Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions
Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions
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DOI：
10.1001/jamanetworkopen.2019.6972
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发表时间：
2019-07-01
影响因子：
13.8
通讯作者：
Zhou, Li
中科院分区：
文献类型：
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作者：
Wang, Liqin;Sha, Long;Zhou, Li
早期姑息治疗干预措施推动高价值护理，但目前未得到充分利用。卫生保健专业人员面临着识别可能受益于姑息治疗的患者的挑战。目的开发一种深度学习算法，使用纵向电子健康记录来预测死亡风险，作为识别可能受益于姑息治疗的痴呆患者的代理指标。设计，设置和参与者在这项回顾性队列研究中，6个月，1年，和2年死亡率预测模型与循环神经网络使用患者人口统计信息和主题从合作伙伴医疗保健系统，一个综合的医疗保健提供系统在波士顿，马萨诸塞州内的临床笔记。这项研究包括26921名成年痴呆症患者，他们从2011年1月1日到2017年12月31日访问了医疗保健系统。这些模型使用24229名患者的数据集进行训练，并使用另一个2692名患者的数据集进行验证。分析时间为2018年9月18日至2019年5月15日。主要结果和指标6个月、1年和2年死亡预测模型的受试者工作特征曲线下面积（AUC）及影响预测的因素。（16 263例女性[60.4%];平均[SD]年龄，74.6 [13.5]岁）。对于训练数据集中的24229例患者，平均（SD）年龄为74.8（13.2）岁，14632（60.4%）例为女性。对于验证数据集中的2692例患者，平均（SD）年龄为75.0（12.6）岁，1631例（60.6%）为女性。6个月模型的AUC为0.978（95% CI，0.977-0.978）; 1年模型为0.956（95% CI，0.955-0.956）; 2年模型为0.943（95% CI，0.942-0.944）。与痴呆患者6个月、1年和2年死亡率相关的潜在主题包括姑息治疗和临终关怀、认知功能、谵妄、胆固醇水平测试、癌症、疼痛、医疗保健服务的使用、关节炎、营养状况、皮肤护理、家庭会议、休克、呼吸衰竭、结论和相关性基于患者人口统计信息和纵向临床记录的深度学习算法似乎在预测不同时间范围内痴呆患者的死亡率方面显示出有希望的结果。进一步的研究是必要的，以确定在临床环境中应用该算法的可行性，以确定未满足的姑息治疗需求更早。
IMPORTANCE Early palliative care interventions drive high-value care but currently are underused. Health care professionals face challenges in identifying patients who may benefit from palliative care.OBJECTIVE To develop a deep learning algorithm using longitudinal electronic health records to predict mortality risk as a proxy indicator for identifying patients with dementia who may benefit from palliative care.DESIGN, SETTING, AND PARTICIPANTS In this retrospective cohort study, 6-month, 1-year, and 2-year mortality prediction models with recurrent neural networks used patient demographic information and topics generated from clinical notes within Partners HealthCare System, an integrated health care delivery system in Boston, Massachusetts. This study included 26 921 adult patients with dementia who visited the health care system from January 1, 2011, through December 31, 2017. The models were trained using a data set of 24 229 patients and validated using another data set of 2692 patients. Data were analyzed from September 18, 2018, to May 15, 2019.MAIN OUTCOMES AND MEASURES The area under the receiver operating characteristic curve (AUC) for 6-month and 1- and 2-year mortality prediction models and the factors contributing to the predictions.RESULTS The study cohort included 26 921 patients (16 263 women [60.4%]; mean [SD] age, 74.6 [13.5] years). For the 24 229 patients in the training data set, mean (SD) age was 74.8 (13.2) years and 14 632 (60.4%) were women. For the 2692 patients in the validation data set, mean (SD) age was 75.0 (12.6) years and 1631(60.6%) were women. The 6-month model reached an AUC of 0.978 (95% CI, 0.977-0.978); the 1-year model, 0.956 (95% CI, 0.955-0.956); and the 2-year model, 0.943 (95% CI, 0.942-0.944). The top-ranked latent topics associated with 6-month and 1- and 2-year mortality in patients with dementia include palliative and end-of-life care, cognitive function, delirium, testing of cholesterol levels, cancer, pain, use of health care services, arthritis, nutritional status, skin care, family meeting, shock, respiratory failure, and swallowing function.CONCLUSIONS AND RELEVANCE A deep learning algorithm based on patient demographic information and longitudinal clinical notes appeared to show promising results in predicting mortality among patients with dementia in different time frames. Further research is necessary to determine the feasibility of applying this algorithm in clinical settings for identifying unmet palliative care needs earlier.