Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY; Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY; Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY.
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
Am J Obstet Gynecol. 2024 Jun;230(6):671.e1-671.e10. doi: 10.1016/j.ajog.2023.10.033. Epub 2023 Oct 23.
Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification.
This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach.
We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use.
The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications.
The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.
孕产妇发病率和死亡率的种族差异在产后期间仍然存在，导致黑人和西班牙裔人群产后住院率更高。分娩住院提供了筛查和识别高风险人群的机会，以预防不良的产后结局。目前的模型没有充分纳入健康的社会和结构性决定因素，有些模型包括种族，这可能导致风险分层存在偏差。
本研究旨在开发一种利用社会和结构性决定因素并采用公平方法预测产后住院的风险预测模型。
我们进行了一项回顾性队列研究，使用 2016-2018 年纽约市活产儿的关联出生证明和医院出院数据。我们将 2016 年至 2017 年的分娩纳入模型开发，将 70%/30%的分娩随机分配为训练/测试数据。我们使用 2018 年的分娩数据进行时间模型验证。我们将“产后医院综合使用”定义为分娩出院后 30 天内至少有 1 次再入院或急诊就诊。我们根据国际疾病分类第十版诊断代码将首次住院的诊断分为 14 类。我们测试了 72 个候选变量，包括社会决定因素、人口统计学、合并症、产科并发症和严重产妇发病率。结构性决定因素是极端集中指数，这是邮政编码层面种族经济隔离的指标，以及公共可用的邻里建设/自然和社会/经济环境儿童机会指数。我们使用 4 种统计和机器学习算法来预测“产后医院综合使用”，并使用集成方法来预测“产后特定原因医院使用”。我们模拟了每种风险分层方法与有效干预相结合对产后医院使用种族-族裔公平性的影响。
产后医院使用的总体发生率为 5.7%；黑人、西班牙裔和白人的发生率分别为 8.8%、7.4%和 3.3%。最常见的住院诊断是一般围产期并发症（17.5%）、高血压/子痫（12.0%）、非妇科感染（10.7%）和伤口感染（8.4%）。使用最小绝对收缩和选择算子选择的逻辑回归保留了 22 个预测变量，在训练、测试和验证数据中，接收者操作特征曲线下面积分别为 0.69、0.69 和 0.69。其他机器学习算法表现相似。选定的社会和结构性决定因素特征包括极端集中指数、保险支付人、抑郁症状和进入产前护理的孕早期。“产后特定原因医院使用”模型选择了 14 种结局诊断中的 6 种（急性心血管疾病、胃肠道疾病、高血压/子痫、精神疾病、败血症和伤口感染），使用交叉验证方法，在训练、测试和验证数据中，接收者操作特征曲线下面积分别为 0.75、0.77 和 0.75。在黑人和西班牙裔亚组中，模型的表现略低。当使用产后干预模拟风险分层模型的使用时，与“产后特定原因医院使用”模型或使用常见妊娠并发症识别高风险个体的标准方法相比，使用“产后医院综合使用”模型识别高风险个体可最大程度地减少产后医院使用的种族-族裔差异。
纳入社会和结构性决定因素的“产后医院综合使用”预测模型可在分娩出院时用于识别有产后住院风险的个体。
