Abstract：
BACKGROUND: To develop and validate a support tool for healthcare providers, enabling them to make precise and critical decisions regarding intensive care unit (ICU) admissions for high-risk pregnant women, thus enhancing maternal outcomes.
METHODS: This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission. Subsequently, sensitivity analysis was conducted to compare the accuracy, predictive power, sensitivity, and specificity of these models, with differences analyzed using the Wilcoxon test.
RESULTS: The XGBoost algorithm demonstrated superior efficiency, achieving an accuracy rate of 85%, sensitivity of 42%, specificity of 97%, and an area under the receiver operating characteristic curve of 86.7%. Notably, the estimated prevalence of ICU utilization by the model (11.6%) differed from the prevalence of ICU use from the study (21.52%).
CONCLUSIONS: The developed risk engine yielded positive results, emphasizing the need to optimize intensive care bed utilization and objectively identify high-risk pregnant women requiring these services. This approach promises to enhance the effective and efficient management of pregnant women, particularly in resource-constrained regions worldwide. By streamlining ICU admissions for high-risk cases, healthcare providers can better allocate critical resources, ultimately contributing to improved maternal health outcomes.
METHODS: This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission. Subsequently, sensitivity analysis was conducted to compare the accuracy, predictive power, sensitivity, and specificity of these models, with differences analyzed using the Wilcoxon test.
RESULTS: The XGBoost algorithm demonstrated superior efficiency, achieving an accuracy rate of 85%, sensitivity of 42%, specificity of 97%, and an area under the receiver operating characteristic curve of 86.7%. Notably, the estimated prevalence of ICU utilization by the model (11.6%) differed from the prevalence of ICU use from the study (21.52%).
CONCLUSIONS: The developed risk engine yielded positive results, emphasizing the need to optimize intensive care bed utilization and objectively identify high-risk pregnant women requiring these services. This approach promises to enhance the effective and efficient management of pregnant women, particularly in resource-constrained regions worldwide. By streamlining ICU admissions for high-risk cases, healthcare providers can better allocate critical resources, ultimately contributing to improved maternal health outcomes.
摘要：
背景：为了开发和验证医疗保健提供者的支持工具，使他们能够就高风险孕妇的重症监护病房（ICU）入院做出准确而关键的决定，从而提高产妇的结局。
方法：这项回顾性研究涉及对9550名孕妇收集的信息进行二次数据分析，谁有严重的产妇发病率（任何意外的并发症，在分娩和生产，导致实质性的短期或长期的健康问题的母亲），2009年至2010年从巴西严重孕产妇发病率监测网络收集，包括巴西的27个产科参考中心。机器学习模型，包括决策树，随机森林,梯度增压机（GBM），和极端梯度提升（XGBoost），用于创建ICU入院风险预测工具。随后，进行了敏感性分析，以比较准确性，预测能力，灵敏度,以及这些模型的特异性，差异分析使用Wilcoxon检验。
结果：XGBoost算法表现出卓越的效率，达到85%的准确率，灵敏度为42%,97%的特异性，接收器工作特性曲线下面积为86.7%。值得注意的是,该模型估计的ICU使用率(11.6%)与研究中ICU使用率(21.52%)不同.
结论：开发的风险引擎产生了积极的结果，强调需要优化重症监护病床的利用，并客观地识别需要这些服务的高风险孕妇。这种方法有望加强对孕妇的有效和高效管理，特别是在全球资源有限的地区。通过简化高风险病例的ICU入院，医疗保健提供者可以更好地分配关键资源，最终有助于改善孕产妇健康结果。
方法：这项回顾性研究涉及对9550名孕妇收集的信息进行二次数据分析，谁有严重的产妇发病率（任何意外的并发症，在分娩和生产，导致实质性的短期或长期的健康问题的母亲），2009年至2010年从巴西严重孕产妇发病率监测网络收集，包括巴西的27个产科参考中心。机器学习模型，包括决策树，随机森林,梯度增压机（GBM），和极端梯度提升（XGBoost），用于创建ICU入院风险预测工具。随后，进行了敏感性分析，以比较准确性，预测能力，灵敏度,以及这些模型的特异性，差异分析使用Wilcoxon检验。
结果：XGBoost算法表现出卓越的效率，达到85%的准确率，灵敏度为42%,97%的特异性，接收器工作特性曲线下面积为86.7%。值得注意的是,该模型估计的ICU使用率(11.6%)与研究中ICU使用率(21.52%)不同.
结论：开发的风险引擎产生了积极的结果，强调需要优化重症监护病床的利用，并客观地识别需要这些服务的高风险孕妇。这种方法有望加强对孕妇的有效和高效管理，特别是在全球资源有限的地区。通过简化高风险病例的ICU入院，医疗保健提供者可以更好地分配关键资源，最终有助于改善孕产妇健康结果。
