Kuan Liao1, Tianxiao Wang2, Jake Coomber-Moore3, David C Wong2,4, Fabio Gomes5, Corinne Faivre-Finn6,7, Matthew Sperrin2, Janelle Yorke3,8, Sabine N van der Veer2. 1. Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. kuan.liao@manchester.ac.uk. 2. Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. 3. Patient-Centred Research Centre, The Christie NHS Foundation Trust, Manchester, UK. 4. Department of Computer Science, University of Manchester, Manchester, UK. 5. Medical Oncology Department, The Christie NHS Foundation Trust, Manchester, UK. 6. The Christie NHS foundation Trust, Manchester, UK. 7. Division of Cancer Science, The University of Manchester, Manchester, UK. 8. Division of Nursing, Midwifery and Social Work, University of Manchester, Manchester, UK.
Abstract
BACKGROUND: There is growing interest in the collection and use of patient-reported outcome measures (PROMs) to support clinical decision making in patients with non-small cell lung cancer (NSCLC). However, an overview of research into the prognostic value of PROMs is currently lacking. AIM: To explore to what extent, how, and how robustly the value of PROMs for prognostic prediction has been investigated in adults diagnosed with NSCLC. METHODS: We systematically searched Medline, Embase, CINAHL Plus and Scopus for English-language articles published from 2011 to 2021 that report prognostic factor study, prognostic model development or validation study. Example data charting forms from the Cochrane Prognosis Methods Group guided our data charting on study characteristics, PROMs as predictors, predicted outcomes, and statistical methods. Two reviewers independently charted the data and critically appraised studies using the QUality In Prognosis Studies (QUIPS) tool for prognostic factor studies, and the risk of bias assessment section of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) for prognostic model studies. RESULTS: Our search yielded 2,769 unique titles of which we included 31 studies, reporting the results of 33 unique analyses and models. Out of the 17 PROMs used for prediction, the EORTC QLQ-C30 was most frequently used (16/33); 12/33 analyses used PROM subdomain scores instead of the overall scores. PROMs data was mostly collected at baseline (24/33) and predominantly used to predict survival (32/33) but seldom other clinical outcomes (1/33). Almost all prognostic factor studies (26/27) had moderate to high risk of bias and all four prognostic model development studies had high risk of bias. CONCLUSION: There is an emerging body of research into the value of PROMs as a prognostic factor for survival in people with NSCLC but the methodological quality of this research is poor with significant bias. This warrants more robust studies into the prognostic value of PROMs, in particular for predicting outcomes other than survival. This will enable further development of PROM-based prediction models to support clinical decision making in NSCLC.
BACKGROUND: There is growing interest in the collection and use of patient-reported outcome measures (PROMs) to support clinical decision making in patients with non-small cell lung cancer (NSCLC). However, an overview of research into the prognostic value of PROMs is currently lacking. AIM: To explore to what extent, how, and how robustly the value of PROMs for prognostic prediction has been investigated in adults diagnosed with NSCLC. METHODS: We systematically searched Medline, Embase, CINAHL Plus and Scopus for English-language articles published from 2011 to 2021 that report prognostic factor study, prognostic model development or validation study. Example data charting forms from the Cochrane Prognosis Methods Group guided our data charting on study characteristics, PROMs as predictors, predicted outcomes, and statistical methods. Two reviewers independently charted the data and critically appraised studies using the QUality In Prognosis Studies (QUIPS) tool for prognostic factor studies, and the risk of bias assessment section of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) for prognostic model studies. RESULTS: Our search yielded 2,769 unique titles of which we included 31 studies, reporting the results of 33 unique analyses and models. Out of the 17 PROMs used for prediction, the EORTC QLQ-C30 was most frequently used (16/33); 12/33 analyses used PROM subdomain scores instead of the overall scores. PROMs data was mostly collected at baseline (24/33) and predominantly used to predict survival (32/33) but seldom other clinical outcomes (1/33). Almost all prognostic factor studies (26/27) had moderate to high risk of bias and all four prognostic model development studies had high risk of bias. CONCLUSION: There is an emerging body of research into the value of PROMs as a prognostic factor for survival in people with NSCLC but the methodological quality of this research is poor with significant bias. This warrants more robust studies into the prognostic value of PROMs, in particular for predicting outcomes other than survival. This will enable further development of PROM-based prediction models to support clinical decision making in NSCLC.
Authors: O P Geerse; D Brandenbarg; H A M Kerstjens; A J Berendsen; S F A Duijts; H Burger; G A Holtman; J E H M Hoekstra-Weebers; T J N Hiltermann Journal: Lung Cancer Date: 2019-02-10 Impact factor: 5.705
Authors: Sean O'Mahony; Susan Nathan; Roozbeh Mohajer; Philip Bonomi; Marta Batus; Mary Jo Fidler; Kalani Wells; Naomi Kern; Shannon Sims; Darpan Amin Journal: Am J Hosp Palliat Care Date: 2015-02-10 Impact factor: 2.500
Authors: Young Ho Yun; Young Ae Kim; Jin Ah Sim; Ae Sun Shin; Yoon Jung Chang; Jongmog Lee; Moon Soo Kim; Young Mog Shim; Jae Lll Zo Journal: BMC Cancer Date: 2016-07-20 Impact factor: 4.430