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van de Water L, Kuijper S, Henselmans I, van Alphen E, Kooij E, Calff M, Beerepoot L, Buijsen J, Eshuis W, Geijsen E, Havenith S, Heesakkers F, Mook S, Muller K, Post H, Rütten H, Slingerland M, van Voorthuizen T, van Laarhoven H, Smets E. Effect of a prediction tool and communication skills training on communication of treatment outcomes: a multicenter stepped wedge clinical trial (the SOURCE trial). EClinicalMedicine 2023; 64:102244. [PMID: 37781156 PMCID: PMC10539636 DOI: 10.1016/j.eclinm.2023.102244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/09/2023] [Accepted: 09/12/2023] [Indexed: 10/03/2023] Open
Abstract
Background For cancer patients to effectively engage in decision making, they require comprehensive and understandable information regarding treatment options and their associated outcomes. We developed an online prediction tool and supporting communication skills training to assist healthcare providers (HCPs) in this complex task. This study aims to assess the impact of this combined intervention (prediction tool and training) on the communication practices of HCPs when discussing treatment options. Methods We conducted a multicenter intervention trial using a pragmatic stepped wedge design (NCT04232735). Standardized Patient Assessments (simulated consultations) using cases of esophageal and gastric cancer patients, were performed before and after the combined intervention (March 2020 to July 2022). Audio recordings were analyzed using an observational coding scale, rating all utterances of treatment outcome information on the primary outcome-precision of provided outcome information-and on secondary outcomes-such as: personalization, tailoring and use of visualizations. Pre vs. post measurements were compared in order to assess the effect of the intervention. Findings 31 HCPs of 11 different centers in the Netherlands participated. The tool and training significantly affected the precision of the overall communicated treatment outcome information (p = 0.001, median difference 6.93, IQR (-0.32 to 12.44)). In the curative setting, survival information was significantly more precise after the intervention (p = 0.029). In the palliative setting, information about side effects was more precise (p < 0.001). Interpretation A prediction tool and communication skills training for HCPs improves the precision of treatment information on outcomes in simulated consultations. The next step is to examine the effect of such interventions on communication in clinical practice and on patient-reported outcomes. Funding Financial support for this study was provided entirely by a grant from the Dutch Cancer Society (UVA 2014-7000).
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Affiliation(s)
- L.F. van de Water
- Department of Medical Psychology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Department of Medical Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - S.C. Kuijper
- Department of Medical Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - I. Henselmans
- Department of Medical Psychology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - E.N. van Alphen
- Department of Medical Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - E.S. Kooij
- Department of Medical Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - M.M. Calff
- Department of Medical Psychology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - L.V. Beerepoot
- Department of Medical Oncology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, the Netherlands
| | - J. Buijsen
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Centre, GROW School for Oncology and Developmental Biology, Maastricht, the Netherlands
| | - W.J. Eshuis
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - E.D. Geijsen
- Department of Radiation Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - S.H.C. Havenith
- Department of Medical Oncology, Flevoziekenhuis, Almere, the Netherlands
| | - F.F.B.M. Heesakkers
- Department of Surgery, Department of Intensive Care Medicine, Catharina Ziekenhuis, Eindhoven, the Netherlands
| | - S. Mook
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - K. Muller
- Department of Radiation Oncology, Radiotherapiegroep, Deventer, the Netherlands
| | - H.C. Post
- Department of Medical Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - H. Rütten
- Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, Netherlands
| | - M. Slingerland
- Department of Medical Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - H.W.M. van Laarhoven
- Department of Medical Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
| | - E.M.A. Smets
- Department of Medical Psychology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
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van de Water LF, van den Boorn HG, Hoxha F, Henselmans I, Calff MM, Sprangers MAG, Abu-Hanna A, Smets EMA, van Laarhoven HWM. Informing Patients With Esophagogastric Cancer About Treatment Outcomes by Using a Web-Based Tool and Training: Development and Evaluation Study. J Med Internet Res 2021; 23:e27824. [PMID: 34448703 PMCID: PMC8433928 DOI: 10.2196/27824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/07/2021] [Accepted: 05/24/2021] [Indexed: 11/17/2022] Open
Abstract
