Li Qingfeng, Alfonso Y Natalia, Wolfson Carrie, Aziz Khyzer B, Creanga Andreea A
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Johns Hopkins Children's Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Healthcare (Basel). 2025 Jan 31;13(3):284. doi: 10.3390/healthcare13030284.
Severe maternal morbidity (SMM) is increasing in the United States. The main objective of this study is to test the use of machine learning (ML) techniques to develop models for predicting SMM during delivery hospitalizations in Maryland. Secondarily, we examine disparities in SMM by key sociodemographic characteristics.
We used the linked State Inpatient Database (SID) and the American Hospital Association (AHA) Annual Survey data from Maryland for 2016-2019 (N = 261,226 delivery hospitalizations). We first estimated relative risks for SMM across key sociodemographic factors (e.g., race, income, insurance, and primary language). Then, we fitted LASSO and, for comparison, Logit models with 75 and 18 features. The selection of SMM features was based on clinical expert opinion, a literature review, statistical significance, and computational resource constraints. Various model performance metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall values were computed to compare predictive performance.
During 2016-2019, 76 per 10,000 deliveries (1976 of 261,226) were in patients who experienced an SMM event. The Logit model with a full list of 75 features achieved an AUC of 0.71 in the validation dataset, which marginally decreased to 0.69 in the reduced model with 18 features. The LASSO algorithm with the same 18 features demonstrated slightly superior predictive performance and an AUC of 0.80. We found significant disparities in SMM among patients living in low-income areas, with public insurance, and who were non-Hispanic Black or non-English speakers.
Our results demonstrate the feasibility of utilizing ML and administrative hospital discharge data for SMM prediction. The low recall score is a limitation across all models we compared, signifying that the algorithms struggle with identifying all SMM cases. This study identified substantial disparities in SMM across various sociodemographic factors. Addressing these disparities requires multifaceted interventions that include improving access to quality care, enhancing cultural competence among healthcare providers, and implementing policies that help mitigate social determinants of health.
在美国，严重孕产妇发病率（SMM）呈上升趋势。本研究的主要目的是测试使用机器学习（ML）技术开发模型，以预测马里兰州分娩住院期间的SMM。其次，我们通过关键的社会人口学特征来研究SMM的差异。
我们使用了马里兰州2016 - 2019年的关联州住院数据库（SID）和美国医院协会（AHA）年度调查数据（N = 261,226例分娩住院）。我们首先估计了关键社会人口学因素（如种族、收入、保险和主要语言）下SMM的相对风险。然后，我们拟合了LASSO模型，并作为比较，拟合了具有75个和18个特征的Logit模型。SMM特征的选择基于临床专家意见、文献综述、统计显著性和计算资源限制。计算了各种模型性能指标，包括受试者工作特征曲线下面积（AUC）、准确性、精确性和召回值，以比较预测性能。
在2016 - 2019年期间，每10,000例分娩中有76例（261,226例中的1976例）发生了SMM事件。具有75个完整特征列表的Logit模型在验证数据集中的AUC为0.71，在具有18个特征的简化模型中略微降至0.69。具有相同18个特征的LASSO算法表现出略优的预测性能，AUC为0.80。我们发现，生活在低收入地区、拥有公共保险、非西班牙裔黑人或非英语使用者的患者中，SMM存在显著差异。
我们的结果证明了利用ML和医院行政出院数据进行SMM预测的可行性。低召回率是我们比较的所有模型的一个局限性，这表明算法在识别所有SMM病例方面存在困难。本研究确定了SMM在各种社会人口学因素方面存在的重大差异。解决这些差异需要多方面的干预措施，包括改善获得优质护理的机会、提高医疗保健提供者的文化能力，以及实施有助于减轻健康的社会决定因素的政策。
