According to the World Health Organization, infertility as a disease of the male and female reproductive system defined by the failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse. It has become a global health issue affecting millions of people of reproductive age worldwide, estimated data shows that one in six people worldwide experiences infertility once in their lifetime
[1]
. The lifetime prevalence of infertility is 17.8% in high-income countries and 16.5% in low and middle-income countries
[2]
. In Cameroon 20% - 30% of couples suffer from infertility and the prevalence varies from region to region [3]. Egbe et al. carried out a hospital-based study in 2016 and reported a 19.2%, the prevalence of infertility in Douala [4]. The Groupe Interafricain de Recherche et d’Application sur la Fertilite (GIERAF) carried a study in 2010 that identified the main cause of female infertility (where) to be tubal stenosis secondary to poorly treated/untreated sexual transmitted diseases [5]. Other causes of female infertility include endometriosis, uterine abnormalities, cervical causes and ovulation disorders. Ovulatory disorders account for approximately 25% of diagnosed infertility, while 70% of women with anovulation have polycystic ovarian syndrome (PCOS)
[3]
. FIGO classifies ovulatory disorders into four types; type 1, corresponds to hypothalamic causes; type 2, pituitary causes; type 3, ovarian causes and type 4, PCOS
[4]
.
The assessment of ovarian reserve to determine the strategy for female infertility treatment has become essential. Ovarian reserve is defined as the number of oocytes remaining in the ovary or oocyte quantity
[5]
. This parameter is actively used in pregnancy planning and in assisted reproductive technology applications
[6]
. The fecundity of a woman decreases with age due to a decrease in the quantity and quality of oocytes, primarily as a result of a continuous process of oocyte atresia [7]. The total number of oocytes is thought to peak in fetal life at 20 weeks of gestation with approximately 5 - 7 million, this number decreases to about 400,000 - 500,000 follicles by puberty
[8]
.
Traditionally, follicle-stimulating hormone (FSH) levels, oestradiol (E2) levels, and antral follicle count (AFC) by ultrasound investigation at the early follicular phase have been used for evaluation of ovarian reserve [9]. Recently, identification of Anti Mullerian hormone (AMH) levels became important as it is considered a more reliable maker in the assessment of ovarian reserve [10]. Anti-Mullerian hormone is a member of the transforming growth factor beta family, it is secreted by the pre-antral and antral follicles and incurs little intra and inter cycle variations, therefore the levels of AMH indirectly represent the quantity of these follicles over time
[10]
[11].
An antral follicle is a resting follicle and appears as a small fluid sac which contains an immature egg. Antral Follicular Count (AFC) is a procedure where a transvaginal ultrasound is used to identify follicles with a mean diameter ranging from 2 to 10mm [12]. AFC is frequently assessed in women of reproductive age for various reasons; it is helpful in infertility and ART work up in predicting ovarian response to gonadotropin stimulation. The ovarian reserve is considered to be adequate when the number of antral follicles identified in each ovary is greater than or equal to five in each ovary
[7]
.
In 2015, Ludmila et al reported that AMH and AFC are preferred methods for predicting ovarian reserve with a varied degree in precision
[9]
and also the type of response to ovarian stimulation. The quantity and quality of oocyte are known to decline with age, however, large variations of oocyte reserves exist between individual patients, as do ovarian responses to gonadotrophin stimulation even among women of the same group. Ovarian stimulation is defined as a pharmacological treatment with the intention of inducing the development of ovarian follicles, it is used in ART to obtain multiple oocytes for follicular aspiration
[12]
. Fleming et al. in 2013 reported that women with low ovarian reserve tend to have low AMH levels and thus respond to ovarian stimulation poorly and may require greater management of their expectations for outcome success while at the other end of the spectrum women with high ovarian response are at risk of excessive ovarian response that can lead to ovarian hyperstimulation syndrome. Similarly, patients presenting over 10 antral follicles in each ovary during AFC will tend to have a high ovarian response to stimulation
[7]
. A study in Cameroon by Kasia et al. in 2020 suggested that a threshold value of 1.1 ng/l was a predictive value for an acceptable response during ovarian stimulation [11]. AMH has been correlated highly with baseline AFC as the two most reliable and valuable markers of ovarian responses, however, there is a paucity of data of this correlation in our country. To ensure safe and efficient ovarian stimulation, our goal during this study is to better understand the correlation between AMH, AFC and the response to ovarian stimulation during IVF in the city of Douala.
