Competing Risks Model in Screening for Preeclampsia

7/26/2019 Competing Risks Model in Screening for Preeclampsia

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OBSTETRICS

Competing risks model in screening for preeclampsiaby maternal factors and biomarkers at 19e24 weeksgestationDahiana M. Gallo, MD; David Wright, PhD; Cristina Casanova, MD; Mercedes Campanero, MD;

Kypros H. Nicolaides, MD

BACKGROUND: Preeclampsia (PE) affects 2e3% of all pregnancies

and is a major cause of maternal and perinatal morbidity and mortality.

The traditional approach to screening for PE is to use a risk-scoring

system based on maternal demographic characteristics and medical

history (maternal factors), but the performance of such an approach is

very poor.

OBJECTIVE: To develop a model for PE based on a combination of

maternal factors with second-trimester biomarkers.

STUDY DESIGN: The data for this study were derived from pro-spective screening for adverse obstetric outcomes in women attending

their routine hospital visit at 19e24 weeks gestation in 3 maternity

hospitals in England between January 2006 and July 2014. We had

data from maternal factors, uterine artery pulsatility index (UTPI), mean

arterial pressure (MAP), serum placental growth factor (PLGF), and

serum soluble fms-like tyrosine kinase-1 (SFLT) from 123,406,

67,605, 31,120, 10,828, and 8079 pregnancies, respectively. Bayes

theorem was used to combine the a priori risk from maternal factors

with various combinations of biomarker multiple of the median (MoM)

values. The modeled performance of screening for PE requiring de-

livery at


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screening by maternal factors alone withthe addition of each biomarker and

combinations of biomarkers. We also

examined the performance of screeningbased on risk factors from the medical

history, as recommended by ACOG.3

Methods

Study design and participantsThe data for this study were derived

from prospective screening for adverseobstetric outcomes in women attending

for routine pregnancy care at 110 to

136 and 190 to 246 weeks gestationin 3 maternity hospitals in the UK

(Kings College Hospital betweenJanuary 2006 and July 2014, Medway

Maritime Hospital between February

2007 and July 2014, and UniversityCollege London Hospital between April

2009 and September 2013). Maternal

characteristics and medical historywere recorded at thevisit at 110 to 136

weeks (n 123,406)5 and measurementsof UTPI, MAP, PLGF, and SFLT at 190

to 246 weeks. Screening evolved over

time in 2 respects. Firstly, there was achange in participating hospitals;

although all 3 hospitals were providing

routine screening of their local pop-

ulations, there were differences in thedistribution of the racial origin of thestudy populations, which would affect

the prior risk for PE. Secondly, there wasa change in the content of the clinics; in

the rst phase of the study, only UTPI

was measured (n 67,605), then mea-surement of MAP was added (n

31,120); and in the nal phase serum

concentration of PLGF was measured(n 10,828) and then SFLT was added

(n 8,079). Measurements of all 4

biomarkers were obtained from 7748pregnancies.

The left and right UTPI weremeasured by transvaginal color Doppler

ultrasound and the mean pulsatility

index was calculated.9 Measurementsof MAP were obtained by validatedautomated devices and a standardized

protocol.10 Measurement of serum

concentration of PLGF and SFLT wereby an automated biochemical analyzer

within 10 minutes of blood sampling

(Cobas e411 system; Roche Diagnostics,Penzberg, Germany). The inter-assay

coefcients of variation for low andhigh concentrations were 5.4% and 3.0%

for PLGF, and 3.0% and 3.2% for SFLT-1,

respectively. Gestational age was deter-mined from measurement of fetal

crown-rump length (CRL) at 11e13

weeks or the fetal head circumferenceat 19e24 weeks.11,12 The women gave

written informed consent to participatein the study, which was approved by the

NHS Research Ethics Committee.The inclusion criteria for this study

were singleton pregnancy delivering a

nonmalformed live birth or stillbirth at24 weeks gestation. We excluded

pregnancies with aneuploidies andmajor fetal abnormalities and those

ending in termination, miscarriage, or

fetal death at40 years, body mass index30 kg/m2,

conception by in vitro fertilization,history of previous pregnancy with PE,

family history of PE, chronic hyperten-

sion, chronic renal disease, diabetesmellitus, systemiclupus erythematosus,or thrombophilia.21

