Estimated maximum failure rates of cycle monitors using daily conception probabilities in the menstrual cycle

G. Freundl1,5, E. Godehardt2, P.A. Kern1, P. Frank-Herrmann3, H.J. Koubenec4 and Ch. Gnoth1

1 Department of Reproductive Medicine and Gynaecological Endocrinology, Staedtische Kliniken Duesseldorf gGmbH, Frauenklinik Benrath and Institute of Natural Family Planning, 2 Biometric Research Group, Clinic for Thoracic and Cardiovascular Surgery and 3 Department of Gynaecological Endocrinology, University of Heidelberg and 4 Stiftung Warentest, Berlin, Germany

5 To whom correspondence should be addressed at: Frauenklinik Städt. Krankenhaus Düsseldorf-Benrath, Urdenbacher Allee 83, 40593 Düsseldorf, Germany. e-mail: freundlg{at}uni-duesseldorf.de


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
BACKGROUND: A number of menstrual cycle monitors have been developed to detect the fertile window of the menstrual cycle, mainly for contraceptive purposes. Reliable data on most of these systems are still missing but are urgently needed because many women use them and the tested systems differ enormously in price and effectiveness. We suggest a new efficacy estimating method to evaluate cycle monitors prior to full prospective clinical trials. METHODS: Sixty-two women prospectively tested seven cycle monitors and the symptothermal method (STM) of natural family planning (NFP) but not more than two different systems at the same time. The clinical fertile window was determined by detecting the day of ovulation using daily urinary LH measurements and daily ultrasonic folliculometry. This was compared to the fertile phase predicted by the systems. Maximum failure rates were estimated for each cycle monitor and the STM, using the daily conception probability rates taken from the European Fecundability Study. Intercourse was assumed to occur on each of all falsely predicted days of infertility. RESULTS: Sixty-two women with a mean age of 31 years (range: 21–42 years) contributed a total of 122 cycles to this study. Monitors based on the microscopic evaluation of saliva or mucus had many more false infertile days than the other methods based on temperature or hormonal measurements (225 versus 42 days). The maximum unintended pregnancy rates per cycle for temperature computers were estimated to be 0.0134–0.0336, for the hormonal computer 0.1155 and for mini-microscopes 0.2313–0.2369. For the STM of NFP, there were no false infertile days. CONCLUSIONS: The STM of NFP proved to be the most effective contraceptive method to detect the fertile window among all the methods tested. The estimated efficacy of the other cycle monitors range from the temperature computers (upper level) to the hormonal computer (medium level) and the mini-microscopes with very low estimated contraceptive efficacy.

Key words: cycle monitor/hormonal computer/mini-microscopes/natural family planning/temperature computers


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
There are only 6–9 days of the menstrual cycle on which intercourse may result in pregnancy. Various devices or cycle monitors have been developed to detect the fertile window in the menstrual cycle (Freundl et al., 1992Go; Barbato et al., 1993Go) in order to time intercourse to avoid or to achieve a pregnancy. Recent prospective studies have estimated daily conception probabilities in the cycle (Bremme, 1991Go; Wilcox et al., 1998Go; Colombo and Masarotto, 2000Go; Wilcox and Dunson, 2000Go; Wilcox et al., 2001Go). The idea is to estimate failure rates of such cycle monitors used to avoid pregnancy by relating the predicted fertile days to the clinical fertile window detected by ultrasound and urinary LH and daily conception probabilities. To our knowledge, this is the first study in which different cycle monitors have been prospectively compared to obtain reliable failure estimates for contraceptive use. An evaluation of such monitors is urgently needed since many women ask for these devices, which differ enormously in price and effectiveness.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Temperature computers are devices with a temperature probe connected to a mini-computer. An internal evaluation program automatically applies rules of the temperature method of natural family planning (NFP) to predict the fertile days in the menstrual cycle. The temperature computers use a data pool of previous measurements and cycle parameters (e.g. cycle length, day of temperature shift) for calculation purposes. The hormonal computer has a photometer reading the blue signal colour generated by an antigen/antibody reaction of the test sticks for detection of the urinary hormones estriol-glucuronide and LH. An internal algorithm using a database of previous cycle length and days of hormonal shifts predicts the fertile days and automatically asks for the next measurement. The mini-microscopes consist of a glass slide and a convex lens used as a microscope with an internal or external light. The user puts a small sample of saliva or cervical mucus on the slide and checks the dry sample for ferning patterns which indicates fertility. Absence of the typical patterns indicates a non-fertile day. The manufacturers of the mini-microscopes claim that ‘ferning’ correlates highly with rising estrogen concentrations in the serum as well as in cervical mucus and saliva and therefore predicts fertility. Table I gives an overview of the different monitors and fertility prediction methods with the principal method, details of the manufacturers’ websites, costs and postal addresses.