2. Methodology
It was a descriptive and analytic cross-sectional study with a retrospective and prospective data collection. The general objective was to determine the relation between AMH, AFC and the response of ovarian stimulation in IVF centres in Douala. Our study period was 3 years and 4 months, from January 2021 to April 2024. The target population included patients who had undergone ovarian stimulation during IVF in the above-mentioned clinics from January 2021 to April 2024. Our Inclusion Criteria where
- Infertile women who had undergone ovarian stimulation during an IVF cycle;
- Patents who had done either AMH serum test, AFC, or the both before ovarian stimulation;
- The presence of bilateral ovaries was confirmed by transvaginal ultrasound.
We excluded infertile patients who had undergone ovarian stimulation for a different ART method (such as artificial insemination) and incomplete patientrecords. We used Cochran’s Formula for the sampling size’s =
. This gave us an approximate sample size of 238 patients.
Z is the statistic corresponding to the level of confidence = 1.96;
P is expected prevalence = 0.192 (a hospital based study carried out by Egbe et al. in 2016 in Douala Cameroon reported a prevalence of 1nfertility of 19.2%) carried in Cameroon;
d is precision (corresponding to effect size) = 0.05 n is sample size.
After approval of the protocol by the research panel of the Faculty of Medicine and Pharmaceutical Sciences Douala, ethical clearance was obtained from the Institutional Review Board of the Faculty of Medicine and Pharmaceutical Sciences, University of Douala (IRB/UD). Administrative approval was obtained from the managing Director of Clinique de l’Aeroport, Clinique Odyssee and Clinique Urogyn. Patients’ files were selected following inclusion criteria above and data was collected for a period of 4 months using questionnaires established by the researcher. The files of patients diagnosed with infertility who had undergone ovarian stimulation for an IVF cycle at Clinique de l’Aeroport, Odyssee and Urogyn were reviewed. We approached the archivist or a nurse at the gynecological unit to obtain the files of patients. Patients’ information was collected using pre-designed questionnaires. Data concerning epidemiology, demography, relevant past history, evaluation of ovarian reserve markers, indication of treatment or management (medical) and evolution was collected.
Patients were divided into three groups based on the number of oocyte retrieved after ovarian stimulation:
Low response = ≤3 oocytes;
Normal response = 4 - 15 oocytes;
High response = >15 oocytes.
Data was collected using the ODK collect application, entered into SPSS version 26 for analysis. Binary logistic regression analysis/chi-square test was used to test association between dependent variable and independent variables and reported as Crude odd ratios with 95% confidence intervals. A two-tailed p-value less than 0.05 was considered statistically significant. Pearson correlation was used to determine the correlation between AMH, AFC and number of retrieved oocytes. ROC analysis curve was used to determine the predictive value of both AMH and AFC. Confusion matrix was used to calculate specificity and sensibility of AMH and AFC.
Both the ethical clearance and the administrative approval documents were used to obtain an approval to carry out this study in the referred clinics. Patient’s confidentiality was respected.
3. Result
For the study, we went through all the records of patients who did ovarian stimulation during an IVF cycle from January 2021 to April 2024 at Clinique de l’Aeroport, Clinique Odyssee and Clinique Urogyn (
Figure 1
).
Figure 1. Recruitment flow chat.
3.1. Sociodemographic Profile
Past History
There was a predominance of 206 pauciparous women (56.7%), 210 women had secondary infertility (57.9%), 56.7% had a history of pelvic inflammatory disease (PID), while 19.3% had a history of PCOS. History of tubal surgery was found in 24% of women, while 32.5% had previously undergone ovarian stimulation. Averagely most women had been dealing with infertility for about 5.28 ± 3.622 years. The most common cause of infertility was mixed causes (60.6%) (
Table 1
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 1. Gynecological past history.
