Objective
The objective of the study was to create a predictive model for preterm birth (PTB) from available clinical data and serum analytes.
Study Design
Serum analytes and routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB.
Results
Serum screening markers remained significant predictors of PTB, even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first-trimester total cholesterol, total cholesterol change between trimesters, and second-trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB.
Conclusion
Using clinical and serum screening data, a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step toward identifying additional women who may benefit from new or currently available interventions.
Preterm birth (PTB) is the leading cause of morbidity and mortality for newborns both worldwide and in the United States. Numerous risk factors are known to be related to PTB, but identifying at risk pregnancies before the onset of labor is difficult. Maternal obstetric history of past PTB is the most easily implemented and widely used screening measure. Cervical length measurement is also beneficial, although not currently universally implemented, despite evidence for its use in all singleton pregnancies.
Interventions in response to both short cervical length and past PTB have proven to be effective. Many other screening measures and biomarkers have been proposed, but none are used ubiquitously or justify additional testing, especially in low-risk individuals. Thus, a screening tool based on the existing and easily available clinical and laboratory data may be useful.
Cholesterol is a common and inexpensive serum test and has been associated with PTB. A recent report indicated that both high (greater than the 90th percentile) and low (less than the 10th percentile) cholesterol levels during the second trimester of pregnancy may identify women at risk for PTB. Each type of abnormal cholesterol measurement conferred an increased odds of PTB by a factor greater than 2.5. A similar finding was subsequently reported using prepregnancy lipids indicating a U-shaped relationship between cholesterol and PTB. These findings are intriguing, given that low-density lipoprotein (LDL) is the precursor to progesterone synthesis during pregnancy. In addition, commonly obtained analytes used in maternal serum screening have yielded similar risk profiles for PTB. Alpha-fetoprotein (AFP) and inhibin A greater than 2.0 multiples of the median (MoM) each more than double the odds of subsequent PTB.
There is an evident need to stratify women at risk for PTB to offer additional screening, or test possible interventions if appropriate. A noninvasive screening tool applicable at the population level and able to improve on obstetric history alone should be of clinical interest. This study proposes a model for predicting PTB derived from data collected prospectively in a cohort of Iowa women who underwent routine serum screening and who had cholesterol measured twice during pregnancy.
Materials and Methods
Population and data sources
All Iowa women undergoing routine prenatal testing in the first and/or second trimester through the Iowa Maternal Serum Screening Program, from May 2009 until November 2010, were included in the initial cohort (12,057 women). Risk factors and outcome information on included women came from 2 distinct sources. The first was serum screening measurements and medical information collected at the time of screening. This included maternal race, ethnicity, weight (both trimesters), age, and gestational age at sampling (both trimesters). Serum measurements captured as part of routine screening included pregnancy-associated plasma protein A (PAPP-A) and human chorionic gonadotropin (hCG) in the first trimester and estriol, AFP, inhibin A, and hCG in the second trimester. These analytes were expressed as adjusted MoM.
Clinical and demographic data were extracted from the neonatal birth certificate. Relevant maternal information included education, ethnicity, race, smoking status, height, previous live births, previous PTB, diabetes (prepregnancy and gestational), prepregnancy hypertension, and sexually transmitted diseases. Information about treatment during pregnancy and delivery outcomes included cerclage, tocolysis, labor onset, prelabor rupture of membranes (PROM), induction, fetal presentation, congenital anomalies, birthweight, gestational age, and plurality. There was no information available for cervical length, fetal fibronectin, maternal characteristics, or serum analytes not collected as part of the maternal serum screening program or recorded on the birth certificate.
This study received approval from the University of Iowa Institutional Review Board (IRB 200812784) and from the Iowa Department of Public Health Congenital and Inherited Disorders Advisory Committee and State Vital Statistics.
Laboratory testing
Maternal serum was sent to the State of Iowa Hygienic Laboratory for testing of the routine pregnancy analytes discussed in the previous text. An additional lipid panel including total cholesterol (TC), LDL, high-density lipoprotein (HDL), and triglycerides (TG) was measured on excess sera. Lipid levels were measured using a Roche Diagnostics c111 Cobas Analyzer (Basel, Switzerland) at a single laboratory. Women were not routinely fasted before these samples were collected. Medical information obtained at the time of serum collection, results of maternal serum screening, and cholesterol and triglyceride levels were combined using unique identifiers with the corresponding birth certificate data. This data set was then provided to the investigators without personal identifiers and stored in a Progeny database (Progeny Software, Delray Beach, FL).
