Multilevel spline models for blood pressure changes in pregnancy

Corrie Macdonald-Wallis

Overview

• Background and aims

• Linear spline models of blood pressure in pregnancy

• Adding maternal characteristics as covariates

• Modelling weight gain and blood pressure changes in pregnancy together

• Summary

Blood pressure in pregnancy

• Hypertensive disorders of pregnancy are associated with maternal and offspring adverse health outcomes

Aims:

• To describe patterns of blood pressure change across pregnancy

• To investigate determinants of blood pressure change

• To relate gestational weight gain to blood pressure changes

ALSPAC 14,541 women living in Avon, UK with expected delivery dates between April 1991 and December 1992 recruited

Routine antenatal BP (median 14 per woman) and weight (median 12 per woman) measurements abstracted from obstetric records

11,789 women with singleton or twin pregnancies who had live term births and no previous diagnosis of hypertension or pre-eclampsia included in BP spline models

Linear spline modelsMultilevel linear spline models represent trajectories of

change over time with linear slopes between knot points

outc

ome

timeknot 1 knot 2

Patterns of change can be estimated for each individual as each individual has a residual for the intercept and each of the slope parameters

These models have been used to describe childhoodgrowth trajectories (McCarthy et al, 2007; Howe et al, 2010)

Choice of knot points for BP models

Knot points (indicating a change in slope) were chosen by:

• Fitting fractional polynomial curves to the data and using the best-fitting curve to estimate approximate locations of knot points

• Comparing the fit of spline models to the fractional polynomial curve

• Comparing the fit of the spline model predicted values to the actual blood pressure measurements across pregnancy

Choice of knot points: fractional polynomial curves

• used to find approximate position of knot points in spline models• suggests knot points at around 22, 30 and 36 weeks gestation

11

21

14

11

61

18

12

01

22

Sys

tolic

blo

od

pre

ssu

re (

mm

Hg

)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

Fractional polynomial predictions for systolic blood pressure

65

70

75

80

Dia

sto

lic b

loo

d p

ress

ure

(m

m H

g)

8 12 16 20 24 28 32 36 40 44

Gestational age (weeks)

Fractional polynomial predictions for diastolic blood pressure

Linear spline models for BP change

6570

7580

Dia

sto

lic b

lood

pre

ssur

e (m

m H

g)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

110

115

120

125

Sys

tolic

blo

od p

ress

ure

(mm

Hg)

8 12 16 20 24 28 32 36 40 44

Gestational age (weeks)

Mean at 8 weeks(mm Hg)

Average change per week (mm Hg/week)

8-18 weeks 18-30 weeks 30-36 weeks 36+ weeks

SBP 112.2 (111.9, 112.4)

-0.131(-0.16, -0.10)

0.153(0.13, 0.17)

0.281(0.24, 0.32)

1.131(1.06, 1.20)

DBP 66.0 (65.8, 66.2)

-0.183(-0.21, -0.16)

0.105(0.09, 0.12)

0.459(0.43, 0.49)

1.279(1.23, 1.33)

Determinants of BP change11

011

512

012

513

0

Sys

tolic

blo

od p

ress

ure

(m

m H

g)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

underweight normal weight overweight obese

SBP by maternal BMI SBP by pregnancy type

11

01

20

13

01

40

15

0

Sys

tolic

blo

od

pre

ssu

re (

mm

Hg

)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

male singleton female singleton twin

model adjusted for maternal pre-pregnancy BMI, age, parity, smoking status, highest educational qualification and pregnancy type

Determinants of BP change11

011

512

012

5

Sys

tolic

blo

od p

ress

ure

(m

m H

g)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

nulliparous multiparous

11

01

15

12

01

25

13

0S

ysto

lic b

loo

d p

ress

ure

(m

m H

g)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

never pre-pregnancy/1st trimester throughout pregnancy

SBP by parity SBP by smoking status

model adjusted for maternal pre-pregnancy BMI, age, parity, smoking status, highest educational qualification and pregnancy type

Spline model for gestational weight gain

60

65

70

75

80

We

ight

(kg

)

8 12 16 20 24 28 32 36 40 44

Gestational age (weeks)

• Weight gain has 2 knots at 18 and 28 weeks-----18 w

eeks

-----28 weeks

Relationships between weight gain and blood pressure change

Is an increase in weight in one period of pregnancy related to a rise in blood pressure in the next?

• Multivariate multilevel spline model with knots at 18, 29 and 36 weeks for SBP and DBP and knots at 18 and 29 weeks for weight

• Relationships between rates of change in weight and BP in different periods of pregnancy derived from variance-covariance matrix of the random effects

SBP changes by baseline weight/ weight gain up to 18 weeks

11

01

15

12

01

25

Pre

dic

ted

sys

tolic

blo

od

pre

ssu

re (

mm

Hg

)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

-1 SD mean +1 SD1

10

11

51

20

12

5P

red

icte

d s

ysto

lic b

loo

d p

ress

ure

(m

mH

g)

8 12 16 20 24 28 32 36 40 44Gestational age (weeks)

-1 SD mean +1 SD

Weight at 8 weeks Weight gain up to 18 weeks

SBP changes by weight gain 18-29 weeks and 29+ weeks

11

01

15

12

01

25

Pre

dic

ted

sys

tolic

blo

od

pre

ssu

re (

mm

Hg

)

20 24 28 32 36 40 44Gestational age (weeks)

-1 SD mean +1 SD

Weight gain 18-29 weeks

11

01

15

12

01

25

Pre

dic

ted

sys

tolic

blo

od

pre

ssu

re (

mm

Hg

)

32 36 40 44Gestational age (weeks)

-1 SD mean +1 SD

Weight gain 29+ weeks

Summary

• Linear spline multilevel models describe patterns of non-linear change in an interpretable way

• We were able to explore associations of maternal characteristics with blood pressure changes in different periods of pregnancy

• We also modelled three response variables together: SBP, DBP and weight to investigate temporal relationships between changes in weight and blood pressure in pregnancy

Future work

• Latent class growth models to group particular patterns of blood pressure change during pregnancy – compare these with definitions of gestational hypertension and pre-eclampsia

• Relate patterns of change in BP to birth weight of offspring and cardiovascular risk factors measured during childhood in offspring

Acknowledgements

PhD supervisors:

Kate Tilling, University of Bristol

Debbie Lawlor, University of Bristol

Also:

Abigail Fraser, University of Bristol

Scott Nelson, University of Glasgow