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

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. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10151-z.


Criterion Inclusion Exclusion
Population Adults aged ≥18 years diagnosed with NSCLC.
We included studies with mixed samples (e.g. patients with different types of cancer) if ≥50% were patients with NSCLC or if results are reported separately for NSCLC.
Adults diagnosed with other types of (lung) cancer; adult with a high risk of NSCLC but not yet diagnosed

Concept
Any generic, cancer-specific or lung cancer-specific patient-reported outcome measures or their components collected in any setting (e.g. routine care, clinical trials) Patient-reported experience of/satisfaction with care; clinicianreported outcomes

Context
Prognostic prediction of any future outcome at individual patient-level Diagnostic prediction of having a certain condition at the time of prediction

Study and publication type
Articles or full conference papers reporting the findings of association studies or prediction model development or validation studies.

Publication date
From 2011 to current a) Before 2011 Language English Other languages a) NSCLC survival has improved because of recent advances in treatment [24][25][26]. For this reason, we limited the publication date to the last ten years

Data source
Where the data in the included study was from Clinical trial (including secondary analyses); Observational study (including cohort, case-control studies); Routinely collected data (e.g., data derived from patient records) Median follow-up The length of time from baseline to half of the patients in a group of patients developed a certain outcome.

Characteristics of PROMs as predictors
Full name of PROMs The full name of the PROMs included in the prognostic analysis

Free text
Abbreviated name of PROMs The well-acknowledged abbreviated name of the PROMs above.

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Construct PROM aims to capture What patient-reported aspects of the PROMs were measured [75].
Health-related quality of liferefers to a multidimensional construct encompassing physical, social, and emotional wellbeing associated with illness and its treatment.
Functional status -refers to a patient's ability to perform both basic and more advanced (instrumental) activities of daily life; Symptoms and symptom burdensymptoms and their presence and intensity Classification of PROMs What level the PROMs were measuring. Generic -measure the wellbeing of all types of patients, regardless of their illness or disorder; Cancer-specificonly measure the wellbeing of patients with cancer; Lung cancer-specificonly measure the wellbeing of patients with lung cancer.
Cross-sectional vs change in score If the included score is cross-sectional was used or a change in score over time

Cross-sectional PROMs score; Change in scores
(for change in scores, describe how this was calculated) PROMs included in the model If single item scores, subdomain scores or overall summary scores of the PROMs were used in the model Single item scoresscore for each item; Subdomain scoresconsist of a group of scores measuring for the same aspect of wellbeing; Overall summary scoresscores summarised all the items.

Time points of PROMs collection
At what time point PROMs scores included in the model had been collected

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Characteristics of outcome(s)

Number of outcomes
The total number of outcomes assessed in the study including both primary and secondary outcomes Name and description of primary outcome The primary outcome(s) is assessed in the prognostic factor study or in the prediction model.

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Type of primary outcome Classified as per the lung cancer standard set of outcomes [76] Acute complications of treatment (e.g., major surgical complications, major radiation complications); Degree of health (e.g., performance status, quality of life), Survival (progression-free survival, overall survival); Quality of death (e.g., place of death, duration of time spent in hospital at end of life), Other (e.g., time from diagnosis to treatment) Name and description of secondary outcome The secondary outcome(s) assessed in the prognostic factor study or in the prediction model.

Free text
Type of secondary outcome Classified as per the lung cancer standard set of outcomes [76].
Acute complications of treatment (e.g., major surgical complications, major radiation complications); Degree of health (e.g., performance status, quality of life), Survival (progression-free survival, overall survival); Quality of death (e.g., place of death, duration of time spent in hospital at end of life), Other (e.g., time from diagnosis to treatment) Time points of outcome assessment Time points at which outcomes were assessed. At the time of occurrence (e.g., death); At the time of clinic visit/follow-up (need to specify the frequency of visits/followups. e.g., treatment discontinuation) Maximum length of observation The maximum time between the end of observation and baseline.

Types of predictive modelling techniques
The statistical techniques are used to evaluate the association between candidate prognostic factors and outcomes or in the prediction model.

Naïve bayes
Uni/multivariable If there are single or multiple independent variables/predictors in a model.

Multivariablemultiple independent variables/predictors in one model
Uni/multivariate If there is single or multiple dependent variables/outcomes in a model.

Univariateonly one dependent variable/outcome in one model
Multivariatemultiple dependent variables/outcomes in one model (i.e. simultaneous prediction of multiple outcomes)

Selection of predictors/independent variables
How the studies selected the predictors/independent variables in the model.

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Other predictors in the prognostic models Describe other predictors included in the prognostic model.

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Type of confounders/other predictors To classify variables other than PROMs in the adjusted analyses or in the prediction model Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.

3-4
Objectives 4 Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.

Protocol and registration 5
Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number.

Eligibility criteria 6
Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale. State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review.

5
Data charting process ‡ 10 Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.

Data items 11
List and define all variables for which data were sought and any assumptions and simplifications made.
Supp. 4-7 Critical appraisal of individual sources of evidence §

12
If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).

5-6
Synthesis of results 13 Describe the methods of handling and summarizing the data that were charted. 5

Selection of sources of evidence 14
Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.

7
Characteristics of sources of evidence 15 For each source of evidence, present characteristics for which data were charted and provide the citations.

8-11
Critical appraisal within sources of evidence Limitations 20 Discuss the limitations of the scoping review process. 14-15 Conclusions 21 Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications and/or next steps.

Funding 22
Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.
17 JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. * Where sources of evidence (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and Web sites. † A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with information sources (see first footnote). ‡ The frameworks by Arksey and O'Malley (6) and Levac and colleagues (7) and the JBI guidance (4, 5) refer to the process of data extraction in a scoping review as data charting. § The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. This term is used for items 12 and 19 instead of "risk of bias" (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).