In our population of 123,406

singleton pregnancies, the screen posi-tive rate with the ACOG recommenda-

tions was 67% and the DR of PE at


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Comment

Principal findings of this studyIn pregnancies that developed PE, the

second-trimester values of UTPI, MAP,

and SFLT were increased and PLGFwas decreased. For all biomarkers the

deviation from normal was greater forearly PE than for late PE, and therefore

the performance of screening was

inversely related to the gestational age atwhich delivery became necessary formaternal and/or fetal indications.

Screening for PE by a combinationof maternal factors, UTPI, MAP, and

PLGF at 19e24 weeks gestation pre-

dicted 99% of early PE, 85% of pretermPE, and 46% of term PE, at an FPR of

10%. Such DRs are superior to the

respective values of 52%, 47%, and 37%achieved by screening with maternal

factors alone. Serum SFLT-1 improvedthe performance of screening for early

PE but not for PE at32 weeks. We havepreviously reported that screening by a

combination of maternal factors, UTPI,

MAP, and PLGF at 11e13 weeksgesta-tion can predict 89% of early PE, 75% of

preterm PE,and 47% of term PE, at anFPR of 10%.8 Consequently, the perfor-

mance of screening for early and preterm

PE, but not for term PE, is superior at19e24 vs at 11e13 weeksgestation.

In the application of Bayes theorem,

the maternal factorederived prior riskhas a strong inuence on the posterior

risk and, therefore, the performance ofscreening. The study has highlighted that

in screening for PE the FPR and DR are

inuenced by the characteristics of thestudy population and for a given risk

cutoff they are both higher in nullipa-

rous than in parous women and in those

of Afro-Caribbean than in those of whiteracial origin. Although the risk of PEis higher in nulliparous than parous

women, the contribution of the lattergroup to PE should not be under-

estimated, because 38% of cases of PE

were from parous women, including13% from parous women with history of

PE in a previous pregnancy and 25%

from parous women without a history ofPE. In all groups, after combined

screening, the risk of being affected given

a screen positive result was considerablyincreased and if the screen result was

negative the risk was considerablyreduced.

Strengths and limitationsThe strengths of this second-trimesterscreening study for PE are, rst, exami-

nation of a large population of pregnant

women attending for routine care in agestational age range that is widely used

for assessment of fetal anatomy and

growth; second, recording of data onmaternal characteristics and medical

TABLE 4

Model-based performance of screening by an algorithm combining maternalfactors, uterine artery pulsatility index, mean arterial pressure, and serumplacental growth factor for preeclampsia with delivery at


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history to identify known risk factorsassociated with PE and use of multivar-

iable logistic model to dene the prior

risk; third, use of a specic methodologyand appropriately trained doctors to

measure UTPI and MAP; fourth, use of

automated machines to provide accuratemeasurement within 40 minutes of

sampling of maternal serum concentra-tion of PLGF and SFLT; fth, expression

of the values of the biomarkers as MoMsafter adjustment for factors that affect

the measurements; and sixth, use of

Bayes theorem to combine the prior riskfrom maternal factors with biomarkers

to estimate patient-specic risks and theperformance of screening for PE deliv-

ering at different stages of pregnancy.

A limitation of the study is that someof the ndings rely on modeling, which

introduces optimistic bias. We have used

cross validation on the empirical data,which reduces such bias, and demon-

strated that the modeled and empiricalperformance were similar.

