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

Development of a real-time reporting system of the reference interval for gestational serum creatinine and estimated glomerular filtration rate using machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development of a real-time reporting system of the reference interval for gestational serum creatinine and estimated glomerular filtration rate using machine learning Young Uh, Kwangjin Ahn, Taesic Lee, Jieun Kang, Seong Jin Choi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2223812/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The evaluation of maternal serum creatinine (SCr) concentrations according to gestational week (GW)-specific reference intervals (RIs) could be helpful in predicting adverse pregnancy outcomes. From January 2010 to December 2020, 1,370 SCr measurements from 940 normal pregnant women were collected from electronic medical records. Data should be processed using the bootstrap resampling method as most of the sample sizes according to GW were too small for obtaining the RIs. To enable resampling, the GWs were divided into 12 gestational periods (GPs). Implementation of resampling, determination of the appropriateness of RIs from the resampled new datasets in every GP, and establishment of GW-specific SCr RI using polynomial regression model analysis of GP-specific SCr RIs were performed using machine learning techniques. As 100 means from two resampled SCr measurements without replacement were made at every GP, 1,200 resampled results were used for developing RIs. The regression equations used for calculating the upper and lower limit of GW-specific SCr RIs were y = 88.8 − 3.75 x + 0.141 x 2 − 0.00157 x 3 and y = 42.3 − 1.48 x + 0.0321 x 2 , respectively. Gestational estimated glomerular filtration rate (eGFR) was defined as the rate of SCr hyperfiltration. The median regression equation for GW-specific eGFR RI was y = 99 + 5.71 x − 0.184 x 2 + 0.00166 x 3 , while the calculation process of SCr hyperfiltration at any GW was added to develop the gestational eGFR formula (GEF). As GW-specific SCr RI and eGFR by GEF with GW-specific eGFR RIs were reported in the laboratory information system in real time, this clinical application can be used as a screening tool for predicting the adverse pregnancy outcomes. Biological sciences/Physiology/Reproductive biology Biological sciences/Computational biology and bioinformatics/Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Pregnancy causes physiological alterations, including blood volume expansion, due to increased cardiac output and decreased systemic vascular resistance 1 , 2 . Systemic vasodilation and glomerular hyperfiltration are normal hemodynamic adaptations during pregnancy 1 . The glomerular filtration rate (GFR) increases during pregnancy, while the serum creatinine (SCr) concentration decreases according to the degree of maternal GFR change. Accordingly, the GFR estimation formula based on the SCr concentration cannot be accurately applied to pregnant women 3 . The Modification of Diet in Renal Disease (MDRD) formula 4 , which estimates the GFR using a combination of serum markers and clinical parameters, has become the standard clinical method for estimating the renal function in patients with chronic kidney disease (CKD). However, the feasibility of using this formula in the pregnant population has not been well studied, and guidelines on the application of the MDRD formula specifically exclude its interpretation in pregnant women 5 , 6 . Accurate determination of SCr concentrations in pregnant women according to gestational age is crucial because the condition of women with adverse pregnancy outcomes or abnormal kidney function can be misinterpreted as normal if the reference interval (RI) for non-pregnant women that does not reflect normal physiological changes during pregnancy is applied. This makes it difficult to diagnose CKD early and provide proper treatment for complications that can affect the fetus and mother. The Clinical and Laboratory Standards Institute has recommended that the best way to establish an RI is to collect samples from a sufficient number of qualified reference individuals in order to yield a minimum of 120 observations for analysis 7 . The parametric method (mean ± 2 standard deviation) was used to compare the RI if the values were normally distributed, whereas the nonparametric method (median and the range of 2.5th − 97.5th percentile) was used when the values were not normally distributed 7 . However, this method is difficult to apply in pregnant women due to the need to obtain a sufficient number of reference individuals by gestational age. Therefore, the estimated GFR (eGFR) values of pregnant women are possibly inaccurate as the urine output and body weight vary according to the gestational week (GW), and the amount of muscle occupied by the total body surface area in pregnant women is different from that in non-pregnant women. The rapid advancements in computer technology, stimulated by the availability of cheaper and smaller devices for processing and memory, have introduced new methods in the field of statistics. The bootstrap method, a computer-intensive resampling technique, is a statistical method based on sampling of the original dataset 8 , 9 . Many subsets of fixed sizes are generated by randomly drawing numbers (with replacement) from the original data 9 . The estimator of interest was calculated for each subset. With this large number of estimator values, the mean or median of the estimator, variance or standard deviation, and confidence intervals can easily be calculated without any assumptions regarding the original data distribution 9 . The bootstrap method was used to estimate the RIs for small sample sizes 9 , 10 . This study involved two main tasks. First, a database table was created to manage the GW-specific SCr concentrations and eGFR normal RIs in the laboratory information system. Second, a real-time reporting system for eGFR was established according to the GW-specific SCr concentration and eGFR RI by obtaining the pregnant women’s data, such as GW, body weight, and height, from the electronic medical records. Results Analysis of raw data 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. Supplementary Table 1 shows the general characteristics of the 940 pregnant women. The SCr concentration from 70.0% of pregnancy (658/940) was only measured once before labor. The kurtosis and skewness of raw 1,370 SCr measurements were 4.7 and 0.5, respectively. The p -value of the Shapiro–Wilk test was < 0.001 (Supplementary Fig. 1). Accordingly, the statistical summaries of 1,370 SCr measurements were expressed as median and interquartile range, which were 45.1 and 38.9–53.0, respectively, as their distribution was non-parametric. The distribution of SCr measurements by GW exhibited a left-skewed pattern, indicating that most examinations were conducted between 34 and 38 GW (Fig. 1 ). Figure 2 shows the GW-specific SCr distribution. Among the 41 distributions of GW-specific SCr concentrations, the following parameters were assumed to indicate no Gaussian distributions: 2.5 ≤ kurtosis < 3.5, − 0.5 ≤ skewness 0.05 (Fig. 2 ). The total GW (0–40) was divided into three trimesters, and each trimester was split into four gestational periods (GP) (Supplementary Table 2). This division was intended to increase the samples within the divided groups, and each group was expected to show measurements with normal distributions. The SCr measurement distributions of the 12 GPs were evaluated based on the normality indices (i.e., kurtosis, skewness, and p -value of the Shapiro–Wilk test, Fig. 3 ). Among the 12 GPs, GP1, GP5, GP11, and GP12 showed a normal distribution based on the kurtosis and skewness (Fig. 3 ). Based on the Shapiro–Wilk test, GP1, GP5, and GP12 were considered to exhibit Gaussian distributions satisfying the mathematical and statistical criteria (Fig. 3 ). Additional data processing was required to obtain normality for all GPs to establish the RIs. Resampling Process The resampling process was not a simple re-combination of the original data, but the creation of a new dataset, which followed the central limit theorem. Based on this, Colugnati et al. 12 developed a confidence interval for applicable cutoff points from 418 patients using the bootstrap resampling method. A bootstrap resampling method was used to overcome this obstacle. The new datasets were established by resampling the original SCr concentrations in 12 GPs using two different hyper-parameters: resample size (RS) and resample times (RTs) (Fig. 4 ). The mean value (defined as the derived value (DV) in Fig. 4 ) of the resampled SCr concentrations according to the RS increased during the RTs (described as m in Fig. 4 ). The number of RS was 19 when increased by 1 (from 2 to 20), while the number of RTs was 20 when increased by 50 increments (from 50 to 1,000). A total of 380 combinations were used. The new datasets from 380 combinations were created for every 12 GPs, and the total number of new datasets was 4,560. Among these new datasets, results falling between the 2.5th and 97.5th percentiles of the new dataset should be included within the 90% confidence interval of the original SCr concentrations. Only those that met the three normality indices (i.e., 2.5 ≤ kurtosis < 3.5, − 0.5 ≤ skewness 0.05) were finally selected. Every selected new dataset had its own resampling condition, which was composed of the RS, RTs, and GP. To reduce the time of the resampling process, conditions that create normally distributed new datasets were excluded during the next resampling process. To create reasonable RIs, new datasets of 12 GPs were constructed with coincident RS and RTs. After each selection, the number of GPs belonging to each combination was counted. If any combination was gathered on all 12 GPs, the iteration was stopped. As a result, the resampling process ended with 2 in RS and 100 in RTs. The new datasets of SCrs for all 12 GPs were normally distributed (Fig. 5 ). Ri Of Gw-specific Scr Concentration Supplement Fig. 2 illustrates the boxplots of the SCr concentration distributions according to GP. 1, 2, 7, 9, and 10 GPs had four GWs, while the other GPs had three GWs. When two virtual lines were drawn connecting the upper limits (values of the 97.5th percentile) and lower limits (values of the 2.5th percentile), respectively, according to the GPs, the shapes of both limit lines were jagged. Although the RIs of GP-specific SCr concentrations have been established, they are impractical for clinical use. A polynomial regression model of GP-specific SCr was developed to construct continuous RIs according to the GW. The first GP was excluded from the GW-specific RIs because implantation begins in the first 4–5 weeks of pregnancy; this exclusion is indicated as a fainted color in Supplementary Fig. 2. Because the method used for curve fitting was k-fold cross-validation, the value of k was 10. The range of the polynomial degree was set from 1 to 5, while the mean square errors (MSEs) according to the degree were calculated. The degree with the smallest MSE value was defined as the best degree for the polynomial regression model (Supplementary Fig. 3). A 100-time of searching for the best degree according to the upper and lower limits were processed, and the best degrees of the polynomial regression model were 3 and 2, respectively (Supplementary Fig. 4). The polynomial regression equations for the upper and lower limits generated by curve fitting are shown in Fig. 6 . The RIs of SCr for 4–40 GWs are listed in Table 1 . Table 1 Reference intervals of SCr concentration and eGFR according to the GWs GWs SCr concentration (µmol/L) eGFR (mL/min) Median 2.5th percentile 97.5th percentile Increment (%) a Median 2.5th percentile 97.5th percentile Increment (%) a 4 56.4 36.9 76.0 0.0 120.9 78.9 162.8 0.0 5 54.5 35.7 73.4 −3.3 124.8 83.0 166.5 3.2 6 52.8 34.6 71.0 −6.4 128.3 86.7 170.0 6.2 7 51.2 33.5 68.9 −9.2 131.6 89.9 173.4 8.9 8 49.8 32.5 67.0 −11.8 134.6 92.8 176.5 11.4 9 48.5 31.6 65.3 −14.1 137.3 95.3 179.4 13.6 10 47.3 30.7 63.8 −16.2 139.8 97.5 182.1 15.7 11 46.2 29.9 62.5 −18.1 142.0 99.3 184.6 17.5 12 45.3 29.2 61.4 −19.8 143.9 100.9 187.0 19.1 13 44.5 28.5 60.4 −21.2 145.6 102.1 189.1 20.5 14 43.8 27.9 59.6 −22.5 147.1 103.1 191.0 21.7 15 43.2 27.3 59.0 −23.5 148.3 103.9 192.8 22.7 16 42.7 26.8 58.5 −24.4 149.3 104.4 194.3 23.6 17 42.3 26.4 58.1 −25.1 150.2 104.7 195.6 24.2 18 41.9 26.1 57.8 −25.7 150.8 104.8 196.8 24.8 19 41.7 25.8 57.7 −26.1 151.2 104.8 197.7 25.1 20 41.6 25.5 57.6 −26.3 151.5 104.6 198.4 25.4 21 41.5 25.4 57.7 −26.4 151.6 104.3 199.0 25.4 22 41.6 25.3 57.8 −26.4 151.6 103.8 199.3 25.4 23 41.6 25.2 58.0 −26.2 151.4 103.3 199.5 25.2 24 41.8 25.3 58.3 −25.9 151.0 102.7 199.4 25.0 25 42.0 25.4 58.6 −25.6 150.6 102.0 199.2 24.6 26 42.3 25.5 59.0 −25.1 150.0 101.3 198.7 24.1 27 42.6 25.7 59.4 −24.5 149.3 100.5 198.1 23.5 28 43.0 26.0 59.9 −23.9 148.5 99.8 197.3 22.9 29 43.4 26.4 60.3 −23.2 147.7 99.1 196.2 22.2 30 43.8 26.8 60.8 −22.4 146.7 98.4 195.0 21.4 31 44.3 27.3 61.3 −21.5 145.7 97.8 193.6 20.5 32 44.8 27.8 61.7 −20.7 144.6 97.3 191.9 19.6 33 45.3 28.4 62.2 −19.7 143.5 96.8 190.1 18.7 34 45.8 29.1 62.6 −18.8 142.3 96.5 188.1 17.7 35 46.4 29.8 63.0 −17.8 141.1 96.3 185.9 16.7 36 47.0 30.6 63.3 −16.8 139.8 96.2 183.4 15.7 37 47.5 31.5 63.6 −15.8 138.6 96.4 180.8 14.7 38 48.1 32.4 63.8 −14.8 137.3 96.7 178.0 13.6 39 48.6 33.4 63.9 −13.8 136.1 97.2 175.0 12.6 40 49.2 34.5 63.9 −12.8 134.9 97.9 171.8 11.6 a Increment (%) indicates the percentage of overfiltration of SCr based on the baseline SCr concentration. eGFR estimated glomerular filtration rate, GW gestational week, SCr serum creatinine Ri Of Gw-specific Egfr Cortinovis et al. 13 mentioned that the GFR increased during pregnancy due to absolute hyperfiltration. Porrini et al. 14 revealed that more than 70 formulae used for estimating GFR had large errors, which did not improve over the past 60 years. In this reaserch, we focused on determining the value of hyperfiltrated SCr concentration as a marker of gestational GFR. As the prefix “hyper” indicated a value over the baseline, the median of 4 GW SCr RI (56.4 µmol/L in Table 1 , red horizontal line in Supplementary Fig. 5a) was set as the baseline SCr concentration (BSC). The hyperfiltration value in the BSC was defined as 100% (Supplementary Fig. 5b). Likewise, the first GP was excluded because it occurred before implantation (shown as gray box in the plots, Supplementary Fig. 5). First, the gaps between the BSC and a median of 2–12 GPs were calculated (blue arrows, Supplementary Fig. 5c). Second, the proportion of each gap against the BSC was calculated, and these rates were set as hyperfiltrations (Supplementary Fig. 5d). Third, the gaps between the median and the 2.5th percentile (purple arrows, Supplementary Fig. 5e), 25th percentile (magenta arrows, Supplementary Fig. 5e), 75th percentile (orange arrows, Supplementary Fig. 5e), and 97.5th percentile (brown arrows, Supplementary Fig. 5e) were calculated. Finally, the proportions of these gaps against each median were calculated and plotted as box plots for SCr hyperfiltration (Supplementary Fig. 5f). The box plots for SCr hyperfiltration created based on four steps were used as the source for deriving the regression equations for the lower limits, medians, and upper limits. However, the unit of hyperfiltration was expressed in percentage, and 120.1 mL/min/1.73 m 2 , as the normal eGFR of non-pregnant women 15 , was multiplied to generate the eGFR in mL/min. In the same way that the establish RI of GW-specific SCr concentration was obtained, the MSEs for lower limits, medians, and upper limits were calculated 100 times, the best degrees of polynomial regression model were 3, 3, and 2, respectively (Supplementary Fig. 6). The polynomial regression equations are presented in Fig. 7 . The RIs of eGFR for 4–40 GWs are shown in Table 1 . Establishment Of Gestational Egfr Formula And Comparison With Other Egfr Equations Although Porrini et al. 14 argued that more than 70 eGFR formulae had obvious limitations, Levey et al. 16 recommended the use of eGFR as a screening tool for identifying patients for whose GFR should be confirmed by directly GFR measuring. The concept used to develop the GW-specific RI of eGFR was applied to establish the gestational eGER formula (GEF). A real case was used to explain the process of establishing a GEF. The patient was a 32-year-old woman at 31 GW with an SCr concentration of 58.3 µmol/L (brown point A, Supplementary Fig. 7). First, when an SCr concentration from any pregnant woman was tested, the hyperfiltration rate against the mean of RI was calculated (brown arrow, Supplementary Fig. 7a). Next, this rate multiplied by 120.1 mL/min was applied on the median regression line at the GW-specific RI of eGFR on a specific GW (brown arrow on 31 GW at Supplementary Fig. 7b). Finally, the actual formula used was as follows: \(Gestational eGFR formula \left(GEF\right)=\) \(\left(1-\frac{SCr concentration \left(\mu mol/L\right)}{65.55-2.615\times GW+0.08655 \times {GW}^{2}-0.000785 \times {GW}^{3}}\right)\times 120.1\) \(+(99+5.71 \times GW-0.184 \times {GW}^{2}+0.00166 \times {GW}^{3})\) SCr, serum creatinine; GW, gestational week When the correlation coefficients were compared with those of other eGFR equations, such as the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 17 , modified MDRD (mMDRD) 18 , and Nanra equations 19 , mMDRD was the most correlated equation with GEF (Fig. 8 ). Figure 9 shows the real-time reporting form provided by the clinician. To avoid confusion, an SCr concentration expressed in mg/dL and an eGFR obtained using the mMDRD equation were provided. Discussion Both GFR and renal plasma flow increased early during pregnancy. Serial observations suggest that GFR progressively reaches a peak level in mid-pregnancy and remains constant thereafter throughout the duration of pregnancy 20 , 21 . On average, the GFR during the second half of pregnancy increases beyond the non-gravid levels (40–50%) 20 . As failure to evaluate this physiological increase is an important risk factor for morbidity and mortality for both the mother and child, it is crucial to quickly and accurately identify and manage their abnormalities through the provision of periodic prenatal care. However, determining the RI for pregnant women remains challenging as the major physiological, hormonal, and biochemical characteristics of the mother and fetus continuously change as pregnancy progresses. When pregnancy deviates from its normal course, several biochemical markers can be used to assess these abnormalities. SCr concentration is mainly used to assess for certain maternal conditions such as renal diseases and preeclampsia (PE). Women with renal disorders experience several problems during pregnancy due to increased physiological changes associated with renal dysfunction and the risk of disease progression, the potential teratogenicity of medications, and the increased risk of developing complications, such as PE and preterm delivery 2 , 15 , 22 , 23 . Although the prevalence of CKD in women of childbearing age seems relatively low, with estimates of 0.1–4%, CKD significantly increases the risk of adverse maternal and perinatal outcomes 23 . Pregnancy-related acute kidney injury (AKI) is one of the most common causes of AKI in young women and is associated with future risks of CKD, hypertension, and cardiovascular diseases 24 . PE is associated with maternal perinatal morbidity and mortality and affects 5–7% of pregnant women worldwide 25 . PE is divided into two classifications. Early-onset PE (before 34 GWs) is commonly associated with abnormal uterine artery Doppler findings, fetal growth restriction, and adverse maternal and neonatal outcomes 26 . By contrast, late-onset PE (after 34 GWs) is mostly associated with a normal or slightly increased uterine resistance index, low rate of fetal involvement, and more favorable perinatal outcomes 26 . In patients with PE, renal perfusion and GFR are reduced, although the levels that are much lower than those of normal non-pregnant women are infrequent and are a consequence of severe disease. The histological findings of PE include glomerular endothelial cell swelling and detachment, subendothelial fibrinoid deposits, occlusion of glomerular capillaries, reduced density and size of endothelial fenestrae, and thickening of the glomerular basement membrane 27 , 28 . These changes, typical of thrombotic microangiopathy, impair the glomerular capillary hydraulic permeability and reduce the filtration surface area, resulting in diminished GFR 29 . These abnormal findings in PE are due to a pre-renal mechanism; hence, they return to normal after childbirth. If early-onset PE is not diagnosed at an early stage, fetal growth restriction or preterm birth (PTB) may result. The absence of glomerular hyperfiltration may reflect the antecedent presence of intraglomerular hypertension due to vascular dysfunction, impaired endothelium, and faulty angiogenesis, all of which are associated with underlying comorbid conditions, such as CKD, hypertension, and diabetes 30 – 32 . Vascular dysfunction can also lead to abnormal placental implantation or development, resulting in placental dysfunction 33 and maternal PE, which is the leading cause of provider-initiated PTB 34 . Therefore, it is important to accurately evaluate the renal function during pregnancy. However, Smith et al. 5 suggested that the sole application of SCr concentration or SCr-based equations tends to substantially underestimate the renal function status during pregnancy. Park et al. 15 reported that the absence of prominent midterm renal hyperfiltration, marked by an extremely high eGFR, might be a significant risk factor for poor pregnancy outcomes in women without evident functional renal impairment. Harel et al. 22 reported that blunted glomerular hyperfiltration in early pregnancy may be associated with an increased risk of PTB and perinatal mortality. In our previous study 11 , we identified that SCr levels could predict the risk of adverse pregnancy outcomes and the number of co-occurring adverse pregnancy outcomes. When using the GW-specific SCr distribution, the predictive power of adverse pregnancy outcomes was more robust based on the beta coefficients and their p- values compared with the raw SCr distribution 11 . In this study, eGFR was calculated as the percentage of SCr hyperfiltration. A 24-h urine creatinine clearance is the most commonly used GFR test method in clinical practice, but it is affected by muscle mass, weight, height, and food intake. The weight of pregnant women changes according to the GW; hence, it is impractical to perform a the 24-h urine collection 19 . The change in muscle mass during pregnancy is not well known and varies greatly depending on an individual’s diet and exercise habits. As our eGFRs were derived only from the SCr hyperfiltration value, the eGFRs were not affected by the bias caused by non-renal aspects. As our eGFR unit is reported as mL/min, without reflecting the body surface area, this may be a limitation of this study. However, one way to set an RI that can exclude the adverse pregnancy outcomes is by retrospectively analyzing the SCr data of each pregnant woman in each institution. The real-time clinical application of the normal RI of SCr concentration and eGFR