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Table I. General information about the monitors to test the fertility status of a woman
 
This study was designed to test each monitor or method in 15 women who had no prior experience with the respective systems. In addition to this, the users of the symptothermal method (STM) of NFP were also beginners. They were instructed by a qualified NFP teacher. The participants were between 21 and 42 years of age with cycle lengths ranging between 17 and 40 days (Table II). The study was conducted in accordance with the principles of the Declaration of Helsinki.


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Table II. Statistical characteristics of the women per monitor or method for age (in years) and cycles (in days)
 
Every woman was asked to test two differently working monitors to minimize a probable bias in interpretation of partly subjective methods such as the mini-microscopes or NFP. The testing combinations are shown in Table III. Women who wished to avoid pregnancy were asked to use barrier methods additionally. The women were asked to test the system for 7–8 cycles. The first six cycles were ‘learning cycles’ which were necessary to set up an internal database for the temperature computers and Persona®. The seventh cycle usually was the test cycle, in which ovulation was detected by folliculometry and LH measurement in urine. However, for the mini-microscope systems for mucus and saliva observation, a training time of >=3 months was needed and was deemed sufficient. The days predicted as fertile by each of the systems were compared with the fertile time revealed by folliculometry and detection of the LH surge in urine.


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Table III. Test combinations and distribution (testing cycle)
 
Ultrasound scanning
Using a 3.5 MHz vaginal probe, ultrasound scanning of the ovaries and follicular tracking were carried out starting from day 8 in women with generally short cycles (according to their cycle history of the last 12 months) and from day 10 in women with normal and longer cycles. Initially, the examination was performed every second day. When the follicle size reached or just passed 12 mm, the scanning was performed daily and was continued until the observation of a follicular diameter of >=20 mm, followed by its disappearance within 24 h. Almost all ultrasound scanning (95% of cycles) was performed by the same investigator. No cycle had to be excluded because of missing values.

Hormonal assays
The LH surge in urine was determined semi-quantitatively using the commercially available monoclonal antibody test ‘ClearPlan’. Urine samples were collected daily beginning at the same time as ultrasound tracking by the participants, in collecting tubes prepared with thiomersal for conservation and stored in a refrigerator until common examination of all probes in the laboratory. The day with the most intensive coloured signal was called the LH peak day.

Use of the various devices
The devices were used according to the manufacturers’ instructions. Each day of the cycle was computed as fertile or infertile as predicted by the device.

Definitions
As suggested by the World Health Organization (1980Go), the probable fertile window is defined as day –5 to day +2 inclusively, related to the LH peak (day 0) which roughly corresponds to the day on which the dominant follicle nearly reaches its maximum diameter, and is ~1 day prior to ovulation (24–28 h). This definition of the complete and ‘objective’ fertile window assumes a maximum of 6 days of sperm and 24 h of oocyte survival. Time of ovulation is defined as being between maximum follicular diameter plus 12 h and LH peak + 24 h (see Table IV).


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Table IV. Estimates of day-specific probabilities of conception (with a cycle viability k = 0.277)
 
Clinical and biometric estimations, statistical analysis
If a monitor assigns a day of the cycle as ‘not fertile’ which is indeed fertile according to clinical examination and previous definitions, this is called a ‘false infertile day’. Using the Schwartz model (which follows Barrett and Marshall, 1969Go), we computed ‘worst case conception probabilities’ for the cycles of each woman. If we suggest that on every day in the fertile window that is indicated as ‘infertile’ by the cycle tester, a woman has intercourse, then we get

