Impact of Immunoassay Selection on the sFlt-1/PlGF Ratio and Preeclampsia Risk Classification

The soluble fms-like tyrosine kinase-1/ placental growth factor (sFlt-1/PlGF) ratio is widely used to aid in preeclampsia (PE) prognosis. Many laboratories apply cutoffs derived from the PRediction of short-term Outcome in preGNant wOmen with Suspected preeclampsIa Study (PROGNOSIS) using Elecsys results. However, differences between immunoassays and lack of international standardization introduce variability in sFlt-1/PlGF values and cutoffs, potentially affecting clinical interpretation. This study compared Elecsys and Kryptor immunoassays for sFlt-1 and PlGF and assessed their impact and the best cutoff for their ratio in PE risk classification.

We compared Elecsys and Kryptor immunoassays in 160 samples from pregnant women with suspected PE. Correlation, receiver operating characteristic (ROC) performance (area under the curve [AUC]), and concordance across risk categories (low/intermediate/high) were assessed using Elecsys-based PROGNOSIS study cutoffs and a Kryptor-specific threshold.

Kryptor yielded significantly lower PlGF and sFlt-1 concentrations than Elecsys (medians: 103.5 ng/L vs 146.3 ng/L and 3199 ng/L vs 4034 ng/L; both P < 0.001). Conversely, sFlt-1/PlGF ratios were higher with Kryptor (31 vs 25; P < 0.001). Diagnostic accuracy was comparable (AUC: Elecsys 0.809; Kryptor 0.810). Applying Elecsys cutoffs to Kryptor results shifted risk categorization, with Kryptor classifying fewer women as low risk (54.4%) compared with Elecsys (58.2%), and more women as high risk (24.3% vs 18.1%). Overall concordance was high (85%; κ = 0.750), but among Elecsys-classified intermediate-risk samples, Kryptor agreed in only 63.2% and 31.6% were upgraded to high risk, reflecting Kryptor's tendency toward higher ratios.

Elecsys and Kryptor show similar discriminative performance; however, immunoassay selection can alter PE risk stratification, particularly for intermediate risk cases. Method-specific thresholds are essential to avoid misclassification and optimize clinical decisions.