Inclusion and exclusion criteria
In Iowa, all infants born alive at a gestational age (GA) of 20 weeks or longer receive a birth certificate and thus were eligible for inclusion in this cohort. Thus, 6 infants with a birthweight less than 500 g were included. Only women with serum analyte results collected in both the first and second trimester were included in this analysis. Many women received only the second-trimester screening (quad screen), and this reduced the available cohort for analysis from 12,057 women with at least 1 screen to 2976 with 2 screenings.
Additional exclusion criteria included multiple gestations (n = 173, 5.8%) or congenital anomaly (n = 17, 0.6%) because these would have different management as at-risk pregnancies; unknown birthweight (n = 1, less than 0.01%) or birthweight for gestational age greater than 3 SD from the mean (n = 18, 0.6%) considered to be unreliable birth records; any serious infection (gonorrhea, syphilis, hepatitis, etc) (n = 48, 1.6%); and the second pregnancy of any mothers with more than 1 pregnancy during collection (n = 10, 0.3%). Also, term births that were treated with either cerclage (n = 1, less than 0.01%) or tocolysis (n = 6, 0.02%) were excluded. Finally, 3 women were excluded by inspection for aberrant cholesterol measures between trimesters (TC decrease of 170 mg/dL, LDL increase of 184 mg/dL, and HDL decrease of 57 mg/dL). This left a total of 2699 women.
Outcome definitions
PTB was the primary outcome for our predictive model. PTB was defined as birth less than 37 weeks of completed gestational age and term birth (TB) was considered 37 completed weeks or more of gestational age as indicated on the birth certificate. There were 2499 TB and 200 PTB in the complete cohort. In addition to the primary outcome of PTB, nonspontaneous PTB was identified as follows:
- 1.
Delivered vaginally with induction but without preterm PROM (PPROM), tocolysis, or precipitous labor.
- 2.
Delivered via cesarean section after labor by induction without PPROM, tocolysis, or precipitous labor.
- 3.
Delivered by cesarean section without labor and no history of cesarean section, tocolysis, or breech presentation.
All PTBs remaining after excluding the nonspontaneous PTBs mentioned in the previous text were considered to be spontaneous PTB (sPTB). The predictive model was also assessed for predicting sPTB, and 153 of the 200 total PTB were classified as sPTB. Defining sPTB in this way has not yet been validated but is meant as a sensitivity analysis to test the prediction in a population less likely to have preeclampsia or indicated PTB. However, sPTB being 76.5% of all total PTB is similar to the 70% figure reported in recent reviews.
Statistical analysis
Covariate adjustment
Many of the serum analytes examined in this study are related to gestational age at the time of sampling. To adjust for this relationship, covariates were standardized to the mean gestational age (in days) at sampling. First, they were examined graphically and then linear regression, with a log transformation of the analyte as the outcome variable, and GA in days as the explanatory variable was used for standardization. If the regression coefficient was significant at the P = .10 level, the analyte measurement was adjusted to the mean GA at sampling before being tested in the predictive model. Again, these were visually inspected to see that the linear relationship was no longer present. This was done for all lipid analytes (TC, LDL, HDL, and TG) in each semester and for the serum screening measures of PAPP-A in the first trimester and AFP in the second trimester.
Serum analysis
Before selecting covariates for inclusion in a final predictive model, the lipid measurements (TC, LDL, HDL, and TG) and routine screening analytes were examined to characterize any relationship to PTB. The highest and lowest quartiles of each lipid measurement were compared with the middle quartiles to assess whether the previously reported U-shaped relationship existed in the current cohort. Linear relationships were considered as well, both with and without log transformation. For routine screening analytes, model fit, based on the Aikike Information Criterion (AIC), was compared between specifying them as dichotomous vs continuous variables. Again, the linear relationships were considered, both with and without log transformation.