Comparison with previous studiesSeveral studies have documented that

development of PE is associated with

second-trimester increase in UTPI,

MAP, and SFLT and decrease in serumPLGF.22-33 In this study we used Bayestheorem to combine the a priori risk

from maternal factors with all 4 bio-markers and conducted 5-fold cross

validation to assess performance of

screening.

Clinical implications of the studyScreening and diagnosis of PE is tradi-tionally based on the demonstration of

elevated blood pressure and proteinuria

during a routine clinical visit in the latesecond or third trimester of pregnancy.

In a proposed new pyramid of pregnancycare,34 an integrated clinic at 22 weeksgestation, in which biophysical and

biochemical markers are combined withmaternal factors, aims to estimate thepatient-specic risk of developing PE

and, on the basis of such risk, dene the

subsequent management of pregnancy,including the timing and content of

subsequent visits. The objective would

be to minimize adverse perinatal eventsfor those that develop PE by determining

the appropriate time and place for

delivery.

We found that the performance ofsecond-trimester screening for PE is

good for preterm PE but poor for term

PE. We assume that the performance of

screening for term PE would be better ifassessment is undertaken at 36, ratherthan 22, weeks. A previous screening

study in the third trimester by a combi-nation of maternal factors, MAP, UTPI,

PLGF, and SFLT demonstrated a high

performance in the prediction of PEwithin 6 weeks of screening but poor

performance for PE developing beyond

this interval.35 Since the majority of casesof PE occurr at term, it may be necessary

that all pregnancies be reassessed at 36

weeks. In this context, the main value ofthe 22 weeks assessment is to identify,

rst, the high-risk group for develop-ment of early PE that would then require

close monitoring for development of

high blood pressure and proteinuria at24e32 weeks; and second, the high-riskgroup for preterm PE that would

require reassessment at around 32 weeks

and, on the basis of such assessment,stratication into a high-risk group in

need of close monitoring at 32e36

weeks and a low-risk group that wouldbe reassessed at 36 weeks.

Performance of screening for PE by

our method is by far superior to those

recommended by ACOG3,21 or NICE.4

Use of a multivariable logistic model

to dene the prior risk attributes the

appropriate relative importance to each

maternal factor and allows estimationof the patient-specic risk of PErequiring delivery before a specied

gestation. The prior risk can then beadjusted according to the results of

biophysical and biochemical testing.

The software for such estimation ofprior and adjusted risk is freely avail-

able (American Journal of Obstetrics

and Gynecology website). Recordingmaternal history and measurement of

blood pressure are universally carried

out as part of routine pregnancy care;measurement of MAP requires adher-

ence to a protocol, but it can be un-dertaken by healthcare assistants after

minimal training, with the use of

inexpensive equipment, and takes a fewminutes to perform. In contrast, mea-surement of UTPI requires specic

training by sonographers and quality

assurance of their results; nevertheless,this test can be undertaken within a few

minutes by the same sonographers and

machines as part of the routine second-trimester scan. Measurement of serum

TABLE 4

Model-based performance of screening by an algorithm combining maternalfactors, uterine artery pulsatility index, mean arterial pressure, and serumplacental growth factor for preeclampsia with delivery at


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PLGF can be undertaken on the samemachines as for free -human chori-

onic gonadotropin and pregnancy-

associated plasma protein-A, whichare widely used in screening for Down

syndrome, but there is an inevitable

increase in cost. The study providesdata on performance of screening for

any combinations of the biomarkers.Ultimately, the choice of test for

screening will depend not only on thebasis of performance, but also on the

feasibility of implementation and health

economic considerations. n

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Author and article information

From the Harris Birthright Research Centre for FetalMedicine, Kings College, London, United Kingdom

Original Research OBSTETRICS ajog.org

619.e10 American Journal of Obstetrics &Gynecology MAY 2016
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(Drs Gallo, Casanova, Campanero, and Nicolaides), and

the Institute of Health Research, University of Exeter,

Exeter, United Kingdom (Dr Wright).

