Personalised cancer follow-up: risk stratification, needs assessment or both?

A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing

BACKGROUND

Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients' earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty.

METHODS

We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient's longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes.

RESULTS

When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an "always test" strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point.

CONCLUSIONS

A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care.

HIGHLIGHTS

In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk.We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)-based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs.A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

User experiences on implementation of patient reported outcome measures (PROMs)in a Haematological outpatient clinic

Background

PROMs can help healthcare professionals gain an improved understanding of patients’ physical burdens, functional levels, and (health-related) quality of life throughout disease and medical treatment. The aim of this study was to investigate the barriers and potential opportunities PROMs may present in a haematological outpatient clinic from three different perspectives: patients, nurses and haematologists.

Methods

The present study synthesizes three previously published studies that separately explored the experiences of patients, nurses and haematologists when implementing PROMs. The studies were all guided by the qualitative methodology Interpretive Description, including a focused ethnographic approach, to develop implications for future practice.

Results

The overall themes that emerged from the analysis were “Structural similarities influence the adoption of PROMs” and “Different perspectives on the potential of PROMs.”

Conclusion

Across the different user groups in the haematological outpatient clinic, the use of PROMs was thwarted due to an unquestioned commitment to biomedical knowledge and the system’s rationality and norms: PROM data was not used in patient consultations. Nurses and haematologists expressed different preferences related to potential future PROMs and different objectives for PROMs in clinical practice. From the different perspectives of the patients, nurses and haematologists, PROMs were not compatible with clinical practice. Further research is recommended to develop PROMs validated for use in haematological outpatient clinics. Moreover, implementation strategies adjusted to the structural barriers of the system are crucial.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s41687-020-00256-z.

Optimal scheduling of post-therapeutic follow-up of patients treated for cancer for early detection of relapses

Post-therapeutic surveillance is one important component of cancer care. However, there still is no evidence-based strategies to schedule patients’ follow-up examinations. Our approach is based on the modeling of the probability of the onset of relapse at an early asymptotic or preclinical stage and its transition to a clinical stage. For that we consider a multistate homogeneous Markov model, which includes the natural history of relapse. The model also handles separately the different types of possible relapses. The optimal schedule is provided by the calendar visit that maximizes a utility function. The methodology has been applied to laryngeal cancer. The different follow-up strategies revealed to be more efficient than those proposed by different scientific societies.