ORIGINAL RESEARCH
Diagnosis of gestational diabetes mellitus: Can we avoid the glucose challenge test? Joan E. Crete, DNP, MSN, WHNP (Women’s Health Nurse Practitioner) & James N. Anasti, MD (Residency Program Director) Department of Obstetrics and Gynecology, St. Luke’s Hospital and Health Network, Bethlehem, Pennsylvania
Keywords Diabetes; risk factors; screening; obstetrics and gynecology (OB/GYN). Correspondence Joan E. Crete, DNP, MSN, WHNP, Department of Obstetrics and Gynecology, Tripler Army Medical Center, 1 Jarrett White Rd., Honolulu, HI 96859-5000. Tel: 808-433-6661; E-mail: [email protected] Received: March 2011; accepted: June 2011 doi: 10.1111/j.1745-7599.2012.00792.x
Abstract Objective: To identify risk factors for gestational diabetes mellitus (GDM) in patients who had either a normal or abnormal glucose tolerance test (GTT) after failing the initial glucose challenge test (GCT). If identified, consideration can be given to circumvent the glucose challenge test for those at risk. Data sources: A chart review was performed on 557 patients with abnormal GCT, 278 had an abnormal GTT (cases), and 279 had normal GTT (controls). The following risk factors were extracted: patients’ age, body mass index (BMI), ethnicity, selected personal history, and family history. A primary logistic regression and secondary exploratory logistic regression were used to analyze the data. Conclusions: Of the risk factors reviewed age, BMI, and prior history of GDM were predictive of GDM in the current pregnancy. Age 30–34 had an odds ratio (OR) of 1.95, 95% confidence interval (CI) [1.25,3.05] and over 35 had an OR 3.87 CI [2.12,7.05]. BMI over 30 had an OR 1.95, CI [1.25,3.05] and prior GDM had an OR 2.82 CI [1.55,5.13]. The combination of age and BMI had a significant OR, but not a significant increase over individual risk factors. Implications for practice: Screening by risk factors to circumvent glucose challenge testing may cause unnecessary testing and cost.
Gestational diabetes mellitus (GDM) is identified in pregnancy regardless of preexisting diabetes or of persistence of diabetes subsequent to pregnancy. The diagnosis is associated with maternal and fetal complications of varying degrees (Cheng & Caughey, 2008; Ecker & Greene, 2008; Hollander, Paarlberg, & Huisjes, 2007; Reece, 2010). This medical disorder affects 5%–12% of pregnancies (Getahun, Fassett, & Jacobsen, 2010; Perkins, Dunn, & Shubhada, 2007). The incidence of GDM may be associated with undiagnosed pregestational conditions in the general population (Langer, 2010; Metzger et al., 2007; Palkhivala, 2009; Robinson & Dornhorst, 2007). Langer (2010) took this association further to suggest the possibility that GDM and type 2 diabetes are the same disease representing different points on the glucose tolerance continuum. Of additional concern is the evidence associating GDM with the development of type 2 diabetes later in life (Ecker & Greene, 2008; van Leeuwen et al., 2007). The literature is replete with information reflecting the incidence and impact of GDM but screening recom-
mendations remain inconsistent. Consensus is lacking regarding guidelines thus creating frustration for both patient and provider. Support for universal screening at 26–28 weeks gestation exists, reflecting known physiologic changes. Insulin resistance develops during this time in pregnancy and for some women the increased demand on the pancreas exposes a transient abnormality in carbohydrate tolerance (Cheng & Caughey, 2008; Griffin et al. (2000); Perkins et al., 2007; Russell, Carpenter, & Constant, 2007). Other authors have identified specific characteristics associated with the risk of developing GDM and support selective screening based on these characteristics. The characteristics include older age, diverse ethnic groups, marked obesity, a family history of diabetes, and previous adverse pregnancy outcomes including gestational diabetes or the birth of an infant with macrosomia (Naylor, Sermer, Chen, & Farine, 1997; Perkins et al., 2007; Williams et al. 1999). A recent study by Getahun et al. (2010) concluded that a subsequent pregnancy, after a past history of GDM, has a 41.3% increased risk of a
C 2012 The Author(s) Journal of the American Association of Nurse Practitioners 25 (2013) 329–333
