Hum. Reprod. Advance Access originally published online on November 13, 2007
Human Reproduction 2008 23(1):85-90; doi:10.1093/humrep/dem361
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Defining poor and optimum performance in an IVF programme
1 Unidad de Reproducción, HU Virgen de las Nieves, E-18014 Granada, Spain 2 Servicio de Ginecologia y Obstetricia, Hospital San Millan, Logroño, Spain 3 FIV Recoletos, Madrid, Spain 4 Dynamic Solutions, Madrid, Spain 5 Instituto Balear de Infertilidad, Palma, Mallorca, Spain 6 Servicio de Medicina de la Reproducción, Departamento de Obstetricia, Ginecología y Reproducción, Institut Universitari Dexeus, Barcelona, Spain
7 Correspondence address. E-mail: josea.castilla.sspa{at}juntadeandalucia.es
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Abstract |
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BACKGROUND: At present there is considerable interest in healthcare administration, among professionals and among the general public concerning the quality of programmes of assisted reproduction. There exist various methods for comparing and analysing the results of clinical activity, with graphical methods being the most commonly used for this purpose. As yet, there is no general consensus as to how the poor performance (PP) or optimum performance (OP) of assisted reproductive technologies should be defined.
METHODS: Data from the IVF/ICSI register of the Spanish Fertility Society were used to compare and analyse different definitions of PP or OP. The primary variable best reflecting the quality of an IVF/ICSI programme was taken to be the percentage of singleton births per IVF/ICSI cycle initiated. Of the 75 infertility clinics that took part in the SEF-2003 survey, data on births were provided by 58. A total of 25 462 cycles were analysed. The following graphical classification methods were used: ranking of the proportion of singleton births per cycles started in each centre (league table), Shewhart control charts, funnel plots, best and worst-case scenarios and state of the art methods.
RESULTS: The clinics classified as producing PP or OP varied considerably depending on the classification method used. Only three were rated as providing ‘PP’ or ‘OP’ by all methods, unanimously. Another four clinics were classified as ‘poor’ or ‘optimum’ by all the methods except one.
CONCLUSIONS: On interpreting the results derived from IVF/ICSI centres, it is essential to take into account the characteristics of the method used for this purpose.
Key words: IVF/league table/outcome assessment/quality of care
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Introduction |
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Clinical indicators can be defined as measures that assess a particular health care process or outcome (Mainz, 2003
). Indicators may be used for different purposes, one of which is to make comparisons between healthcare centres. However, the differences observed between centres are not always due to variations in quality; thus, when interpreting data for purposes of comparison we must take into account four crucial issues: measurement properties, controlling for case mix and other relevant factors, coping with chance variability and data quality (Powell et al., 2003). The final stage in measuring health care quality is that of applying a standard of quality that embodies the acceptability of a particular performance or outcome rate. In this respect, standards can be derived from the academic literature or from consensus among health professionals (Mainz, 2003). If a performance rate falls below the established standard (i.e. the performance is ‘poor’) further evaluation or action is triggered. Consequently, identifying those centres that are above average (i.e. yielding optimum performance, OP) for benchmarking, and thus defining poor and OP is a fundamental aspect of healthcare management.
Recent legislation in Spain (the 14/2006 Law on Techniques for Assisted Human Reproduction, passed in 2006) requires healthcare authorities to evaluate assisted reproduction clinics, without specifying which variables or which methodology should be used for this purpose. The following are some of the methods used to analyse the results of IVF programmes carried out at different clinics: ranked histograms or points with error bars representing the 95% confidence interval (CI) (league tables) (Marshall and Spiegelhalter, 1998), control chart (Mohammed et al., 2001), funnel plot (Mohammed and Leary, 2006), and the best-case/worst-case scenario (Lemmers et al., 2007). It is still an open question as to which of these methods is better, as there are significant differences in the interpretation of the results achieved by each (Marshall et al., 2004).
Another methodology, which has been used to compare the quality achieved by different centres, especially with respect to clinical laboratories, is that of state of the art graphs; these present a cumulative view of the proportion of laboratories that achieve a given level of quality (Castilla et al., 2005). The aim of this study is to determine the influence of the method used when a clinic is classified as producing poor performance (PP) or OP and to present a new method to evaluate IVF data.
