# Presentation: 715356

## Achieving Pregnancy Using Fresh Versus Cryopreserved Semen for Donor Insemination

- Blood Products Advisory Committee, 12/14/01
- Emmet J. Lamb, M.D.
- Professor of Gyn- Ob, Emeritus
- Stanford University, Stanford, California

## Indications in 2001for donor insemination 

- Azoospermia or severe oligospermia
- ICSI not available
- No sperm
- Not successful
- Too Costly
- Genetic problem of male
- No male partner

**Notes:**
Over the past decade there has been a major change in the need for donor insemination for azoospermia and severe oligospermia.  Now, with the help of ICSI, intracytoplasmic injection of sperm, a man formerly considered hopelessly sterile can father a child who possesses his genes.  This requires a surgical procedure to obtain sperm directly from the testicular tubules where they are present in a substantial proportion of cases even with no sperm found in the semen.  An individual sperm cell is injected into the unfertilized egg of his partner.  She must undergo the risks involved with in vitro fertilization.  If live sperm are found, the success rate is in the range of 30 to 40%.  
Donor insemination, therefore, is indicated when no sperm are obtained,  when ICSI has not been successful, or is not elected for whatever reason.  
Genetic problems inherited through the male account for a small number of cases and lesbian or straight women without a male partner account for the remainder.

##  Variables that improve fecundity Stratify in analysis 

- Azoospermic Male
- Young female
- Normal female
- Parous female
- Short duration infertility
- First cycle

**Notes:**
Fecundity, fecundability, or the chance to conceive per unit of exposure, here today an insemination cycle, is greater in these 6 circumstances that we can not change for the individual patient but must take into account in the analysis of data about insemination.  I have included in your materials a reprint of mine that goes into detail on analysis methods.  
If the male has no sperm, the probability is higher that his partner has normal fertility.  The first five are indirect measures of female fecundity. The duration of infertility is a marker only if the male has sperm in the ejaculate.  
A proportion of the females have low fecundity and as the more fertile members of the cohort conceive, the low fecundity women represent a larger share of those exposed.  Therefore, the first cycle almost always has the highest fecundity and is the least biased estimate of fecundity.  The degree of the decrease in cycle pregnancy rate after the first cycle is an indication of the magnitude of the effect of these other factors.

## Variables that can be changed to improve fecundity 

- Donor selection
- Fertile
- High motile sperm count
- Good cryosurvival
- Fresh sperm
- Cryopreservation Methods
- Sperm preparation

- Insemination Methods
- Ovulation monitoring
- Cervical mucus and BBT
- Urinary LH
- Pelvic ultrasound
- Ovulation enhancement
- Clomiphene
- Gonadotropins

**Notes:**
There are a number of ways to improve fecundity for the individual patient:  Using sperm from a highly selected donor, using fresh sperm, using optimal methods of freezing and thawing, optimizing sperm preparation methods for intrauterine insemination, monitoring ovulation to assure that the sperm are in place when the egg is ready to receive them.  The ovulation monitoring methods are listed in order of accuracy and cost.  Drugs improve the accuracy of timing and increase the number of eggs produced, an effect that increases cycle fecundity but also increases the rate of multiple pregnancy, a serious public health problem.

## Comparing Fecundity

- Cycle fecundity
- Total pregnancies / Total cycles of exposure
- Assumes constant hazard rate.
- Easy calculation
- Easy explanation
- Close to reality
- Kaplan Meier survival analysis
- Cox proportional hazard regression analysis

**Notes:**
We list here in order of increasing sophistication and complexity the methods of comparing fecundity.  
Cycle fecundity is the most commonly used, easiest to determine and to explain.  In studies of donor insemination with azoospermic partners, it is at least 90% accurate.  The more female fertility problems present, the less accurate it is. 
Survival analysis stratifies the data by cycle of treatment and avoids this source of bias.  
Cox analysis provides information on the effect of any variable we have just listed.  
If one reviews published reports, one usually must deal with cycle fecundity for the comparison because that is what is out there

