Results
Study population consists of 195 women with a history of at least one
delivery. PM women constituted 8.2% (n=16), MP women constituted 37.4%
(n=73), GMP women constituted 23.6% (n=46) and GGMP women constituted
30.8% (n=60) of the study population. The mean age was 50.6±16.3 and
mean parity was 6.5±4.2. The characteristics of the study population
were given in Table I.
The E velocity (p=0.017), A velocity (p=0.000), lateral e’ velocity
(p=0.000), lateral s’ (p=0.027), septal e’ (p=0.000), septal s’
(p=0.000) and EF (p=0.000) values were significantly different among all
parity groups. The results were shown in Table II. Binary comparison of
the study groups evaluating the echocardiographic parameters can be seen
in Table III.
Diastolic dysfunction classification was done according to the
echocardiographic parameters. For the PM group, 87.5% (n=14) had normal
diastolic function, 6.25% (n=1) had grade 1 diastolic dysfunction and
6.25% (n=1) had grade 2 diastolic dysfunction. For the MP women; 71.2%
(n=52) had normal diastolic function, 12.4% (n=9) had grade 1 diastolic
dysfunction and 16.4% (n=12) had grade 2 diastolic dysfunction. For the
GMP women; 56.5% (n=26) had normal diastolic function, 10.9% (n=5) had
grade 1 diastolic dysfunction and 32.6% (n=15) had grade 2 diastolic
dysfunction. For the GGMP women; 28.6% (n=17) had normal diastolic
function, 33.2% (n=20) had grade 1 diastolic dysfunction and 38.2%
(n=23) had grade 2 diastolic dysfunction (Table IV). There were no women
with grade 3 diastolic dysfunction among the study population.
Spearman correlation analysis showed that diastolic dysfunction has
positive correlations with parity, age, hypertension, and diabetes
mellitus (Table V).
Table VI and Table VII report the findings of the binary and multinomial
logistic regressions. Explanatory variables in both models are age,
number of parity, hypertension and diabetes mellitus. The difference
among the models stems from how the dependent variable is handled. In
the binary logistic regression, dependent variables are grouped into two
categories: diastolic dysfunction existence or the patient has normal
diastolic function. On the other hand, multinomial logistic regression
in this study separates the patients into three groups: patients without
diastolic dysfunction, patients with grade 1 and grade 2 diastolic
dysfunction. Both models show that only number of parity and age are
statistically significant.
ROC analysis showed that the best cut-off value of the parity number for
predicting left ventricular diastolic dysfunction was 6.5, with 66.3%
sensitivity and 66.7% specificity (Figure 1).