Variable
Category
Frequency (N)
Percentage (%)
Gravidity
Nulligravida (0)
143
39.4
Paucigravid (1 - 4)
206
56.7
Multigravida (˃4)
14
3.9
Type of infertility
Primary
153
42.1
Secondary
210
57.9
Duration of infertility (years)
1 to 5
242
66.7
6 to 10
98
27.0
11 to 15
16
4.4
>15
7
1.9
History of PCOS
Yes
70
19.3
No
293
80.7
History of PID
Yes
206
56.7
No
157
43.3
History of pelvic surgery
Tubal
87
24.0
Ovarian
17
4.7
Myomectomy
51
14.0
Other abdominopelvic
48
13.2
None
160
44.1
Etiology of infertility
Female factor
93
25.6
Male factor
47
12.9
Mixed causes
220
60.6
Unexplained cause
3
0.8
Previous ovarian stimulation
Yes
118
32.5
No
245
67.5
<xref ref-type="bibr" rid="scirp.140413-"></xref>3.2. Different Protocols Used during Ovarian Stimulation
311 women (85.7%) were stimulated using the antagonist protocol while 51 women were stimulated using either the long (8.0%) or short agonist (6.1%) protocol. Recombinant FSH gonadotropin was the most frequently used gonadotropin with a percentage of about 88.4%. HMG was used for ovarian stimulation in 11.6% of cycles, nearly always in combination with FSH. Ovulation was induced 93.9% of the times with HCG. Duration of stimulation ranged from 8 - 16 days (
Table 2
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 2. Protocols used during ovarian stimulation.
Variables
Category
Frequency (N)
Percentage (%)
Type of protocol
Antagonist
311
85.7
Long-agonist
29
8.0
PPOS
1
0.3
Short agonist
22
6.1
Type of gonadotropin
Highly Purified HMG
4
1.1
Urinary gonadotropin (HMG)
38
10.5
Recombinant gonadotropin (FSH)
321
88.4
Posologie of gonadotropin (IU)
≤150
151
41.6
151 - 225
192
52.9
>225
20
5.5
˃225
16
4.4
Molecule used to trigger oocyte maturation
GnRH agonist
22
6.1
Human chorionic gonadotropin (HCG)
341
93.9
Duration of stimulation (days)
<10
8
2.2
10 to 12
262
72.2
13 to 15
84
23.1
>15
9
2.5
Age
The ages of participants ranged from 20 - 47 years, with a mean age of 34.11 ± 5.11 years. The median age of the population was 34 years, with a predominant age range of [30 - 40] (66.6%) (
Table 3
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 3. Distribution according to age.
Mean
Standard deviation
Median
Min
Max
Age
34.11
5.11
34
20
47
Age group
Frequency
Percentage (%)
<25
10
2.8
[25 - 30]
56
15.4
[30 - 35]
121
33.3
[35 - 40]
121
33.3
≥40
55
15.2
Total
363
100.0
3.3. Factors Associated with Ovarian Response
On bivariate analysis: From the odd ratios displayed women within the age groups [25 - 35] were less likely to have a low ovarian response as compared to women above 40 years. Women who had PCOS where less likely to have a low ovarian response to stimulation compared to women who did not have PCOS. In addition, women who had been previously stimulated had lower chances of responding poorly to ovarian stimulation as compared to women who had not been previously stimulated. Furthermore, patients who had been stimulated for 10 - 12 - 15 days were less likely to have a poor response than patients who were stimulated for less than 10 days. After multi-regression analysis, we found out that older age, not having PCOS and previous ovarian stimulation were independent predictor variables of low ovarian response (
Table 4
,
Table 5
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 4. Bivariate analysis of factors associated with low ovarian response.