## Fresh versus thawed semenMeta-analysis of cycle fecundity

|   |   |   | Thawed |  | Fresh |  | Odds |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Type | Study | Year | Preg. | Total | Preg. | Total | Ratio |
| R | Subak | 1992 | 9 | 96 | 21 | 102 | 0.40 |
| R | Iddenden | 1985 | 13 | 151 | 10 | 153 | 1.35 |
| R | Keel | 1989 | 44 | 917 | 18 | 310 | 0.82 |
| RA | Brown | 1988 | 59 | 566 | 153 | 559 | 0.31 |
| RA | Richter | 1984 | 54 | 1767 | 126 | 711 | 0.15 |
| C | Smith | 1981 | 38 | 500 | 121 | 613 | 0.33 |
| C | Schoysman | 1980 | 415 | 2027 | 148 | 610 | 0.80 |
| C | Bordson | 1986 | 17 | 165 | 19 | 165 | 0.88 |
| C | Jackson | 1977 | 19 | 540 | 336 | 6760 | 0.70 |
| C | Chong | 1985 | 54 | 176 | 73 | 290 | 1.32 |
| H | Kossoy | 1989 | 14 | 110 | 43 | 445 | 1.36 |
| H | Dimarzo | 1990 | 21 | 371 | 332 | 3040 | 0.49 |
| H | Bartlet | 1994 | 32 | 372 | 18 | 177 | 0.83 |
| CD | Leeton | 1980 | 229 | 1502 | 36 | 179 | 0.72 |
|   |   |   |   |   |   |   |   |
|   | Total subjects | 23,374 | 1,018 | 9,260 | 1,454 | 14,114 | 0.62 |

**Notes:**
We used cycle fecundity here.  An expanded and legible version of this busy slide is included in your handout. In the first column, note that the first three studies are randomized clinical trials, the next two studies randomized the first cycle and then alternated subsequent cycles.  The rest were cohort studies or studies that used historical controls.  The last used a design that introduced a considerable bias. 
In your handout is an annotated bibliography that notes the positive and negative features of each study and documents which data we pulled for this table.  
In the Subak study in the first row, note that 9 of 96 insemination cycles with thawed semen resulted in pregnancy as did 21 of 102 cycles with fresh.  The odds ratio, 0.4, is the ratio of these two fractions.  

## Meta-analysisSummary Statistics 

- Total subjects		        23,374
- Summary odds ratio		0.62  (0.43 to 0.90)
- 2p Fresh v. Thaw		0.011  (Z= -2.55)
- Heterogeneity, Q		   129  (Tau sq= 0.42)

**Notes:**
The summary odds ratio based on all 23,374 cycles of insemination, weighted by the size of the study, is 0.62.  Frozen semen has 62% of the cycle fecundity of fresh semen.  The p value and the 95% confidence limits indicate that the odds ratio is statistically significantly different from 1.  The Q test for heterogeneity has a chi square distribution and one this large indicates that the series are not all similar.  We used the random effects model of Dersimonean and Laird that is recommended in this situation.  
To approach the question of weighting by quality of the study rather than size of the study, we did a cumulative analysis.

## Fresh versus Thawed SemenCumulative Meta-analysis

**Notes:**
The left hand graph shows the individual means and 95% confidence limits fluctuating about the summary odds ratio of 0.62.  An odds ratio less than one indicates that fresh had higher fecundity than thawed.
 The right hand graph shows a cumulative plot in which a summary odds ratio is calculated for the second and the first combined, then another summary odds ratio is calculated after the third study, etc.  Since the studies are listed in order of quality, we can get some idea of the need for the less rigorous studies. 
If we had stopped after the last randomized study, study 5, the results would be significant, confidence limits not crossing 1 and the odds ratio would be less than the one we used.  The less rigorous studies added little information.

## Cycle fecundity with donor frozen semen in clomiphene or unstimulated cycles

- O’Brien in Cochrane Library, 2001

**Notes:**
The one systematic review of methods to improve cycle fecundity that I found was this comparison of IUI versus cervical insemination using donor frozen semen. The odds ratio was 2.6 in favor of the intrauterine method with confidence limits indicating statistical significance.

## Frozen Semen

- Lower fecundity = more cycles
- More drop-outs
- Fewer children
-  Greater use of limited resources
- More multiple pregnancies if gonadotropin stimulation used to compensate for lower fecundity

**Notes:**
Today, you are addressing concerns raised about the appropriateness of current guidelines mandating frozen semen.
Lower fecundity means that more cycles are needed to achieve the same chance of pregnancy and, thus, there is a greater likelihood that a woman will drop out of treatment, that there will be fewer children, and greater use of limited medical resources with a policy that mandates frozen semen.
Efforts to offset this reduction in fecundity by using ovulation–inducing drugs multiplies the resource use per insemination by five to ten fold and increase the risk of multiple pregnancy.

