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"Wherever the art of Medicine is loved, there is also a love of Humanity."
— Hippocrates

Accurately measuring heart failure quality of life is essential for effective clinical management and personalized care. In many settings, particularly when patients are elderly or significantly impaired, clinicians rely on caretakers to provide information. However, this method introduces the risk of proxy response bias. Understanding the direction and magnitude of this bias helps doctors interpret health data more effectively and make better treatment decisions.
A recent study focused on outpatients in Malaysia to compare self-reported and proxy-reported scores using the AQoL-6D questionnaire. Researchers utilized propensity score matching to ensure that the two groups were comparable in terms of sociodemographic and clinical characteristics. Notably, the study found that proxies consistently reported a poorer quality of life than the patients themselves. These discrepancies were most evident in the Relationships and Coping dimensions, which initially scored the lowest across the entire cohort.
Furthermore, the data indicated that proxy reporting bias was particularly pronounced in physical and functional domains. Specifically, proxies tended to exaggerate the severity of physical limitations. Consequently, the relative risk of reported problems was significantly higher in proxy reports compared to self-reports. Although propensity score matching reduced the magnitude of these differences, the bias remained statistically significant across most categories.
Clinicians should approach proxy-reported data with a degree of caution. While caretakers provide a vital perspective, their assessments may not perfectly align with the patient’s lived experience. In the Indian clinical context, where family involvement in healthcare is deep-rooted, doctors should strive to validate functional assessments with the patient directly whenever possible. This ensures that interventions targeted at improving physical health are based on accurate data rather than overestimated caregiver burden.
This research underscores the high prevalence of proxy responses in chronic disease management. While proxy reporting remains a valuable tool, especially for patients with cognitive or communication barriers, its limitations are clear. By acknowledging the small-to-moderate deviations in physical and functional domains, healthcare providers can better refine their diagnostic and therapeutic strategies for heart failure patients.
Proxy response bias occurs when a caretaker's assessment of a patient's health-related quality of life differs from the patient's own assessment, often resulting in an overestimation of symptoms or disability.
Proxies often focus more on observable physical limitations and the perceived burden of care, which can lead them to rate the patient’s health more poorly than the patient perceives it.
Doctors can minimize bias by using standardized self-report tools for patients who are capable and by cross-referencing proxy reports with objective clinical measurements and direct patient interviews.
Disclaimer: This content is for informational and educational purposes only and does not constitute medical advice or a professional relationship. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Refer to the latest local and national guidelines for clinical practice.
References
Tan YJ et al. Assessment of Quality of Life Among Chronic Heart Failure Outpatients in Malaysia: Propensity Score Matched Analysis Between Self- and Proxy-Reports in 6 Health Dimensions. Value Health Reg Issues. 2026 Mar 27. doi: undefined. PMID: 41893397.
Prichard RA et al. Discrepancies between proxy estimates and patient reported, health related, quality of life: minding the gap between patient and clinician perceptions in heart failure. Quality of Life Research. 2021.
Karthikeyan G et al. Development and validation of heart failure-specific quality-of-life measurement tool in India. Journal of Clinical Epidemiology. 2023.
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