Received Sept. 3, 2015; revised Oct. 3, 2015;

accepted Nov. 19, 2015.

The authors report no conflict of interest.

The study was supported by a grant from the Fetal

Medicine Foundation (Charity No: 1037116). The re-

agents and equipment for the measurement of serum

placental growth factor and soluble fms-like tyrosine

kinase-1 were provided by Roche Diagnostics Limited.

This paper is part of the PhD thesis of Dahiana Gallo for

Universidad de Granada, Spain.

Corresponding author: Kypros H. Nicolaides, MD.

[email protected]

ajog.org OBSTETRICS Original Research

MAY 2016 American Journal of Obstetrics &Gynecology 619.e11
mailto:[email protected]://www.ajog.org/http://www.ajog.org/mailto:[email protected]


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SUPPLEMENTAL TABLE 1

Characteristics of the population with complete data on all biomarkers

Variable Unaffected (n 7295) Preeclampsia (n 268) PIH (n 185)

Maternal age in years, median (IQR) 30.9 (26.4, 34.6) 31.5 (26.5, 35.6) 31.2 (27.1, 35.7)

Maternal weight in kg, median (IQR) 71.0 (63.0, 82.0) 78.0 (68.5, 91.5)a 77.0 (69.0, 87.8)a

Maternal height in cm, median (IQR) 165 (160, 169) 164 (160, 168) 164 (160, 169)

Body mass index, median (IQR) 26.1 (23.4, 29.9) 28.7 (25.4, 33.2)a 28.1 (25.7, 32.6)a

Gestational age in weeks, median (IQR) 21.8 (21.2, 22.1) 22.0 (21.1, 22.2) 22.0 (21.2, 22.1)

Racial origin a a

White, n (%) 5596 (76.7) 170 (63.4%) 121 (65.4%)

Afro-Caribbean, n (%) 1127 (15.5) 79 (29.5%) 44 (23.8%)

South Asian, n (%) 299 (4.1) 9 (3.4%) 13 (7.0%)

East Asian, n (%) 134 (1.8) 6 (2.2%) 2 (1.1%)

Mixed, n (%) 139 (1.9) 4 (1.5%) 5 (2.7%)

Medical historyChronic hypertension, n (%) 80 (1.1) 30 (11.2)a 0 (0.0)

Diabetes mellitus, n (%) 73 (1.0) 8 (3.0)a

1 (0.5)

SLE/APS, n (%) 10 (0.1) 0 (0.0) 1 (0.5)

Conception a a

Natural, n (%) 7050 (96.6) 253 (94.4) 174 (94.1)

In vitro fertilization, n (%) 181 (2.5) 9 (3.4) 4 (2.2)

Ovulation induction drugs, n (%) 64 (0.9) 6 (2.2) 7 (3.8)

Family history of preeclampsia, n (%) 215 (3.0) 16 (6.0)a 11 (6.0)

Parity a a

Nulliparous, n (%) 3433 (47.1) 169 (63.06%) 123 (66.5)

Parous with no previous PE, n (%) 3623 (49.7) 58 (21.64%) 49 (26.5)

Parous with previous PE, n (%) 239 (3.3) 41 (15.30%) 13 (7.0)

Inter-pregnancy interval in years, median (IQR) 3.1 (2.0, 5.0) 4.3 (2.5, 6.3)a 3.3 (2.2, 5.5)

Outcome

Delivery at


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Fitted regression models for marker log10multiple of the median (MoM)values on gestation at time of delivery for pregnancies with preeclampsia

Biomarker Estimate (95% confidence interval)

Uterine artery pulsatility index

Intercept 0.34798 (0.324785, 0.37117)

Slope -0.0195256 (-0.021237, -0.01781)

Mean arterial pressure

Intercept 0.063088 (0.049141, 0.07704)

Slope -0.002842 (-0.00377, -0.00191)

Placental growth factor

Intercept -1.11759 (-1.436384, -0.7988)

Slope 0.078571 (0.048763, 0.10838)

Soluble fms-like tyrosine kinase-1

Intercept 0.585767 (0.621931, 1.73667)

Slope -0.052772 (-0.097567, -0.05974)

In the regression models, gestational age was centered at 24 weeks so the intercept represents the mean at 24 weeks.