## The use of frozen semen to avoid HIV transmission 

- Stanford University School of Medicine
-  Michael Payne
- Health Research and Policy
- Douglas Owens
- Internal Medicine and HRP
- Emmet Lamb
- Gynecology and Obstetrics

**Notes:**
These collaborators and I have submitted a Markov computer model analysis of this problem for publication in Fertility and Sterility, the journal of the American Society for Reproductive Medicine.  I am a member of that society but do not represent it in my appearance here today.

## Methods of study

- Gauge health benefit in terms of QALYs gained
- Assign quality-adjusted utility to health states
- Calculate quality-adjusted life expectancy
-  Discount utilities at 3%

- Gold, et al. Cost-effectiveness in Health and Medicine.  Oxford University Press , 1996

**Notes:**
We chose the societal perspective and quality-adjusted life years (QALYs) to gauge health benefit.
For each treatment option (fresh or thawed semen), the computer model calculated  quality-adjusted survival time and other variables in a Markov model.

## Markov model

- Events unfold over time at fixed intervals -- insemination cycles
- Cohort of 80,000 women
- Monthly cycles (insemination) up to 12
- Yearly follow-up for a lifetime
- Two treatment options
- Frozen semen.  Donor screened twice with EIA
- Fresh semen.  Donor screened once with EIA.

**Notes:**
Markov analytical models represent recursive processes, a series of events which unfold over time at fixed intervals.  They are particularly well suited to decision  problems with numerous probabilities and utilities that may change over the course of observation.  
 For this simulation, we chose a cohort of 80,000 to represent all women having donor insemination each year in the U.S. 
We compared two options for insemination: 1.  The protocol under consideration today that requires one enzyme immunoassay for HIV before freezing and a second EIA after the mandated quarantine period.  2.  A protocol that is identical except for freezing and the second EIA.  Donors are screened and tested once and fresh semen is used for insemination. 

## Slide 14

**Notes:**
Each woman starts in the Insemination state and at the end of each 1-month cycle must be in one of the Markov states shown. The model compares the effectiveness and other outcome measures of the fresh and frozen semen treatment choices. 
We assigned 2 reward sets for each passage through a state: QALYs and another that for the moment we leave unnamed.
 If the subject  does not drop out she continues to treat until pregnancy occurs or 12 inseminations have been done.   If she becomes pregnant, she may experience a spontaneous abortion or ectopic pregnancy and may then drop out or return for further inseminations. 
At the end of the treatment period, women enter the follow-up period in one of 5 health states. Those in the uninfected states remain there until death (at a time determined by national gender- and age-specific mortality tables) and those in the infected states start in phase 1 and progress through the five HIV phases until death. The model runs until all women have entered the Dead state. 
For each of the transitions, the arrows, we estimated the probability of its occurrence.  The most difficult of these is the probability of infection with HIV from insemination.

## Probability of Transmission of HIV-1 by insemination

- Product of two elements:
- Probability of infection from 1 insemination with an infected donor
- Probability of an infected donor
- Sum of two elements
- Missed by EIA:  Prevalence of HIV X (1-sensitivity) EIA
- In window and not “False” Positive :  Prevalence of window donors X specificity

**Notes:**
The model calculates the risk of HIV transmission via donor insemination from a donor who has passed all screens including EIA as ...
The probability of infection from one insemination using semen from an infected  donor multiplied by the probability of the donor being infected.  
Infected donors are of two types that we added.   First, those missed by the antibody test.   In the case of frozen semen, the second element, (1 - sensitivity of  EIA), is squared.    Second, those in the window period who do not have “false” positive antibody test. 
In the next few slides, we present our estimates for each element in the calculation.

## Heterosexual transmission

- Uninfected partners of infected men
- Per episode risk = 1 in 500 to 1 in 1000
- Mastro & Kitayaporn.  Aids Research & Human Retroviruses 1998;14 (Suppl 3):S223-7
- Royce, Sena, Cates, Cohen.  New Engl J Med 1997;336:1072-8

**Notes:**
For the per-insemination rate of transmission of HIV-1 given an infected donor, we chose a base case value of 1 in 200, or 0.5%.  This is a conservative estimate considering that recent analyses estimated a rate of 1 in 500 to 1 in 1000.