C 2012 American Association of Nurse Practitioners
329
Diagnosis of gestational diabetes mellitus
repeat diagnosis. Screening by risk factors is common in the United Kingdom where 80% of perinatal units utilize this method (Russell et al., 2007). A third option, offered by Hollander et al. (2007), defends the suspension of all screening until a consensus is reached. Lastly, a pragmatic approach suggested by Robinson and Dornhorst (2007) is for antenatal units to review their patients’ characteristics and trends to develop guidelines that reflect their particular patient population and outcomes. Development of GDM screening guidelines remains a primary focus of obstetrics and diabetes specialty groups. The International Workshop-Conference on GDM supports studies that examine the incidence of GDM and risk factors associated with the disorder (Metzer et al., 2007). Collaboration among researchers is required to develop high-level evidence. In response to this need, The Diabetes in Pregnancy Group of North America (DPSGNA) was established in 1997 (Langer, Miodovnik, Reece, & Rosenn, 2010). This group has taken on the task of monitoring new developments in the area of GDM. Major medical organizations also maintain a close watch on the research and continue to modify their recommendations based on current data. The United States Preventative Services Task Force (USPSTF; United States Department of Health and Human Services, 2010) states that current evidence is insufficient to assess the balance of benefits and harm of screening for GDM. These updated 2010 recommendations also recognize populations at risk for GDM and suggest providers consider screening on a case-by-case basis. They encourage dialogue between provider and patient to arrive at screening decisions. According to the most recent American College of Obstetricians and Gynecologists (ACOG) Practice Bulletin No. 30 (2010), screening all women for GDM, whether by patient’s history, clinical risk factors, or a laboratory testing is recommended. ACOG states that selective screening based on risk is appropriate and low-risk women may be excluded. They also acknowledge that high-risk women may benefit from earlier screening. The American Diabetes Association’s (ADA) position is aligned with the ACOG guidelines (Metzer et al., 2007), but the World Health Organization (WHO) has a slightly different recommendation. It suggests screening all women at 24– 28 weeks with earlier screening for those considered high risk (Mulholland, Njoroge, Mersereau, & Williams, 2007). In an attempt to provide the evidence needed in this debate, the recent hyperglycemia and adverse pregnancy outcome (HAPO) study has started to release its findings. Preliminary review of the data suggest a continuum of risk for GDM patients resulting in adverse outcomes (Constant, Lowe, & Metzger, 2010). The data also cor330
J. E. Crete & J. N. Anasti
relate earlier diagnosis to more severe forms of glucose intolerance (Langer, 2010; Moore, 2010). These findings may provide, in part, the convincing data that the medical organizations have been searching for concerning the incidence of GDM and outcomes (Javanovic, 2009; Moore 2010). The purpose of this study was to identify risk factors for GDM in patients who had either a normal or abnormal GTT, after failing the initial glucose challenge test (GCT). If identified, consideration can be given to selectively screen by risk factors, and move directly to the diagnostic 3-h oral GTT (OGTT) for those considered high risk. Additionally, we can consider not performing glucose screening on those who are at low risk. Currently, our institution, like most in this country, performs universal screening for GDM at 28 weeks using a two-step method. The first step is a nonfasting, 1 h 50 g oral glucola test. Patients who fail this first step with a value between 135 and 179 mg/dL, (Carpenter & Constant, 1982) proceed on to the diagnostic test. This second step is a fasting, 3 h 100 g OGTT. Patients with a 1-h glucola value of ≥ 180 mg/dL are diagnosed with GDM and are not given an OGTT. The upper limit cut-off values for the 3-h OGTT is: fasting— 95 mg/dL, 1 h—180 mg/dL, 2 h—155 mg/dL, 3 h— 140 mg/dL (O’Sullivan, Mahan, Charles, & Dandrow, 1973). The diagnosis of GDM is made if two of the four values are elevated.