according to the GW can be helpful as a screening test in two aspects. When the SCr concentration and eGFR are lower than those of RIs or show blunted glomerular hyperfiltration, this may imply the possibility of pregnancy-induced kidney disease or PE. By contrast, if the SCr concentration and eGFR are higher than those of RIs, severe anemia and/or hypoproteinemia may be suspected. The real-time clinical application of the RIs of SCr concentration and eGFR according to the GW may help in the early detection of adverse pregnancy outcomes in pregnant women. In conclusion, by applying the bootstrap resampling process, a type of machine learning technique, we were able to establish the SCr concentration and eGFR RIs according to the GW among normal pregnant Korean women. However, our study is limited in that it cannot represent all pregnant women, as our data were obtained from a single center with limited geographical coverage. Therefore, to accurately evaluate the normal RIs for SCr concentration and eGFR among pregnant women, big data analytical studies that represent pregnant women and verification studies using our data are needed in the future. Methods Data collection From January 2010 to December 2020, relevant data were collected from the electronic medical records and laboratory information systems at Wonju Severance Christian Hospital (WSCH), and all data were decoded automatically. Women who had singleton or multiple births at > 38 weeks of gestation and were aged 16–50 years at the time of delivery were included in the analysis; the SCr concentration was measured at least once during the pregnancy period. Pregnant women with adverse pregnancy outcomes such as PE, gestational diabetes, gestational hypertension, and other underlying medical and surgical problems were excluded. If the SCr test was performed two or more times a week, only one of the initially tested SCr results was included. This study was approved by the Institutional Review Board (IRB) of WSCH (CR321084). This study was conducted in accordance with the principles of the Declaration of Helsinki. This was an observational study without medical interventions; therefore, the need to obtain informed consent from patients was waived. The waiver of informed consent was confirmed by the IRB of the WSCH (CR321084). The SCr concentrations were measured using the Cobas ® 8000 system (Cobas ® c 702 and e 801 module; Roche Diagnostics, Switzerland), Modular DPE analyzer (Roche Diagnostics), Vitros FS 5.1 (Ortho Clinical Diagnostics, Raritan, NJ, USA), Vista 1500 (Siemens Healthineers, Erlangen, Germany), and Atellica CH 930 analyzer (Siemens Healthineers). Data of the SCr concentrations were analyzed by converting mg/dL to µmol/L. Analysis Of Original Data For the enrolled normal pregnant women, the general characteristics, number of cases examined, and distribution of SCr concentrations according to GW were analyzed. The total GWs (0–40) was divided into three trimesters, and each trimester was split into four periods (Supplementary Table 2). The Gaussian distribution of SCr concentrations was defined according to the following three criteria: met 35 , kurtosis 3 (2.5–3.5), skewness 0 (− 0.5 to 0.5), and a p -value of > 0.05 in normality tests. Normality tests were performed using the Shapiro–Wilk test. Computational statistics and graphics were performed using R language and environment for statistical computing version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). Resampling Process Three hyperparameters were required for the resampling process: DV as new data, RS, and RTs. DV type was set as the mean value in this study. Step 1) A resampled dataset was extracted from the original data by setting n as the RS without replacement (Fig. 4 ). Step 2) The mean value (defined as DV in this study) was obtained for a resampled dataset. Step 3) Steps 1–2 m (referred to as RTs) were repeated. Thus, the new dataset included m DVs of SCr. According to the central limit theorem, when sufficient numbers of m and n are set, the values in the new dataset follow a Gaussian distribution, although the SCr concentrations in the original data do not show normality. Step 4) RS ( n ) was increased by 1 (from 2 to 20), while RT ( m ) was increased by 50 increments (from 50 to 1,000). As a result, the total number of newly created datasets was 380. Step 5) The creation of 380 new datasets (steps 1–4) was performed in each of the 12 GPs. After all the 4,560 new datasets were obtained, all distributions were evaluated to determine whether the new dataset satisfied the criteria of an RI. The first criterion of RI was that results falling between the 2.5th and 97.5th percentiles of the new dataset should be included within the 90% confidence interval of the original data. The second criterion was that the p- value of the normality test should be > 0.05. The final criterion was whether the distribution showed kurtosis (2.5 to 3.5) and skewness (− 0.5 to 0.5) (Fig. 4 ). Establishment Of Gp-specific Scr Ri From New Datasets Obstetric experts manually categorized GWs (0–40 GWs) into 12 GPs. As each GP had three or four GWs, the estimated RIs of the total GWs in each period resembled the angular cliffs. Polynomial linear regression equations were applied to develop the smooth-curved RIs. Because implantation was not completed at 0–3 GWs, 1 GP (containing 0–3 GWs) was excluded. The independent variable was the middle GW of each of the 12 GP, while the dependent variables were resampled values of the 2.5th and 97.5th percentiles in each GP. In order to identify the best degree of polynomial linear regression, the MSE was calculated based on the following degrees: 1 to 5. As k-fold cross-validation was chosen as the methodology, the degree with the smallest MSE was defined as the best degree. This cross-validation was repeated 100 times for every polynomial regression model. Establishment Of Gp-specific Egfr Ri From Gp-specific Scr Ri Our research team focused on the value of hyperfiltrated SCr concentration as a marker of gestational GFR. To calculate the “hyper” filtrated rate, we defined the BSC as the median of 4 GW SCr RI (56.4 µmol/L). Similarly, the first GP (0–3 GWs) was excluded. Step 1) The gaps between BSC and the median of 2–12 GPs were calculated. Step 2) As the proportion of each gap from step 1 was calculated based on the BSC, these rates were set in hyperfiltrations. Step 3) The gaps between the median and the 2.5th percentile, 25th percentile, 75th percentile, and 97.5th percentile were calculated. Step 4) The gaps from step 3 were calculated to identify the proportion of each gap using the median value in each GP. Step 5) Boxplots were constructed according to the proportions from step 4. However, hyperfiltration was expressed in percentage; the eGFR of 120.1 mL/min/1.73 m 2 , as the normal value for non-pregnant women 15 , was multiplied to generate the eGFR in mL/min. These boxplots were used as the source for deriving the regression equations for the lower limits, medians, and upper limits. In the same way that the RI of GW-specific SCr concentration was established, the MSEs for lower limits, medians, and upper limits were calculated 100 times. Development Of Gw-specific Scr And Egfr Ris The RI was constructed according to the 2.5th and 97.5th percentiles, which were the lower and upper limits, respectively. As GWs that required RIs were 4–40, the GW-specific RIs were derived using the best polynomial regression equations of the upper and lower limits. Creation Of The Gef The median regression equation from the GP-specific RI of eGFR could help obtain the median gestational eGFR for any GW. As every SCr concentration was not the same as the median SCr concentration, extra adjustment was necessary to determine a more precise eGFR. Step 1) We calculated the proportion of maternal SCr concentration relative to the median value of the GW-specific SCr RI. Step 2) Because gestational eGFR was expressed in mL/min, this proportion was transformed into mL/min. Hence, 120.1 mL/min, as the normal eGFR value of non-pregnant women 15 , was multiplied. Step 3) The formula was created based on the sum of the median regression equation of the GP-specific eGFR RI and the calculation process for steps 1–2. Declarations Data availability Detailed data that support the findings and equations of this research are not available because of privacy concerns and hospital regulation restrictions to protect patients. Anonymized data may be available from the permission of the corresponding author upon reasonable request. Acknowledgements We would like to thank Editage (www.editage.co.kr) for English language editing. Author contributions Conceptualization: Uh Y; Data curation and formal analysis: Kang J and Hwang S; Investigation: Choi SJ; Methodology: Ahn K and Lee T; Resources: Seo DM and Cho J; Software and validation: Hwang S and Seo DM; Visualization: Ahn K; Writing – original draft: Uh Y; Writing – review and editing: Ahn K and Lee T. All authors approved the final manuscript. Competing interests No potential conflicts of interest relevant to this article are reported. References Cheung, K. L. & Lafayette, R. A. Renal physiology of pregnancy. Adv Chronic Kidney Dis 20 , 209–214 (2013). Gonzalez Suarez, M. L., Kattah, A., Grande, J. P. & Garovic, V. Renal disorders in pregnancy: Core curriculum 2019. Am J Kidney Dis 73 , 119–130 (2019). Wiles, K. et al. Serum creatinine in pregnancy: A systematic review. Kidney Int Rep 29 , 408–419 (2018). Levey, A. S. et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 145 , 247–254 (2006). Smith, M. C., Moran, P., Ward, M. K. & Davison, J. M. Assessment of glomerular filtration rate during pregnancy using the MDRD formula. BJOG 115 , 109–112 (2008). Maynard, S. E. & Thadhani, R. Pregnancy and the kidney. J Am Soc Nephrol 20 , 14–22 (2009). Clinical and Laboratory Standards Institute. Defining, establishing, and verifying reference intervals in the clinical laboratory; Approved Guideline – Third Edition (Clinical and Laboratory Standards Institute, 2010). Efron, B. & Tibshirani, R. Statistical data analysis in the computer age. Science 253 , 390–395 (1991). Pavlov, I. Y., Wilson, A. R. & Delgado, J. C. Resampling approach for determination of the method for reference interval calculation in clinical laboratory practice. Clin Vaccine Immunol 17 , 1217–1222 (2010). Coskun, A., Ceyhan, E., Inal, T. C., Serteser, M. & Unsal, I. The comparison of parametric and nonparametric bootstrap methods for reference interval computation in small sample size groups. Accred Qual Assur 18 , 51–60 (2013). Kang, J. et al. Gestational age-specific serum creatinine can predict adverse pregnancy outcomes. Sci Rep 12 , 11224 (2022) Colugnati, F. A. B., Louzada-Neto, F. & de Aguiar Carrazedo Taddei, J. A. An application of bootstrap resamling method to obtain confidence interval for percentile fatness cutoff points in childhood and adolescence overweight diagnoses. Int J Obes 29 , 340–347 (2005). Cortinovis, M., Perico, N., Ruggeneti, P., Remuzzi, A. & Remuzzi G. Glomerular hyperfiltration. Nat Rev Nephrol 18 , 435–451 (2022). Porrini, E. et al. Estimated GFR: time for a critical appraisal. Nat Rev Nephrol 15 , 177–190 (2019). Park, S. et al. Midterm eGFR and adverse pregnancy outcomes: The clinical significance of gestational hyperfiltration. Clin J Am Soc Nephrol 12 , 1048–1056 (2017). Levey, A. S., Coresh, J., Tighiouart, H., Greene, T. & Inker, L. A. Measured and estimated glomerular filtration rate: current status and future directions. Nat Rev Nephrol 16 , 51–64 (2020). Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med 385 , 1737–1749 (2021). Levey, A. S. et al. Expressing the modification of diet in renal disease study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem 53 , 766–772 (2007). Gao, M., Vilayur, E., Ferreira, D., Nanra, R. & Hawkins, J. Estimating the glomerular filtration rate in pregnancy: The evaluation of the Nanra and CKD-EPI serum creatinine-based equations. Obstet Med 14 , 31–34 (2021). Lafayette, R. A., Malik, T., Druzin, M., Derby, G. & Myers, B. D. The dynamics of glomerular filtration after Caesarean section. J Am Soc Nephrol 10 , 1561–1565 (1999). Odutayo, A. & Hladunewich, M. Obstetric nephrology: renal hemodynamic and metabolic physiology in normal pregnancy. Clin J Am Soc Nephrol 7 , 2073–2080 (2012). Harel, Z., Park, A. L. & Ray, J. G. Blunted glomerular hyperfiltration in pregnancy and risk of adverse perinatal outcomes. Am J Kidney Dis 76 , 297–299 (2020). Hui, D. & Hladunewich, M. A. Chronic kidney disease and pregnancy. Obstet Gynecol 133 , 1182–1194 (2019). Piccoli, G. B. et al. Acute kidney injury in pregnancy: The need for higher awareness. A pragmatic review focused on what could be improved in the prevention and care of pregnancy-related AKI, in the year dedicated to women and kidney diseases. J Clin Med 7 , 318 (2018). Walker, J. J. Pre-eclampsia. Lancet 356 , 1260–1265 (2000). Valensise, H., Vasapollo, B., Gagliardi, G. & Novelli, G. P. Early and late preeclampsia: two different maternal hemodynamic states in the latent phase of the disease. Hypertension 52 , 873–880 (2008). Packham, D. K., Mathews, D. C., Fairley, K. F., Whitworth, J. A. & Kincaid-Smith, P. S. Morphometric analysis of pre-eclampsia in women biopsied in pregnancy and post-partum. Kidney Int 34 , 704–711 (1988). Lafayette, R. A. et al. Nature of glomerular dysfunction in pre-eclampsia. Kidney Int 54 , 1240–1249 (1998). Hussein, W. & Lafayette, R. A. Renal function in normal and disordered pregnancy. Curr Opin Nephrol Hypertens 23 , 46–53 (2014). Conrad, K. P., Novak, J., Danielson, L. A., Kerchner, L. J. & Jeyabalan, A. Mechanisms of renal vasodilation and hyperfiltration during pregnancy: current perspectives and potential implications for preeclampsia. Endothelium 12 , 57–62 (2005). Perni, U. et al. Angiogenic factors in superimposed preeclampsia: a longitudinal study of women with chronic hypertension during pregnancy. Hypertension 59 , 740–746 (2012). Di Marco, G. S. et al. The soluble VEGF receptor sFlt1 contributes to endothelial dysfunction in CKD. J Am Soc Nephrol 20 , 2235–2245 (2009). Maynard, S. E. et al. Excess placental soluble fms-like tyrosine kinase 1 (sFlt1) may contribute to endothelial dysfunction, hypertension, and proteinuria in preeclampsia. J Clin Invest 111 , 649–658 (2003). Ray, J. G., Park, A. L. & Fell, D. B. Mortality in infants affected by preterm birth and severe small-for-gestational age birth weight. Pediatrics 140 , e20171881 (2017). Rifai, N. et al. Tietz textbook of clinical chemistry and molecular diagnostics, sixth edition (Elsevier, 2018). Additional Declarations (Not answered) Supplementary Files NPJ221101supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2223812","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":150699515,"identity":"becd51c2-f678-40ba-bc45-64ef5e8e78fc","order_by":0,"name":"Young Uh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3QsWoCMRjA8U+EuBy63lHw+gifBJxO+io5hLgo+AgHQm+xuMa3yCNEvuE6hM63+gaBLh1uMK3QbjndCuY/JCTkR0IAYrH/2Kg2RmAxva7we2A9JLGlcVvJ7yCp4CflqKz+tnoItkJQgnKlmzdybgv5pGLSBYk9G0+KjbYfMlMIM2UYqSBprrdsdLueDxOEgYbRLvww/12e0ArbNf/sEF76ybvAk0ISnuCT/7FSA6MgyawVxqGcHa2dZ3tMl4rYMkjGTU1OdEU+bvbcfXXF4lC/8iD57dn8TCnA8DYAkFe3nozFYrGH6wJZ81Cft9aY8wAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Young","middleName":"","lastName":"Uh","suffix":""},{"id":150699516,"identity":"8ec22c69-3daa-4dd6-98bf-2bb43c4bbacc","order_by":1,"name":"Kwangjin Ahn","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kwangjin","middleName":"","lastName":"Ahn","suffix":""},{"id":150699517,"identity":"c637bd44-1050-4207-ac7d-7ea38fa07769","order_by":2,"name":"Taesic Lee","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Taesic","middleName":"","lastName":"Lee","suffix":""},{"id":150699518,"identity":"a9156019-0596-4a13-939b-85252a80444f","order_by":3,"name":"Jieun Kang","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jieun","middleName":"","lastName":"Kang","suffix":""},{"id":150699519,"identity":"8c256bd6-f9f1-42fc-8bd0-b47ca00af4ba","order_by":4,"name":"Seong Jin Choi","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Seong","middleName":"Jin","lastName":"Choi","suffix":""},{"id":150699520,"identity":"5260a78a-0126-4df5-91ad-d6538e82a48f","order_by":5,"name":"Sangwon Hwang","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sangwon","middleName":"","lastName":"Hwang","suffix":""},{"id":150699521,"identity":"5aef2346-3c54-4864-b77d-eef27294cd90","order_by":6,"name":"Dong Min Seo","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Min","lastName":"Seo","suffix":""},{"id":150699522,"identity":"c6563c96-b647-4463-aa22-dab0de74f180","order_by":7,"name":"Jooyoung Cho","email":"","orcid":"","institution":"Yonsei University Wonju College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jooyoung","middleName":"","lastName":"Cho","suffix":""}],"badges":[],"createdAt":"2022-11-01 01:45:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2223812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2223812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28973200,"identity":"d3cb424e-1b71-4591-95f8-424de14337ce","added_by":"auto","created_at":"2022-11-11 22:25:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":652552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of serum creatinine (SCr) measurements in every gestational week (GW). \u003c/strong\u003eAs 60.2% (825/1,370) of total measurements are within 34–38 GWs, the distribution shows a left-skewed pattern. Before 34 GWs, 6 and 32 GWs have more than 30 SCr measurements. Colors indicates three trimesters.\u003c/p\u003e","description":"","filename":"NPJ221101Fig01.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/c3649834fc664cf20b17a59e.jpg"},{"id":28973941,"identity":"33fe5972-29fb-4008-97bf-524fd46a543e","added_by":"auto","created_at":"2022-11-11 22:33:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1422661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of serum creatinine (SCr) measurements according to the gestational week (GW). \u003c/strong\u003eColors indicate the groups determined by the number of SCr measurements in each GW. Among GWs of which number of SCr measurements is more than 30, only six GWs (32, 35, 37, 38, 39, and 40 GWs) satisfy the normality indices (2.5 ≤ kurtosis \u0026lt; 3.5, −0.5 ≤ skewness \u0026lt; 0.5, and a \u003cem\u003ep\u003c/em\u003e-value of Shapiro–Wilk test of \u0026gt;0.05).\u003c/p\u003e","description":"","filename":"NPJ221101Fig02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/bfd62eb7a535443e3aec7cb7.jpg"},{"id":28973201,"identity":"c271c8d3-0136-41a7-b891-82d076ffa930","added_by":"auto","created_at":"2022-11-11 22:25:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":379573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNormality evaluations for the categorized 12 gestational period (GP)-specific serum creatinine distributions.\u003c/strong\u003e To increase the number of groups at the normality analysis, the 0–40 gestational weeks were divided into 12 GPs by obstetrics experts. The green square area indicates the zone that satisfied the two normality indices (i.e., 2.5 ≤ kurtosis \u0026lt; 3.5 and −0.5 ≤ skewness \u0026lt; 0.5). Round dot means a \u003cem\u003ep\u003c/em\u003e-value of \u0026gt;0.05, while the triangle dot means a \u003cem\u003ep\u003c/em\u003e-value of ≤0.05 measured by the Shapiro–Wilk test.\u003c/p\u003e","description":"","filename":"NPJ221101Fig03.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/5eeec20371a31ad4130cadc4.jpg"},{"id":28973940,"identity":"0a76b040-7472-4d68-9142-fe827e85217c","added_by":"auto","created_at":"2022-11-11 22:33:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1729812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram of the resampling process for the development of serum creatinine (SCr) concentration reference intervals (RIs) according to the gestational periods (GPs).\u003c/strong\u003e RS (resampling size) \u003cem\u003en\u003c/em\u003e indicates a dataset including \u003cem\u003en\u003c/em\u003eSCr measurements obtained by random sampling without replacement. \u003cem\u003em\u003c/em\u003e DVs include \u003cem\u003em\u003c/em\u003e cases of SCr mean, defined as the derived value (DV), calculated repeatedly from RS\u003cem\u003en\u003c/em\u003e. As the number of \u003cem\u003en\u003c/em\u003e is 19 (from 2 to 20, by 1) and the number \u003cem\u003em\u003c/em\u003e is 20 (from 50 to 1,000 by 50), the total number of created new datasets is 380. The new datasets from 380 combinations are created in each of the 12 GPs, while the total number of new datasets are 4,560. Among these new datasets, only new datasets that satisfied all three normality indices (i.e., 2.5 ≤ kurtosis \u0026lt; 3.5, −0.5 ≤ skewness \u0026lt; 0.5, and a \u003cem\u003ep\u003c/em\u003e-value of Shapiro–Wilk test of \u0026gt;0.05) were selected as RIs. RI is separately made for each of the 12 GP categories.\u003c/p\u003e","description":"","filename":"NPJ221101Fig04.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/ee96d0d6d2862fee349be2fc.jpg"},{"id":28974143,"identity":"f4a94ad3-af46-41d6-9079-0dcf9e724573","added_by":"auto","created_at":"2022-11-11 22:41:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1031828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of new datasets according to the gestational periods (GPs). \u003c/strong\u003eAll distributions are satisfying the normality indices (2.5 ≤ kurtosis \u0026lt; 3.5, −0.5 ≤ skewness \u0026lt; 0.5, and a \u003cem\u003ep\u003c/em\u003e-value of Shapiro–Wilk test of \u0026gt;0.05). As each new dataset of GP is constructed 100 times from 2 resampled serum creatinine measurements, a total of 1,200 resampled results were generated. Colors indicate three trimesters.\u003c/p\u003e","description":"","filename":"NPJ221101Fig05.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/3c0b501f55977aee54a2c39b.jpg"},{"id":28973203,"identity":"481f62bd-b863-4b8e-91b6-01f10e5f1f30","added_by":"auto","created_at":"2022-11-11 22:25:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":904070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReference intervals of 12 gestational periods and best regression equations of the supper and lower limits serum creatinine concentration.\u003c/strong\u003e As the 0–3 gestational weeks were considered before implantation, the first period was excluded when the polynomial regression models were developed. In the plot diagram, the thick horizontal line in the central rectangle indicates the median. The central rectangle is the interquartile range (25\u003csup\u003eth\u003c/sup\u003e–75\u003csup\u003eth\u003c/sup\u003e percentile). The whiskers above and below the rectangle show the 97.5\u003csup\u003eth\u003c/sup\u003e and 2.5\u003csup\u003eth\u003c/sup\u003e percentiles, respectively. Colors indicates three trimesters.\u003c/p\u003e","description":"","filename":"NPJ221101Fig06.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/b8eee88e572becd01b1d8152.jpg"},{"id":28973206,"identity":"32e9eb13-45a2-4ef5-9954-16f7f8b41b36","added_by":"auto","created_at":"2022-11-11 22:25:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":924756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReference intervals of predicted estimated glomerular filtration rate (eGFR) for gestational periods.