Low ovarian response ≤ 3 oocytes retrieved
(CI)
Variables
Yes
%
No
%
P value
OR
Inf
Sup
Age
<25
2
20.0
8
80.0
0.374
0.5
0.1
2.5
[25 - 30]
4
7.1
52
92.9
0.001
0.1
0.0
0.5
[30 - 35]
15
12.4
106
87.6
0.001
0.3
0.1
0.6
[35 - 40]
21
17.4
100
82.6
0.013
0.4
0.2
0.8
≥40
19
34.5
36
65.5
1
History of PCOS
Yes
3
4.3
67
95.7
0.005
0.3
0.1
0.6
No
58
19.8
235
80.2
1
Etiology of infertility
Male factor
8
17.0
39
83.0
0.399
1.5
0.6
4.1
Mixed causes
42
19.1
178
80.9
0.121
1.8
0.9
3.6
Unexplained cause
0
0.0
3
100.0
0.999
0.0
0.0
Female factor
11
11.8
82
88.2
1
Previous ovarian stimulation
Yes
10
8.7
105
91.3
0.006
0.4
0.2
0.8
No
51
20.6
197
79.4
1.000
Type of protocol
Long-agonist
6
20.7
23
79.3
0.654
1.2
0.5
3.2
PPOS
0
0.0
1
100.0
1.000
0.0
0.0
Short agonist
1
4.5
21
95.5
0.151
0.2
0.0
1.7
Antagonist
54
17.4
257
82.6
1.000
Type of gonadotropin
Highly Purified HMG
0
0.0
4
100.0
0.999
-
-
-
Recombinant gonadotropin
55
16.4
280
83.6
0.284
0.6
0.2
1.6
Urinary gonadotropin
6
25.0
18
75.0
1
Dosage of gonadotropin
≤150
31
19.255
130
80.745
0.584
0.7
0.2
2.4
151-225
26
13.978
160
86.022
0.243
0.5
0.1
1.6
>225
4
25
12
75
1
Duration of stimulation
10 to 12
44
16.8
218
83.2
0.028
0.2
0.0
0.8
13 to 15
12
14.3
72
85.7
0.020
0.2
0.0
0.8
>15
1
11.1
8
88.9
0.103
0.1
0.0
1.5
<10
4
50.0
4
50.0
1
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 5. Multivariate logistic regression of predictor variables with low ovarian response.
Variable
P. value
OR
(CI) 95%
Inf
Sup
History of PCOS
0.025
0.250
0.074
0.841
Previous ovarian stimulation
0.003
0.318
0.150
0.673
Duration of stimulation
0.0099
0.857
0.713
1.030
Age (years)
0.001
1.122
1.053
1.195
On bivariate analysis age, history of PCOS and duration of stimulation were significantly associated with high ovarian response. From the odd ratio displayed, women within the age groups [25 - 30] years were four times more likely to have a high ovarian response than women above 40 years. Women who had PCOS were 11 times more likely to have a higher ovarian response than those who did not have PCOS. Women who had been stimulated for 13 - 15 days were 3 times more likely to have a high ovarian response than those stimulated for 10 – 12 days. After multivariate logistic regression analysis, we found out that being younger, having of PCOS were independent predictors of high ovarian response (
Table 6
,
Table 7
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 6. Independent predictors of high ovarian response.
<xref ref-type="bibr" rid="scirp.140413-"></xref>3.4. Correlation Analysis between AMH, AFC and the Response to Ovarian Stimulation
In our study 65.01% of our population had a normal ovarian response, 19.56% had a high ovarian response while 15.3% were low responders. Using Pearson correlative analysis, the number of oocytes had a highly significant correlation with AFC (r = 0.545, P = 0.001) (r = 0.643, P = 0.001) than with AMH (r = 0.519, P = 0.001) (r = 0.572, P = 0,001) in both the low and high ovarian response group respectively. The number of oocytes had a higher statistically more significant correlation with AMH (r = 0.490, P = 0.001) than with AFC (r = 0.430, P = 0.001) in patients with normal response (see
Table 13
). Furthermore, Age had a statistically significant inverse correlation with number of oocytes (r: −0.261, P: <0.001), AMH (r = −0.247, P < 0.001) and AFC (r = −0.452, P < 0.001) (see
Table 8
,
Table 9
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 8. Number of oocytes retrieved.
