Head and neck cancer patients’ preferences for individualized prognostic information: a focus group study

Hoesseini, Arta; Dronkers, Emilie A. C.; Sewnaik, Aniel; Hardillo, José A.; Jong, Robert J. Baatenburg de; Offerman, Marinella P. J.

doi:10.1186/s12885-020-6554-8

Cited by 31 publications

(34 citation statements)

References 41 publications

(59 reference statements)

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“…33 In this updated version smoking, PY, BMI, weight loss, WHO performance and SES were added. As patients 15 and doctors expressed different presentation wishes during our qualitative research, two separate pages were made: one with a survival graph (doctors) and one with a pie chart (patients). Figure 3 shows a screenshot of the online prediction model.…”

Section: Resultsmentioning

confidence: 99%

“…The second aim of this study was: (2) to explore whether adding new prognostic factors would improve OncologIQ's performance: model revision . During our qualitative research on prognostication both patients 15 and doctors' addressed the wish to explore new predictors like smoking and alcohol consumption, in order to further personalize the models' estimations. The third aim was to integrate the updated model in a clinically useful tool.…”

Section: Introductionmentioning

confidence: 99%

“…The present study builds on our prior prognostic and qualitative research. 15 Finally, our line of research is described in the discussion which illustrates the steps that were necessary to develop a valued tool for use in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Predicting survival in head and neck cancer: External validation and update of the prognostic model OncologIQ in 2189 patients

et al. 2021

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Background Disclosing prognostic information is necessary to enable patients to make well‐informed treatment decisions. OncologIQ is a prognostic model that predicts the overall survival (OS) probability in patients with head and neck squamous cell carcinoma (HNSCC). We aimed to externally validate and update the model with new prognostic factors and translate it to a clinically useful tool. Methods A consecutive retrospective sample of 2189 patients eligible for curative treatment of a primary HNSCC were selected. Discriminative performance was determined using the C‐statistic. Results External validation showed systematic underestimation of OS in the new population, and reasonable discrimination (C‐statistic 0.67). Adding smoking, pack years, BMI, weight loss, WHO performance, socioeconomic status, and p16 positivity to the recalibrated multivariable model, improved the internally validated C‐statistic to 0.71. The model showed minor optimism and was translated in an online tool ( www.oncologiq.nl ). Conclusions The updated model enables personalized patient counseling during treatment decision consultations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting survival in head and neck cancer: External validation and update of the prognostic model OncologIQ in 2189 patients

et al. 2021

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“…This could be due to misinterpretation or lack of information, as patients often do not receive prognostic information from their physicians [ 31 – 34 ]. Patients’ denial affects misunderstanding the prognosis and goals of treatment [ 35 , 36 ]. Although patients often do not receive prognostic information, they generally have high levels of information need about life expectancy and they want at least some discussion of this topic at the time of diagnosis or shortly after [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…, others prefer quantitative information like survival rates or other statistics [ 14 , 36 , 38 ]. Curatively treated HNC patients that participated in our focus group research had a stronger preference for quantitative prognostic information in the hypothetical case of cancer recurrence and a poor prognosis [ 36 ]. Besides meeting patients’ information needs, providing prognostic information in the palliative phase can affect patients’ end-of-life choices.…”

Section: Discussionmentioning

confidence: 99%

Physicians’ clinical prediction of survival in head and neck cancer patients in the palliative phase

