Advertisement

Reply

Published:August 05, 2019DOI:https://doi.org/10.1016/j.ajog.2019.07.048
      To the Editors:
      The risks produced from our competing risks model
      • Wright D.
      • Tan M.Y.
      • O’Gorman N.
      • et al.
      Predictive performance of the competing risk model in screening for preeclampsia.
      are for delivery with preeclampsia before a specific gestational age, assuming no other cause for delivery. Because other-cause deliveries are effectively censored observations, the actual incidence of preeclampsia would be expected to be lower than predicted. For early gestations, when there are few other-cause deliveries, the effects would be small. At later gestations, with many other-cause deliveries, the effect of censoring may be substantial.
      We welcome the contribution from Krantz and Hallahan who show how, by incorporating a survival function, our model can be extended to predict the risk of birth with preeclampsia that allows for deliveries because of other causes.
      • Krantz D.A.
      • Hallahan T.W.
      Incorporating the probability of competing event(s) into the preeclampsia competing risk algorithm.
      However, we would not advocate the adjustment in general because, from the perspective of clinical management and decision-making, the risk of assuming no other-cause delivery is more meaningful. For example, the estimated risk of preeclampsia at <41 weeks gestation addresses the question of what is the risk of having to deliver with preeclampsia at <41 weeks gestation if the pregnancy were to continue. In this instance, the finding of a high risk result could lead to medically indicated delivery at an earlier gestational age, such as 38 weeks gestation, thereby avoiding the development of term preeclampsia. Consequently, the no other-cause risk is being used to modify the other-cause delivery process.
      A technical issue with the method suggested by Krantz and Hallahan is the specification of Soth(g), which is the probability that delivery from other causes occurs after gestational age g. This should be conditional on the maternal features and biomarker data available. This presents a difficult technical challenge that is avoided by the use of no other-cause delivery risks.

      References

        • Wright D.
        • Tan M.Y.
        • O’Gorman N.
        • et al.
        Predictive performance of the competing risk model in screening for preeclampsia.
        Am J Obstet Gynecol. 2019; 220: 199.e1-199.e13
        • Krantz D.A.
        • Hallahan T.W.
        Incorporating the probability of competing event(s) into the preeclampsia competing risk algorithm.
        Am J Obstet Gynecol. 2019; 221: 532-533

      Linked Article

      • Incorporating the probability of competing event(s) into the preeclampsia competing risk algorithm
        American Journal of Obstetrics & GynecologyVol. 221Issue 5
        • Preview
          We read with great interest the paper by Wright et al1 in which the authors assessed the predictive performance of their competing risk algorithm for preeclampsia. Using such an approach, the authors estimated the detection rate for early, preterm, and all preeclampsia to be 90%, 75%, and 50%, respectively. The authors are to be congratulated for introducing the important concept of survival analysis and competing risks into this emerging field of prenatal care. However, further refinement of the algorithm is warranted.
        • Full-Text
        • PDF