Number of oocytes retrieved
Number of patients
Percentage
Low response = ≤3
56
15.43
Normal response = 4 - 17
236
65.01
High response = ≥15
71
19.56
Total
363
100.0
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 9. Correlation between the number of oocytes, AMH and AFC using pearson correlation.
Frequency
Mean
Number of oocytes
Coefficient
P-value
AMH
38
0.89 ± 0.12
≤ 3
0.519
0.001
173
2.31 ± 0.13
4 - 15
0.490
0.001
44
6.69 ± 0.64
>15
0.572
0.001
Total
255
2.85 ± 0.18
AFC
45
9.13 ± 0.61
≤ 3
0.545
0.001
192
13.01 ± 0.35
4 - 15
0.430
0.001
53
20.28 ± 0.67
>15
0.643
0.001
Total
290
13.74 ± 0.34
AMH had the highest accuracy for predicting low ovarian response (AUC: 0.856, 95%CI: 0.79 - 0.92, P < 0.001) and was significantly better than AFC (AUC: 0.774, 95%CI: 0.71 - 0.84, P < 0.001). An AMH cut-off value of ≤0.5 ng/ml had a sensitivity of 52.60% and a specificity of 98.61%. Corresponding values for an AFC of ≤3 were 62.50% and 85.81% (
Table 10
,
Table 11
).
AMH had the highest accuracy for predicting high ovarian response (AUC: 0.917, 95%CI: 0.88 - 0.95, P < 0.001) and was significantly better than AFC (AUC: 0.874, 95%CI: 0.83 - 0.92, P < 0.001). An AMH h cut-off value of ≥6.4 ng/ml had a sensibility of 61.29% and a specificity of 88.84%. Corresponding values for an AFC of ≥21 were 84.21% and 86.34% (
Table 12
,
Table 13
).
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 10. Confusion matrix for AMH in predicting low response.
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 11. Confusion matrix for AFC in predicting low response.
Predicted values
Observed values
0
1
Total
0
234
3
237
1
37
16
53
Total
271
20
290
Specificity = 85.81%, Sensitivity = 62.50%.
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 12. Confusion matrix for AMH in predicting high ovarian response.
Predicted values
Observed values
0
1
Total
0
199
12
211
1
25
19
44
Total
224
31
255
Specificity = 61.29%, Sensitivity = 88.84%.
<xref ref-type="bibr" rid="scirp.140413-"></xref>Table 13. Confusion matrix for AFC in predicting high ovarian response.
Predicted values
Observed values
0
1
Total
0
234
3
237
1
37
16
53
Total
271
20
290
Specificity = 84.21%, Sensitivity = 86.34%.
4. Discussion
We carried out a hospital based descriptive and analytic cross-sectional study which set out to identify the correlation between AMH, AFC and the response to ovarian stimulation. Our study population consisted of patient files with 510 patient files excluded, and 363 patient files included and analyzed.
<xref ref-type="bibr" rid="scirp.140413-"></xref>4.1. Sociodemographic and Clinical Profile
The mean age of our study was 34.11 ± 5.11 years, the age of participants ranged from 20 - 47 years, with a predominant age of 30 - 40 years (66.6%). This is consistent with the findings of Vuong et al. [13], who found their mean age to be 34.3 ± 5.2 years in a hospital based prospective study in Vietnam in 2015. Permadi et al. [14] also had a mean age of 34.8 ± 2.8 years in a hospital based retrospective study in 2021. Himabindu et al. [15] in India had a mean age of 34.61 ± 3.62 years. This similarity can be explained by the fact that female ovarian reserve declines progressively with increasing chronological age. Above 30 years there is a significant decline in the quality and quantity of oocytes which decreases the probability of a spontaneous conception. By 35 years a woman’s fecundity declines by 50%, which is why optimal fertility is accepted to be between late teens and late 20 s.