et al. 2020

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Background The prognosis of patients with incurable head and neck cancer (HNC) is a relevant topic. The mean survival of these patients is 5 months but may vary from weeks to more than 3 years. Discussing the prognosis early in the disease trajectory enables patients to make well-considered end-of-life choices, and contributes to a better quality of life and death. However, physicians often are reluctant to discuss prognosis, partly because of the concern to be inaccurate. This study investigated the accuracy of physicians’ clinical prediction of survival of palliative HNC patients. Methods This study was part of a prospective cohort study in a tertiary cancer center. Patients with incurable HNC diagnosed between 2008 and 2011 (n = 191), and their treating physician were included. Analyses were conducted between July 2018 and February 2019. Patients’ survival was clinically predicted by their physician ≤3 weeks after disclosure of the palliative diagnosis. The clinical prediction of survival in weeks (CPS) was based on physicians’ clinical assessment of the patient during the outpatient visits. More than 25% difference between the actual survival (AS) and the CPS was regarded as a prediction error. In addition, when the difference between the AS and CPS was 2 weeks or less, this was always considered as correct. Results In 59% (n = 112) of cases survival was overestimated. These patients lived shorter than predicted by their physician (median AS 6 weeks, median CPS 20 weeks). In 18% (n = 35) of the cases survival was correctly predicted. The remaining 23% was underestimated (median AS 35 weeks, median CPS 20 weeks). Besides the differences in AS and CPS, no other significant differences were found between the three groups. There was worse accuracy when predicting survival closer to death: out of the 66 patients who survived 6 weeks or shorter, survival was correctly predicted in only eight (12%). Conclusion Physicians tend to overestimate the survival of palliative HNC patients. This optimism can result in suboptimal use of palliative and end-of-life care. The future development of a prognostic model that provides more accurate estimates, could help physicians with personalized prognostic counseling.

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Key Aspects of Prognostic Model Development and Interpretation From a Clinical Perspective

Hoesseini

Leeuwen

Sewnaik

et al. 2022

JAMA Otolaryngol Head Neck Surg

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lthough crucial in clinical decision-making, prognostication is often challenging. Research in cancer care shows that both patients 1,2 and physicians [3][4][5][6][7] tend to be too optimistic about survival chances. We increasingly rely on prognostic models' outcomes, while a thorough understanding of the development, possibilities, and limitations is often limited. This can lead to misinterpretation and erroneous explanation of the models' estimates. In head and neck cancer (HNC), several prognostic models have been developed. These models predict, for example, overall or progression-free survival chances 8-14 or the risk of recurrence. 15 They can be used to inform patients and support medical decisionmaking. It is expected that the development of prognostic models will increase over time 16 with the shift toward data-driven medicine. The growing availability of large and rich data sets, such as electronic health records, facilitates this process. 17 Many excellent books [18][19][20] and comprehensive methodological articles 16,[21][22][23][24][25][26] are available on prognostic model research. These may not be accessible enough for the clinical audience. Herein, we aim to provide an overview on the main issues regarding prediction research for health care professionals. We provide illustrations with clinical examples to achieve better understanding and improve the development, interpretation, and implementation of prognostic models that intend to support clinical decision-making. DiscussionWe consider 7 necessary steps to develop valid prediction models with regression analysis. 18,20 These steps are used as a framework, accompanied by clinical examples.Step 1: Defining the Research Question and Initial Data InspectionThe first step in the development of a prognostic model is to carefully state the research question and consider the available data, predictor definitions, and the outcome of interest. 18,20 Overall survival is commonly used as the outcome of interest in prediction research, while disease-free survival is preferable when comparing 2 treatments. Overall survival is simple to measure, easy to interpret, relevant for patients, and straightforward to explain. Disease-IMPORTANCE Prognostication is an important aspect of clinical decision-making, but it is often challenging. Previous studies show that both patients and physicians tend to overestimate survival chances. Prediction models may assist in estimating and quantifying prognosis. However, insufficient understanding of the development, possibilities, and limitations of such models can lead to misinterpretations. Although many excellent books and comprehensive methodological articles on prognostic model research are published, they may not be accessible enough for the clinical audience. Our aim is to provide an overview on the main issues regarding prediction research for health care professionals to achieve better interpretation and increase the use of prognostic models in daily clinical practice.OBSERVATIONS The first steps of model developme...

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Head and neck cancer patients’ preferences for individualized prognostic information: a focus group study

Cited by 31 publications

References 41 publications

Predicting survival in head and neck cancer: External validation and update of the prognostic model OncologIQ in 2189 patients

Predicting survival in head and neck cancer: External validation and update of the prognostic model OncologIQ in 2189 patients

Physicians’ clinical prediction of survival in head and neck cancer patients in the palliative phase

Key Aspects of Prognostic Model Development and Interpretation From a Clinical Perspective

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