Methods Study design, sample, and measures This was a descriptive study consisting of a retrospective patient chart review. The patients received prenatal care and delivered in a community hospital in eastern Pennsylvania between 2001 and 2009. Services were provided by private practice groups affiliated with the hospital or by the in-house staff in the hospital’s outpatient women’s health center. The results of 1-h glucola and 3-h OGTT test results were collected from the hospital’s laboratory data bank and yielded 1700 results of patients who had failed the screening nonfasting 1 h 50 g oral glucose test and had a follow-up diagnostic 100 g OGTT. A random sample of 600 patients (300 case and 300 control) was chosen. The cases failed the 3-h OGTT with two or more elevated values and the controls passed the 3-h OGTT with a minimum of three normal values, using the guidelines cited. Excluded from the study were patients with a 1-h 50 g glucose results ≥ 180 mg/dL and patients with known pregestational diabetes or a current multiple gestation pregnancy. Selected independent predictor variables consisting of the patient’s age, BMI, ethnicity, personal history, and
Diagnosis of gestational diabetes mellitus
J. E. Crete & J. N. Anasti
Table 1 Independent variable and subgroups (N = 557) AGE ≤ 24 years 25–29 years 30–34 years ≥35 years BMI ≤24: NL 25–29: Overweight ≥30: Obese ETHNIC CATEGORY African American Caucasian Hispanic/Mexican Asian, Indian/Eastern, or Othera Family history Negative Diabetes mellitus Personal history Negative Macrosomia Gestational diabetes mellitus PIH/HTN Nullipara IUFD, PTL/PTD, PCOSa TOTAL
Table 2 Predicted probability of GDM—primary logistic regression re-
GDM
No GDM
28 56 86 99
67 77 90 54
68 83 118
106 85 97
12 215 31 11
19 205 53 11
217 52
244 44
107 27 42 10 55 28 269
168 20 21 19 44 16 288
a
Combined because of small numbers. IUFD, intrauterine fetal demise; PTL, preterm labor; PTD, preterm delivery; PCOS, polycystic ovary syndrome; BMI, body mass index.
family history were entered into Excel spread sheets and coded by the principal investigator. To adhere to HIPAA guidelines, the patients’ personal identifying information was excluded. The spreadsheets were kept on the computers of the investigators and statistician who were all hospital employees and compliant with HIPAA guidelines. The research study received approval from both the hospital’s IRB committee and Robert Morris University’s IRB committee with expedited status. The five variables were all self-reported. They included patients’ age, prepregnant BMI, ethnicity, selected personal history, and family history. Each variable also included subgroups (see Table 1). The patient’s age at time of delivery was taken from the date of birth on record. The four age subgroups consisted of ≤24, 25–29, 30–34, and ≥35 years of age. The BMI (WHO, 2000) was calculated from the patient’s prepregnant weight and height and formed three subgroups: normal with a BMI of ≤ 24, overweight with a BMI between 25 and 29 and obese with a BMI of ≥ 30. Six ethnic groups were included: African American, Asian, Caucasian, Hispanic/Mexican/Latino, Eastern Indian, and Other. The patients’ personal obstetrical history included previous gestational diabetes, delivery of a macrosomic infant (wt.
sults (N = 557) Variable Age 30–34 Age ≥ 35 BMI 25–29 BMI ≥ 30 Phx GDM
Adjusted OR 95% CI
p value
2.04 (1.15, 3.61) 3.87 (2.12, 7.05) 1.53 (.96, 2.43) 1.95 (1.25, 3.05) 2.82 (1.55, 5.13)
.015 .000 .075 .003 .001
≥ 8.5 lbs), gestational hypertension or pregnancy induced hypertension, fetal demise, preterm labor or delivery, and the gynecologic diagnosis of polycystic ovary syndrome.
