Data-driven tool to help oncologists identify high-risk periods for metastatic breast cancer patients
Oncologists know that intensive treatment rarely helps patients in their final days, but recognizing when someone is entering that stage is difficult, especially in metastatic breast cancer, where an increasing number of effective treatments has helped prolong life and given patients and oncologists more treatment options. Physicians often overestimate survival, and uncertainty can delay…
A new predictive tool for oncologists aims to identify high-risk periods for metastatic breast cancer patients as they approach the end of their lives. This regression-based model, developed by Emily Ray, MD, MPH, leverages routinely collected clinical data from electronic health records to estimate the probability of death within 30 or 90 days.
Ray explains that the tool draws on variables such as worsening liver function, increased heart rate, rising opioid needs, and declining functional status - signs that clinicians often recognize but may miss. Unlike existing models that combine multiple cancer types, this tool is specifically designed for metastatic breast cancer and is based on real-world data from a national oncology database.
By identifying these high-risk periods, the model aims to prompt timely conversations between oncologists, patients, and caregivers about care needs, preferences, and support. Ray hopes the tool will help oncologists have these difficult conversations, even when patients are reluctant to discuss prognosis, and ensure care plans align with patients' values and goals.
The researchers plan to conduct further studies to determine how best to integrate the model into clinical practice and evaluate its impact on decision-making for oncologists, patients, and caregivers.
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