Departments of Obstetrics and Gynecology and Pediatrics and the California Maternal Quality Care Collaborative, Stanford University, Stanford, and the Division of Epidemiology and Biostatistics and the Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, California.
Obstet Gynecol. 2020 Sep;136(3):440-449. doi: 10.1097/AOG.0000000000004022.
To develop and validate an expanded obstetric comorbidity score for predicting severe maternal morbidity that can be applied consistently across contemporary U.S. patient discharge data sets.
Discharge data from birth hospitalizations in California during 2016-2017 were used to develop the score. The outcomes were severe maternal morbidity, defined using the Centers for Disease Control and Prevention index, and nontransfusion severe maternal morbidity (excluding cases where transfusion was the only indicator of severe maternal morbidity). We selected 27 potential patient-level risk factors for severe maternal morbidity, identified using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used a targeted causal inference approach integrated with machine learning to rank the risk factors based on adjusted risk ratios (aRRs). We used these results to assign scores to each comorbidity, which sum to a single numeric score. We validated the score in California and national data sets and compared the performance to that of a previously developed obstetric comorbidity index.
Among 919,546 births, the rates of severe maternal morbidity and nontransfusion severe maternal morbidity were 168 and 74 per 10,000 births, respectively. The highest risk comorbidity was placenta accreta spectrum (aRR of 30.5 for severe maternal morbidity and 54.7 for nontransfusion severe maternal morbidity) and the lowest was gestational diabetes mellitus (aRR of 1.06 for severe maternal morbidity and 1.12 for nontransfusion severe maternal morbidity). Normalized scores based on the aRR were developed for each comorbidity, which ranged from 1 to 59 points for severe maternal morbidity and from 1 to 36 points for nontransfusion severe maternal morbidity. The overall performance of the expanded comorbidity scores was good (C-statistics were 0.78 for severe maternal morbidity and 0.84 for nontransfusion severe maternal morbidity in California data and 0.82 and 0.87, respectively, in national data) and improved on prior comorbidity indices developed for obstetric populations. Calibration plots showed good concordance between predicted and actual risks of the outcomes.
We developed and validated an expanded obstetric comorbidity score to improve comparisons of severe maternal morbidity rates across patient populations with different comorbidity case mixes.
开发和验证一种扩展的产科合并症评分系统,用于预测严重产妇发病率,该评分系统可在当代美国患者出院数据集中一致应用。
使用 2016-2017 年加利福尼亚州分娩住院患者的数据来开发评分系统。结局为严重产妇发病率,使用疾病控制与预防中心指数定义,非输血严重产妇发病率(排除仅输血是严重产妇发病率指标的病例)。我们使用国际疾病分类、第十次修订版临床修正诊断代码确定了 27 种可能的产妇风险因素,使用基于机器学习的靶向因果推理方法根据调整后的风险比(aRR)对这些风险因素进行排名。我们根据这些结果为每种合并症分配分数,分数总和为一个单一的数字评分。我们在加利福尼亚州和全国数据集进行了评分验证,并将表现与之前开发的产科合并症指数进行了比较。
在 919546 例分娩中,严重产妇发病率和非输血严重产妇发病率分别为每 10000 例 168 例和 74 例。风险最高的合并症是胎盘附着异常(严重产妇发病率的 aRR 为 30.5,非输血严重产妇发病率的 aRR 为 54.7),风险最低的是妊娠期糖尿病(严重产妇发病率的 aRR 为 1.06,非输血严重产妇发病率的 aRR 为 1.12)。为每种合并症开发了基于 aRR 的标准化评分,严重产妇发病率为 1 至 59 分,非输血严重产妇发病率为 1 至 36 分。扩展合并症评分的整体性能良好(加利福尼亚州数据中严重产妇发病率的 C 统计量为 0.78,非输血严重产妇发病率的 C 统计量为 0.84,全国数据中分别为 0.82 和 0.87),并优于之前为产科人群开发的合并症指数。校准图显示,预测结果与实际结局风险之间具有良好的一致性。
我们开发和验证了一种扩展的产科合并症评分系统,以提高不同合并症病例组合的患者人群之间严重产妇发病率的比较。