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. 2023 Sep:7:e2300070.
doi: 10.1200/CCI.23.00070.

Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice

Katie L Spencer  1   2 , Kate L Absolom  1 , Matthew J Allsop  1 , Samuel D Relton  3 , Jessica Pearce  2   4 , Kuan Liao  5 , Sairah Naseer  6 , Omolola Salako  7 , Daniel Howdon  1 , Jenny Hewison  1 , Galina Velikova  2   4 , Corinne Faivre-Finn  8 , Hilary L Bekker  1 , Sabine N van der Veer  5
Affiliations

Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice

Katie L Spencer et al. JCO Clin Cancer Inform. 2023 Sep.

Abstract

Purpose: This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice.

Methods: We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions.

Results: Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population.

Conclusion: Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.

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Conflict of interest statement

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/cci/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Katie L. Spencer

Research Funding: EuroQoL Foundation (less than $10,000 USD in a single calendar year)

Omolola Salako

Employment: Pearl Oncology

Consulting or Advisory Role: Stack Diagnostics ($10,000 USD or above in a single calendar year)

Speakers' Bureau: Janssen and Janssen (less than $10,000 USD in a single calendar year)

Research Funding: Pfizer Global

Patents, Royalties, Other Intellectual Property: I have intellectual property rights to Oncopadi Digital Cancer Clinic ($10,000 USD or above in a single calendar year)

Travel, Accommodations, Expenses: Global Health Catalyst

Galina Velikova

Honoraria: Eisai (less than $10,000 USD in a single calendar year), Pfizer (less than $10,000 USD in a single calendar year), Novartis (less than $10,000 USD in a single calendar year)

Consulting or Advisory Role: Roche UK (less than $10,000 USD in a single calendar year), Eisai (less than $10,000 USD in a single calendar year), Novartis (less than $10,000 USD in a single calendar year), Sanofi (less than $10,000 USD in a single calendar year), Pfizer (less than $10,000 USD in a single calendar year), AstraZeneca, Seagen (less than $10,000 USD in a single calendar year)

Speakers' Bureau: Novartis

Research Funding: Pfizer ($10,000 USD or above in a single calendar year), IQVIA ($10,000 USD or above in a single calendar year)

Travel, Accommodations, Expenses: Roche UK, Novartis, Eisai

Other Relationship: University of Leeds

Corinne Faivre-Finn

Consulting or Advisory Role: AstraZeneca (Inst)

Research Funding: AstraZeneca/MedImmune (Inst) ($10,000 USD or above in a single calendar year), Merck Sharp & Dohme (Inst) ($10,000 USD or above in a single calendar year), Elekta (Inst) (less than $10,000 USD in a single calendar year)

Travel, Accommodations, Expenses: AstraZeneca/MedImmune (less than $10,000 USD in a single calendar year), Elekta (less than $10,000 USD in a single calendar year)

No other potential conflicts of interest were reported.

Figures

FIG 1.
FIG 1.
Conceptual framework for the role of PROs in predictive modeling. PROs, patient-reported outcomes.
FIG 2.
FIG 2.
The leaky pipe—why developed prognostic and predictive models do not make it into clinical practice. IT, information technology; PRO, patient-reported outcome. Adapted with permission from Royen et al.
See this image and copyright information in PMC

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