Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review.

Clinical decision rules Lung neoplasms Non-small cell lung cancer Patient-reported outcome measures Prognosis Review

Journal

BMC cancer
ISSN: 1471-2407
Titre abrégé: BMC Cancer
Pays: England
ID NLM: 100967800

Informations de publication

Date de publication:
19 Oct 2022
Historique:
received: 18 05 2022
accepted: 08 09 2022
entrez: 19 10 2022
pubmed: 20 10 2022
medline: 22 10 2022
Statut: epublish

Résumé

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. To explore to what extent, how, and how robustly the value of PROMs for prognostic prediction has been investigated in adults diagnosed with NSCLC. 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. 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. 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.

Sections du résumé

BACKGROUND 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 OBJECTIVE
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 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 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 CONCLUSIONS
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.

Identifiants

pubmed: 36261794
doi: 10.1186/s12885-022-10151-z
pii: 10.1186/s12885-022-10151-z
pmc: PMC9580146
doi:

Types de publication

Systematic Review Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1076

Informations de copyright

© 2022. The Author(s).

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Auteurs

Kuan Liao (K)

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.

Tianxiao Wang (T)

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.

Jake Coomber-Moore (J)

Patient-Centred Research Centre, The Christie NHS Foundation Trust, Manchester, UK.

David C Wong (DC)

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.
Department of Computer Science, University of Manchester, Manchester, UK.

Fabio Gomes (F)

Medical Oncology Department, The Christie NHS Foundation Trust, Manchester, UK.

Corinne Faivre-Finn (C)

The Christie NHS foundation Trust, Manchester, UK.
Division of Cancer Science, The University of Manchester, Manchester, UK.

Matthew Sperrin (M)

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.

Janelle Yorke (J)

Patient-Centred Research Centre, The Christie NHS Foundation Trust, Manchester, UK.
Division of Nursing, Midwifery and Social Work, University of Manchester, Manchester, UK.

Sabine N van der Veer (SN)

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.

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