Majority of our patients had secondary infertility with 57.9%, which was similar to the findings of Kasia et al.
[11]
in 2020 where 62.9% of patients had secondary infertility in a hospital based retropective study in Cameroon. These findings were contrary to that of Siddiqui et al. [16] and Vuong et al.
[13]
who found that majority of their patients had primary infertility (68.3% and 55.4%). Our findings can be explained by the fact that women in low-income countries have a high rate of PID, practice unsafe abortions and inadequate maternity care leading to post-abortive and postpartum infections. Due to the lack of adequate medical treatment these infections tend to cause significant damaged to reproductive organs.
In our study, 66.7% of our patients had been diagnosed with infertility for about 1 - 5 years. The mean infertility years was 5.28 ± 3.622 years which was similar to the findings of Vuong et al.
[13]
where their mean infertility years was 5.28 ± 4.133.
The majority of our study population had mixed factors as the predominant etiology of infertility with a frequency of 60.6%, followed by female factors with a frequency of 25.6% which was not in accordance with the findings of Kasia et al.
[11]
where female factors were the predominant cause of infertility with a frequency of 52.9%. This difference in results may be attributed to the patient selection bias in the various study sites.
<xref ref-type="bibr" rid="scirp.140413-"></xref>4.2. Types of Protocol Used during Ovarian Stimulation
The antagonist protocol was the most prescribed ovarian stimulation protocol with a frequency of 85.7%, followed by the long protocol with a frequency of 8.0%. This finding was similar to that of Baker et al. [17], in this study the antagonist protocol had a frequency of 86% while the long agonist had a frequency of 9%. This can be explained by the observation that the antagonist protocol is associated with a lower risk of ovarian hyperstimulation.
For ovarian stimulation, 88.4% of cycles used FSH. HMG was used for ovarian stimulation in 11.6% of cycles, nearly always in combination with FSH. This was similar to the studies done by Baker et al.
[17]
in the United States of America in 2018, where 95% of cycles used FSH. The similarity in both studies can be explained by the fact that recombinant FSH results in shorter duration of treatment, lower gonadotropin dose and better follicular characteristics on the day of the ovulation trigger shot.
To induce ovulation, 93.9% of cycles used human chorionic gonadotrophin. GnRH agonist (6.1%) was mostly used to induce ovulation in patients who had risk factors of developing ovarian hyperstimulation syndrome. This finding was very similar to that of Vuong et al.
[13]
, where 93.1% of cycles used HCG to trigger oocyte release while 6.9% used GnRH agonist. This is because HCG induced cycles have been associated with higher rates of implantation, clinical pregnancy, lower rates of early pregnancy loss and higher risk of OHSS due to their prolonged half-life which is responsible for higher LH activity. However, GnRH has a lower LH activity due to its short half-life of 60 mins hence lower risk of OHSS and high rates of pregnancy loss.
<xref ref-type="bibr" rid="scirp.140413-"></xref>4.3. Factors Associated with Ovarian Response to Stimulation
Our study revealed that there was a significant association between age and number of oocyte. Women within the age group [25 - 30[ years (OR: 0.1, 95%CI: 0.0 - 0.5, P = 0.001), and 30 - 35 [years (OR: 0.3, 95%CI: 0.1 - 0.6, P = 0.001) were less likely to have a low ovarian response as compared to women above 40 years. Women within the age group [25 - 30 [years (OR: 4.5, 95%CI: 1.7 - 12.4, P = 0.004) were more likely to have a higher ovarian response than women above 40 years. These findings can be explained by the fact that a woman’s ovarian reserve gradually decreases with age.
History of PCOS was significantly associated with the number of oocytes. Patients who had PCOS were 11 times more likely to have a high response to ovarian stimulation than those who did not have PCOSPCOS is known to be a risk factor of excessive ovarian response.