\u003c/strong\u003e The median regression equation is used to establish the equation of gestational eGFR. In the plot diagram, the thick horizontal line in the central rectangle indicates the median. The central rectangle shows the interquartile range (25\u003csup\u003eth\u003c/sup\u003e–75\u003csup\u003eth\u003c/sup\u003e percentiles). The whiskers above and below the rectangle show the 2.5\u003csup\u003eth\u003c/sup\u003e and 97.5\u003csup\u003eth\u003c/sup\u003e percentiles, respectively. As 0–3 gestational weeks were considered before implantation, the first period was excluded when the polynomial regression models were developed. Colors indicates three trimesters.\u003c/p\u003e","description":"","filename":"NPJ221101Fig07.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/8a17831bf0715980dd9aa58c.jpg"},{"id":28973943,"identity":"8acaaac5-b7af-47f7-81d5-c68647c5a489","added_by":"auto","created_at":"2022-11-11 22:33:16","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1060066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analyses among the estimated glomerular filtration rates (eGFRs) derived from the gestational eGFR equation (GEF) using others.\u003c/strong\u003e Others are the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation\u003csup\u003e12\u003c/sup\u003e, modified Modification of Diet in Renal Disease (mMDRD) equation\u003csup\u003e13\u003c/sup\u003e, and Nanra equation\u003csup\u003e14\u003c/sup\u003e. The distributions placed in the diagonal indicate the eGFR values according to the gestational periods (GPs). A correlation plot in the x\u003csup\u003eth\u003c/sup\u003e row and y\u003csup\u003eth\u003c/sup\u003e column describes comparison between x\u003csup\u003eth\u003c/sup\u003e and y\u003csup\u003eth\u003c/sup\u003e GP-specific eGFR distributions placed diagonally, while the matched Spearman’s correlation coefficient (SCC) is described on the x\u003csup\u003eth\u003c/sup\u003e column and y\u003csup\u003eth\u003c/sup\u003e row.\u003c/p\u003e","description":"","filename":"NPJ221101Fig08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/044c8b3748ac6314f2ec7d80.jpg"},{"id":28973944,"identity":"63d9b911-b5bc-4f06-b68c-5695eb33e8b9","added_by":"auto","created_at":"2022-11-11 22:33:16","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":472845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic reported results showing the real-time clinical application of gestational week (GW)-specific reference interval of serum creatinine (SCr) concentration and estimated glomerular filtration rate (eGFR).\u003c/strong\u003e Age, height, weight, and GW were automatically derived from the electronic medical records of patients. When the SCr concentration with conventional unit (mg/dL) is received, it is converted into international system unit (μmol/L). In addition, the eGFR by modified the Modification of Diet in Renal Disease (mMDRD) equation is listed.\u003c/p\u003e","description":"","filename":"NPJ221101Fig09.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/44fdcb7173f6db601efffa61.jpg"},{"id":31729887,"identity":"7108f37f-bec1-467d-8e55-1121bf5d8da7","added_by":"auto","created_at":"2023-01-18 07:51:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1849431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/49e9a560-395f-4e03-96f9-e15bc9f0497c.pdf"},{"id":28973209,"identity":"5223b2e7-6f5c-41fa-9384-ece1f143742e","added_by":"auto","created_at":"2022-11-11 22:25:16","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":2101262,"visible":true,"origin":"","legend":"","description":"","filename":"NPJ221101supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-2223812/v1/0dd02482176410e7088077c6.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Development of a real-time reporting system of the reference interval for gestational serum creatinine and estimated glomerular filtration rate using machine learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePregnancy causes physiological alterations, including blood volume expansion, due to increased cardiac output and decreased systemic vascular resistance\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Systemic vasodilation and glomerular hyperfiltration are normal hemodynamic adaptations during pregnancy\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The glomerular filtration rate (GFR) increases during pregnancy, while the serum creatinine (SCr) concentration decreases according to the degree of maternal GFR change. Accordingly, the GFR estimation formula based on the SCr concentration cannot be accurately applied to pregnant women\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The Modification of Diet in Renal Disease (MDRD) formula\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, which estimates the GFR using a combination of serum markers and clinical parameters, has become the standard clinical method for estimating the renal function in patients with chronic kidney disease (CKD). However, the feasibility of using this formula in the pregnant population has not been well studied, and guidelines on the application of the MDRD formula specifically exclude its interpretation in pregnant women\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccurate determination of SCr concentrations in pregnant women according to gestational age is crucial because the condition of women with adverse pregnancy outcomes or abnormal kidney function can be misinterpreted as normal if the reference interval (RI) for non-pregnant women that does not reflect normal physiological changes during pregnancy is applied. This makes it difficult to diagnose CKD early and provide proper treatment for complications that can affect the fetus and mother. The Clinical and Laboratory Standards Institute has recommended that the best way to establish an RI is to collect samples from a sufficient number of qualified reference individuals in order to yield a minimum of 120 observations for analysis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The parametric method (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;2 standard deviation) was used to compare the RI if the values were normally distributed, whereas the nonparametric method (median and the range of 2.5th \u0026minus;\u0026thinsp;97.5th percentile) was used when the values were not normally distributed\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, this method is difficult to apply in pregnant women due to the need to obtain a sufficient number of reference individuals by gestational age. Therefore, the estimated GFR (eGFR) values of pregnant women are possibly inaccurate as the urine output and body weight vary according to the gestational week (GW), and the amount of muscle occupied by the total body surface area in pregnant women is different from that in non-pregnant women.\u003c/p\u003e \u003cp\u003eThe rapid advancements in computer technology, stimulated by the availability of cheaper and smaller devices for processing and memory, have introduced new methods in the field of statistics. The bootstrap method, a computer-intensive resampling technique, is a statistical method based on sampling of the original dataset\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Many subsets of fixed sizes are generated by randomly drawing numbers (with replacement) from the original data\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The estimator of interest was calculated for each subset. With this large number of estimator values, the mean or median of the estimator, variance or standard deviation, and confidence intervals can easily be calculated without any assumptions regarding the original data distribution\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The bootstrap method was used to estimate the RIs for small sample sizes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study involved two main tasks. First, a database table was created to manage the GW-specific SCr concentrations and eGFR normal RIs in the laboratory information system. Second, a real-time reporting system for eGFR was established according to the GW-specific SCr concentration and eGFR RI by obtaining the pregnant women\u0026rsquo;s data, such as GW, body weight, and height, from the electronic medical records.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch3\u003eAnalysis of raw data\u003c/h3\u003e\n\u003cp\u003eWe previously analyzed 10,126 SCr measurements from 4,004 pregnant women to predict the adverse pregnancy outcomes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. 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. Supplementary Table\u0026nbsp;1 shows the general characteristics of the 940 pregnant women. The SCr concentration from 70.0% of pregnancy (658/940) was only measured once before labor. The kurtosis and skewness of raw 1,370 SCr measurements were 4.7 and 0.5, respectively. The \u003cem\u003ep\u003c/em\u003e-value of the Shapiro\u0026ndash;Wilk test was \u0026lt;\u0026thinsp;0.001 (Supplementary Fig.\u0026nbsp;1). Accordingly, the statistical summaries of 1,370 SCr measurements were expressed as median and interquartile range, which were 45.1 and 38.9\u0026ndash;53.0, respectively, as their distribution was non-parametric.\u003c/p\u003e\n\u003cp\u003eThe distribution of SCr measurements by GW exhibited a left-skewed pattern, indicating that most examinations were conducted between 34 and 38 GW (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the GW-specific SCr distribution. Among the 41 distributions of GW-specific SCr concentrations, the following parameters were assumed to indicate no Gaussian distributions: 2.5\u0026thinsp;\u0026le;\u0026thinsp;kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;3.5, \u0026minus;\u0026thinsp;0.5\u0026thinsp;\u0026le;\u0026thinsp;skewness\u0026thinsp;\u0026lt;\u0026thinsp;0.5, and a \u003cem\u003ep\u003c/em\u003e-value of Shapiro\u0026ndash;Wilk test of \u0026gt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The total GW (0\u0026ndash;40) was divided into three trimesters, and each trimester was split into four gestational periods (GP) (Supplementary Table\u0026nbsp;2). This division was intended to increase the samples within the divided groups, and each group was expected to show measurements with normal distributions. The SCr measurement distributions of the 12 GPs were evaluated based on the normality indices (i.e., kurtosis, skewness, and \u003cem\u003ep\u003c/em\u003e-value of the Shapiro\u0026ndash;Wilk test, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the 12 GPs, GP1, GP5, GP11, and GP12 showed a normal distribution based on the kurtosis and skewness (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the Shapiro\u0026ndash;Wilk test, GP1, GP5, and GP12 were considered to exhibit Gaussian distributions satisfying the mathematical and statistical criteria (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Additional data processing was required to obtain normality for all GPs to establish the RIs.