连续血糖监测与人工智能风险指数相关：早产儿脑室出血的早期迹象。
Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates.
机构信息
1 Department of Pediatrics, Yale University, New Haven, Connecticut.
2 Neonatal Intensive Care Unit, Department of Woman's and Child's Health, University of Padova, Padova, Italy.
出版信息
Diabetes Technol Ther. 2019 Mar;21(3):146-153. doi: 10.1089/dia.2018.0383.
OBJECTIVE
To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm neonates based on continuous glucose monitoring (CGM).
STUDY DESIGN
We retrospectively analyzed CGM traces obtained from 50 very preterm neonates, grouped into two sub-cohorts started on CGM within 12 and 48 h of birth, respectively. A CGM linked to an Artificial Intelligence Risk (CLAIR) index was developed to quantify glucose variability during the first 72 h of life in neonates with and without IVH. Brain-US was performed at least twice a day for the first 5 days of birth. An integrated remote monitoring platform was developed to capture major clinical events in real time and gather data for the risk index. The new score performance was further compared with other measures of glucose variability (coefficient of variation [CV] and standard deviation [SD]) and with a clinical risk index for babies II (CRIB-II) as a predictor of IVH event. The two cohorts were analyzed separately for internal validation of the method.
RESULTS
The primary cohort consisted of 26 neonates (gestational age 30 [28, 31] weeks; BW1275 g[1090, 1750]). Controls (n = 23) exhibited higher CLAIR index than cases (P = 0.004). A cut-off of 0.69 for the new CLAIR index allowed a 100% sensitivity and an 83% specificity for IVH prediction. The CLAIR index was the sole significant predictor for IVH (P = 0.003) when compared with clinical variables, CV, SD, and CRIB-II. In a subgroup analysis in very low-birth-weight infants, the CLAIR index was the sole variable significantly associated with IVH (P = 0.009). Analysis on the secondary cohort (five cases and 16 controls) confirmed a higher CLAIR index in the controls (P = 0.008), in the absence of a difference for CV, SD, and CRIB-II between the two groups.
CONCLUSION
CGM, combined with the AI-algorithm, provides a high-sensitivity index for risk detection of IVH that reflects the glycemic impairment preceding IVH.
目的
基于连续血糖监测（CGM）开发并验证一种新的早产儿脑室内出血（IVH）风险评分。
研究设计
我们回顾性分析了 50 例极早产儿的 CGM 轨迹，这些早产儿分别在出生后 12 小时和 48 小时内开始进行 CGM 监测，分为两个亚组。通过对伴有和不伴有 IVH 的新生儿在出生后前 72 小时内的血糖变异性进行量化，开发了一种与人工智能风险（CLAIR）指数相关的 CGM 链接。在出生后的前 5 天，每天至少进行两次脑超声检查。开发了一个集成远程监测平台，以实时捕获主要临床事件并为风险指数收集数据。该新评分的性能还与其他血糖变异性指标（变异系数 [CV] 和标准差 [SD]）和婴儿 II 临床风险指数（CRIB-II）进行了比较，作为 IVH 事件的预测指标。两个队列分别进行了内部验证。
结果
主要队列包括 26 名新生儿（胎龄 30[28，31]周；BW1275 g[1090，1750]）。对照组（n=23）的 CLAIR 指数高于病例组（P=0.004）。新的 CLAIR 指数为 0.69 的截断值可使 IVH 预测的敏感性达到 100%，特异性为 83%。与临床变量、CV、SD 和 CRIB-II 相比，CLAIR 指数是唯一与 IVH 相关的显著预测因子（P=0.003）。在极低出生体重儿的亚组分析中，CLAIR 指数是唯一与 IVH 显著相关的变量（P=0.009）。对次要队列（5 例和 16 例对照组）的分析证实，对照组的 CLAIR 指数较高（P=0.008），两组间 CV、SD 和 CRIB-II 无差异。
结论
CGM 结合人工智能算法为 IVH 风险检测提供了一种高灵敏度指数，反映了 IVH 发生前的血糖损害。
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