P = k [1 – (1 – P–5)x–5(1 – P–4)x–4 ... (1 – P0)x0 (1 – P1)x1 (1 – P2)x2]

as the worst case probability. In this formula, the index i runs from –5, –4, ..., to 2, and xi takes the value of 1 if day i is indicated as infertile by the cycle tester (assuming the woman has intercourse on such a ‘false infertile ‘ day), and xi takes the value of 0 if this day is predicted correctly as fertile (and she had no intercourse on that day). Using the ideas outlined by Colombo and Masarotto (2000Go), we need the day-specific conditional conception probabilities p–5, ..., p2 (under the condition that the oocyte is fertilized) of each day of the menstrual cycle in the fertile window and we need the cycle viability k, which is roughly the maximum probability of pregnancy in any given cycle even if intercourse occurs on every fertile day in order to calculate the worst case probability, P, of becoming pregnant for every woman. For details of the model, the reader is referred to Colombo (1989Go) and Colombo and Masarotto (2000Go). They give estimates of the day-specific conception probabilities pi = k Pi (with p0 denoting the conception probability of the day of ovulation).

Higher values of P indicate a higher risk of conception for those women who want to use such a monitor to avoid pregnancy. In this sense, the worst case probability P is also an indicator for the quality of a monitor, indicating better ‘contraception quality’ if P is smaller. We used this formula to obtain estimates of maximum pregnancy rates for every woman and device together with the specific values for k and p–5, ..., p2 from Table 10 of Colombo and Masarotto (2000Go). These values are shown in Table IV. For example, if in the test cycle, days –5, –4, –3 and 2 of the fertile window (clinically detected by ultrasound scans and urinary LH) had not been detected as ‘fertile’ by the device, and days –2 to 1 had been detected correctly, then

P = k [1 – (1 – P–5) (1 – P–4) (1 – P3) (1 – P2)] = 0.277 [1 – (1 – 0.2455) (1 – 0.6354) (1 – 0.8556) (1 – 0.1264)] = 0.2674

was calculated as the individual risk of an unintended pregnancy, which is the maximum estimated pregnancy rate in this cycle for the user of this device and is very close to the cycle viability factor k = 0.277. This is a ‘worst case analysis’ since we assume intercourse occurred in each of the false infertile days, thus maximizing the individual risk of each participant.

A quality measure (quality index, QI) can be derived using P:

QI = P/k

for each woman and device. QI ranges between 0 and 1 with small values indicating a good method for preventing a pregnancy. A value close to 1 indicates that the device or method is close to ‘no method at all’. Thus, QI serves as a normalization between 0 and 1 with the cycle viability as normalizing factor. For the example above, QI = 0.2674/0.277 = 0.9653, indicating that the use of the device is virtually no better than ‘no use of any device’.

Performing these calculations for each combination of women and devices, the means and SD of these risks and quality measures for every device were computed. For the calculation of the individual risks of becoming pregnant, a Mathematica notebook was written for the product formula above (Mathematica program package from Wolfram Research for symbolic and numerical mathematics, version 4.2, www.wolfram.com). In addition, a Microsoft Excel table was written, into which the data from the notebook could be transferred to perform further statistical analysis with the SAS program package for statistics from SAS Institute (www.sas.com, version 8.2). The data from the participants were described by the usual means of descriptive statistics: we computed the means and SD of these risks and quality measurements for every cycle in which each device was tested.

For inferential statistics, the Kruskal–Wallis test as a non-parametric one-way test was used. After demonstrating significant differences for the means of the logarithms of the worst case probabilities for unwanted pregnancies between the methods, the one-factorial analysis of variance and Duncan’s a-posteriori test was used to find the possible grouping of the methods according to these average conception probabilities. This was considered as the main outcome for this research. The logarithms of the probabilities were computed to homogenize the variances between the groups instead of the original values. In addition, an analysis of variance for the percentage of false negative days was performed. The results were virtually the same as the primary analysis and are not reported here.


    Results
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Altogether, 65 women entered the study and three women dropped out early: one woman conceived in the third cycle, one lost the device and the third withdrew for personal reasons. The mean age of the final 62 participants was 31 years (range: 21–42 years) and they contributed a total of 122 test cycles.

In all cycles, ovulation could be detected by the LH surge and maximum follicular diameter occurring on the same day (see Table IV).