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResampling Process\u003c/h3\u003e\n\u003cp\u003eThe resampling process was not a simple re-combination of the original data, but the creation of a new dataset, which followed the central limit theorem. Based on this, Colugnati et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e developed a confidence interval for applicable cutoff points from 418 patients using the bootstrap resampling method. A bootstrap resampling method was used to overcome this obstacle. The new datasets were established by resampling the original SCr concentrations in 12 GPs using two different hyper-parameters: resample size (RS) and resample times (RTs) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The mean value (defined as the derived value (DV) in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) of the resampled SCr concentrations according to the RS increased during the RTs (described as \u003cem\u003em\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The number of RS was 19 when increased by 1 (from 2 to 20), while the number of RTs was 20 when increased by 50 increments (from 50 to 1,000). A total of 380 combinations were used. The new datasets from 380 combinations were created for every 12 GPs, and the total number of new datasets was 4,560. Among these new datasets, results falling between the 2.5th and 97.5th percentiles of the new dataset should be included within the 90% confidence interval of the original SCr concentrations. Only those that met the three normality indices (i.e., 2.5\u0026thinsp;\u0026le;\u0026thinsp;kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;3.5, \u0026minus;\u0026thinsp;0.5\u0026thinsp;\u0026le;\u0026thinsp;skewness\u0026thinsp;\u0026lt;\u0026thinsp;0.5, and a \u003cem\u003ep\u003c/em\u003e-value of Shapiro\u0026ndash;Wilk test of \u0026gt;\u0026thinsp;0.05) were finally selected. Every selected new dataset had its own resampling condition, which was composed of the RS, RTs, and GP. To reduce the time of the resampling process, conditions that create normally distributed new datasets were excluded during the next resampling process. To create reasonable RIs, new datasets of 12 GPs were constructed with coincident RS and RTs. After each selection, the number of GPs belonging to each combination was counted. If any combination was gathered on all 12 GPs, the iteration was stopped. As a result, the resampling process ended with 2 in RS and 100 in RTs. The new datasets of SCrs for all 12 GPs were normally distributed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRi Of Gw-specific Scr Concentration\u003c/h3\u003e\n\u003cp\u003eSupplement Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the boxplots of the SCr concentration distributions according to GP. 1, 2, 7, 9, and 10 GPs had four GWs, while the other GPs had three GWs. When two virtual lines were drawn connecting the upper limits (values of the 97.5th percentile) and lower limits (values of the 2.5th percentile), respectively, according to the GPs, the shapes of both limit lines were jagged. Although the RIs of GP-specific SCr concentrations have been established, they are impractical for clinical use. A polynomial regression model of GP-specific SCr was developed to construct continuous RIs according to the GW. The first GP was excluded from the GW-specific RIs because implantation begins in the first 4\u0026ndash;5 weeks of pregnancy; this exclusion is indicated as a fainted color in Supplementary Fig.\u0026nbsp;2. Because the method used for curve fitting was k-fold cross-validation, the value of k was 10. The range of the polynomial degree was set from 1 to 5, while the mean square errors (MSEs) according to the degree were calculated. The degree with the smallest MSE value was defined as the best degree for the polynomial regression model (Supplementary Fig.\u0026nbsp;3). A 100-time of searching for the best degree according to the upper and lower limits were processed, and the best degrees of the polynomial regression model were 3 and 2, respectively (Supplementary Fig.\u0026nbsp;4). The polynomial regression equations for the upper and lower limits generated by curve fitting are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The RIs of SCr for 4\u0026ndash;40 GWs are listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReference intervals of SCr concentration and eGFR according to the GWs\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGWs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eSCr concentration (\u0026micro;mol/L)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eeGFR (mL/min)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2.5th percentile\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e97.5th percentile\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIncrement (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2.5th percentile\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e97.5th percentile\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIncrement (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;9.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;11.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e176.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;14.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e13.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;16.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e182.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;18.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;19.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e19.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;21.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e20.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;22.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e21.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e22.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;24.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e194.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e24.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;25.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e24.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;25.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;25.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e24.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e24.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;24.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e22.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e22.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;22.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e21.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;21.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e20.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e19.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;19.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e18.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;18.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;17.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e185.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e16.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;16.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e183.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;15.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e14.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;14.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e13.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;13.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e12.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;12.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Increment (%) indicates the percentage of overfiltration of SCr based on the baseline SCr concentration.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e\u003cem\u003eeGFR\u003c/em\u003e estimated glomerular filtration rate, \u003cem\u003eGW\u003c/em\u003e gestational week, \u003cem\u003eSCr\u003c/em\u003e serum creatinine\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRi Of Gw-specific Egfr\u003c/h3\u003e\n\u003cp\u003eCortinovis et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e mentioned that the GFR increased during pregnancy due to absolute hyperfiltration. Porrini et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e revealed that more than 70 formulae used for estimating GFR had large errors, which did not improve over the past 60 years. In this reaserch, we focused on determining the value of hyperfiltrated SCr concentration as a marker of gestational GFR. As the prefix \u0026ldquo;hyper\u0026rdquo; indicated a value over the baseline, the median of 4 GW SCr RI (56.4 \u0026micro;mol/L in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, red horizontal line in Supplementary Fig.\u0026nbsp;5a) was set as the baseline SCr concentration (BSC). The hyperfiltration value in the BSC was defined as 100% (Supplementary Fig.\u0026nbsp;5b). Likewise, the first GP was excluded because it occurred before implantation (shown as gray box in the plots, Supplementary Fig.\u0026nbsp;5). First, the gaps between the BSC and a median of 2\u0026ndash;12 GPs were calculated (blue arrows, Supplementary Fig.\u0026nbsp;5c). Second, the proportion of each gap against the BSC was calculated, and these rates were set as hyperfiltrations (Supplementary Fig.\u0026nbsp;5d). Third, the gaps between the median and the 2.5th percentile (purple arrows, Supplementary Fig.\u0026nbsp;5e), 25th percentile (magenta arrows, Supplementary Fig.\u0026nbsp;5e), 75th percentile (orange arrows, Supplementary Fig.\u0026nbsp;5e), and 97.5th percentile (brown arrows, Supplementary Fig.\u0026nbsp;5e) were calculated. Finally, the proportions of these gaps against each median were calculated and plotted as box plots for SCr hyperfiltration (Supplementary Fig.\u0026nbsp;5f). The box plots for SCr hyperfiltration created based on four steps were used as the source for deriving the regression equations for the lower limits, medians, and upper limits. However, the unit of hyperfiltration was expressed in percentage, and 120.1 mL/min/1.73 m\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, as the normal eGFR of non-pregnant women\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, was multiplied to generate the eGFR in mL/min. In the same way that the establish RI of GW-specific SCr concentration was obtained, the MSEs for lower limits, medians, and upper limits were calculated 100 times, the best degrees of polynomial regression model were 3, 3, and 2, respectively (Supplementary Fig.\u0026nbsp;6). The polynomial regression equations are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The RIs of eGFR for 4\u0026ndash;40 GWs are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eEstablishment Of Gestational Egfr Formula And Comparison With Other Egfr Equations\u003c/h3\u003e\n\u003cp\u003eAlthough Porrini et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e argued that more than 70 eGFR formulae had obvious limitations, Levey et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e recommended the use of eGFR as a screening tool for identifying patients for whose GFR should be confirmed by directly GFR measuring. The concept used to develop the GW-specific RI of eGFR was applied to establish the gestational eGER formula (GEF). A real case was used to explain the process of establishing a GEF. The patient was a 32-year-old woman at 31 GW with an SCr concentration of 58.3 \u0026micro;mol/L (brown point A, Supplementary Fig.\u0026nbsp;7). First, when an SCr concentration from any pregnant woman was tested, the hyperfiltration rate against the mean of RI was calculated (brown arrow, Supplementary Fig.\u0026nbsp;7a). Next, this rate multiplied by 120.1 mL/min was applied on the median regression line at the GW-specific RI of eGFR on a specific GW (brown arrow on 31 GW at Supplementary Fig.