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 (Q1–4raw), and then GA-specific (Q1–4adj) SCr distribution. Linear regression analysis revealed that Q1-4adj groups had better predictive outcomes than the Q1–4raw groups. In logistic regression model, the Q1–4adj groups exhibited a robust non-linear U-shaped relationship with the risk of adverse pregnancy outcomes, compared to the Q1–4raw 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.

Development of a formula for estimated glomerular filtration rate in pregnant women from physiological hyperfiltration of serum creatinine

“…CLSI recommends that utilizing two approaches to establish a 95% con dence interval (CI) for each subclass (e.g., age group and sex): either selecting quali ed reference individuals or including a su cient number of participants, with a recommended minimum of 120 subjects 7 . We previously analyzed the SCr concentrations in 4,004 pregnant women to predict adverse pregnancy outcomes 11 . We attempted to satisfy the rst recommendation of the CLSI by carefully selecting reference SCr measurements in healthy pregnant women without a medical diagnosis of a chronic disease or adverse pregnancy outcomes.…”

“…Kang et al 11 demonstrated that the SCr concentration could predict the risk of adverse pregnancy outcomes and the number of co-occurring adverse pregnancy outcomes. When using the GW-speci c SCr distribution, the predictive power of adverse pregnancy outcomes was more robust based on beta coe cients and their p-values compared to the raw SCr distribution 11 .…”

Development of a real-time reporting system of the reference interval for gestational serum creatinine and estimated glomerular filtration rate using machine learning

“…Harel et al 22 reported that blunted glomerular hyper ltration in early pregnancy may be associated with an increased risk of PTB and perinatal mortality. In our previous study 11 , we identi ed that SCr levels could predict the risk of adverse pregnancy outcomes and the number of co-occurring adverse pregnancy outcomes. When using the GWspeci c SCr distribution, the predictive power of adverse pregnancy outcomes was more robust based on the beta coe cients and their p-values compared with the raw SCr distribution 11 .…”

“…We previously analyzed 10,126 SCr measurements from 4,004 pregnant women to predict the adverse pregnancy outcomes 11 . In this study, only 1,370 SCr measurements, which were obtained from 940 normal pregnant women with a healthy full-term baby, were extracted and analyzed.…”

Development of a formula for estimated glomerular filtration rate in pregnant women from physiological hyperfiltration of serum creatinine

“…The analysis included women who had singleton or multiple births at > 38 weeks of gestation, were aged 16–50 years at the time of delivery, and had at least one SCr measurement during pregnancy. We previously analyzed the SCr concentrations in 4,004 pregnant women to predict adverse pregnancy outcomes 11 . EMR extracted data were reviewed by an expert from the Department of Medical Information, and LIS data were reviewed by laboratory medicine specialists.…”