Covariate selection
All covariates potentially related to PTB were screened for entry into a final predictive model. All covariates related to PTB at the P < .10 level using a χ 2 test or simple logistic regression were considered for selection of the final model. The following covariates, all reported to be related to PTB in previous studies, underwent initial screening: the lipid markers as described above (TC, LDL, HDL, and TG in the first and second trimesters as well as a change between trimesters for each), prepregnancy hypertension, maternal age, maternal education, smoking status, prepregnancy diabetes, gestational diabetes, previous PTB, previous birth outcomes, maternal body mass index (BMI; both low and high; as indicated by past research), weight change between trimesters, infertility treatment, hCG (both trimesters and change), PAPP-A, AFP, estriol, inhibin-A, time between pregnancies, maternal race, and maternal ethnicity.
Model selection and performance
Multivariable logistic regression using a logit link function was used to determine which covariates significantly predicted PTB. The final model was determined using forward, backward, and stepwise selection with AIC being used to assess model fit. Parameter estimates for predictive covariates were determined in the entire cohort, and these were used for testing the model in all subgroups. Model performance was assessed based on sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating curve (AUC). The predictive model was compared with an obstetric history of prior PTB as the only predictor, both in the entire cohort and the subgroup of women with a past live birth (in which an obstetric history would be most useful). This comparison is justified, given that a past PTB is sufficient to initiate treatment to prevent PTB, and thus, our model may identify a population similarly at risk.
For ease of comparison with past PTB, the predictive model’s performance was evaluated at equal specificity. Performance was also calculated setting specificity equal to 90% for the model. In addition, the model was assessed in the subgroup of nulliparous women and those with only past term births. Here specificity was given a lower bound of 90% or 95% and the other performance metrics evaluated.
Sensitivity analysis and reclassification
To test the stability of the predictive model for different theoretical populations, a bootstrapping procedure was performed. The cohort was sampled with replacement, creating a new cohort of equal size 1000 times, and the estimated AUC and 95% confidence interval for the complete model using all predictors (maternal characteristics, serum screening, and cholesterol) were calculated. In addition, all analyses were performed using sPTB as the outcome variable.
To assess whether the predictive model assigned higher probabilities of PTB to cases vs controls, the AUC was compared from 4 prediction scenarios. Model I consisted of past PTB alone; model II, past PTB plus other maternal characteristics found to be significant predictors; model III, all predictors from model II plus results from routine serum screening (excluding cholesterol); and model IV, all predictors from model III plus cholesterol measures. AUC was compared between models using a standard method for correlated AUCs. Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also compared between models. Continuous NRI between 2 models can be thought of as the percentage of individuals whose probability of PTB moves in the correct direction (ie, increases for cases, decreases for controls) minus the percentage that moved in the wrong direction (ie, decreases for cases, increases for controls). IDI is the increase, from one predictive model to another, of the difference in average predicted probabilities of PTB between cases and controls.
All analyses were performed in R version 2.15 with the aid of packages predictABEL, pROC, and ROCR.
Results
The average birthweights for TB vs PTB are 3449 g (SD, 444 g; range, 1960–4805 g) vs 2334 g (SD, 828; range, 275–3914 g). The average GAs for TB vs PTB are 39.1 weeks (SD, 1.1 weeks; range, 37–43 weeks) vs 33.7 weeks (SD, 3.6 weeks, range, 20–36 weeks).
Results from serum lipid association with PTB are available in Table 1 . Neither the highest nor lowest quartile of any lipid measurement was significantly associated with PTB ( P > .05). Thus, no U-shaped relationship was observed for any serum lipids. Only first-trimester cholesterol, as a continuous variable, showed a trend for association with PTB and was eligible for inclusion in the final model. Using a log transformation did not improve model fit for any of the lipid measurements.
