| 建立子痫前期不良结局风险预警模型的初步研究 |
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| 引用本文： | 廖 媛,刘兴会,谭 婧,等. 建立子痫前期不良结局风险预警模型的初步研究[J]. 四川大学学报(医学版), 2018, 49(5): 797-802 |
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| 作者姓名： | 廖 媛 刘兴会 谭 婧 等 |
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| 作者单位： | 1.四川大学华西第二医院 妇产科 |
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| 摘 要： | 目的 分析子痫前期患者发生不良结局的预后因素，并建立不良结局的风险预警模型及探讨其运用价值。方法 回顾性分析2005年1月至2014年12月我院收治的2 532例子痫前期患者的临床资料，根据患者是否出现不良结局分为有不良结局组990例（39.1%）与无不良结局组1 542例（60.9%）。统计分析两组患者的一般特征，并对可能导致不良结局发生的相关临床指标进行单因素分析，再将研究对象随机分为80%的建模组与20%的验证组，对建模组进行多因素logistic回归分析，并建立风险预警模型。最后用20%的验证组对所建立模型进行评价，绘制ROC曲线并结合临床意义寻找最佳预测点。结果 单因素分析结果显示，子痫前期发生不良结局的预测指标包括初产妇、产检次数、双胎、水肿、胸闷胸痛、呼吸困难、头昏头痛、视力模糊、合并妊娠期肝内胆汁淤积症（ICP）、合并妊娠期糖代谢异常、合并心血管疾病、入院时血压值、尿蛋白定性定量结果、肝肾功能结果等。多因素logistic回归分析显示，有统计学意义的预测指标有双胎、水肿、呼吸困难、视力模糊、合并心血管疾病、尿蛋白定量及血生化结果异常（P<0.05）。建立的Logit（P）模型预测患者不良结局的准确度为77.1%，拟合优度检验示该模型拟和良好。用此模型的风险值预测20%的验证组患者的不良结局ROC曲线的曲线下面积（AUC）为0.804（P＜0.01, 95%可信区间:0.758～0.849）。结合临床对高敏感度的要求，当诊断点取风险值为0.300时，58.6%将发生不良结局，其敏感度为83.8%，假阳性率为46.8%。结论 可通过患者是否为双胎妊娠，是否出现水肿、呼吸困难、视力模糊，是否合并心血管疾病及尿蛋白定量和血生化结果对子痫前期不良结局进行预测。运用此风险预警模型进行风险预测时，对风险值≥0.300的患者应予以重视。
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| 关 键 词： | 子痫前期 预后因素 预测 不良结局 |
Development of a Predictive Model for Adverse Outcomes of Preeclampsia |
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| LIAO Yuan,LIU Xing-hui,TAN Jing,et al. Development of a Predictive Model for Adverse Outcomes of Preeclampsia[J]. Journal of Sichuan University. Medical science edition, 2018, 49(5): 797-802 |
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| Authors: | LIAO Yuan LIU Xing-hui TAN Jing et al |
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| Abstract: | Objective To determine factors associated with adverse outcomes of preeclampsia and develop a predictive model. Methods Clinical data of 2 532 patients with preeclampsia who were admitted to our hospital from 2005 to 2014 were extracted for the study. The patients were divided into two groups, including 990 (39.1%) with adverse outcomes and 1 542 (60.9%) without adverse outcomes. Factors associated with adverse outcomes were identified through univariate analyses. The predictive model was developed through multivariate logistic regression analyses using a randomly selected sample containing 80% of the cases. The remaining 20% of cases served for the purpose of validation and the establishment of the ROC curve. Results Primiparas, educational attainments, prenatal care, multiple births, edema, chest pain, dyspnea, dizziness, headache, blurred vision, intrahepatic cholestasis of pregnancy, gestational diabetes, cardiovascular disease, blood pressure, urine protein, liver and kidney functions were found to be associated with adverse outcomes of preeclampsia. Multiple births, edema, dyspnea, blurred vision, cardiovascular disease, liver and kidney functions entered into the logistic regression model (P<0.05). The Logit(P) model had a good fitness of data and 77.1% accuracy in predicting adverse outcomes. The area under the curve (AUC) of the ROC curve was 0.804 〔P<0.01, 95% confidence interval CI): 0.758 to 0.849〕. The highest sensitivity was achieved when the cut-off point set risk value at 0.300, with 58.6% patients having adverse outcomes representing 83.8% true positive rate and 46.8% false positive rate. Conclusion Adverse outcomes of preeclampsia can be predicted through multiple births, edema, dyspnea, blurred vision, cardiovascular disease, liver and kidney functions. Risk value ≥0.300 is recommended. |
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| Keywords: | Preeclampsia Prognostic factors Predictive model Adverse outcomes |
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