## Valleroy.  FDA BPAC, 12/14/01

| Dates | Group | Percentage
HIV + | Reference |
| --- | --- | --- | --- |
| 1990-94 | Blood donors | 0.007, 0.04% | Parrish 1995, Glynn 1998 |
| 1997  (P) | Semen donors | 0.05 | Used in Payne, et al. CEA |
| 1988-94 | General population | 0.15, 0.3 | Karon 1996, Davis 1998 |
| 1996 (P), 1998 (P) | STD clinic heterosexual males | 1.1, 2.3 | Holmberg 1996, Valdessari 1998 |
| 1999 (P) | Prisoners | 2.6 | Ruiz 1999 |
| 1996 (P), 1998 (P) | Homosexual males (MSM) | 6 to 18 | Holmberg 1996, Valdessari 1998, Katz 1998 |
| 1996 (P), 1998 (P) | Injection drug users | 5 to 20 | Holmberg 1996, Valdessari 1998, Garfein 1996 |
| 1995 (P) | Commercial sex workers | 23 | Onorato 1995 |

- Payne, Owens, Lamb.  In review 2001.

**Notes:**
We used an estimated prevalence of HIV-1 in the semen donor population similar to that in other groups that had self screening and lab testing such as cornea donors and blood donors.  Their prevalence is lower than that of the general population. 
The other data in this slide are from Dr. Valleroy’s presentation.  
Using sensitivity analysis, in which the values of one variable are systematically varied in the computer model while all others are held constant, we determined that if we had chosen a prevalence in the model as high as that seen in the highest risk groups, the risk would, of course, be higher but because of the rarity of transmission, not sufficiently high to change our conclusions.

## Prevalence of donors in the window period

-     50,000 new HIV cases in males
- 35,000 high risk.
- 8,750 (25%)  escape screen (by self selection, history, STD tests)
- +   8,750 high risk
- + 15,000 low risk
- = 23,750 infected potential donors
- 		Peterson, et al Transfusion 1994;34:1721
- 		CDC HIV/AIDS Surveillance Report 1997

**Notes:**
Because of the improved characteristics of HIV tests, the window is the largest reason for HIV transmission from seronegative donors. 
To estimate the prevalence of infected potential donors in the window period we considered the following.  Among the 50,000 new male cases of HIV in the U.S. each year, about 35,000 are men who have sex with men or are IV-drug users.  We assumed that self-selection and screening by medical history, and STD testing  would eliminate 75% of such men from the potential donor pool, leaving 8750.
Thus, 25% of the high risk population and all the 15,000 men from the low-risk population comprise the pool of 23,750 infected men who are potential donors. 

## Prevalence of donors in the window period

- 23,750 potential donors / 12 months =
-    1,980 per month
- X 3 months window period
- = 5,940 per year
- About 1 in 10,000 of the 65 million males of reproductive age -- 0.0091%
- US Department of Census.  Statistical Abstract of United States 1997.  Tables 14,169,170

**Notes:**
Assuming  no seasonal variation, 1,980 new potential donors become infected each month. 
Assuming that infected potential donors remain in the window period for no more than three months, the total number of potential donors in the window period at one time is not likely to exceed 5,940 males in the U.S. 
Assuming further that all of the males who acquire HIV are of reproductive age (the conservative approach from the modeling standpoint) and that the total U.S. population of males of reproductive age is 65 million, then the estimated prevalence of infected potential donors in the window period is (5,940/65,000,000)=0.00914%.  We tested a wide range in sensitivity analysis.

## Outcomes if All 80,000 Inseminations Were Fresh

|  | Fresh | Frozen | Diff. |
| --- | --- | --- | --- |
|                      |  |  |  |
| Number of live births | 39,792 | 31,084 | +8,708 |
|  |  |  |  |
|  |  |  |  |
| Number of infected women | 0.14302 | 0.00002 | 0.143 |
| Number of infected newborns | 0.0048416 | 0.0000006 | 0.004841 |
|  |  |  |  |

**Notes:**
Let us look at the outcomes of this model if all 80,000 women who have donor insemination each year in the U.S. were to have insemination with fresh semen from EIA-negative donors.  
There would be 8,708 more births. 
The incremental risk of HIV-1 is equivalent to 1 woman infected every 7 years, an interval during which over a quarter million children conceived with fresh semen would be born.  Over 6 million uninfected infants would be born for every infected one.  