<xref ref-type="bibr" rid="scirp.140413-"></xref>4.4. Correlation Analysis between AMH, AFC and Number of Oocyte
Our study revealed that number of oocytes was positively and significantly correlated With AFC and AMH in the low and high ovarian response groups. However AFC had a stronger correlation coefficient, This is in accordance with the findings of, Permadi et al.
[14]
, Himanbindu et al.
[15]
, Tsakos et al.
[16]
and Sun et al.
[17]
who found out that number of oocytes had a stronger correlation with AFC. This can be explained by the fact that AFC has less variability than AMH and also it is a direct ultrasound measurement of the number of antral follicles in the ovaries in contrast to AMH which indirectly estimates ovarian reserve [ACOG]. These findings were in contrary to those of Nelson et al. [18], Siddiquie et al.
[16]
and Vuong et al.
[13]
who found out that AMH had a stronger correlation coefficient than AFC. These differences can be attributed to the difference in our study population and size since AMH level can be influenced by factors such as age, variability in assay results, hormonal changes and ethnicity. [ACOG]. In addition, the Pearson correlation coefficient showed that age was significant but inversely correlated with number of oocytes, AMH and AFC. This is in accordance with the findings of Himanbindu et al.
[15]
, Baker et al.
[17]
and Vuong et al.
[13]
who reported a statistically significant and inverse correlation between age and number of oocytes. This is understandable as there is a progressive decline in the quality and quantity of oocyte as age increases.
AMH had the highest accuracy for predicting ovarian response (AUC: 0.856, 95%CI: 0.79 - 0.92, P < 0.001); (AUC:0.917, 95%CI: 0.88 - 0.95, P < 0.001) and was significantly better than AFC (AUC: 0.774, 95%CI: 0.71 - 0.84, P < 0.001); (AUC: 0.874, 95%CI: 0.83 - 0.92, P < 0.001) in the low and high ovarian response group respectively. This was accordance to the findings of Vuong et al.
[13]
where AMH was a better predictor of ovarian response than AFC. Also Nelson et al. [20] in a multicenter analysis, assessed the relative capacity of AMH and AFC for predicting oocyte yield which demonstrated that AMH dominated the model: AMH, R = 0.29 and 0.23; AFC: R = 0.07 and 0.07 for long GnRH agonist and GnRH antagonist trials respectively. In contrast Himabindu et al.
[15]
, Tsakos et al
[16]
and Sun et al.
[17]
, in single center studies found out that AFC was a better predictor of ovarian response. Our findings can be explained by the observation that AMH can detect smaller follicles at earlier stages of follicular development, providing a wider range of detection and potentially a stronger predictive value. Also AMH is less operator dependent compared to AFC, which requires ultrasound assessment and can be influenced by operator expertise and ultrasound technology
[18]
-
[20]
.
An AMH cut-off value of ≤0.5 ng/ml had a sensitivity of 52.60% and a specificity of 98.61% in predicting low ovarian response. Corresponding values for an AFC of ≤3 was 62.50% and 85.81%. Our findings were within the limits of the Bologna criteria, which states that an AMH levels < 0.5 ng/ml and AFC < 5 is a predictor of poor ovarian response to stimulation (POR ≥ 3 oocyte). This is in contrast with Poseidon’s classification, where an AMH level <1.2 ng/ml is a predictor of poor ovarian stimulation (POR ≥ 3). Also Vuong et al.
[13]
, found an AMH cut off value of ≤1.25 ng/mL with sensitivity of 86.7% and specificity of 84.8%. Corresponding values for AFC was ≤5, sensibility 78.8% and specificity 86.0%. These differences can be explained by the lack of an international standardization assay for AMH causing inter-assay variabilities. It can also be attributed to marked sonographer-dependent variability across centers. Furthermore different study designs and difference in ethnicities between study populations may contribute to these variations.
