Use of an Automated Database to Evaluate Markers for Early Detection of Pregnancy

Jeanne M. Manson1, Bentson McFarland2 and Sheila Weiss3

1 Department of Obstetrics and Gynecology, Thomas Jefferson University, Philadelphia, PA.
2 Kaiser Permanente Center for Health Research, Portland, OR.
3 Schools of Pharmacy and Medicine, University of Maryland, Baltimore, MD.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The objective of this study was to develop and validate algorithms to detect pregnancies from the time of first clinical recognition by using Kaiser Permanente automated databases from Portland, Oregon. In 1993–1994, the authors evaluated these databases retrospectively to identify markers indicative of initial clinical detection of pregnancy and pregnancy outcomes. Pregnancy markers were found for 99% of the women for whom pregnancy outcomes were included in the automated databases, and pregnancy outcomes were identified for 77% of the women for whom there were pregnancy markers. The earliest marker most predictive of a pregnancy outcome was a positive human chorionic gonadotropin test; least predictive was an obstetric outpatient visit. Medical record review indicated that in a sample of women with pregnancy markers in the database, an estimated 6% of pregnancy outcomes (primarily early fetal deaths and elective terminations) were lost. Pregnancies were first captured in automated databases 6–8 weeks after the last menstrual period, and a combination of a positive human chorionic gonadotropin test and an outpatient obstetric visit was the most sensitive and specific early marker of pregnancy. When combined with automated pharmacy records, these databases may be valuable tools for evaluating prescription drug effects on all major outcomes of clinically recognized pregnancies.

abortion, legal; abortion, missed; abortion, spontaneous; medical record systems, computerized; pregnancy; pregnancy outcome; pregnancy tests; pregnancy trimester, first

Abbreviations: AFP, alpha-fetoprotein; hCG, human chorionic gonadotropin; LMP, last menstrual period


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
For the majority of prescription drugs, whether newly marketed or available commercially for an extended time, laboratory animal studies and isolated case reports are the only sources of information on human health effects during pregnancy. Pregnant women are actively excluded from trials during clinical development of most pharmaceutical products; if pregnancy occurs during a trial, the usual response is to discontinue treatment and drop the patient from the study. Consequently, at the time of marketing, there are usually no meaningful data on the effects of prescription drugs during pregnancy. Prescription drug use by women prior to clinical recognition of pregnancies can be extensive and can involve inadvertent exposure during the organogenesis period, when susceptibility to birth defects occurs (1Go). In addition, therapeutically essential treatment for some chronic conditions may continue even after clinical recognition of the pregnancy (2Go).

Randomized, controlled trials to evaluate adverse effects of prescription drugs during pregnancy are usually not possible, and spontaneous reports of prescription drug use during pregnancy that manufacturers receive during postmarketing surveillance have many limitations (1Go). Pregnancy registries are becoming recognized as effective approaches to use in evaluating prescription drug effects during pregnancy (3Go). Prenatal exposures are reported voluntarily to these registries by physicians or patients, and patients are followed prospectively to identify the pregnancy outcome. However, because pregnancy registries are based on voluntary reporting, ascertainment of exposed pregnancies can be incomplete, and the full spectrum of pregnancy outcomes can be missed (4Go). Automated data systems containing prescription drug use and health outcome information offer many advantages for monitoring the health effects of prescription drugs during pregnancy (5Go, 6Go). The number of women with specific prescription drug exposures during pregnancy can be measured accurately, and maternal and infant health status can be determined without differential recall bias (7Go).

Most studies in which automated data systems have been used to determine prescription drug effects during pregnancy have focused on adverse events identified in liveborn infants (8GoGoGo–11Go). However, with improvements in prenatal diagnostics, evaluation of liveborn infants alone is likely to lead to substantial underestimation of many adverse effects. For example, in the United States, the birth prevalence of anencephaly has been reduced by approximately 70 percent and of spina bifida by 30 percent because of elective termination of affected fetuses identified via prenatal diagnostic tests (12GoGo–14Go). In addition, fetal deaths occurring prior to 28 weeks of pregnancy are not evaluated routinely but can be an important result of prenatal exposure to prescription drugs. Ideally, pregnancies should be identified from the time of first clinical recognition and followed to obtain information on all major outcomes, including spontaneous abortions, elective terminations, fetal deaths/stillbirths, and livebirths. It is recognized that early spontaneous abortions occurring prior to clinical recognition of pregnancies will not be detected accurately by using automated databases (15Go).

