion Although a majority of published analyses indicated that ODX is cost effective, they incorporated study designs that can increase the risk of bias.

Use of the BOADICEA Web Application in clinical practice: appraisals by clinicians from various countries

The 'BOADICEA' Web Application (BWA) used to assess breast cancer risk, is currently being further developed, to integrate additional genetic and non-genetic factors. We surveyed clinicians' perceived acceptability of the existing BWA v3. An online survey was conducted through the BOADICEA website, and the British, Dutch, French and Swedish genetics societies. Cross-sectional data from 443 participants who provided at least 50% responses were analysed. Respondents varied in age and, clinical seniority, but mainly comprised women (77%) and genetics professionals (82%). Some expressed negative opinions about the scientific validity of BOADICEA (9%) and BWA v3 risk presentations (7-9%). Data entry time (62%), clinical utility (22%) and ease of communicating BWA v3 risks (13-17%) received additional negative appraisals. In multivariate analyses, controlling for gender and country, data entry time was perceived as longer by genetic counsellors than clinical geneticists (p < 0.05). Respondents who (1) considered hormonal BC risk factors as more important (p < 0.01), and (2) communicated numerical risk estimates more frequently (p < 0.001), judged BWA v3 of lower clinical utility. Respondents who carried out less frequent clinical activity (p < 0.01) and respondents with '11 to 15 years' seniority (p < 0.01) had less favourable opinions of BWA v3 risk presentations. Seniority of '6 to 10 years' (p < 0.05) and more frequent numerical risk communication (p < 0.05) were associated with higher fear of communicating the BWA v3 risks to patients. The level of genetics training did not affect opinions. Further development of BWA should consider technological, genetics service delivery and training initiatives.

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

BACKGROUND

PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands.

METHODS

All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from the nationwide Netherlands Cancer Registry (NCR). Predicted and observed 5- and 10-year overall survival (OS) were compared for the overall cohort, separated by oestrogen receptor (ER) status, and predefined subgroups. A >5% difference was considered as clinically relevant. Discriminatory accuracy and goodness-of-fit were determined using the area under the receiver operating characteristic curve (AUC) and the Chi-squared-test.

RESULTS

We included 8834 patients. Discriminatory accuracy for 5-year OS was good (AUC 0.80). For ER-positive and ER-negative patients, AUCs were 0.79 and 0.75, respectively. Predicted 5-year OS differed from observed by -1.4% in the entire cohort, -0.7% in ER-positive and -4.9% in ER-negative patients. Five-year OS was accurately predicted in all subgroups. Discriminatory accuracy for 10-year OS was good (AUC 0.78). For ER-positive and ER-negative patients AUCs were 0.78 and 0.76, respectively. Predicted 10-year OS differed from observed by -1.0% in the entire cohort, -0.1% in ER-positive and -5.3 in ER-negative patients. Ten-year OS was overestimated (6.3%) in patients ≥75 years and underestimated (-13.%) in T3 tumours and patients treated with both endocrine therapy and chemotherapy (-6.6%).

CONCLUSIONS

PREDICT predicts OS reliably in most Dutch breast cancer patients, although results for both 5-year and 10-year OS should be interpreted carefully in ER-negative patients. Furthermore, 10-year OS should be interpreted cautiously in patients ≥75 years, T3 tumours and in patients considering endocrine therapy and chemotherapy.

Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature

INTRODUCTION

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making.

METHODS

We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer.

RESULTS AND CONCLUSIONS

Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy.

Accuracy of the online prognostication tools PREDICT and Adjuvant! for early-stage breast cancer patients younger than 50 years

IMPORTANCE

Online prognostication tools such as PREDICT and Adjuvant! are increasingly used in clinical practice by oncologists to inform patients and guide treatment decisions about adjuvant systemic therapy. However, their validity for young breast cancer patients is debated.

OBJECTIVE

To assess first, the prognostic accuracy of PREDICT's and Adjuvant! 10-year all-cause mortality, and second, its breast cancer-specific mortality estimates, in a large cohort of breast cancer patients diagnosed <50 years.

DESIGN

Hospital-based cohort.

SETTING

General and cancer hospitals.

PARTICIPANTS

A consecutive series of 2710 patients without a prior history of cancer, diagnosed between 1990 and 2000 with unilateral stage I-III breast cancer aged <50 years.

MAIN OUTCOME MEASURES

Calibration and discriminatory accuracy, measured with C-statistics, of estimated 10-year all-cause and breast cancer-specific mortality.

RESULTS

Overall, PREDICT's calibration for all-cause mortality was good (predicted versus observed) mean_difference: -1.1% (95%CI: -3.2%-0.9%; P = 0.28). PREDICT tended to underestimate all-cause mortality in good prognosis subgroups (range mean_difference: -2.9% to -4.8%), overestimated all-cause mortality in poor prognosis subgroups (range mean_difference: 2.6%-9.4%) and underestimated survival in patients < 35 by -6.6%. Overall, PREDICT overestimated breast cancer-specific mortality by 3.2% (95%CI: 0.8%-5.6%; P = 0.007); and also overestimated it seemingly indiscriminately in numerous subgroups (range mean_difference: 3.2%-14.1%). Calibration was poor in the cohort of patients with the lowest and those with the highest mortality probabilities. Discriminatory accuracy was moderate-to-good for all-cause mortality in PREDICT (0.71 [95%CI: 0.68 to 0.73]), and the results were similar for breast cancer-specific mortality. Adjuvant!'s calibration and discriminatory accuracy for both all-cause and breast cancer-specific mortality were in line with PREDICT's findings.