Background Due to the increasing use of shared decision-making, patients with esophagogastric cancer play an increasingly important role in the decision-making process. To be able to make well-informed decisions, patients need to be adequately informed about treatment options and their outcomes, namely survival, side effects or complications, and health-related quality of life. Web-based tools and training programs can aid physicians in this complex task. However, to date, none of these instruments are available for use in informing patients with esophagogastric cancer about treatment outcomes. Objective This study aims to develop and evaluate the feasibility of using a web-based prediction tool and supporting communication skills training to improve how physicians inform patients with esophagogastric cancer about treatment outcomes. By improving the provision of treatment outcome information, we aim to stimulate the use of information that is evidence-based, precise, and personalized to patient and tumor characteristics and is communicated in a way that is tailored to individual information needs. Methods We designed a web-based, physician-assisted prediction tool—Source—to be used during consultations by using an iterative, user-centered approach. The accompanying communication skills training was developed based on specific learning objectives, literature, and expert opinions. The Source tool was tested in several rounds—a face-to-face focus group with 6 patients and survivors, semistructured interviews with 5 patients, think-aloud sessions with 3 medical oncologists, and interviews with 6 field experts. In a final pilot study, the Source tool and training were tested as a combined intervention by 5 medical oncology fellows and 3 esophagogastric outpatients. Results The Source tool contains personalized prediction models and data from meta-analyses regarding survival, treatment side effects and complications, and health-related quality of life. The treatment outcomes were visualized in a patient-friendly manner by using pictographs and bar and line graphs. The communication skills training consisted of blended learning for clinicians comprising e-learning and 2 face-to-face sessions. Adjustments to improve both training and the Source tool were made according to feedback from all testing rounds. Conclusions The Source tool and training could play an important role in informing patients with esophagogastric cancer about treatment outcomes in an evidence-based, precise, personalized, and tailored manner. The preliminary evaluation results are promising and provide valuable input for the further development and testing of both elements. However, the remaining uncertainty about treatment outcomes in patients and established habits in doctors, in addition to the varying trust in the prediction models, might influence the effectiveness of the tool and training in daily practice. We are currently conducting a multicenter clinical trial to investigate the impact that the combined tool and training have on the provision of information in the context of treatment decision-making.
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Affiliation(s)
- Loïs F van de Water
- Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Héctor G van den Boorn
- Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Florian Hoxha
- Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Inge Henselmans
- Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Mart M Calff
- Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Mirjam A G Sprangers
- Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Ellen M A Smets
- Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
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van den Boorn HG, Dijksterhuis WPM, van der Geest LGM, de Vos-Geelen J, Besselink MG, Wilmink JW, van Oijen MGH, van Laarhoven HWM. SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data. J Natl Compr Canc Netw 2021; 19:1045-1053. [PMID: 34293719 DOI: 10.6004/jnccn.2020.7669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/12/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. MATERIALS AND METHODS Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. RESULTS Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. CONCLUSIONS A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.
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Affiliation(s)
- Héctor G van den Boorn
- 1Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam
| | - Willemieke P M Dijksterhuis
- 1Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam.,2Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht
| | - Lydia G M van der Geest
- 2Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht
| | - Judith de Vos-Geelen
- 4Division of Medical Oncology, Department of Internal Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Marc G Besselink
- 3Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam; and
| | - Johanna W Wilmink
- 1Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam
| | - Martijn G H van Oijen
- 1Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam.,2Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht
| | - Hanneke W M van Laarhoven
- 1Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam
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Lim B, Lee KS, Lee YH, Kim S, Min C, Park JY, Lee HS, Cho JS, Kim SI, Chung BH, Kim CS, Koo KC. External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival. Cancer Res Treat 2020; 53:558-566. [PMID: 33070560 PMCID: PMC8053858 DOI: 10.4143/crt.2020.637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/05/2020] [Indexed: 12/23/2022] Open
Abstract
Purpose Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.
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Affiliation(s)
- Bumjin Lim
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kwang Suk Lee
- Department of Urology, Yonsei University College of Medicine, Seoul, Korea
| | - Young Hwa Lee
- Department of Urology, Yonsei University College of Medicine, Seoul, Korea
| | | | | | - Ju-Young Park
- Biostatistics Collaboration Unit, Yonsei University, Seoul, Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University, Seoul, Korea
| | - Jin Seon Cho
- Department of Urology, Hallym University College of Medicine, Chuncheon, Korea
| | - Sun Il Kim
- Department of Urology, Ajou University School of Medicine, Suwon, Korea
| | - Byung Ha Chung
- Department of Urology, Yonsei University College of Medicine, Seoul, Korea
| | - Choung-Soo Kim
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyo Chul Koo
- Department of Urology, Yonsei University College of Medicine, Seoul, Korea
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