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Incorporating the probability of competing event(s) into the preeclampsia competing risk algorithm

      To the Editors:
      We read with great interest the paper by Wright et al
      • Wright D.
      • Tan M.Y.
      • O’Gorman N.
      • et al.
      Predictive performance of the competing risk model in screening for preeclampsia.
      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.
      In the authors’ study, the observed incidence of early-onset preeclampsia was well aligned with predicted risks but lower than predicted for all preeclampsia. The authors rightly attribute this phenomenon to the fact that as pregnancies get closer to term, there is more of a chance that delivery will occur before the opportunity for onset of preeclampsia. Indeed, in the authors’ model, the average gestational age at delivery with preeclampsia is 54 weeks, well beyond the time frame of normal pregnancy.
      Use of the cumulative incidence function (CIF),
      • Pintilie M.
      Competing risks: a practical perspective.
      which factors in the probability that the competing event (delivery for other causes) will occur after delivery because of preeclampsia, would result in more appropriate estimates of individual-specific risks, and these risks will be more in line with observed incidence.
      CIF=24Gp(g)Soth(g)dg24p(g)dg,where p(g) is described by the authors.
      • Rolnik D.L.
      • Wright D.
      • Poon L.C.
      • et al.
      Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia.
      The CIF is equivalent to the formula used by the authors except for the Soth term. Soth is the probability of delivery after a given gestational age for other causes (determined using survival analysis with preeclampsia cases censored). Soth is near 1.0 in early gestation and close to zero near term. Thus, the risk estimate based on the CIF will be similar to the authors’ estimate for early preeclampsia, while the risk estimate of all preeclampsia will be lower and more in line with observed incidence. In a subpopulation in which the risk of earlier birth is increased, there would be a further reduction in estimated all preeclampsia risk using the CIF calculation.
      Use of the CIF will not only provide a more precise estimate of the risk of observing preeclampsia but also introduce a more easily understood paradigm for risk estimation. Estimate of preeclampsia risk would now not only incorporate the assumption that all pregnancies will eventually be affected by preeclampsia (in most cases at unrealistic gestational ages) but that delivery because of other causes reduces the chance of observed occurrence.

      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
        • Pintilie M.
        Competing risks: a practical perspective.
        John Wiley and Sons Ltd, West Sussex, England2006
        • Rolnik D.L.
        • Wright D.
        • Poon L.C.
        • et al.
        Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia.
        N Engl J Med. 2017; 377: 613-622

      Linked Article

      • Predictive performance of the competing risk model in screening for preeclampsia
        American Journal of Obstetrics & GynecologyVol. 220Issue 2
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          The established method of screening for preeclampsia is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors the patient is classified as high risk and in their absence as low risk. However, the performance of such an approach is poor. We developed a competing risks model, which allows combination of maternal factors (age, weight, height, race, parity, personal and family history of preeclampsia, chronic hypertension, diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, method of conception and interpregnancy interval), with biomarkers to estimate the individual patient-specific risks of preeclampsia requiring delivery before any specified gestation.
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        American Journal of Obstetrics & GynecologyVol. 221Issue 5
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          The risks produced from our competing risks model1 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.
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