## Outcomes if All 80,000 Inseminations Were Fresh

-              Mean                                    Fresh     Frozen   Difference
- Number of insemination cycles         3.83	  4.33	   -0.51
- Life years after age 30                      44.597	43.601    -0.004
- Discounted QALYs                         21.02 	20.95       0.07

**Notes:**

The average patient would be exposed 3.8 cycles with fresh semen half a cycle more with frozen.  The difference in life expectancy is a matter of one and a half days in favor of frozen but when adjusted for quality of life the difference is 26 days in favor of fresh.

## Donor inseminationMicromorts from HIV-1

|  |  |  |  |  |
| --- | --- | --- | --- | --- |
|  | Fresh |  | Thawed |  |
| One Cycle | 0.5 |  | 0.00006 |  |
| Average Course | 1.8 |  | 0.0002 |  |
| Maximum (12) | 5.6 |  | 0.0007 |  |

- Howard RA.  Intn J Tech Assessment Health Care 1989;5:357

**Notes:**
Howard suggested that the small chance of death from voluntary activities can be expressed in micromorts, each micromort representing a one-in-a-million chance of death.
When we translate into micromorts the risks to a woman electing to use fresh semen screened as we have outlined, the results are as shown here.  With one cycle about one chance in two million.  For the average course of 3.8 inseminations, 1.8 chances of death per million.  For the maximum that we modeled, 12 cycles, 5.6 micromorts.

## Annual Risks at Home

|                  Event | 
Micromort |      1/n/year |
| --- | --- | --- |
| 1 charbroiled steak/week* | 0.4 | 2500000 |
| Drown in tub | 1.7 | 600000 |
| Plane crashes on home | 4 | 250000 |
| Denver versus NYC* | 10 | 100000 |
|  1 peanut butter sandwich/day* | 10 | 100000 |
|  1 lite bear/day* | 20 | 50000 |
| Lightening strike | 111 | 9000 |
| Accidents at home | 118 | 8500 |
| * Excess Cancer risk  |  |  |

- Stallings & Paling.  Obstet Gynecol 2001;98:345-9

**Notes:**
Recently in a journal article about a method of explaining the risks of operations to Ob-Gyn patients, I came across this list of at-home risks.  Those with the asterisk are excess risk of cancer deaths.  I translated the risks to micromorts.

## Annual Micromorts at Home

**Notes:**
A woman committing to a course of 12 cycles of donor insemination with fresh semen accepts a potential risk, 5.6 micromorts, somewhere between the risk she would accept by living where airplanes fly over her house and the risk she would accept by living in the Mile-High City rather than a sea level city.

## OB-Gyn Risks

| Surgery | Micromorts | 1/n |
| --- | --- | --- |
| Legal abortion | 4 | 250,000 |
| Tubal sterilization | 20 | 50,000 |
| Pregnancy | 50 | 20,000 |
| Vaginal hysterectomy | 222 | 4,500 |
| Ectopic pregnancy | 400 | 2,500 |
| Abdominal hysterectomy | 800 | 1,250 |
| Puerperal hysterectomy | 3030 | 330 |
| Hysterectomy for cancer | 4000 | 250 |

- Stallings & Paling.  Obstet Gynecol 2001;98:345

**Notes:**
The risk of a general anesthetic, 6.7 micromorts in a healthy person, is between that of abortion and sterilization and a bit higher than the 5.6 micromort risk assumed by the patient who commits to 12 inseminations with fresh semen.

## Micromorts from Gyn Surgery

**Notes:**
So that the smaller risks would appear on the slide, I deleted those associated with hysterectomies for obstetrical problems or cancer.  The risk for a patient having insemination with fresh semen for a year, 5.6 micromorts, is less than one third that for a tubal sterilization. 

## Conclusions

- The risk of using fresh semen in donor insemination is quite small, in the range of risks we accept on a daily basis.
- The reward, higher fecundity, is substantial.
- Informed patients may reasonably accept the risk in order to get the reward.

**Notes:**
The risk of using fresh semen in donor insemination is quite small, in the range of risks we accept on a daily basis.  
The reward, a  better chance of having a family, is substantial.
Informed patients may reasonably accept this risk to obtain this reward, if given the opportunity.