CONCLUSIONS

Although imprecise at the extremes, PREDICT's estimates of 10-year all-cause mortality seem reasonably sound for breast cancer patients <50 years; Adjuvant! findings were similar. Prognostication tools should be used with caution due to the intrinsic variability of their estimates, and because the threshold to discuss adjuvant systemic treatment is low. Thus, seemingly insignificant mortality overestimations or underestimations of a few percentages can significantly impact treatment decision-making.

The impact of doctor-patient communication on patients' perceptions of their risk of breast cancer recurrence

PURPOSE

Doctor-patient communication is the primary way for women diagnosed with breast cancer to learn about their risk of distant recurrence. Yet little is known about how doctors approach these discussions.

METHODS

A weighted random sample of newly diagnosed early-stage breast cancer patients identified through SEER registries of Los Angeles and Georgia (2013-2015) was sent surveys about ~2 months after surgery (Phase 2, N = 3930, RR 68%). We assessed patient perceptions of doctor communication of risk of recurrence (i.e., amount, approach, inquiry about worry). Clinically determined 10-year risk of distant recurrence was established for low and intermediate invasive cancer patients. Women's perceived risk of distant recurrence (0-100%) was categorized into subgroups: overestimation, reasonably accurate, and zero risk. Understanding of risk and patient factors (e.g. health literacy, numeracy, and anxiety/worry) on physician communication outcomes was evaluated in multivariable regression models (analytic sample for substudy = 1295).

RESULTS

About 33% of women reported that doctors discussed risk of recurrence as "quite a bit" or "a lot," while 14% said "not at all." Over half of women reported that doctors used words and numbers to describe risk, while 24% used only words. Overestimators (OR .50, CI 0.31-0.81) or those who perceived zero risk (OR .46, CI 0.29-0.72) more often said that their doctor did not discuss risk. Patients with low numeracy reported less discussion. Over 60% reported that their doctor almost never inquired about worry.

CONCLUSIONS

Effective doctor-patient communication is critical to patient understanding of risk of recurrence. Efforts to enhance physicians' ability to engage in individualized communication around risk are needed.

Disclosing the Uncertainty Associated with Prognostic Estimates in Breast Cancer

Background. Treatment decision making is often guided by evidence-based probabilities, which may be presented to patients during consultations. These probabilities are intrinsically imperfect and embody 2 types of uncertainties: aleatory uncertainty arising from the unpredictability of future events and epistemic uncertainty arising from limitations in the reliability and accuracy of probability estimates. Risk communication experts have recommended disclosing uncertainty. We examined whether uncertainty was discussed during cancer consultations and whether and how patients perceived uncertainty. Methods. Consecutive patient consultations with medical oncologists discussing adjuvant treatment in early-stage breast cancer were audiotaped, transcribed, and coded. Patients were interviewed after the consultation to gain insight into their perceptions of uncertainty. Results. In total, 198 patients were included by 27 oncologists. Uncertainty was disclosed in 49% (97/197) of consultations. In those 97 consultations, 23 allusions to epistemic uncertainty were made and 84 allusions to aleatory uncertainty. Overall, the allusions to the precision of the probabilities were somewhat ambiguous. Interviewed patients mainly referred to aleatory uncertainty if not prompted about epistemic uncertainty. Even when specifically asked about epistemic uncertainty, 1 in 4 utterances referred to aleatory uncertainty. When talking about epistemic uncertainty, many patients contradicted themselves. In addition, 1 in 10 patients seemed not to realize that the probabilities communicated during the consultation are imperfect. Conclusions. Uncertainty is conveyed in only half of patient consultations. When uncertainty is communicated, oncologists mainly refer to aleatory uncertainty. This is also the type of uncertainty that most patients perceive and seem comfortable discussing. Given that it is increasingly common for clinicians to discuss outcome probabilities with their patients, guidance on whether and how to best communicate uncertainty is urgently needed.

Validity of the online PREDICT tool in older patients with breast cancer: a population-based study

Background:

Predicting breast cancer outcome in older patients is challenging, as it has been shown that the available tools are not accurate in older patients. The PREDICT tool may serve as an alternative tool, as it was developed in a cohort that included almost 1800 women aged 65 years or over. The aim of this study was to assess the validity of the online PREDICT tool in a population-based cohort of unselected older patients with breast cancer.

Methods:

Patients were included from the population-based FOCUS-cohort. Observed 5- and 10-year overall survival were estimated using the Kaplan–Meier method, and compared with predicted outcomes. Calibration was tested by composing calibration plots and Poisson Regression. Discriminatory accuracy was assessed by composing receiver-operator-curves and corresponding c-indices.

Results:

In all 2012 included patients, observed and predicted overall survival differed by 1.7%, 95% confidence interval (CI)=−0.3–3.7, for 5-year overall survival, and 4.5%, 95% CI=2.3–6.6, for 10-year overall survival. Poisson regression showed that 5-year overall survival did not significantly differ from the ideal line (standardised mortality ratio (SMR)=1.07, 95% CI=0.98–1.16, P=0.133), but 10-year overall survival was significantly different from the perfect calibration (SMR=1.12, 95% CI=1.05–1.20, P=0.0004). The c-index for 5-year overall survival was 0.73, 95% CI=0.70–0.75, and 0.74, 95% CI=0.72–0.76, for 10-year overall survival.

Conclusions:

PREDICT can accurately predict 5-year overall survival in older patients with breast cancer. Ten-year predicted overall survival was, however, slightly overestimated.

The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.