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Material and Methods |
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In this study, we used data obtained from the Assisted Reproductive Technology register of the Spanish Fertility Society (ART SEF register) for 2003, a total of 75 clinics. However, data on births were provided by only 57 of these. A total of 25 462 cycles were analysed. The variable used to classify the clinics was the singleton birth rate per started cycle (SBR/SC). They were classified in accordance with the following five criteria.
League table
Each SBR/SC is represented by a point including error bars representing the 95% CI. We calculated the mean proportion (sum of all rates/number of clinics) and the 99% CI. Clinics were considered ‘PP’ when the SBR/SC obtained was below the lower 99% CI of the mean proportion and ‘OP’ when it surpassed the upper 99% CI of the mean proportion.
Shewhart control chart
A control chart can indicate when a result is ‘unusual’. Ideally, such a situation would be detected whenever it occurred, but it is also desirable to have as few ‘false alarms’ as possible. The use of statistics allows us to strike a balance between the two. The upper and lower lines are termed the ‘alarm’ control limits (±3
) and represent the limits of common cause variation. In the present study, the control chart was constructed using the methodology described by Mohammed et al. (2001) for binomial data. Such control charts can be drawn on double square-root paper designed on the assumptions of a binomial distribution. The raw binomial data are plotted on the paper, and a central line, representing the mean, is drawn. For more precision, a least squares line can also be computed and drawn. Since the SD on this type of paper is usefully regarded as a constant 0.5 mm, the resulting 3 control limits are parallel lines 1.5 mm above and below the mean. A clinic was considered PP when the performance obtained was located below the lower control limit, and OP when the performance obtained was above the upper control limit.
Funnel plot
The SBR/SC is plotted against the volume of cases, with the CI being calculated by exact statistical methods, using a single baseline estimate of SBR/SC corresponding to the mean SBR/SC of all clinics (total number of singleton births/total number of started cycles). The exact binomial control limits corresponding to 3 SD around the mean of all the centres' SBR/SC are plotted to indicate possible thresholds for ‘alarm’ (Spiegelhalter, 2002). The same criteria are used in the control chart to classify the centres.
Best and worst-case scenarios
We used the methods described by Lemmers et al. (2007). First, we calculated the 95% CI of the difference between the SBR/SC for one clinic and that for each of the others. The performance of clinic A was considered better (or worse) than that of clinic B in the best-case scenario (or worst-case scenario) when the right-hand margin (or left-hand margin) of the 95% CI was positive (or negative). Secondly, the best-case scenario rating was calculated by counting the number of intervals with a positive right-hand margin. Clinic A was given the benefit of the doubt when its performance was better than a number of clinics equal to the count obtained. For example, if we obtain a count of 56 intervals with a positive right-hand margin, in the best-case scenario, clinic A is in position number 1 (57–56 = 1). The worst-case scenario rating was calculated by counting the number of intervals with a negative left-hand margin. When clinic A was given the benefit of all the doubts, its performance was worse than the number of clinics equal to the count obtained. For example, if we obtain a count of 56 intervals with a negative left-hand margin, in the worst-case scenario, clinic A is in position number 57 (56 + 1 = 57). A clinic was classified as PP if in the worst-case scenario its rating was 57, and as OP if in the best-case scenario its rating was 1.
State of the art
A graph was drawn of the cumulative percentage of participating clinics that reached a given level of lower and upper 95% CI of SBR/SC, from which the 50th percentile of the lower and upper CI of SBR/SC was calculated. These values were then taken as the lower and upper limits, respectively, in a league table. A clinic was classified as PP when the upper 95% CI of SBR/SC was below the lower 95% CI of the SBR/SC obtained by 50% of the best clinic (50th percentile of the lower 95% CI of SBR/SC). A clinic was classified as OP when the lower 95% CI of SBR/SC was above the upper 95% CI of the SBR/SC obtained by 50% of the best centres (50th percentile of the upper 95% CI of SBR/SC).
