Department of Obstetrics and Gynecology, University of Cincinnati College of Medicine, and the Center for Prevention of Preterm Birth, Perinatal Institute, and the Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Obstet Gynecol. 2019 Aug;134(2):216-224. doi: 10.1097/AOG.0000000000003319.
Severe maternal morbidity has increased in the United States over the past two decades by approximately 200%, to 144 cases per 10,000 delivery hospitalizations. There are limited data available to assist in identifying at-risk women before parturition. We sought to evaluate risk factors associated with maternal admission to an intensive care unit (ICU).
We conducted a population-based cohort study of all live births delivered between 20 and 44 weeks of gestation in the United States during 2012-2016. Our primary objective was to identify prenatal factors associated with increased risk of maternal ICU admission to build a multivariable predictive model to estimate the association of these factors with ICU admission risk. We performed k-fold cross-validation for internal validation and then externally validated the model on a separate live birth cohort (2006-2011, n=856,255).
There were 18,745,615 live births in the United States between 2012 and 2016. Among the mothers of these live newborns, 27,602 (0.15%) were admitted to the ICU in the peripartum period. Fourteen variables were selected for inclusion in the predictive model for maternal ICU admission. The predicted minimal and maximal risk for ICU admission ranged 0-25%. The receiver operating characteristic curve for these 14 variables achieved an area under the curve (AUC) of 0.81 (95% CI 0.79-0.81). External validation with a separate live birth cohort demonstrated a consistent measure of discrimination with an AUC of 0.83 (95% CI 0.82-0.84). Using a relatively high cut point of 5.0% or more predicted risk for ICU admission, achieved a positive predictive value (PPV) of only 4.0%.
This model provides insight as to the cumulative effect of multiple risk factors on maternal ICU admission risk. The predictive model achieves an AUC of 0.81, discriminating women with significantly increased risk (30-fold) for ICU admission. Nonetheless, because of the low frequency of maternal ICU admission, the PPV of the model was low and therefore whether models such as ours may be beneficial in future efforts to reduce the prevalence and burden of maternal morbidity is uncertain.
在过去的二十年中，美国严重产妇发病率增加了约 200%，每 10000 例分娩住院中有 144 例。目前可用的数据有限，无法在分娩前帮助识别高危妇女。我们试图评估与产妇入住重症监护病房（ICU）相关的危险因素。
我们对 2012 年至 2016 年期间美国 20 至 44 周妊娠的所有活产进行了基于人群的队列研究。我们的主要目的是确定产前因素与产妇 ICU 入院风险增加的关系，以建立一个多变量预测模型来估计这些因素与 ICU 入院风险的关系。我们对内进行了 k 折交叉验证，然后在另一个活产队列（2006-2011 年，n=856255）上对外验证模型。
2012 年至 2016 年期间，美国有 18745615 例活产。在这些新生儿的母亲中，27602 例（0.15%）在围产期入住 ICU。有 14 个变量被纳入预测产妇 ICU 入院的模型。ICU 入院的预测最小和最大风险范围为 0-25%。这些 14 个变量的受试者工作特征曲线（ROC）获得了 0.81（95%CI 0.79-0.81）的曲线下面积（AUC）。使用另一个独立的活产队列进行外部验证，发现 AUC 为 0.83（95%CI 0.82-0.84），具有一致的判别能力。使用相对较高的预测 ICU 入院风险（5.0%或以上）的切点，阳性预测值（PPV）仅为 4.0%。
该模型提供了关于多种危险因素对产妇 ICU 入院风险的累积影响的见解。该预测模型的 AUC 达到 0.81，可区分 ICU 入院风险显著增加（30 倍）的女性。然而，由于产妇 ICU 入院的频率较低，因此该模型的阳性预测值较低，因此，我们的模型等是否可能有助于降低产妇发病率和负担的未来努力尚不确定。