The purpose of this study was to evaluate whether pregnancies could be identified accurately from the time of first clinical recognition by using Kaiser Permanente automated databases. Development and validation of algorithms to detect pregnancies from the time of first clinical recognition are essential steps in the use of automated databases to evaluate health effects of prescription drugs and other medical interventions on all major outcomes of clinically recognized pregnancies.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The study was conducted with automated databases from the Northwest Division of Kaiser Permanente. This group-model health maintenance organization has a current membership of about 400,000, and approximately 20 percent of residents in the greater Portland, Oregon, area are enrolled. The health insurance plan covers a full spectrum of services related to pregnancy, which are available to all members who are pregnant or may become pregnant. The automated databases include enrollment records, outpatient visits, laboratory test results, ultrasound reports, hospital discharges, elective termination procedures, and drug prescriptions filled by the pharmacy. A single paper medical record is maintained for each member, containing printouts of computerized clinical records and handwritten notes.

Data sources and codes used to identify pregnancy markers and outcomes in automated databases are presented in table 1. Markers were defined as diagnoses and procedures included in the databases that were indicative of the occurrence/recognition of a pregnancy. Outcomes of pregnancy were defined as diagnoses and procedures indicative of the ending of a pregnancy, including livebirths, fetal deaths, or claims for an elective termination submitted by an abortion provider under contract to Kaiser Permanente. Because the automated databases did not contain sufficient information to date pregnancies (date of the last menstrual period (LMP), expected delivery date, etc.), spontaneous abortions occurring <=28 weeks post-LMP were not separated from fetal deaths/stillbirths occurring >28 weeks post-LMP. All spontaneous losses from the time of first clinical recognition of pregnancy to the time of pregnancy outcome were categorized as "fetal deaths."


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TABLE 1. Data sources used to identify pregnancy markers and outcomes in automated databases, Portland, Oregon, 1993–1994

 
The procedures used to select study subjects are diagrammed in figure 1. Identified first were a total of 57,678 women aged 15–44 years with continuous enrollment in Kaiser Permanente. Automated databases were queried by using algorithms for pregnancy markers and outcomes; 47,695 (83 percent) identified women had no pregnancy markers or outcome, and 9,983 (17 percent) had any pregnancy marker and/or outcome. The latter group was then sampled to select women for whom there was any pregnancy marker (9,711; 17 percent of the total) or any pregnancy outcome (5,162; 9 percent of the total) in the automated databases. These two groups were not mutually exclusive and were formed by sampling the 9,983 women based on the presence of markers or outcomes of pregnancy. When more than one pregnancy was detected for a subject, information on markers and outcomes from the first pregnancy alone was included in the study.



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FIGURE 1. Procedures used to select the population studied to evaluate markers in an automated database for early detection of pregnancy, Portland, Oregon, 1993–1994. HMO, health maintenance organization.

 
The first step in evaluating pregnancy markers was to select women whose pregnancy outcomes occurring between October 1, 1993, and December 31, 1994, were included in automated databases; 3,267 women were identified, and pregnancy outcomes were categorized according to whether a fetal death, elective termination, or livebirth occurred (table 2). Pregnancy outcomes that could not be placed clearly in any of these categories (4; 0.12 percent) were identified as "outcome uncertain." The automated databases were then searched from January 1, 1993, to December 31, 1994, to identify the earliest pregnancy marker that could be found for women whose pregnancy outcomes were included. By allowing a 9-month window from the earliest marker of pregnancy to the first pregnancy outcome, time was sufficient to identify the earliest pregnancy marker for women with full-term to post-term deliveries. For each category of pregnancy outcome, the proportion first identified by a specific marker was calculated.


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TABLE 2. Identification of earliest pregnancy markers from pregnancy outcome data in automated databases, Portland, Oregon, 1993–1994*,{dagger}

 
The second step in evaluation was to select women for whom the automated databases included any pregnancy marker from January 1, 1993, to March 31, 1994; a total of 7,317 women were identified and categorized according to the earliest markers found (table 3). The automated databases were then searched from January 1, 1993, to December 31, 1994, to identify pregnancy outcomes for women with markers. By allowing a 9-month window from the earliest pregnancy marker to the latest pregnancy outcome, sufficient time was allotted to identify pregnancy outcomes for those women with database markers who had full-term to post-term deliveries. For each type of marker, the proportion of women with a pregnancy outcome was calculated.