Gallo et al. Second-trimester screening for preeclampsia. Am J Obstet Gynecol 2016.


Standard deviations and correlations, with 95% confidence limits, for log10multiples of the median biomarker values

No preeclampsia Preeclampsia

Pooledn Value n Value

Standard deviation

MAP 30,261 0.036279 (0.035992, 0.03657) 859 0.040576 (0.038741, 0.042593) 0.036403 (0.036119, 0.036692)

UTPI 65,762 0.113026 (0.112418, 0.113641) 1843 0.137039 (0.13275, 0.141616) 0.113746 (0.113143, 0.114357)

PLGF 9947 0.199612 (0.196865, 0.202438) 335 0.243466 (0.226296, 0.263476) 0.201017 (0.198296, 0.203815)

SFLT 7797 0.212704 (0.209404, 0.216111) 282 0.22947 (0.211936, 0.250191) 0.213306 (0.210053, 0.216661)

Correlations

MAP and UTPI 28,631 -0.0412 (-0.05246, -0.02993) 817 -0.02828 (-0.09514, 0.03885) -0.0412 (-0.05246, -0.02993)

MAP and PLGF 9667 -0.05417 (-0.06542, -0.04292) 324 -0.08371 (-0.14991, -0.01675) -0.05417 (-0.06542, -0.04292)

MAP and SFLT 7621 0.0439 (0.03264, 0.05516) 271 0.04954 (-0.01757, 0.1162) 0.0439 (0.03264, 0.05516)

UTPI and PLGF 9735 -0.07356 (-0.08116, -0.06595) 329 -0.07031 (-0.11566, -0.02467) -0.07356 (-0.08116, -0.06595)

UTPI and SFLT 7639 -0.16083 (-0.16827, -0.15336) 277 -0.14624 (-0.19069, -0.10119) -0.16083 (-0.16827, -0.15336)

PLGF and SFLT 7790 0.19361 (0.17454, 0.21253) 282 0.08523 (-0.02262, 0.19112) 0.19361 (0.17454, 0.21253)

Pooled refers to estimates obtained from pooling data for the preeclampsia and no preeclampsia groups.

MAP, mean arterial pressure; PLGF, placental growth factor;SFLT, soluble fms-like tyrosine kinase-1;UTPI, uterine artery pulsatility index.


ajog.org OBSTETRICS Original Research

MAY 2016 American Journal of Obstetrics &Gynecology 619.e13
http://www.ajog.org/http://www.ajog.org/


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Empirical detection rate with 95% confidence interval, at false-positive rate of 5% and 10%, in screening forpreeclampsia with delivery at


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Empirical detection rate with 95% confidence interval, at false-positive rate of 5% and 10%, in screening forpreeclampsia with delivery at


16/17


Empirical detection rate with 95% confidence interval, at false-positive rate of 5% and 10%, in screening forpreeclampsia with delivery at 37D0 to 39D6 and at 40 weeks gestation by maternal factors and combinationsof biomarkers

Method ofscreening

Preeclampsia at 370 to 396 weeks Preeclampsia at 40 weeks

n

FPR 5% FPR 10%

n

FPR 5% FPR 10%

History Combined History Combined History Combined History Combined

History 1315 31 (29, 34) 31 (29, 34) 41 (38, 44) 41 (38, 44) 643 19 (16, 22) 20 (17, 23) 30 (27, 34) 30 (27, 34)

MAP 435 35 (31, 40) 39 (34, 44) 47 (42, 51) 52 (47, 57) 201 18 (13, 24) 18 (13, 24) 29 (23, 36) 35 (28, 42)

