Department of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.
Department of Pediatrics, McGill University, Montreal, Quebec, Canada.
JAMA Netw Open. 2022 Jun 1;5(6):e2219203. doi: 10.1001/jamanetworkopen.2022.19203.
Perinatal arterial ischemic stroke (PAIS) is a focal brain injury in term neonates that is identified postnatally but is presumed to occur near the time of birth. Many pregnancy, delivery, and fetal factors have been associated with PAIS, but early risk detection is lacking; thus, targeted treatment and prevention efforts are currently limited.
To develop and validate a diagnostic risk prediction model that uses common clinical factors to predict the probability of PAIS in a term neonate.
DESIGN, SETTING, AND PARTICIPANTS: In this diagnostic study, a prediction model was developed using multivariable logistic regression with registry-based case data collected between January 2003, and March 2020, from the Alberta Perinatal Stroke Project, Canadian Cerebral Palsy Registry, International Pediatric Stroke Study, and Alberta Pregnancy Outcomes and Nutrition study. Criteria for inclusion were term birth and no underlying medical conditions associated with stroke diagnosis. Records with more than 20% missing data were excluded. Variable selection was based on peer-reviewed literature. Data were analyzed in September 2021.
Clinical pregnancy, delivery, and neonatal factors associated with PAIS as common data elements across the 4 registries.
The primary outcome was the discriminative accuracy of the model predicting PAIS, measured by the concordance statistic (C statistic).
Of 2571 term neonates in the initial analysis (527 [20%] case and 2044 [80%] control individuals; gestational age range, 37-42 weeks), 1389 (54%) were male, with a greater proportion of males among cases compared with controls (318 [60%] vs 1071 [52%]). The final model was developed using 1924 neonates, including 321 cases (17%) and 1603 controls (83%), and 9 clinical factors associated with risk of PAIS in term neonates: maternal age, tobacco exposure, recreational drug exposure, preeclampsia, chorioamnionitis, intrapartum maternal fever, emergency cesarean delivery, low 5-minute Apgar score, and male sex. The model demonstrated good discrimination between cases and controls (C statistic, 0.73; 95% CI, 0.69-0.76) and good model fit (Hosmer-Lemeshow P = .20). Internal validation techniques yielded similar C statistics (0.73 [95% CI, 0.69-0.77] with bootstrap resampling, 10-fold cross-validated area under the curve, 0.72 [bootstrap bias-corrected 95% CI, 0.69-0.76]), as did a sensitivity analysis using cases and controls from Alberta, Canada, only (C statistic, 0.71; 95% CI, 0.65-0.77).
The findings suggest that clinical variables can be used to develop and internally validate a model to predict the risk of PAIS in term neonates, with good predictive performance and strong internal validity. Identifying neonates with a high probability of PAIS who could then be screened for early diagnosis and treatment may be associated with reductions in lifelong morbidity for affected individuals and their families.
围产期动脉缺血性卒中（PAIS）是一种发生在足月新生儿的局灶性脑损伤，在产后被识别，但据推测是在出生时发生的。许多妊娠、分娩和胎儿因素与 PAIS 有关，但早期风险检测却缺乏；因此，目前靶向治疗和预防措施受到限制。
开发和验证一种使用常见临床因素预测足月新生儿 PAIS 概率的诊断风险预测模型。
设计、地点和参与者：在这项诊断研究中，我们使用多变量逻辑回归开发了一个预测模型，该模型使用了基于登记的病例数据，这些数据是 2003 年 1 月至 2020 年 3 月期间从艾伯塔围产期卒中项目、加拿大脑瘫登记处、国际儿科卒中研究和艾伯塔妊娠结局和营养研究中收集的。纳入标准为足月出生和无与卒中诊断相关的潜在医疗条件。记录中超过 20%的数据缺失的记录被排除在外。变量选择基于同行评议的文献。数据分析于 2021 年 9 月进行。
与 4 个登记处的 PAIS 相关的常见临床妊娠、分娩和新生儿因素。
主要结局是模型预测 PAIS 的判别准确性，通过一致性统计量（C 统计量）来衡量。
在最初的分析中，有 2571 名足月新生儿（527[20%]例和 2044[80%]例对照个体；胎龄范围为 37-42 周），其中 1389 名（54%）为男性，与对照组相比，病例组中男性比例更高（318[60%]与 1071[52%]）。最终模型使用了 1924 名新生儿，包括 321 例（17%）病例和 1603 例（83%）对照，以及 9 个与足月新生儿 PAIS 风险相关的临床因素：母亲年龄、吸烟、滥用药物、子痫前期、绒毛膜羊膜炎、产时母亲发热、急诊剖宫产、5 分钟 Apgar 评分低和男性。该模型在病例和对照组之间表现出良好的区分能力（C 统计量为 0.73；95%CI，0.69-0.76），并且具有良好的模型拟合度（Hosmer-Lemeshow P=0.20）。内部验证技术产生了类似的 C 统计量（bootstrap 重采样为 0.73[95%CI，0.69-0.77]，10 折交叉验证曲线下面积为 0.72[bootstrap 偏校正 95%CI，0.69-0.76]），使用仅来自加拿大艾伯塔省的病例和对照的敏感性分析也是如此（C 统计量为 0.71；95%CI，0.65-0.77）。
研究结果表明，临床变量可用于开发和内部验证预测足月新生儿 PAIS 风险的模型，具有良好的预测性能和较强的内部有效性。识别出具有高 PAIS 概率的新生儿，然后对其进行早期诊断和治疗的筛查，可能会降低受影响个体及其家庭的终生发病率。
