Eye-tracking analyses of physician face gaze patterns in consultations.

Face gaze is a fundamental non-verbal behaviour and can be assessed using eye-tracking glasses. Methodological guidelines are lacking on which measure to use to determine face gaze. To evaluate face gaze patterns we compared three measures: duration, frequency and dwell time. Furthermore, state of the art face gaze analysis requires time and manual effort. We tested if face gaze patterns in the first 30, 60 and 120 s predict face gaze patterns in the remaining interaction. We performed secondary analyses of mobile eye-tracking data of 16 internal medicine physicians in consultation with 100 of their patients. Duration and frequency of face gaze were unrelated. The lack of association between duration and frequency suggests that research may yield different results depending on which measure of face gaze is used. Dwell time correlates both duration and frequency. Face gaze during the first seconds of the consultations predicted face gaze patterns of the remaining consultation time (R2 0.26 to 0.73). Therefore, face gaze during the first minutes of the consultations can be used to predict face gaze patterns over the complete interaction. Researchers interested to study face gaze may use these findings to make optimal methodological choices.

Prognostic value of patient-reported quality of life for survival in oesophagogastric cancer: analysis from the population-based POCOP study.

BACKGROUND: Accumulating evidence of trials demonstrates that patient-reported health-related quality of life (HRQoL) at diagnosis is prognostic for overall survival (OS) in oesophagogastric cancer. However, real-world data are lacking. Moreover, differences in disease stages and tumour-specific symptoms are usually not taken into consideration. The aim of this population-based study was to assess the prognostic value of HRQoL, including tumour-specific scales, on OS in patients with potentially curable and advanced oesophagogastric cancer. METHODS: Data were derived from the Netherlands Cancer Registry and the patient reported outcome registry (POCOP). Patients included in POCOP between 2016 and 2018 were stratified for potentially curable (cT1-4aNallM0) or advanced (cT4b or cM1) disease. HRQoL was measured with the EORTC QLQ-C30 and the tumour-specific OG25 module. Cox proportional hazards models assessed the impact of HRQoL, sociodemographic and clinical factors (including treatment) on OS. RESULTS: In total, 924 patients were included. Median OS was 38.9 months in potentially curable patients (n = 795) and 10.6 months in patients with advanced disease (n = 129). Global Health Status was independently associated with OS in potentially curable patients (HR 0.89, 99%CI 0.82-0.97), together with several other HRQoL items: appetite loss, dysphagia, eating restrictions, odynophagia, and body image. In advanced disease, the Summary Score was the strongest independent prognostic factor (HR 0.75, 99%CI 0.59-0.94), followed by fatigue, pain, insomnia and role functioning. CONCLUSION: In a real-world setting, HRQoL was prognostic for OS in patients with potentially curable and advanced oesophagogastric cancer. Several HRQoL domains, including the Summary Score and several OG25 items, could be used to develop or update prognostic models.

Communicating treatment risks and benefits to cancer patients: a systematic review of communication methods.

PURPOSE: Cancer patients are increasingly involved in decision-making processes. Hence, clinicians need to inform patients about the risks and benefits of different treatment options in order for patients to make well informed decisions. The aim of this review is to determine the effects of methods of communicating prognostic information about (1) disease progression (survival, progression, recurrence and remission), (2) side effects and complications and (3) health-related quality of life (HRQL) on cognitive, affective and behavioral outcomes in cancer patients. METHODS: A literature search was performed to select articles that were published up to  November 2019 and that examined verbal and/or visual risk communication interventions in an oncological clinical setting. RESULTS: The search yielded 14,875 studies; 28 studies were ultimately included. For disease progression information, we found that framing affects treatment choice. Furthermore, limiting the amount of progression information in a graphical display could benefit patients' understanding of risks and benefits. For prognostic information about side effects and complications, precise and defined risk information was better understood than information presented in words. When displaying HRQL data, no consensus was found on which graph type to use. CONCLUSION: Great heterogeneity in the results and methodology and in the compared communication formats precluded us from drawing any further conclusions. Practical implications for clinicians are to consider the effects that different types of framing might have on the patient and to not rely exclusively on words to describe risks, but rather include at least some form of numbers or visualization.

External Validation of the Dutch SOURCE Survival Prediction Model in Belgian Metastatic Oesophageal and Gastric Cancer Patients.

The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Registry. Data of Belgian patients diagnosed with metastatic disease between 2004 and 2014 were extracted (n = 4097). Model calibration and discrimination (c-indices) were determined. A total of 2514 patients with oesophageal cancer and 1583 patients with gastric cancer with a median survival of 7.7 and 5.4 months, respectively, were included. The oesophageal cancer model showed poor calibration (intercept: 0.30, slope: 0.42) with an absolute mean prediction error of 14.6%. The mean difference between predicted and observed survival was -2.6%. The concordance index (c-index) of the oesophageal model was 0.64. The gastric cancer model showed good calibration (intercept: 0.02, slope: 0.91) with an absolute mean prediction error of 2.5%. The mean difference between predicted and observed survival was 2.0%. The c-index of the gastric cancer model was 0.66. The SOURCE gastric cancer model was well calibrated and had a similar performance in the Belgian cohort compared with the Dutch internal validation. However, the oesophageal cancer model had not. Our findings underscore the importance of evaluating the performance of prediction models in other populations.

Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis.

BACKGROUND: Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. The aim of this study was threefold. First, to perform a systematic review to identify available clinical prediction models for patients with esophageal and/or gastric cancer. Second, to evaluate sources of bias in the included studies. Third, to investigate the predictive performance of the prediction models using meta-analysis. METHODS: MEDLINE, EMBASE, PsycINFO, CINAHL, and The Cochrane Library were searched for publications from the year 2000 onwards. Studies describing models predicting survival, adverse events and/or health-related quality of life (HRQoL) for esophageal or gastric cancer patients were included. Potential sources of bias were assessed and a meta-analysis, pooled per prediction model, was performed on the discriminative abilities (c-indices). RESULTS: A total of 61 studies were included (45 development and 16 validation studies), describing 47 prediction models. Most models predicted survival after a curative resection. Nearly 75% of the studies exhibited bias in at least 3 areas and model calibration was rarely reported. The meta-analysis showed that the averaged c-index of the models is fair (0.75) and ranges from 0.65 to 0.85. CONCLUSION: Most available prediction models only focus on survival after a curative resection, which is only relevant to a limited patient population. Few models predicted adverse events after resection, and none focused on patient's HRQoL, despite its relevance. Generally, the quality of reporting is poor and external model validation is limited. We conclude that there is a need for prediction models that better meet patients' information needs, and provide information on both the benefits and harms of the various treatment options in terms of survival, adverse events and HRQoL.