泰国南部的孕产妇近乎失踪和孕产妇死亡的预测因素和评分模型:一个病例控制研究
Wipawan Raktong1, Rapphon Sawaddisan2, Krantarat Peeyananjarassri1
1Department of Obstetrics and Gynecology, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.
Archives of gynecology and obstetrics
|May 7, 2024
概括
这项研究确定了孕产妇近乎失踪 (MNM) 和孕产妇死亡率的关键预测因素,并开发了一个评分模型来评估风险. 该模型在识别高风险怀孕方面表现出可接受的性能.
科学领域:
- 产科和妇科 产科和妇科
- 孕产妇健康 孕产妇健康
- 公共卫生 公共卫生
背景情况:
- 孕产妇近乎失踪 (MNM) 和孕产妇死亡率仍然是全球重大健康挑战.
- 识别风险因素和开发预测工具对于及时干预和改善结果至关重要.
研究的目的:
- 确定孕产妇近乎失踪 (MNM) 和孕产妇死亡率的预测因素.
- 开发和验证一个评分模型来预测MNM和产妇死亡率.
主要方法:
- 一项涉及1420名在2014年至2020年间分娩的妇女的病例控制研究.
- 使用多变量逻辑回归分析了产前特征和并发症.
- 开发并评估了风险评分,以预测绩效.
主要成果:
- 确定的关键预测因素包括晚年,肥胖,平价≥3,有子宫切或产后出血史,贫血,孕期糖尿病,心脏病,多胎妊娠,胎盘前/后和高血压疾病.
- 具有≥4的截止值的评分模型实现了74.4%的灵敏度和90.4%的特异性,用于识别高风险女性.
- 妊娠高血压/孕产前和早产 (<34周) 的严重特征也是显著的预测因素.
结论:
- 已经确定了MNM和孕产妇死亡率的预测因素.
- 已经开发出了一种可接受的MNM和产妇死亡率预测性能的评分模型.
相关概念视频
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
125
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
125
Comparing the Survival Analysis of Two or More Groups
177
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177
Assumptions of Survival Analysis
124
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
124
Strategies for Assessing and Addressing Confounding
93
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
93
Censoring Survival Data
85
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
85


