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Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
npj Digital Medicine ( IF 15.2 ) Pub Date : 2021-07-01 , DOI: 10.1038/s41746-021-00477-6
Ravi B Parikh 1, 2, 3, 4 , Manqing Liu 4 , Eric Li 2 , Runze Li 5 , Jinbo Chen 6
Affiliation  

Machine learning algorithms may address prognostic inaccuracy among clinicians by identifying patients at risk of short-term mortality and facilitating earlier discussions about hospice enrollment, discontinuation of therapy, or other management decisions. In the present study, we used prospective predictions from a real-time machine learning prognostic algorithm to identify two trajectories of all-cause mortality risk for decedents with cancer. We show that patients with an unpredictable trajectory, where mortality risk rises only close to death, are significantly less likely to receive guideline-based end-of-life care and may not benefit from the integration of prognostic algorithms in practice.



中文翻译:

癌症患者死亡风险轨迹及相关临终利用

机器学习算法可以通过识别有短期死亡风险的患者并促进有关临终关怀入组、停止治疗或其他管理决策的早期讨论来解决临床医生的预后不准确问题。在本研究中,我们使用实时机器学习预后算法的前瞻性预测来确定癌症死者全因死亡风险的两条轨迹。我们表明,具有不可预测轨迹的患者,即死亡率风险仅在接近死亡时才上升的患者,接受基于指南的临终关怀的可能性显着降低,并且可能无法从实践中预后算法的整合中受益。

更新日期:2021-07-01
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