In a previously published study we have compared the correlation between the symptoms of self-observation and the ovulation detected by ultrasound/maximum follicle diameter/LH (Gnoth et al., 1996Go) in 87 cycles. The basal body temperature (BBT) rise identified according to the three-over-six rule was detected +0.92 (± 1.17) days around objective ovulation by ultrasound and LH monitoring.

Table V shows the total number of computed menstrual cycle days for each system and gives their ‘false infertile’ (true fertile days predicted as not fertile days) and ‘false fertile’ (true infertile days predicted as fertile days) days. It is obvious that the mini-microscopes had much more false infertile days than the temperature or hormonal devices.


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Table V. Detection of cycle days as fertile or infertile in relation to the clinical status as revealed by ultrasound and daily urinary LH
 
In contraceptive use, if a system assigns a cycle as ‘false infertile’ and the couple have intercourse, it has a certain probability of pregnancy, depending on the cycle day relative to the ovulation. Using the formula derived from the Schwartz model, a probability of unintended conception can be calculated for each cycle, for each device or system, with the assumption that the couple have intercourse on every day with false infertile information. This value is closely related to the efficiency of the systems when used to avoid pregnancy, which is a worst case rate for unintended pregnancies since we assume that intercourse occurred on every false infertile day. Instead of an upper limit of k = 0.277, the quality index QI can be used to limit the value of t between 0 and 1. For calculation purposes, we have used the daily probability values of the European Fecundability Study reported by Colombo and Masarotto (2000Go).

These recently published probabilities (Table IV) correlate well with other previously published figures (Schwartz et al., 1979Go; Bremme, 1991Go; Miolo et al., 1993Go; Weinberg et al., 1994Go). Table V summarizes the results of this worst case analysis. Values obtained from the mini-microscopes with their relatively high rates of false infertile cycle days did not differ much from the estimated cycle viability with a maximal probability for pregnancy values. These high false negative rates and low false positive rates account for their low sensitivity detecting truly fertile cycle days. In contrast, the temperature computers and the STM of NFP are highly sensitive but less specific in detecting the fertile window which accounts for their optimal use in contraception. Persona was found to have only a medium sensitivity and specificity. The non-parametric Kruskal–Wallis test showed significant differences between the different devices and fertility prediction methods (P < 0.0001). Using one-factorial analysis of variance together with Duncan’s method of a-posteriori testing to validate a grouping for these ‘worst case probabilities’, the analysis showed a significant difference between three groups of methods: group A consisting of the mini-microscopes PG 53, PC 2000 and Maybe Baby, group B consisting of Persona only, and group C consisting of the temperature computers and natural family planning methods. We used the Kruskal–Wallis test to prove possible differences between the means of the different methods since we could not assume normal distribution of the worst case probabilities. We additionally transformed the data by adding a constant to each value and then taking the logarithm to obtaining homogeneous variances for the groups.

We performed the Kruskal–Wallis test to find possible differences in the expectations of the worst case probabilities (and thus in the quality measures) per device. We calculated the worst case probability in the test cycle of every participant. The results of the Kruskal–Wallis test for differences and the Duncan test for a-posteriori grouping were the same for the transformed as well as for the original data. In Table VI shows the descriptive statistics for the original data together with the statistical grouping for easier understanding since a table of the Kruskal–Wallis test (rankings of all women, together with the average ranking per group) does not give as much information as a table with the means and SD of the worst case probabilities and quality measures. For all women in the NFP group, the worst case probability for pregnancy was 0 (thus giving 0 also as the mean and SD of this probability in this group according to the usual formulas used for descriptive statistics). Compared with other methods, Table V shows that NFP does not predict too many infertile days to be fertile.


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Table VI. A-posteriori grouping of the risk of unwanted pregnancy (worst case per cycle pregnancy rate) and of the quality measure for the risk of unwanted pregnancy as compared to the cycle viability (maximal probability of getting pregnant) for the various systems
 

    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
We have developed a new quality index, QI, and suggest a new method to test different cycle monitors or fertility prediction methods generally used to detect the fertile window for contraception. We compared the fertile days in the menstrual cycle predicted by the different monitors to the clinical fertile window revealed by ultrasound and urinary LH measurements. Sensitivity and specificity were calculated and analysed statistically. This procedure was shown to be effective and yielded good initial estimates of maximum unintended pregnancy rates for each monitor or method. Only cycle monitors or fertility prediction methods with a low maximum pregnancy rate in this primary efficacy estimation analysis are worth undertaking in view of the effort and expense of a full prospective clinical trial. To our knowledge, to date there has been no similar approach to compare the different cycle monitors or fertility prediction methods for contraception.

