Harvard Medical School, Boston, MA, USA.
Massachusetts General Hospital, Boston, MA, USA.
Neurocrit Care. 2024 Dec;41(3):866-879. doi: 10.1007/s12028-024-02007-0. Epub 2024 Jun 6.
Social determinants of health (SDOH) have been linked to neurocritical care outcomes. We sought to examine the extent to which SDOH explain differences in decisions regarding life-sustaining therapy, a key outcome determinant. We specifically investigated the association of a patient's home geography, individual-level SDOH, and neighborhood-level SDOH with subsequent early limitation of life-sustaining therapy (eLLST) and early withdrawal of life-sustaining therapy (eWLST), adjusting for admission severity.
We developed unique methods within the Bridge to Artificial Intelligence for Clinical Care (Bridge2AI for Clinical Care) Collaborative Hospital Repository Uniting Standards for Equitable Artificial Intelligence (CHoRUS) program to extract individual-level SDOH from electronic health records and neighborhood-level SDOH from privacy-preserving geomapping. We piloted these methods to a 7 years retrospective cohort of consecutive neuroscience intensive care unit admissions (2016-2022) at two large academic medical centers within an eastern Massachusetts health care system, examining associations between home census tract and subsequent occurrence of eLLST and eWLST. We matched contextual neighborhood-level SDOH information to each census tract using public data sets, quantifying Social Vulnerability Index overall scores and subscores. We examined the association of individual-level SDOH and neighborhood-level SDOH with subsequent eLLST and eWLST through geographic, logistic, and machine learning models, adjusting for admission severity using admission Glasgow Coma Scale scores and disorders of consciousness grades.
Among 20,660 neuroscience intensive care unit admissions (18,780 unique patients), eLLST and eWLST varied geographically and were independently associated with individual-level SDOH and neighborhood-level SDOH across diagnoses. Individual-level SDOH factors (age, marital status, and race) were strongly associated with eLLST, predicting eLLST more strongly than admission severity. Individual-level SDOH were more strongly predictive of eLLST than neighborhood-level SDOH.
Across diagnoses, eLLST varied by home geography and was predicted by individual-level SDOH and neighborhood-level SDOH more so than by admission severity. Structured shared decision-making tools may therefore represent tools for health equity. Additionally, these findings provide a major warning: prognostic and artificial intelligence models seeking to predict outcomes such as mortality or emergence from disorders of consciousness may be encoded with self-fulfilling biases of geography and demographics.
社会决定因素健康（SDOH）与神经危重症护理结局相关。我们试图研究 SDOH 在多大程度上解释了与生命维持治疗相关的决策差异，这是一个关键的结局决定因素。我们特别调查了患者家庭地理位置、个体层面的 SDOH 和邻里层面的 SDOH 与随后早期限制生命维持治疗（eLLST）和早期停止生命维持治疗（eWLST）的关联，同时调整了入院严重程度。
我们在 Bridge to Artificial Intelligence for Clinical Care（Bridge2AI for Clinical Care）合作医院资源库统一公平人工智能标准（CHoRUS）项目中开发了独特的方法，从电子健康记录中提取个体层面的 SDOH，并从隐私保护的地理映射中提取邻里层面的 SDOH。我们在马萨诸塞州东部医疗系统的两家大型学术医疗中心的 7 年回顾性连续神经科学重症监护病房入院队列（2016-2022 年）中对这些方法进行了试点，检查家庭普查区与随后发生的 eLLST 和 eWLST 之间的关联。我们使用公共数据集将邻里层面的 SDOH 信息与每个普查区相匹配，量化社会脆弱性指数的总分和子分数。我们通过地理、逻辑和机器学习模型检查个体层面的 SDOH 和邻里层面的 SDOH 与随后的 eLLST 和 eWLST 的关联，同时使用入院格拉斯哥昏迷量表评分和意识障碍等级调整入院严重程度。
在 20660 例神经科学重症监护病房入院患者（18780 例患者）中，eLLST 和 eWLST 在地理上存在差异，与个体层面的 SDOH 和邻里层面的 SDOH 在不同诊断中独立相关。个体层面的 SDOH 因素（年龄、婚姻状况和种族）与 eLLST 密切相关，比入院严重程度更能预测 eLLST。个体层面的 SDOH 比邻里层面的 SDOH 更能预测 eLLST。
在不同的诊断中，eLLST 因家庭地理位置而异，并且可以通过个体层面的 SDOH 和邻里层面的 SDOH 比入院严重程度更准确地预测。因此，结构化的共同决策工具可能代表了公平卫生保健的工具。此外，这些发现提供了一个重要的警告：试图预测死亡率或从意识障碍中恢复等结局的预后和人工智能模型可能会嵌入与地理位置和人口统计学相关的自我实现偏见。
