Augmented intelligence to predict 30-day mortality in patients with cancer.

Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients' electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible.

Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting.

PURPOSE: For patients with advanced cancer, timely referral to palliative care (PC) services can ensure that end-of-life care aligns with their preferences and goals. Overestimation of life expectancy may result in underutilization of PC services, counterproductive treatment measures, and reduced quality of life for patients. We assessed the impact of a commercially available augmented intelligence (AI) tool to predict 30-day mortality risk on PC service utilization in a real-world setting. METHODS: Patients within a large hematology-oncology practice were scored weekly between June 2018 and October 2019 with an AI tool to generate insights into short-term mortality risk. Patients identified by the tool as being at high or medium risk were assessed for a supportive care visit and further referred as appropriate. Average monthly rates of PC and hospice referrals were calculated 5 months predeployment and 17 months postdeployment of the tool in the practice. RESULTS: The mean rate of PC consults increased from 17.3 to 29.1 per 1,000 patients per month (PPM) pre- and postdeployment, whereas the mean rate of hospice referrals increased from 0.2 to 1.6 per 1,000 PPM. Eliminating the first 6 months following deployment to account for user learning curve, the mean rate of PC consults nearly doubled over baseline to 33.0 and hospice referrals increased 12-fold to 2.4 PPM. CONCLUSION: Deployment of an AI tool at a hematology-oncology practice was found to be feasible for identifying patients at high or medium risk for short-term mortality. Insights generated by the tool drove clinical practice changes, resulting in significant increases in PC and hospice referrals.

Overview of cancer-related anemia: focus on the potential role of darbepoetin alfa.

Cancer-associated anemia is common and has many causes, including the effects of the underlying disease and cancer treatment. The effect of anemia on patients with cancer was not appreciated fully until relatively recently. Several well-designed studies have demonstrated the relationship between anemia and fatigue, and the effect of fatigue on quality of life. These data have resulted in a greater awareness of anemia in cancer and have increased the use of recombinant human erythropoietin (r-HuEPO, epoetin alfa) therapy for the treatment of anemia. Recombinant HuEPO produces a hemoglobin response in 50-60% of patients with cancer; however, to obtain this response rate, frequent dosing is required. Darbepoetin alfa, a recently developed erythropoietic protein, has a longer half-life than that of r-HuEPO, enabling less frequent dosing, and has a greater in vivo activity. In studies of patients with cancer who develop anemia, darbepoetin alfa has proved to be well tolerated and effective, and its advantages make it a potential improved treatment option for anemia in these patients.