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Results |
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The clinics were numbered in ascending order in accordance with the SBR/SC obtained. The number of SC at each one ranged from 10 (at Clinic No. 10) to 3054 (at Clinic No. 11). The highest number of clinics classified as OP and PP was obtained with the league table method (Fig. 1), and the lowest such number was with the state of the art method.
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The Shewhart control chart (Fig. 2) and Funnel plot (Fig. 3) methods classified some clinics as OP or PP despite their having SBR/SC with intermediate values, because the CI were very narrow; thus, the SBR/SC obtained by Clinics Nos. 11, 12 and 15 were considered OP even though they were far from the maximum SBR/SC values. Similarly, Nos. 41 and 49 were classified as PP despite their not having the lowest SBR/SC values.
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Figure 4 shows the best and worst-case scenarios. Clinic No. 2 presented the smallest variation in this respect, with only five positions of difference, from 1 to 5. Number 10 had the largest variation, being first in the best-case scenario and position 57 in the worst.
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The state of the art graphs (Fig. 5) were used to establish the level of quality obtained by the best clinics. Figure 6 presents these values on a graph showing the ranking of proportions with CIs (league table).
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Table I shows the clinics classified as OP or PP by the different methods of analysis that were applied. Numbers 2 and 3 were classified as OP, and No. 57 was classified as PP, by all methods. Number 4 was classified as OP and Nos. 52, 53 and 56 were classified as PP by all the methods except one.
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Discussion |
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In the field of assisted reproductive technology (ART), healthcare authorities (e.g. HFEA) and scientific societies (e.g. ESHRE, SEF) publish the results of different indicators of the quality of IVF programmes. The best way to present ‘success rates’ is currently under international discussion, and it seems, in fact, that various forms of expression are necessary. Among the issues discussed are whether only singleton pregnancies and deliveries should be described as ‘success’ and whether twins pregnancy should be reported among ‘side-effects and/or risks’ (Winston, 1998
; ESHRE Capri Workshop Group, 2000; Davies et al., 2004; Heijnen et al., 2004; Land and Evers, 2004; Gleicher and Barad, 2006; Nygren et al., 2006). Among the solutions proposed are the development of an agreed standard patient group and outcome (Sharif and Afnan, 2003) and the definition of a new end-point (Min et al., 2004). In the present study, we utilized the rate of single births per started cycle as the variable outcome, considering a multiple birth as an adverse event in a programme of assisted reproduction. We were unable to carry out SBR/SC fitting (age, causes of sterility, number of previous cycles of IVF, number of embryos transferred, etc) because the data from the ART SEF register are gathered per centre and not per cycle.
Another point that should be considered in the ART SEF register is the possible bias in its results due to the voluntary participation of the various clinics; as a result of this, it is possible that only those clinics with the best results actually took part. Nevertheless, we do not believe this to be the case, as the ART SEF results are very similar to those reported in the Catalonia (NE Spain) register, for which participation is obligatory (FIVCAT.NET, 2006).
Our study shows that the use of the upper and lower bounds of the CIs of the ratio analysed (in our case, SBR/SC), in league tables is not a suitable means of classifying centres as PP or OP, because those classified within a given category of performance under this criterion are rated differently when the method used takes into account the variability i.e. inherent in any measurement instrument—as is the case with methods such as Shewhart control charts or funnel plots. This finding is corroborated by Goldstein and Spiegelhalter (1996), Mohammed et al. (2001), Adab et al. (2002) and Battersby and Flowers (2004). The league table method classifies a large number of centres as OP or PP, which tends to favour the over-investigation of unusual performance (Marshall et al., 2004). In the light of these limitations, various solutions have been proposed to improve the utility of league tables, including that of carrying out this procedure by groups of centres, taking into account factors such as differences in the volume of patients, staffing policy and staff training, and after adjusting for risk (Parry et al., 1998). However, the difficulty of adjusting for the results of assisted reproduction programmes has been pointed out (Winston, 1998), and it has been suggested that one solution to this might be to establish a homogeneous population (Sharif and Afnan, 2003).
Methods that take into account the natural variation, such as control charts or funnel plots, seem to classify centres with a high level of activity, even if extreme rates are not recorded, as PP or OP (e.g. clinics 11 and 49). This fact might be due to the existence of large differences in the sizes of the centres being analysed. Such is the case of our register, where the difference in the number of cycles analysed, between the centre presenting the lowest level of activity and that where it is highest, is 3044. In other study populations, in which the sizes of the participating centres are more homogeneous, the two methods mentioned above would probably produce better results.
