Gestational age-specific serum creatinine can predict adverse pregnancy outcomes

Abstract

Serum creatinine level (SCr) typically decreases during pregnancy due to physiologic glomerular hyperfiltration. Therefore, the clinical practice of estimated glomerular filtration rate (eGFR) based on SCr concentrations might be inapplicable to pregnant women with kidney disease since it does not take into account of the pregnancy-related biological changes. We integrated the Wonju Severance Christian Hospital (WSCH)-based findings and prior knowledge from big data to reveal the relationship between the abnormal but hidden SCr level and adverse pregnancy outcomes. We analyzed 4004 pregnant women who visited in WSCH. Adverse pregnancy outcomes included preterm birth, preeclampsia, fetal growth retardation, and intrauterine fetal demise. We categorized the pregnant women into four groups based on the gestational age (GA)-unadjusted raw distribution (Q1-4_raw), and then GA-specific (Q1-4_adj) SCr distribution. Linear regression analysis revealed that Q1-4_adj groups had better predictive outcomes than the Q1-4_raw groups. In logistic regression model, the Q1-4_adj groups exhibited a robust non-linear U-shaped relationship with the risk of adverse pregnancy outcomes, compared to the Q1-4_raw groups. The integrative analysis on SCr with respect to GA-specific distribution could be used to screen out pregnant women with a normal SCr coupled with a decreased renal function.

Figure 1

The number of cases according to the number of examinations performed to determine serum creatinine levels.

Figure 2

Figure shows the two methods used to categorize the subjects into four groups based on the levels of serum creatinine (SCr). (A) Figure shows the distribution of our original or raw data. (B) Figure shows the abstract version of the distribution of gestational age-specific serum creatinine made by Harel et al.

Figure 3

Association between SCr groups and the number of adverse pregnancy outcomes. (A,B) Adverse pregnancy outcomes include preterm birth, preeclampsia, fetal growth retardation, and intrauterine fetal death; therefore, the maximum value of the number of pregnancy outcomes is four. (C) Y-axis indicates beta-coefficients obtained from linear regression with the number of adverse pregnancy outcomes and SCr groups (Q1–4_raw or Q1–4_adj) set as dependent and independent variables, respectively. Age, hypertension, diabetes, and parity types are covariates for multivariate models. *, **, *** indicate p value < 0.001, < 0.00001, < 0.0000001, respectively.

Figure 4

Logistic regression. Q2_raw and Q2_adj were used as references in all models. Age, hypertension, diabetes, and parity types are covariates for multivariate models. Logistic regression method was used to reveal the relationship between main outcome and four groups based on serum creatinine measurements. Relationship of the four SCr groups with all types of adverse pregnancy outcomes (A), preterm birth (B), preeclampsia (C), fetal growth retardation (D), and intrauterine fetal demise (E) are illustrated.

Figure 5

Application of the gestational age-specific serum creatinine distribution in clinical setting.