# Cost-Effectiveness of a Quality of Life Predictor to Guide Psychosocial Support in Breast Cancer

## TL;DR

A machine learning tool that predicts quality of life in breast cancer patients can help doctors decide who needs psychosocial support more effectively and cost-effectively.

## Contribution

The study introduces a cost-effective machine learning-based decision-making strategy for targeting psychosocial support in breast cancer patients.

## Key findings

- Combining clinicians with a QoL predictor tool yielded the highest net monetary benefit and QALY gain.
- Clinician-only and predictor-only strategies were dominated by the combined approach.
- The combined strategy had a 69% probability of being cost-effective at EUR 30,000 willingness to pay.

## Abstract

Women diagnosed with breast cancer often experience psychological distress, and some benefit from psychosocial support to help maintain their quality of life. However, identifying which patients are most likely to need this support can be difficult in routine clinical practice. This study examined whether a machine learning tool that predicts future quality of life could help clinicians to make better decisions about offering psychosocial support. We compared four decision-making strategies, including the clinician alone, the machine learning tool alone, and the combination of both. Using health economic modeling based on observational data from women with breast cancer, we estimated the health benefits, healthcare costs, and overall value of each strategy. Our findings suggest that combining clinicians with support from the prediction tool may improve decision-making and help to target psychosocial support to the patients who may benefit most.

Introduction: Women with breast cancer experience psychological distress, and resilience-strengthening psychosocial support may improve their quality of life (QoL). Identifying those at risk of low QoL is challenging. This study evaluated the cost-effectiveness of a machine learning-based QoL predictor to support clinical decision-making regarding psychosocial support (sample size: 660). Methods: A decision tree cost–utility model was developed to compare four decision-making strategies in offering psychosocial support: the clinician alone, the QoL predictor alone, the clinician supported by the predictor, and no prediction with no psychosocial support. QoL after one year was used as a proxy for resilience. Costs, health outcomes, and net monetary benefits (NMBs) were estimated using a one-year time horizon. Incremental cost-effectiveness ratios (ICERs) were calculated and dominance assessed. A societal scenario analysis incorporated productivity losses. A probabilistic sensitivity analysis generated cost-effectiveness acceptability curves. Results: Clinicians supported by the QoL predictor produced the highest NMB (EUR 16,349) and the greatest quality-adjusted life year (QALY) gain (0.759), with an ICER of EUR 22,892 compared with the next least costly strategy. Clinician-only prediction and predictor-only approaches were dominated or extendedly dominated. Under the societal perspective, all strategies produced negative NMB values due to productivity losses, but the overall ranking remained unchanged. The probabilistic sensitivity analysis showed that the combined clinician and predictor strategy had a 69% probability of being cost-effective at a willingness to pay threshold of EUR 30,000. Conclusions: Combining clinician judgement with the machine learning-based QoL predictor improved the targeting of psychosocial support and was the most cost-effective strategy. Further prospective and comparative studies are needed to confirm its long-term effectiveness and cost-effectiveness in clinical practice.