| Lipid concentration first trimester (range mg/dL) | PTB (<37 wks) | Lipid concentration second trimester (range mg/dL) | PTB (<37 wks) | ||
|---|---|---|---|---|---|
| Categorical variables | |||||
| Total cholesterol | OR (95% CI) | P value | Total cholesterol | OR (95% CI) | P value |
| Q4 (193–359) | 1.14 (0.81–1.61) | .440 | Q4 (216–358) | 1.00 (0.70–1.42) | .996 |
| Q2-Q3 (152–192) | Reference | Q2-Q3 (173–215) | Reference | ||
| Q1 (86–151) | 0.90 (0.62–1.30) | .574 | Q1 (96–172) | 1.00 (0.70–1.42) | .996 |
| LDL cholesterol | LDL cholesterol | ||||
| Q4 (111–290) | 1.06 (0.75–1.51) | .726 | Q4 (130–265) | 1.08 (0.76–1.54) | .671 |
| Q2-Q3 (77–110) | Reference | Q2-Q3 (91–129) | Reference | ||
| Q1 (28–76) | 1.02 (0.72–1.45) | .918 | Q1 (33–90) | 1.25 (0.88–1.76) | .210 |
| HDL cholesterol | HDL cholesterol | ||||
| Q4 (68–121) | 0.89 (0.62–1.29) | .533 | Q4 (72–131) | 0.93 (0.64–1.34) | .679 |
| Q2-Q3 (50–67) | Reference | Q2-Q3 (53–71) | Reference | ||
| Q1 (19–49) | 1.16 (0.82–1.62) | .403 | Q1 (20–52) | 1.21 (0.86–1.70) | .266 |
| Triglycerides | Triglycerides | ||||
| Q4 (163–655) | 1.20 (0.85–1.69) | .310 | Q4 (196–690) | 1.03 (0.72–1.47) | .859 |
| Q2-Q3 (98–162) | Reference | Q2-Q3 (121–195) | Reference | ||
| Q1 (38–97) | 1.12 (0.79–1.60) | .514 | Q1 (42–120) | 1.10 (0.78–1.56) | .592 |
| Continuous variables | |||||
|---|---|---|---|---|---|
| Lipid measurement first trimester | OR (95% CI) | P value | Lipid measurement second trimester | OR (95% CI) | P value |
| Total cholesterol | 1.14 (0.99–1.31) | .068 | Total cholesterol | 1.03 (0.89–1.19) | .681 |
| LDL cholesterol | 1.12 (0.97–1.29) | .114 | LDL cholesterol | 1.01 (0.88–1.17) | .856 |
| HDL cholesterol | 0.91 (0.79–1.06) | .218 | HDL cholesterol | 0.96 (0.83–1.11) | .583 |
| Triglycerides | 1.10 (0.96–1.25) | .161 | Triglycerides | 1.02 (0.88–1.17) | .821 |
Routine serum analytes were also assessed and AFP and inhibin A were significantly associated with PTB ( Table 2 ). Specifying each as a categorical variable showed a trend for increasing the odds of PTB with increasing levels of each analyte. The best model fit for each analyte was as a continuous variable; log transformation did not improve the fit for either analyte.
| AFP (MoM) | OR (95% CI) | P value | Inhibin A (MoM) | OR (95% CI) | P value |
|---|---|---|---|---|---|
| Serum analytes, categorical | |||||
| AFP <1.0 | Reference | Inhibin A <1.0 | Reference | ||
| ≤1.0 AFP, <1.5 | 1.44 (1.04–1.98) | .026 | ≤1.0 inhibin A, <1.5 | 1.28 (0.91–1.80) | .163 |
| ≤1.5 AFP, <2.0 | 2.45 (1.51–3.99) | < .001 | ≤1.5 inhibin A, <2.0 | 1.68 (1.05–2.69) | .030 |
| ≤2.0 AFP, <2.5 | 6.27 (2.93–13.4) | < .001 | ≤2.0 inhibin A, <2.5 | 2.37 (1.24–4.53) | .009 |
| AFP ≥2.5 | 6.99 (1.78–27.5) | .005 | Inhibin A ≥2.5 | 3.19 (1.68–6.04) | < .001 |
| Model fit, continuous | |||||
|---|---|---|---|---|---|
| AFP (MoM) | P value | AIC | Inhibin A (MoM) | P value | AIC |
| AFP | 5.81 × 10 −9 | 1398 | Inhibin A | 2.13 × 10 −5 | 1343 |
| Log(AFP) | 1.08 × 10 −7 | 1402 | log(inhibin A) | 9.63 × 10 −5 | 1346 |
After initial screening of potential predictor variables, those with a P < .10 were eligible for testing in the final prediction model and are presented in Table 3 . Some variables with P < .10 were not included because of the high correlation with other explanatory variables. In each case, the predictor with a better model fit, according to the AIC, was included. This resulted in choosing maternal education over maternal age, smoking status during the second trimester in place of smoking status at other time points, and measured BMI during the first trimester over self-reported prepregnancy BMI.
