Department of Obstetrics and Gynecology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
J Ultrasound Med. 2020 Feb;39(2):373-378. doi: 10.1002/jum.15116. Epub 2019 Aug 18.
To identify abnormal amniotic fluid volumes (AFVs), normal volumes must be determined. Multiple statistical methods are used to define normal amniotic fluid curves; however, quantile regression (QR) is gaining favor. We reanalyzed ultrasound estimates in identifying oligohydramnios, normal fluid, and polyhydramnios using normal volumes calculated by QR.
Data from 506 dye-determined or directly measured AFVs along with ultrasound estimates were analyzed. Each was classified as low, normal, or high for both the single deepest pocket (SDP) and amniotic fluid index (AFI). A weighted κ statistic was used to assess the level of agreement between the AFI and SDP compared to actual AFVs by QR.
The overall level of agreement for the AFI was fair (κ = 0.26), and that for the SDP was slight (κ = 0.19). Although not statistically significant (P = .792), the positive predictive value to classify a low volume using the AFI was lower compared to the SDP (35% vs 43%). The positive predictive value for a high volume was higher using the AFI compared to the SDP (55% versus 31%) but not statistically significant. The missed-call rate for high-volume identification by the SDP versus AFI was statistically significant (odds ratio, 5.5; 95% confidence interval, 2.04-14.97). The missed-call rate for low-volume identification by the AFI versus SDP was not statistically significant (odds ratio, 3.3; 95% confidence interval, 0.96-11.53).
Both the AFI and SDP identify actual normal AFVs by QR, with sensitivity higher than 90%. The SDP is superior for identification of oligohydramnios, and the AFI superior for identification of polyhydramnios.
确定异常羊水体积(AFV),必须确定正常体积。多种统计方法用于定义正常羊水曲线;然而,分位数回归(QR)越来越受欢迎。我们使用 QR 计算的正常体积重新分析了超声估计值,以识别羊水过少、正常羊水和羊水过多。
分析了 506 例染料确定或直接测量的 AFV 以及超声估计值的数据。每个均根据 QR 计算的单一最深液囊(SDP)和羊水指数(AFI)进行低、正常或高分类。使用加权κ统计量评估 AFI 和 SDP 与 QR 实际 AFV 的一致性水平。
AFI 的总体一致性为中等(κ=0.26),SDP 的一致性为轻微(κ=0.19)。尽管没有统计学意义(P=0.792),但使用 AFI 分类低容量的阳性预测值低于 SDP(35%对 43%)。使用 AFI 比 SDP 高容量的阳性预测值更高(55%对 31%),但无统计学意义。SDP 与 AFI 相比,高容量识别的漏诊率具有统计学意义(比值比,5.5;95%置信区间,2.04-14.97)。AFI 与 SDP 相比,低容量识别的漏诊率无统计学意义(比值比,3.3;95%置信区间,0.96-11.53)。
AFI 和 SDP 均可通过 QR 识别实际的正常 AFV,灵敏度均高于 90%。SDP 更适合识别羊水过少,而 AFI 更适合识别羊水过多。