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TABLE 3. Correlation of earliest pregnancy markers with pregnancy outcomes in automated databases, Portland, Oregon, 1993–1994*

 
The accuracy of information in automated databases and the time relative to the LMP when pregnancies were first recognized were evaluated by using medical records. A random sample of 400 women was selected from the 47,695 for whom there were no pregnancy markers or outcomes in automated databases, and medical records were located for all 400. Additionally, a random sample of 100 women was selected from the 3,267 for whom the automated databases contained pregnancy outcomes, and records were located for 99 of these women. Finally, a random sample of 100 women was selected from the 1,688 for whom there were pregnancy markers but no pregnancy outcomes in automated databases, and records were located for 99 of them. Trained abstractors collected the LMP dates, dates and types of pregnancy markers, and dates and types of pregnancy outcomes from the medical records. The number of days from the LMP date to the first pregnancy marker and to the pregnancy outcome was calculated for each type of pregnancy outcome. The accuracy of information in automated databases was calculated by using the medical records as the "gold standard" (16Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Of the 3,267 women for whom the automated databases contained pregnancy outcomes, 99 percent had markers that preceded the date of pregnancy outcome (table 2). For all categories of pregnancy outcome, the earliest marker found most frequently was a positive human chorionic gonadotropin (hCG) test. This was the earliest marker in 84 percent of fetal deaths, 88 percent of elective terminations, and 73 percent of livebirths. An obstetric outpatient visit was the next most common marker, and a code for this outpatient service was the earliest marker for 9 percent of fetal deaths, 4 percent of elective terminations, and 23 percent of livebirths. These two markers combined (a positive hCG test and an outpatient obstetric visit) accounted for 95 percent of the earliest markers found for all categories of pregnancy outcome. The remaining markers combined (abortion referral, obstetric ultrasound, threatened abortion, alpha-fetoprotein (AFP) test, and inpatient pregnancy hospital stay) were the earliest found for only 5 percent of the women for whom the databases contained pregnancy outcomes.

Of the women for whom the automated databases included a pregnancy marker, there were indicators of pregnancy outcome for 77 percent (table 3). Pregnancy outcome could be found for 93 percent of women with a positive hCG test, 60 percent with an obstetric outpatient visit, 84 percent with an abortion referral, 89 percent with an obstetric ultrasound, 99 percent with an AFP test, 84 percent with a threatened abortion, and 86 percent with an inpatient pregnancy hospital stay as the earliest marker. The most predictive, earliest markers of pregnancy were a positive hCG test and an AFP test, and least predictive was an outpatient obstetric visit.

Results from reviewing the medical records of the 600 women randomly selected from automated databases are presented in table 4. With respect to identification of pregnancy outcome, the overall accuracy of automated database results compared with medical records was 99 percent. One pregnancy outcome (a fetal death) was found in the databases that could not be located in the medical records. For all other pregnancies, there was complete concordance between the databases and medical records regarding occurrence and type of pregnancy outcome. Among those women for whom there were no pregnancy markers or outcomes in the automated databases, no pregnancy markers or outcomes were found in the medical records. In this comparison, the overall accuracy of the databases for identifying any prenatal care or pregnancy outcome was 100 percent.


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TABLE 4. Comparison of automated databases with medical records* for pregnancy outcome, Portland, Oregon, 1993–1994

 
Among women for whom the databases included pregnancy markers but no outcomes, the overall accuracy of the automated databases, compared with medical records, for detecting pregnancy outcome was 73 percent. For these women, review of medical records revealed 27 outcomes consisting of 20 fetal deaths, 5 elective terminations, and 2 livebirths that were not found in the automated databases. For the remaining 72 women, there was no documentation of a pregnancy outcome in the medical records or the automated databases.

For these women, the types of markers found in automated databases were compared with medical record results for pregnancy outcome (table 5). For the 25 women whose earliest marker in automated databases was a positive hCG test, pregnancy outcome was detected in medical records for 22 of them (88 percent), consisting of 16 fetal deaths, 4 elective terminations, and 2 livebirths. The elective terminations and fetal deaths occurred relatively early in pregnancy, at a mean time of 39 and 61 days post-LMP, respectively. For the 65 women whose earliest marker in automated databases was an obstetric outpatient visit, pregnancy outcome was found in medical records for only 2 (3 percent). For these 2 women, an obstetric outpatient visit was the earliest marker and was subsequently followed by a positive hCG test (data not shown). For the remaining 63 women (97 percent) with no pregnancy outcome documented in medical records, an obstetric outpatient visit was the only pregnancy marker found in the databases.


