Can the use of diagnostic and prognostic categorisation tailor the need for assisted reproductive technology in...

The complications associated with in vitro fertilisation (IVF) for both the offspring and mother, and its high cost make it essential to tailor the technology to those infertile couples who truly benefit from it.

To determine whether a simple prognostic algorithm could discriminate between couples who require immediate fertility treatments and couples in whom less invasive strategies should be offered first.

In this retrospective cohort study, couples were classified into six groups based on the medical necessity of IVF and their prognosis for natural conception: (i) tubal/severe semen factor mandating immediate IVF due to the very low chance of natural conception; (ii) pure anovulation infertility; (iii) female age ≥39 years; and (iv) unexplained/mild male infertility (no indication for immediate treatment) with (4A) good, (4B) moderate or (4C) poor prognosis of natural conception, as per an existing, validated prognostic model. For each group, we constructed Kaplan-Meier curves to measure natural conception and the effect of fertility treatment.

The 12 months cumulative live birth rate for couples with unexplained or mild male infertility and poor prognosis increased from 1% without treatment to 35% after treatment (P < 0.001). In contrast, couples with good prognosis experienced a statistically insignificant increase in their cumulative live birth rate from 40% to 56% (P = 0.07). This demonstrates that a prognostic model could predict a couple's chances of natural conception and the benefit they derive from treatment.

This prognostic mode allows fertility treatment to be individually tailored to reduce unnecessary IVF without compromising fertility chances.