| Variable | Term (n = 2499) | PTB (n = 200) | P value |
|---|---|---|---|
| Dichotomous variables, n (%) | |||
| Prepregnancy HTN | 2.3 (57) | 4.5 (9) | .086 |
| Maternal postsecondary degree | 48.9 (1222) | 37.6 (74) | .003 |
| Prepregnancy DM | 1.8 (45) | 5.5 (11) | .001 |
| Previous PTB | 3.0 (75) | 11.5 (23) | < .001 |
| Previous live birth | 59.4 (1484) | 51.5 (103) | .035 |
| Race (black) | 5.4 (135) | 9.5 (19) | .025 |
| First trimester BMI <18.5 kg/m 2 | 2.3 (53) | 5.6 (10) | .011 |
| First trimester BMI >40 kg/m 2 | 6.0 (146) | 11.6 (22) | .004 |
| Continuous variables, mean (SD) | |||
| Smoking, second trimester | 1.1 (3.5) | 1.8 (4.8) | .011 |
| Weight change to second trimester | 9.0 (12.6) | 10.7 (13.9) | .072 |
| AFP (MoM) | 1.0 (0.3) | 1.2 (0.5) | < .001 |
| Inhibin A (MoM) | 1.1 (0.6) | 1.3 (1.0) | < .001 |
| TC first trimester | 173.8 (30.4) | 177.9 (35.7) | .068 |
| TC change first to second trimester | 22.4 (18.3) | 19.3 (19.5) | .022 |
Using the variables identified in the initial screening procedure, forward, backward, and stepwise selection of logistic regression models was performed. All selection algorithms converged to the same final model. The parameter estimates and odds ratios of the predictors that were included in the final model were similar to their univariate estimates, showing no evidence of confounding or multicollinearity. Parameter estimates were also stable after including different subsets of all final explanatory variables.
This model was then tested in the subgroup of women who had no previous PTB. The results of these models are shown in Table 4 . The serum analytes remain predictive of PTB, even after controlling for clinical risk factors. This is also true in the cohort of women without a previous PTB. The predictors listed in Table 4 constitute the predictive model described in further analysis. These include maternal characteristics (maternal degree, prepregancy diabetes, previous PTB, previous live birth, and maternal BMI); routine serum analytes (AFP and inhibin A); and cholesterol (first-trimester TC and TC change between trimesters [second TC trimester – first trimester TC]). The 2 cholesterol measures were not correlated (Pearson correlation coefficient = –0.12) and thus were kept as independent predictors in the model. Increased first-trimester TC was associated with PTB, whereas decreases in TC change were also associated with PTB.
| Explanatory variable | Beta | OR (95% CI) | P value |
|---|---|---|---|
| Entire cohort | |||
| Intercept | −4.32 | ||
| TC first trimester, mg/dL | 0.005 | 1.17 (1.01–1.36) | .032 |
| TC change first to second trimester, mg/dL | −0.008 | 0.87 (0.74–1.01) | .072 |
| Maternal degree | −0.411 | 0.66 (0.48–0.91) | .012 |
| Prepregnancy DM | 1.140 | 3.13 (1.38–6.51) | .004 |
| Previous PTB | 1.440 | 4.25 (2.34–7.44) | < .001 |
| Previous live birth | −0.494 | 0.61 (0.44–0.84) | .003 |
| First-trimester BMI <18.5 kg/m 2 | 1.058 | 2.88 (1.31–5.77) | .005 |
| First-trimester BMI >40 kg/m 2 | 0.375 | 1.46 (0.84–2.41) | .161 |
| AFP (MoM) | 0.880 | 2.41 (1.71–3.40) | < .001 |
| Inhibin A (MoM) | 0.308 | 1.36 (1.11–1.68) | .004 |
| No previous PTB | |||
| TC first trimester, mg/dL | 1.17 (1.00–1.38) | .049 | |
| TC change first to second Trimester, mg/dL | 0.85 (0.72–0.99) | .044 | |
| Maternal degree | 0.69 (0.49–0.95) | .026 | |
| Prepregnancy DM | 3.36 (1.43–7.17) | .003 | |
| Previous live birth | 0.61 (0.44–0.84) | .003 | |
| First trimester BMI <18.5 kg/m 2 | 2.92 (1.33–5.84) | .004 | |
| First trimester BMI >40 kg/m 2 | 1.33 (0.73–2.29) | .322 | |
| AFP (MoM) | 2.24 (1.57–3.20) | < .001 | |
| Inhibin A (MoM) | 1.33 (1.07–1.67) | .011 | |
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