\u0026nbsp;7b). Finally, the actual formula used was as follows:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Gestational eGFR formula \\left(GEF\\right)=\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\left(1-\\frac{SCr concentration \\left(\\mu mol/L\\right)}{65.55-2.615\\times GW+0.08655 \\times {GW}^{2}-0.000785 \\times {GW}^{3}}\\right)\\times 120.1\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(+(99+5.71 \\times GW-0.184 \\times {GW}^{2}+0.00166 \\times {GW}^{3})\\)\u003c/span\u003e \u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSCr, serum creatinine; GW, gestational week\u003c/p\u003e\n\u003cp\u003eWhen the correlation coefficients were compared with those of other eGFR equations, such as the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, modified MDRD (mMDRD)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and Nanra equations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, mMDRD was the most correlated equation with GEF (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e shows the real-time reporting form provided by the clinician. To avoid confusion, an SCr concentration expressed in mg/dL and an eGFR obtained using the mMDRD equation were provided.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBoth GFR and renal plasma flow increased early during pregnancy. Serial observations suggest that GFR progressively reaches a peak level in mid-pregnancy and remains constant thereafter throughout the duration of pregnancy\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. On average, the GFR during the second half of pregnancy increases beyond the non-gravid levels (40\u0026ndash;50%)\u003csup\u003e20\u003c/sup\u003e. As failure to evaluate this physiological increase is an important risk factor for morbidity and mortality for both the mother and child, it is crucial to quickly and accurately identify and manage their abnormalities through the provision of periodic prenatal care. However, determining the RI for pregnant women remains challenging as the major physiological, hormonal, and biochemical characteristics of the mother and fetus continuously change as pregnancy progresses. When pregnancy deviates from its normal course, several biochemical markers can be used to assess these abnormalities. SCr concentration is mainly used to assess for certain maternal conditions such as renal diseases and preeclampsia (PE).\u003c/p\u003e \u003cp\u003eWomen with renal disorders experience several problems during pregnancy due to increased physiological changes associated with renal dysfunction and the risk of disease progression, the potential teratogenicity of medications, and the increased risk of developing complications, such as PE and preterm delivery\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Although the prevalence of CKD in women of childbearing age seems relatively low, with estimates of 0.1\u0026ndash;4%, CKD significantly increases the risk of adverse maternal and perinatal outcomes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Pregnancy-related acute kidney injury (AKI) is one of the most common causes of AKI in young women and is associated with future risks of CKD, hypertension, and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePE is associated with maternal perinatal morbidity and mortality and affects 5\u0026ndash;7% of pregnant women worldwide\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. PE is divided into two classifications. Early-onset PE (before 34 GWs) is commonly associated with abnormal uterine artery Doppler findings, fetal growth restriction, and adverse maternal and neonatal outcomes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. By contrast, late-onset PE (after 34 GWs) is mostly associated with a normal or slightly increased uterine resistance index, low rate of fetal involvement, and more favorable perinatal outcomes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In patients with PE, renal perfusion and GFR are reduced, although the levels that are much lower than those of normal non-pregnant women are infrequent and are a consequence of severe disease. The histological findings of PE include glomerular endothelial cell swelling and detachment, subendothelial fibrinoid deposits, occlusion of glomerular capillaries, reduced density and size of endothelial fenestrae, and thickening of the glomerular basement membrane\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These changes, typical of thrombotic microangiopathy, impair the glomerular capillary hydraulic permeability and reduce the filtration surface area, resulting in diminished GFR\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These abnormal findings in PE are due to a pre-renal mechanism; hence, they return to normal after childbirth. If early-onset PE is not diagnosed at an early stage, fetal growth restriction or preterm birth (PTB) may result. The absence of glomerular hyperfiltration may reflect the antecedent presence of intraglomerular hypertension due to vascular dysfunction, impaired endothelium, and faulty angiogenesis, all of which are associated with underlying comorbid conditions, such as CKD, hypertension, and diabetes\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Vascular dysfunction can also lead to abnormal placental implantation or development, resulting in placental dysfunction\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and maternal PE, which is the leading cause of provider-initiated PTB\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, it is important to accurately evaluate the renal function during pregnancy. However, Smith et al.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e suggested that the sole application of SCr concentration or SCr-based equations tends to substantially underestimate the renal function status during pregnancy. Park et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e reported that the absence of prominent midterm renal hyperfiltration, marked by an extremely high eGFR, might be a significant risk factor for poor pregnancy outcomes in women without evident functional renal impairment. Harel et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e reported that blunted glomerular hyperfiltration in early pregnancy may be associated with an increased risk of PTB and perinatal mortality. In our previous study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we identified that SCr levels could predict the risk of adverse pregnancy outcomes and the number of co-occurring adverse pregnancy outcomes. When using the GW-specific SCr distribution, the predictive power of adverse pregnancy outcomes was more robust based on the beta coefficients and their \u003cem\u003ep-\u003c/em\u003evalues compared with the raw SCr distribution\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, eGFR was calculated as the percentage of SCr hyperfiltration. A 24-h urine creatinine clearance is the most commonly used GFR test method in clinical practice, but it is affected by muscle mass, weight, height, and food intake. The weight of pregnant women changes according to the GW; hence, it is impractical to perform a the 24-h urine collection\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The change in muscle mass during pregnancy is not well known and varies greatly depending on an individual\u0026rsquo;s diet and exercise habits. As our eGFRs were derived only from the SCr hyperfiltration value, the eGFRs were not affected by the bias caused by non-renal aspects. As our eGFR unit is reported as mL/min, without reflecting the body surface area, this may be a limitation of this study. However, one way to set an RI that can exclude the adverse pregnancy outcomes is by retrospectively analyzing the SCr data of each pregnant woman in each institution.\u003c/p\u003e \u003cp\u003eThe real-time clinical application of the normal RI of SCr concentration and eGFR according to the GW can be helpful as a screening test in two aspects. When the SCr concentration and eGFR are lower than those of RIs or show blunted glomerular hyperfiltration, this may imply the possibility of pregnancy-induced kidney disease or PE. By contrast, if the SCr concentration and eGFR are higher than those of RIs, severe anemia and/or hypoproteinemia may be suspected. The real-time clinical application of the RIs of SCr concentration and eGFR according to the GW may help in the early detection of adverse pregnancy outcomes in pregnant women.\u003c/p\u003e \u003cp\u003eIn conclusion, by applying the bootstrap resampling process, a type of machine learning technique, we were able to establish the SCr concentration and eGFR RIs according to the GW among normal pregnant Korean women. However, our study is limited in that it cannot represent all pregnant women, as our data were obtained from a single center with limited geographical coverage. Therefore, to accurately evaluate the normal RIs for SCr concentration and eGFR among pregnant women, big data analytical studies that represent pregnant women and verification studies using our data are needed in the future.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eFrom January 2010 to December 2020, relevant data were collected from the electronic medical records and laboratory information systems at Wonju Severance Christian Hospital (WSCH), and all data were decoded automatically. Women who had singleton or multiple births at \u0026gt;\u0026thinsp;38 weeks of gestation and were aged 16\u0026ndash;50 years at the time of delivery were included in the analysis; the SCr concentration was measured at least once during the pregnancy period. Pregnant women with adverse pregnancy outcomes such as PE, gestational diabetes, gestational hypertension, and other underlying medical and surgical problems were excluded. If the SCr test was performed two or more times a week, only one of the initially tested SCr results was included. This study was approved by the Institutional Review Board (IRB) of WSCH (CR321084). This study was conducted in accordance with the principles of the Declaration of Helsinki. This was an observational study without medical interventions; therefore, the need to obtain informed consent from patients was waived. The waiver of informed consent was confirmed by the IRB of the WSCH (CR321084). The SCr concentrations were measured using the Cobas\u003csup\u003e\u0026reg;\u003c/sup\u003e 8000 system (Cobas\u003csup\u003e\u0026reg;\u003c/sup\u003e c 702 and e 801 module; Roche Diagnostics, Switzerland), Modular DPE analyzer (Roche Diagnostics), Vitros FS 5.1 (Ortho Clinical Diagnostics, Raritan, NJ, USA), Vista 1500 (Siemens Healthineers, Erlangen, Germany), and Atellica CH 930 analyzer (Siemens Healthineers). Data of the SCr concentrations were analyzed by converting mg/dL to \u0026micro;mol/L.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnalysis Of Original Data\u003c/h3\u003e\n\u003cp\u003eFor the enrolled normal pregnant women, the general characteristics, number of cases examined, and distribution of SCr concentrations according to GW were analyzed. The total GWs (0\u0026ndash;40) was divided into three trimesters, and each trimester was split into four periods (Supplementary Table\u0026nbsp;2). The Gaussian distribution of SCr concentrations was defined according to the following three criteria: met\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, kurtosis 3 (2.5\u0026ndash;3.5), skewness 0 (\u0026minus;\u0026thinsp;0.5 to 0.5), and a \u003cem\u003ep\u003c/em\u003e-value of \u0026gt;\u0026thinsp;0.05 in normality tests. Normality tests were performed using the Shapiro\u0026ndash;Wilk test. Computational statistics and graphics were performed using R language and environment for statistical computing version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\n\u003ch3\u003eResampling Process\u003c/h3\u003e\n\u003cp\u003eThree hyperparameters were required for the resampling process: DV as new data, RS, and RTs. DV type was set as the mean value in this study.\u003c/p\u003e\n\u003cp\u003eStep 1) A resampled dataset was extracted from the original data by setting \u003cem\u003en\u003c/em\u003e as the RS without replacement (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eStep 2) The mean value (defined as DV in this study) was obtained for a resampled dataset.