The method described recently by Lemmers et al. (2007) describes the rating that would be obtained by a centre if it had everything going for it, or if it had everything going against it—in other words, the highest and lowest position that a clinic can reasonably achieve. In our opinion, this method does not sufficiently identify the quality of centres, as those with low levels of activity may be considered as PP or OP at the same time, as is the case with Centre No. 10. Moreover, when this method is applied, centres with extreme rates may be considered as PP or OP despite having scant activity (large CIs). In our study, Centre No. 1 had a high SBR/SC but a low volume of activity, and thus a large CI; nevertheless, its performance was considered optimum by this method. These observations contradict the results obtained by Lemmers et al. (2007), possibly because on the Dutch IVF register the participating clinics present smaller differences as regards levels of activity (from a minimum of 180 to a maximum of 1513, whereas the corresponding figures on the Spanish register are 10 and 3054). In the light of these considerations, we believe this method is not reliable in the case of registers where there is a large difference in activity levels between different clinics.
The method based on the state of the art establishes the upper and lower limits on the basis of the best and worst results possible at all the clinics. A clinic is taken to be PP if its best result (the upper limit of the CI of the SBR/SC obtained) is worse than the worst result reported by 50% of the clinics. Conversely, a clinic is classified as OP if its worst result is better than the best result obtained by 50% of the clinics. Our data show that under this method only those clinics with extreme values of SBR/SC and a narrow CI will be considered PP or OP.
In our study, only three categories were considered: poor, desirable or optimum. Similar discrepancies would have been found, nevertheless, if five rather than three degrees had been distinguished, using, e.g. the 95 and 99% limits in the control chart or funnel plot methods, as has been proposed by other authors (Spiegelhalter, 2002; Battersby and Flowers, 2004). On the other hand, we could have made other interpretations of the analytical methods used, such as that cited by Marshall and Spiegelhalter (1998) in the case of league tables, identifying a clinic as PP or OP if the interval for the assessment of performance does not include the corresponding national average. However, we believe that those we make are straightforward and not illogical. We do not believe that the conclusions drawn from this study would be varied significantly by the application of other, more complicated and less intuitive methods.
The use made of public performance data depends on the stakeholders (consumers, purchasers, physicians and provider organizations) and is highly diverse, ranging from choice of clinic (consumers and purchasers), discussions with patients or referral pattern (physicians) and benchmarking, the promotion of collaboration or for purposes of internal monitoring of performance (provider organizations) (Marshall et al., 2000). Therefore, the choice of the most suitable method of evaluation largely depends on who is to make use of the data. In the case of a provider organization interested in locating an OP clinic in order to carry out benchmarking, it is necessary to be very rigorous when classifying a clinic as OP. Provider organizations are sensitive to their public image and thus any PP classification would need to be highly reliable (control chart, funnel plot or state of the art). However, when a consumer is seeking to choose a clinic, he/she is not only influenced by performance but also by other factors such as cost or distance (Schneider and Epstein, 1998); in this case, it is useful to have various clinics from which to choose, and then less strict methods of analysis may be preferable (such as the league table or the best and worst-case scenario).
In summary, we show that large discrepancies arise between different methods in classifying performance as poor or optimum. At present there is considerable interest in healthcare administration, among professionals and among the general public concerning the quality of programmes of assisted reproduction, and so we consider it necessary to standardize the analytic methods used to classify a clinic as OP or PP. A possible solution to resolve the inconsistency would be for the centres yielding PP or OP to be those classified within the same category by all or at least by all bar one of the methods examined in the present study.
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Acknowledgements |
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We thank Organon Española S.A. for their technical support to the Assisted Reproductive Technology Register of the Spanish Fertility Society. We would also like to thank the Spanish ART clinics whose participation made the work possible (list in supplementary data section of European IVF-monitoring report 2003 at http://humrep.oxfordjournals.org).
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Submitted on June 1, 2007; resubmitted on July 24, 2007; accepted on September 5, 2007.
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