In our study an AMH cut-off value of ≥6.4 ng/ml had a sensibility of 61.29% and a specificity of 88.84% in predicting high ovarian response. Corresponding values for an AFC of ≥21 was 84.21% and 86.34% respectively. Our cut-off value for AFC was similar to that of ASRM
[4]
, which states that AFC > 24 is a predictor of high ovarian response (>15 - 18 oocyte retrieved), whereas AMH > 3.4 ng/ml is indicative of a high ovarian response, in contrast to our findings. Also Vuong et al.
[13]
found that an AMH cut off value of >3.57 ng/mL had sensitivity of 83.7%, and a specificity of 79.8%. Corresponding values for an AFC of ≥12 were 79.2% and 81.7% respectively for predicting high ovarian response. The difference in results may be due to the lack of an international standardized definition of ovarian response. Difference in Ethnicity and study designs may contribute to these variations.
5. Strength
It is one of the few studies in Cameroon that has effectively evaluated the sensitivity, specificity and cut off values of ovarian reserve biomarkers in predicting ovarian response to stimulation in couples attending the infertility clinics in the reproductive age group and selected for IVF treatment. Moreover, the study was conducted in a hospital setting, which allows for access to medical records and patient information that provided a comprehensive understanding of ovarian response to stimulation.
6. Limitations
As this was a multicenter study, one major concern is the lack of international standardization hence variability in AMH results as different assays may have a distinct sensitivity and specificity leading to variabilities in results. However we use a conversion factor to adjust values obtained from different assays to ng/ml to ensure comparability across studies and reduce discrepancies. While our study was limited to a specific population we took steps to increase the generality of our findings by recruiting from multiple sites. This study may be subject to selection bias since it was not a probability sampling.
7. Conclusions
Majority of our study population had secondary infertility, with mixed factors being the most frequent cause of infertility. The mean duration of infertility was 5.28 ± 3.622 years.
In our study, the most prescribed ovarian stimulation protocol was the antagonist protocol, and recombinant FSH was the preferred gonadotropin used during stimulation. Ovulation was induced 93.9% of the time with HCG. GnRH agonist was mostly used to trigger ovulation in patients with high risk of ovarian stimulation. Duration of stimulation ranged from 8 - 16 days.
On multivariate logistic analysis age and history of PCOS were significantly associated with ovarian response in both the low and high ovarian response groups.
The number of oocytes was significantly and positively correlated with AMH and AFC in both low and high ovarian groups. Age was significantly but negatively correlated with number of oocytes, AMH and AFC. Both AMH and AFC were independent predictors of ovarian response to stimulation. AMH cut-off values of ≤0.5 ng/ml and ≥6.3 ng/ml were more specific but less sensitive in predicting low and high ovarian response respectively. Whereas, AFC cut-off values of ≤3 and ≥21 were less specific and more sensitive in predicting low and high ovarian response. Although, AMH was a better predictor of ovarian response to stimulation, due to its lack of an international standardized assay, AFC combined with it may be a more accurate predictor of ovarian response.
8. Recommendation
Patients should seek early consultations to mitigate age-related infertility effects and timely initiation of fertility treatments, such as IVF, after a year of unsuccessful natural conception. Regular monitoring of ovarian reserves through AMH and AFC testing can help adjust fertility plans accordingly. Physicians are encouraged to promote early fertility evaluation in patients’ early to mid-30 s, recommend egg freezing for those concerned about age-related infertility, and utilize both AMH and AFC to improve the accuracy of predicting ovarian response considering their sensitivities and specificities, with AMH’s higher specificity aiding in ruling out poor responders, and AFC’s higher sensitivity helping identify potential high responders. Personalized treatment should be based on combined AMH and AFC information, tailored to each patient’s needs, including parameters such as BMI, contraceptive use, and smoking history. For the government, ensure AMH and AFC testing is widely available and accessible, especially in fertility clinics and ART centers, and promote education and training for healthcare providers on interpreting these results. For researchers, conduct further studies to optimally combine AMH and AFC results, improving and validating predictive models that include other relevant factors to enhance the accuracy of predicting ovarian response.
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