\u003c/p\u003e\n\u003cp\u003eStep 3) Steps 1\u0026ndash;2 \u003cem\u003em\u003c/em\u003e (referred to as RTs) were repeated. Thus, the new dataset included \u003cem\u003em\u003c/em\u003e DVs of SCr. According to the central limit theorem, when sufficient numbers of \u003cem\u003em\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e are set, the values in the new dataset follow a Gaussian distribution, although the SCr concentrations in the original data do not show normality.\u003c/p\u003e\n\u003cp\u003eStep 4) RS (\u003cem\u003en\u003c/em\u003e) was increased by 1 (from 2 to 20), while RT (\u003cem\u003em\u003c/em\u003e) was increased by 50 increments (from 50 to 1,000). As a result, the total number of newly created datasets was 380.\u003c/p\u003e\n\u003cp\u003eStep 5) The creation of 380 new datasets (steps 1\u0026ndash;4) was performed in each of the 12 GPs. After all the 4,560 new datasets were obtained, all distributions were evaluated to determine whether the new dataset satisfied the criteria of an RI. The first criterion of RI was that results falling between the 2.5th and 97.5th percentiles of the new dataset should be included within the 90% confidence interval of the original data. The second criterion was that the \u003cem\u003ep-\u003c/em\u003evalue of the normality test should be \u0026gt;\u0026thinsp;0.05. The final criterion was whether the distribution showed kurtosis (2.5 to 3.5) and skewness (\u0026minus;\u0026thinsp;0.5 to 0.5) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEstablishment Of Gp-specific Scr Ri From New Datasets\u003c/h3\u003e\n\u003cp\u003eObstetric experts manually categorized GWs (0\u0026ndash;40 GWs) into 12 GPs. As each GP had three or four GWs, the estimated RIs of the total GWs in each period resembled the angular cliffs. Polynomial linear regression equations were applied to develop the smooth-curved RIs. Because implantation was not completed at 0\u0026ndash;3 GWs, 1 GP (containing 0\u0026ndash;3 GWs) was excluded. The independent variable was the middle GW of each of the 12 GP, while the dependent variables were resampled values of the 2.5th and 97.5th percentiles in each GP. In order to identify the best degree of polynomial linear regression, the MSE was calculated based on the following degrees: 1 to 5. As k-fold cross-validation was chosen as the methodology, the degree with the smallest MSE was defined as the best degree. This cross-validation was repeated 100 times for every polynomial regression model.\u003c/p\u003e\n\u003ch3\u003eEstablishment Of Gp-specific Egfr Ri From Gp-specific Scr Ri\u003c/h3\u003e\n\u003cp\u003eOur research team focused on the value of hyperfiltrated SCr concentration as a marker of gestational GFR. To calculate the \u0026ldquo;hyper\u0026rdquo; filtrated rate, we defined the BSC as the median of 4 GW SCr RI (56.4 \u0026micro;mol/L). Similarly, the first GP (0\u0026ndash;3 GWs) was excluded.\u003c/p\u003e\n\u003cp\u003eStep 1) The gaps between BSC and the median of 2\u0026ndash;12 GPs were calculated.\u003c/p\u003e\n\u003cp\u003eStep 2) As the proportion of each gap from step 1 was calculated based on the BSC, these rates were set in hyperfiltrations.\u003c/p\u003e\n\u003cp\u003eStep 3) The gaps between the median and the 2.5th percentile, 25th percentile, 75th percentile, and 97.5th percentile were calculated.\u003c/p\u003e\n\u003cp\u003eStep 4) The gaps from step 3 were calculated to identify the proportion of each gap using the median value in each GP.\u003c/p\u003e\n\u003cp\u003eStep 5) Boxplots were constructed according to the proportions from step 4. However, hyperfiltration was expressed in percentage; the eGFR of 120.1 mL/min/1.73 m\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, as the normal value for non-pregnant women\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, was multiplied to generate the eGFR in mL/min. These boxplots were used as the source for deriving the regression equations for the lower limits, medians, and upper limits. In the same way that the RI of GW-specific SCr concentration was established, the MSEs for lower limits, medians, and upper limits were calculated 100 times.\u003c/p\u003e\n\u003ch3\u003eDevelopment Of Gw-specific Scr And Egfr Ris\u003c/h3\u003e\n\u003cp\u003eThe RI was constructed according to the 2.5th and 97.5th percentiles, which were the lower and upper limits, respectively. As GWs that required RIs were 4\u0026ndash;40, the GW-specific RIs were derived using the best polynomial regression equations of the upper and lower limits.\u003c/p\u003e\n\u003ch3\u003eCreation Of The Gef\u003c/h3\u003e\n\u003cp\u003eThe median regression equation from the GP-specific RI of eGFR could help obtain the median gestational eGFR for any GW. As every SCr concentration was not the same as the median SCr concentration, extra adjustment was necessary to determine a more precise eGFR.\u003c/p\u003e\n\u003cp\u003eStep 1) We calculated the proportion of maternal SCr concentration relative to the median value of the GW-specific SCr RI.\u003c/p\u003e\n\u003cp\u003eStep 2) Because gestational eGFR was expressed in mL/min, this proportion was transformed into mL/min. Hence, 120.1 mL/min, as the normal eGFR value of non-pregnant women\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, was multiplied.\u003c/p\u003e\n\u003cp\u003eStep 3) The formula was created based on the sum of the median regression equation of the GP-specific eGFR RI and the calculation process for steps 1\u0026ndash;2.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed data that support the findings and equations of this research are not available because of privacy concerns and hospital regulation restrictions to protect patients. Anonymized data may be available from the permission of the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.co.kr) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Uh Y; Data curation and formal analysis: Kang J and Hwang S; Investigation: Choi SJ; Methodology: Ahn K and Lee T; Resources: Seo DM and Cho J; Software and validation: Hwang S and Seo DM; Visualization: Ahn K; Writing \u0026ndash; original draft: Uh Y; Writing \u0026ndash; review and editing: Ahn K and Lee T. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest relevant to this article are reported. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCheung, K. L. \u0026amp; Lafayette, R. A. Renal physiology of pregnancy. Adv Chronic Kidney Dis \u003cb\u003e20\u003c/b\u003e, 209\u0026ndash;214 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez Suarez, M. L., Kattah, A., Grande, J. P. \u0026amp; Garovic, V. Renal disorders in pregnancy: Core curriculum 2019. Am J Kidney Dis \u003cb\u003e73\u003c/b\u003e, 119\u0026ndash;130 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiles, K. et al. Serum creatinine in pregnancy: A systematic review. 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Mechanisms of renal vasodilation and hyperfiltration during pregnancy: current perspectives and potential implications for preeclampsia. Endothelium \u003cb\u003e12\u003c/b\u003e, 57\u0026ndash;62 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerni, U. et al. Angiogenic factors in superimposed preeclampsia: a longitudinal study of women with chronic hypertension during pregnancy. Hypertension \u003cb\u003e59\u003c/b\u003e, 740\u0026ndash;746 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Marco, G. S. et al. The soluble VEGF receptor sFlt1 contributes to endothelial dysfunction in CKD. J Am Soc Nephrol \u003cb\u003e20\u003c/b\u003e, 2235\u0026ndash;2245 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaynard, S. E. et al. Excess placental soluble fms-like tyrosine kinase 1 (sFlt1) may contribute to endothelial dysfunction, hypertension, and proteinuria in preeclampsia. J Clin Invest \u003cb\u003e111\u003c/b\u003e, 649\u0026ndash;658 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay, J. G., Park, A. L. \u0026amp; Fell, D. B. Mortality in infants affected by preterm birth and severe small-for-gestational age birth weight. Pediatrics \u003cb\u003e140\u003c/b\u003e, e20171881 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRifai, N. et al. \u003cem\u003eTietz textbook of clinical chemistry and molecular diagnostics, sixth edition\u003c/em\u003e (Elsevier, 2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2223812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2223812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe evaluation of maternal serum creatinine (SCr) concentrations according to gestational week (GW)-specific reference intervals (RIs) could be helpful in predicting adverse pregnancy outcomes. From January 2010 to December 2020, 1,370 SCr measurements from 940 normal pregnant women were collected from electronic medical records. Data should be processed using the bootstrap resampling method as most of the sample sizes according to GW were too small for obtaining the RIs. To enable resampling, the GWs were divided into 12 gestational periods (GPs). Implementation of resampling, determination of the appropriateness of RIs from the resampled new datasets in every GP, and establishment of GW-specific SCr RI using polynomial regression model analysis of GP-specific SCr RIs were performed using machine learning techniques. As 100 means from two resampled SCr measurements without replacement were made at every GP, 1,200 resampled results were used for developing RIs. The regression equations used for calculating the upper and lower limit of GW-specific SCr RIs were \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;88.8\u0026thinsp;\u0026minus;\u0026thinsp;3.75\u003cem\u003ex\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.141\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;0.00157\u003cem\u003ex\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e and \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42.3\u0026thinsp;\u0026minus;\u0026thinsp;1.48\u003cem\u003ex\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.0321\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, respectively. Gestational estimated glomerular filtration rate (eGFR) was defined as the rate of SCr hyperfiltration. The median regression equation for GW-specific eGFR RI was \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99\u0026thinsp;+\u0026thinsp;5.71\u003cem\u003ex\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;0.184\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.00166\u003cem\u003ex\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e, while the calculation process of SCr hyperfiltration at any GW was added to develop the gestational eGFR formula (GEF). As GW-specific SCr RI and eGFR by GEF with GW-specific eGFR RIs were reported in the laboratory information system in real time, this clinical application can be used as a screening tool for predicting the adverse pregnancy outcomes.\u003c/p\u003e","manuscriptTitle":"Development of a real-time reporting system of the reference interval for gestational serum creatinine and estimated glomerular filtration rate using machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-11 22:25:11","doi":"10.21203/rs.3.rs-2223812/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51caba3f-0663-4965-acb3-31db150f13de","owner":[],"postedDate":"November 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":16836441,"name":"Biological sciences/Physiology/Reproductive biology"},{"id":16836442,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2023-01-18T07:51:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-11 22:25:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2223812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2223812","identity":"rs-2223812","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}