UTPI 881 32 (29, 35) 34 (31, 37) 42 (39, 46) 46 (43, 49) 442 19 (15, 22) 22 (19, 27) 29 (25, 34) 35 (31, 40)

PLGF 172 32 (25, 40) 32 (25, 40) 42 (35, 50) 42 (34, 50) 82 17 (10, 27) 18 (11, 28) 24 (16, 35) 28 (19, 39)

SFLT 146 32 (25, 40) 32 (25, 40) 42 (34, 50) 42 (34, 50) 67 16 (8, 27) 18 (10, 29) 22 (13, 34) 27 (17, 39)

MAP, UTPI 410 34 (30, 39) 41 (37, 46) 45 (40, 50) 55 (50, 60) 196 18 (13, 25) 18 (13, 25) 30 (23, 37) 34 (28, 41)

MAP, PLGF 168 31 (24, 39) 33 (26, 40) 42 (34, 50) 48 (40, 55) 81 17 (10, 27) 17 (10, 27) 25 (16, 36) 27 (18, 38)

MAP, SFLT 142 31 (24, 39) 32 (25, 41) 41 (33, 49) 47 (39, 56) 66 17 (9, 28) 20 (11, 31) 24 (15, 36) 30 (20, 43)

UTPI, PLGF 168 31 (24, 39) 32 (25, 40) 42 (34, 50) 42 (35, 50) 82 17 (10, 27) 15 (8, 24) 24 (16, 35) 28 (19, 39)

UTPI, SFLT 143 31 (24, 40) 33 (25, 41) 41 (33, 50) 45 (36, 53) 67 16 (8, 27) 15 (7, 26) 24 (14, 36) 7 (17, 39)

PLGF, SFLT 146 32 (25, 40) 32 (25, 40) 42 (34, 50) 41 (33, 50) 67 16 (8, 27) 18 (10, 29) 22 (13, 34) 28 (18, 41)

MAP, UTPI, PLGF 165 30 (23, 38) 33 (26, 40) 41 (34, 49) 50 (42, 58) 81 17 (10, 27) 17 (10, 27) 25 (16, 36) 30 (20, 41)

MAP, UTPI, SFLT 140 31 (23, 39) 36 (28, 44) 41 (32, 49) 49 (40, 57) 66 17 (9, 28) 17 (9, 28) 24 (15, 36) 30 (20, 43)

MAP, PLGF, SFLT 142 31 (24, 39) 31 (24, 39) 41 (33, 49) 48 (39, 56) 66 17 (9, 28) 20 (11, 31) 24 (15, 36) 30 (20, 43)

UTPI, PLGF, SFLT 143 31 (24, 40) 31 (23, 39) 41 (33, 50) 42 (34, 50) 67 16 (8, 27) 15 (7, 26) 22 (13, 34) 28 (18, 41)

MAP, UTPI, PLGF,SFLT

140 31 (23, 39) 32 (25, 41) 41 (32, 49) 49 (41, 58) 66 17 (9, 28) 15 (8, 26) 24 (15, 36) 27 (17, 40)

The performance of screening with history varies with each biomarker or their combination because of differences in composition of the studied populations.

FPR, false-positive rate;MAP, mean arterial pressure;PLGF, placental growth factor; SFLT, soluble fms-like tyrosine kinase-1; UTPI, uterine artery pulsatility index.


Original Research OBSTETRICS ajog.org

619.e16 American Journal of Obstetrics &Gynecology MAY 2016
http://www.ajog.org/http://www.ajog.org/


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Model-based detection rate of preeclampsia, at false-positive rates of 5% and 10%, in screening by maternalfactors and combination of maternal factors and biomarkers

Method ofscreening

Gestational age at delivery with preeclampsia (w)

Documents

Competing Risks Model in Screening for Preeclampsia