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TABLE 5. Comparison of medical records for women with pregnancy markers only in automated databases, Portland, Oregon, 1993–1994

 
For the 5 women with database pregnancy markers of abortion referral, obstetric ultrasound, and threatened abortion, 3 pregnancy outcomes consisting of fetal deaths and elective terminations occurring 38–54 days post-LMP were found in the medical records. For 4 women for whom the databases included AFP tests, no pregnancy outcome could be found in the medical records. There were no other indications of pregnancy-related care for these women, and it is likely that the AFP test was conducted to screen for hepatocellular carcinoma (17Go). An estimated 6 percent of pregnancy outcomes would be missed by identifying women in this study based on pregnancy markers alone. This figure is derived from the 473 women (1,688 for whom there were pregnancy markers but not outcomes in the database x 28 percent with outcomes found in medical records) of the 7,317 women originally identified based on pregnancy markers in the databases.

Timing of the earliest recognition of pregnancy for 96 women for whom pregnancy outcomes were found in medical records is presented in table 6. The LMP date could not be found in medical records for 50 percent of elective terminations, 7 percent of fetal deaths, and 14 percent of livebirths, and the pregnancy outcome date was missing for 9 percent of elective terminations, 10 percent of fetal deaths, and 14 percent of livebirths. These missing data in medical records limited the number of pregnancies that could be evaluated for timing of earliest recognition. The earliest marker identifying pregnancy for each of these outcomes was a positive hCG test. For women who had elective terminations, pregnancies were first recognized a mean of 44 days post-LMP, and pregnancy outcomes occurred a mean of 10 days after first recognition. For fetal deaths, the pregnancies were first recognized a mean of 40 days post-LMP, and pregnancy outcomes occurred a mean of 27 days after first recognition. Pregnancies ending in livebirths were first recognized a mean of 57 days post-LMP, and outcomes occurred a mean of 221 days after first recognition.


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TABLE 6. Number of days from the date of the last menstrual period to the earliest pregnancy marker and pregnancy outcome, Portland, Oregon, 1993–1994*

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The expected annual pregnancy rate is 9 percent for US women aged 15–44 years, and the 9 percent pregnancy rate for women in this study agrees well with national figures (18Go). Approximately twice as many women (17 percent) in this study had pregnancy markers, which may reflect both pregnancies in progress and those with outcomes and that some of the markers evaluated were poor predictors of pregnancy. Among the 3,267 women with pregnancy outcomes documented in automated databases, 10 percent had fetal deaths, 17 percent had elective terminations, and 73 percent had livebirths. These figures are in general agreement with those obtained from vital statistics reporting systems for pregnancy outcomes in the United States (19Go).

In this study, the majority of women had multiple markers of pregnancy, particularly those who delivered liveborn infants. For the 5 percent of fetal deaths for which there was no preoutcome marker, it is possible that these pregnancies were first recognized by the occurrence of an early fetal death and that prenatal care had not been sought prior to this occurrence. There are no apparent explanations, other than missing data, for the remaining women for whom there were pregnancy outcomes without markers (3.0 percent of elective terminations and 0.8 percent of livebirths). Women were continuously enrolled in Kaiser Permanente for the duration of this study, and pregnancies with outcomes were followed back for a sufficient time to detect any pertinent record of a pregnancy marker. These women may have received prenatal care outside the Kaiser Permanente system, either at home or family planning clinics for pregnancy tests, or prenatal care was covered by other insurance providers.

Results from the second phase of evaluation indicated that obstetric outpatient visits were inaccurate markers of pregnancy; for 40 percent of the women who had this marker, a pregnancy outcome was not documented in the databases. A survey of local Kaiser Permanente hospitals in the Portland, Oregon, area revealed that this code was not used uniformly for pregnancy care. In some clinics, for example, this code was used for gynecologic care for menopausal symptoms and for birth control. Likewise, the 11 percent of women who had obstetric ultrasounds and the 14 percent with inpatient pregnancy hospital stays for whom there was no pregnancy outcome in the databases may have had these procedures for gynecologic events not involving pregnancy. The 16 percent of women with an abortion referral who did not have an outcome included in the database may have remained enrolled in Kaiser Permanente but have chosen to have a termination procedure outside the system. Regarding threatened abortion, the 16 percent of women who did not have a pregnancy outcome included in the automated databases may have had early miscarriages for which they did not receive medical care.

In an attempt to resolve some of these problems, the medical records of a sample of women were reviewed to evaluate the accuracy of information on pregnancy markers and outcomes in automated databases. The majority of pregnancy outcomes found in medical records but missing from automated databases were early fetal deaths and elective terminations occurring approximately 6–8 weeks post-LMP. These outcomes presumably were reported to health care providers and entered the medical records but were not associated with any diagnoses or procedures in automated databases. Positive hCG tests were found to be highly predictive, and outpatient obstetric visits poorly predictive, of pregnancies documented in medical records. When these two markers were combined, the occurrence of pregnancy was detected with a high level of accuracy. When pregnancy outcome cannot be found for those few women with both of these markers, review of medical records would be appropriate to ensure that an early outcome was not lost.

