Ellen G Engelhardt1, Dóra Révész2, Hans J Tamminga2, Cornelis J A Punt3, Mirjam Koopman4, Bregje D Onwuteaka-Philipsen5, Ewout W Steyerberg6, Ilse P Jansma7, Henrica C W De Vet2, Veerle M H Coupé2. 1. Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. Electronic address: e.engelhardt@vumc.nl. 2. Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. 3. Department of Medical Oncology, Academic Medical Center, Amsterdam, The Netherlands. 4. Department of Medical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands. 5. The EMGO Institute for Health and Care Research, Department of Public and Occupational Health, Palliative Care Expertise Centre, VU University Medical Centre, Amsterdam, The Netherlands. 6. Department of Public Health, Centre for Medical Decision Making, Erasmus Medical Center, Rotterdam, The Netherlands and Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands. 7. Department of Medical Information and Library, VU University Medical Center, Amsterdam, The Netherlands.
Abstract
BACKGROUND: Decision-making regarding palliative treatment for patients with metastatic colorectal cancer (mCRC) is complex and comprises numerous decisions. Decision-making should be guided by the premise of maintaining and/or improving patients' quality of life, by patient preference, and by the trade-off between treatment benefits and harm. Decision support systems (DSSs) for clinicians (eg, nomograms) can assist in this process. The present systematic review aimed to provide a comprehensive overview of the available DSSs for incurable mCRC and to assess their clinical usefulness. MATERIALS AND METHODS: A systematic literature search was performed in PubMed, Embase, and the Cochrane Library. We extracted information on the DSS characteristics and their discriminatory ability, calibration, and user-friendliness. RESULTS: From 5205 studies, we identified 14 DSSs for decisions regarding palliative resection of the primary tumor (n = 3), radiotherapy for metastases (n = 2), treatment type (invasive vs. symptomatic only; n = 7), and selection of chemotherapy (n = 2). The predictors varied greatly among the DSSs, and only 1 DSS incorporated a genetic marker (ie, UGT1A1). None of the DSSs included > 1 treatment option, nor did any DSS present estimates of treatment benefits and harms. Five tools had not been externally validated, two had only been validated in < 35 patients, and the rest had only been validated in populations similar to the population used for their development. Discriminatory accuracy was generally moderate to poor. Calibration measures were only reported for 2 tools. CONCLUSION: A limited number of DSSs are available to support palliative treatment decisions for patients with mCRC, and the evidence regarding their discriminatory ability and calibration is too limited to recommend their use. New DSSs comparing multiple treatment options and presenting both treatment benefits and harms are needed.
BACKGROUND: Decision-making regarding palliative treatment for patients with metastatic colorectal cancer (mCRC) is complex and comprises numerous decisions. Decision-making should be guided by the premise of maintaining and/or improving patients' quality of life, by patient preference, and by the trade-off between treatment benefits and harm. Decision support systems (DSSs) for clinicians (eg, nomograms) can assist in this process. The present systematic review aimed to provide a comprehensive overview of the available DSSs for incurable mCRC and to assess their clinical usefulness. MATERIALS AND METHODS: A systematic literature search was performed in PubMed, Embase, and the Cochrane Library. We extracted information on the DSS characteristics and their discriminatory ability, calibration, and user-friendliness. RESULTS: From 5205 studies, we identified 14 DSSs for decisions regarding palliative resection of the primary tumor (n = 3), radiotherapy for metastases (n = 2), treatment type (invasive vs. symptomatic only; n = 7), and selection of chemotherapy (n = 2). The predictors varied greatly among the DSSs, and only 1 DSS incorporated a genetic marker (ie, UGT1A1). None of the DSSs included > 1 treatment option, nor did any DSS present estimates of treatment benefits and harms. Five tools had not been externally validated, two had only been validated in < 35 patients, and the rest had only been validated in populations similar to the population used for their development. Discriminatory accuracy was generally moderate to poor. Calibration measures were only reported for 2 tools. CONCLUSION: A limited number of DSSs are available to support palliative treatment decisions for patients with mCRC, and the evidence regarding their discriminatory ability and calibration is too limited to recommend their use. New DSSs comparing multiple treatment options and presenting both treatment benefits and harms are needed.
Authors: Nadia A Nabulsi; Ali Alobaidi; Brian Talon; Alemseged A Asfaw; Jifang Zhou; Lisa K Sharp; Karen Sweiss; Pritesh R Patel; Naomi Y Ko; Brian C-H Chiu; Gregory S Calip Journal: Cancer Causes Control Date: 2020-04-30 Impact factor: 2.506
Authors: Ellen G Engelhardt; Dóra Révész; Hans J Tamminga; Cornelis J A Punt; Miriam Koopman; Bregje D Onwuteaka-Philipsen; Ewout W Steyerberg; Henrica C W de Vet; Veerle M H Coupé Journal: BMC Med Inform Decis Mak Date: 2018-12-14 Impact factor: 2.796