Prospective efficacy studies have been carried out on a few devices. One such device, the Ovarian Monitor (Brown and Blackwell, 1980Go; Brown et al., 1989Go, 1991), which we did not test here, showed a Pearl Index of 7.3 (Brown et al., 1991Go) in a study involving 37 women with 569 cycles. With this system, the beginning of the fertile period was marked by the rise in urinary metabolites of estrogen and the end by the rise in progesterone metabolites. This system still has some technical problems and is currently not available in Europe.

The largest prospective efficacy trial of cycle monitors was done for Persona (Freundl, 1998Go; Bonnar et al., 1999Go; Trussell, 1999Go; Trussell, 2001Go), involving 710 participants in three European countries. The method failure rate was estimated to be 6.4%. In the present study, the failure rate of Persona with daily intercourse on false negative days was in the middle range of all devices tested (average of 0.1155 for the worst case probability and of 0.4169 for the quality index). Essentially, a modified device is now used to identify the fertile days to achieve pregnancy (ClearPlan Fertility Monitor: Behre et al., 2000Go; Behre, 2001Go; May, 2001Go).

Another prospective trial was reported for Bioself (Drouin et al., 1994Go). This study included 83 women with 745 cycles. The pregnancy rate was 9.02 (Pearl Index). Another study by Flynn et al. (1991Go) involving 131 women with 1238 cycles, showed a Pearl Index of 23. However, out of 24 unplanned pregnancies, only two could be definitely considered as method failures.

For the other temperature computers tested in the present trial, only small prospective and retrospective efficacy finding studies (EFS) (Freundl et al., 1992Go, 1998a,b) have been performed. However, it is noteworthy that the most effective devices (and the STM) in the present study are based on BBT and that the estimates from Colombo and Masarotto (2000Go) are likewise derived from a BBT reference point.

No efficacy studies have been performed for the mini-microscopes prior to this study.

Recent research by Braat et al. (1998Go) investigated the reliability of predicting fertile days by observing ‘ferning’ in saliva. In 30 women with regular menstrual cycles, the day of ovulation was confirmed either by ultrasound or by BBT recordings. Every morning a drop of saliva was dried and assessed with a mini-microscope in group 1 (17 women) and a normal light microscope in group 2 (13 women). Tests were judged positive with the appearance of ferning or intermediate (some) ferning. The sensitivity was 53% for group 1 and 86% for group 2. They reported a strong correlation between saliva estradiol and serum estradiol values but no correlation was detected between the estradiol concentrations in saliva and the ferning pattern, and they concluded that ‘... the saliva ferning is unreliable for predicting the fertile period and its use should therefore be discouraged’.

The Cue Fertility Monitor uses the changes in salivary electrical resistance. Presently, it is not available in Europe but may be of interest after some technical changes. A computerized version, OvaCue, also exists. Two small studies with 42 cycles of 19 women (Moreno et al., 1997Go) and 21 cycles of 11 women (Fehring, 1996Go) reported astonishing effectiveness. However, our small prospective study (Freundl et al., 1996Go) could not prove these results.

In summary, there is an urgent need to test systems which are developed to detect the fertile window in the menstrual cycle. To undertake the efforts and expenses of a full prospective clinical trial, a primary efficacy estimation analysis as proposed in this paper should be performed. The QI should be <=0.5 (Persona, temperature computers, STM of NFP). Systems with a QI >0.5 cannot be expected to have a reasonable failure rate in a prospective efficacy finding study (pEFS), should not be offered to patients and are not worth further investigations.


    Acknowledgements
 
The authors whish to thank Prof. Dr J.B.Stanford, Family Department, University of Salt Lake City, Utah, USA and Ms S.Devarajoo, PhD for proof-reading the manuscript.


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
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Submitted on December 23, 2002; resubmitted on May 27, 2003; accepted on August 26, 2003.





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