When a positive hCG test was used as the earliest marker for clinical identification of pregnancy, elective terminations and fetal deaths were first identified approximately 6 weeks and livebirths 8 weeks post-LMP. The trend toward a slightly later identification of pregnancies ending in livebirths is interesting, but results from the present study are not sufficient to determine whether this trend is real. Clinical identification of pregnancies prior to the organogenesis period (5–10 weeks post-LMP) is highly desirable but does not seem feasible given the results from the present study. More likely is that pregnancies can be identified reliably from automated databases 6–8 weeks post-LMP, shortly after the organogenesis period begins.

Rates of early spontaneous abortion can be expected to vary substantially depending on when the pregnancy is first identified (20Go, 21Go). These rates are estimated to be 25 percent for healthy women whose pregnancies are recognized less than 6 weeks post-LMP and 8 percent for pregnancies identified more than 6 weeks post-LMP. Efforts should be made to normalize the rate of early spontaneous abortion for the time when pregnancies were first detected. Doing so would be particularly important to avoid biases in finding increased spontaneous abortion rates for women who detected their pregnancies early because they were concerned about drug exposures or other health conditions related to an adverse pregnancy outcome.

Strengths of this study are that methodologies were developed and assessed by using Kaiser Permanente automated databases to capture pregnancies from the time of first clinical recognition 6–8 weeks post-LMP and to identify all major categories of pregnancy outcome. Identifying pregnancies from the time of first clinical recognition is preferable to using conventional database approaches in which pregnancies are captured on the basis of the occurrence of livebirth/stillbirth outcomes (3Go, 6Go). Elements of the automated databases critical to conducting this study were access to laboratory test results, diagnosis/procedure codes for inpatient and outpatient medical care, and the paper medical record. Specificity of referral and billing records for correct identification of elective terminations versus spontaneous abortions was also important. Otherwise, these outcomes can be lumped into a single category of "abortions."

A limitation of this study is that the automated databases did not contain information to date pregnancies. The LMP date had to be obtained from the limited sample of women for whom the medical record was reviewed to identify timing of the earliest recognition of pregnancy. The medical record was a poor source of information for dating pregnancies, and the LMP date and pregnancy outcome date were missing from a substantial number of records. This shortcoming has also been identified in other automated databases used for pharmacoepidemiology research (3Go). Adding the LMP date (or the expected delivery date) to automated data systems will be essential to their widespread use for identifying the major outcomes of clinically recognized pregnancies. (The LMP date was added to the Kaiser Permanente Northwest database as of October 1999. (Dr. Barbara Valanis, Kaiser Permanente, personal communication, 1999)). Another limitation of the present study is that the procedure code for an outpatient obstetric visit frequently was not accompanied by a diagnosis code to indicate the purpose of the visit. This omission may be the cause of the poor predictive value of this code for identifying pregnancy. Finally, medical record review will be an essential component of any study of the early outcomes of pregnancy, as information on early losses of clinically recognized pregnancies can be missed in automated databases. However, by judicious choice of markers, the number of medical records requiring review will be relatively small.

Results from this study indicate that Kaiser Permanente automated databases are reasonably accurate for detecting a pregnancy from the time of first clinical recognition 6–8 weeks post-LMP and for identifying pregnancy outcome. Given the large proportion of pregnancies that are unplanned (14Go), drug exposures are not likely to be evaluated or modified until pregnancies are first recognized clinically. The date of first clinical recognition is likely to be an important landmark influencing the pattern and extent of drug use during pregnancy. Future directions for research will be to characterize prescribing patterns before and after this landmark occurs. Determination of the health impact of these inadvertent exposures should be a high priority for future research.


    ACKNOWLEDGMENTS
 
This work was supported by grant K01ES00352-01 from the National Institute of Environmental Health Sciences to Dr. Manson and by cooperative agreement FD-U-000739 from the US Food and Drug Administration to Dr. McFarland.

This research was performed by J. Manson to partially fulfill the requirement for a Master of Science in Clinical Epidemiology degree from the University of Pennsylvania, Philadelphia, Pennsylvania.

The authors gratefully acknowledge review of this manuscript by Drs. Michelle Berlin, Mary Sammel, and Brian Strom.


    NOTES
 
Correspondence to Dr. Jeanne M. Manson, Department of Obstetrics and Gynecology, Thomas Jefferson University, 909 Walnut Street, Room 220, Philadelphia, PA 19107 (e-mail: jmanson{at}earthlink.net).


    REFERENCES
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication July 26, 2000. Accepted for publication January 17, 2001.





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