Data analysis Direct logistic regression analysis was conducted using PASW (formally known as SPSS) statistical software version 17. The Omnibus Tests of Model Coefficients was used to determine overall model fit, followed by reporting of adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). The primary logistic regression was calculated on the single variables and subgroups (see Table 1). Of the 600 charts, 557 were included (N = 557) while 43 (7.3%) were excluded based on missing or incomplete data. The group split was 269 cases and 288 controls. For exploratory purposes, a second multiple logistic regression (ML) was calculated with a multicategorical predictor variable combining age and BMI in addition to the previously included variables of ethnicity, personal history, and family history. The multicategorical predictor was distributed as follows: the ML. 1 group (n = 68) was aged 30–34 years with a BMI ≥ 30 and the ML. 2 group (n = 63) was aged ≥ 35 years with a BMI ≥ 30. This exploratory analysis had an N = 586 with 14 (2.3%) charts excluded because of missing data. The group split was 291 cases and 295 controls.
Results Primary logistic regression Testing of the full model (Omnibus Tests of Model Coefficients) with all predictors, compared with the constantonly model, yielded statistically significant results (χ 2 = 69.38, p < .0001), indicating that the predictor variables taken together differentiate the case and control groups in a reliable manner and suggesting good overall model fit. Table 2 presents results for the individual predictors. The two age groups of 30–34 and ≥ 35 years old had adjusted ORs of 2.04, 95% CI [1.15, 3.61], p = .015 and 3.87, 95% CI [2.12, 7.05], p < .0001, respectively, 331
Diagnosis of gestational diabetes mellitus
J. E. Crete & J. N. Anasti
Table 3 Predicted probability of GDM—exploratory logistic regression results (N = 586) Variable Age 30–34/BMI ≥ 30 Age ≥ 35/BMI ≥ 30
Adjusted OR 95% CI
p value
1.52 (.88, 2.59) 2.54 (1.41, 4.57)
.131 .002
indicating statistically significant prediction of gestational diabetes. The BMI category of ≥30 also had a statistically significant adjusted OR of 1.95, 95% CI [1.25, 3.05], p = .003, as did the personal history of gestational diabetes, with an adjusted OR of 2.82, 95% CI [1.55, 5.13], p = .001. The BMI category 25–29 showed a trend toward statistical significance with an adjusted OR of 1.53, 95% CI [.96, 2.43], p = .075. However, neither ethnicity nor family history of gestational diabetes significantly predicted gestational diabetes, p > .05.
Exploratory logistic regression Testing of the full model (Omnibus Tests of Model Coefficients) with all predictors, compared with the constantonly model, yielded statistically significant results, (χ 2 ) = 47.99, p < .0001, indicating that the predictor variables taken together differentiate the case and control groups in a reliable manner and suggesting good overall model fit. Of the multicategorical variables (see Table 3) reflecting both age and BMI, only the age ≥ 35 years with a BMI of ≥ 30 were significantly more likely to have gestational diabetes with an adjusted OR of 2.54, 95% CI [1.41, 4.57], p = .002. None of the remaining variables were significant predictors, p > .05.
tively based on risk factors, is a logical approach and would avoid unnecessary testing. Also, it is reasonable to offer earlier screening to those identified as higher risk. Using risk factors alone to circumvent the glucose challenge and proceed directly to the diagnostic OGTT is not supported and may cause unnecessary testing and cost to the care of the pregnant women. The results did not correlate family history of diabetes, personal history of poor obstetrical outcomes, or those from ethnically diverse populations with a significantly higher risk of GDM. One possible explanation for these results is the small sample size in each of the given subgroups. Previous studies have demonstrated an increased risk in these groups. The primary strength of this study was that the number of subjects was large enough to provide significant results and that the laboratory testing was done at the same institution. Several potential limitations to this study exist. All of the variables were self-reported by the subjects and the accuracy is dependent upon the historian’s memory. In addition, documentation on the chart by the hospital staff was not verified. As mentioned earlier, there were small numbers in some of the subgroups. Replicating this study in other populations is recommended. It is important to mention that since the start of this study the HAPO data have been published with the authors’ recommendations. The one step 75 g OGTT, popular in other countries and used in the HAPO study, may soon gain acceptance in the United States. Several authors support adoption of this test (Ecker & Greene, 2008; Hadar et al, 2009). Also reinforced is the importance of early diagnosis for populations at risk. Furthermore, based on these results, Constant et al. (2010) recommended lowering the threshold values for the diagnosis of GDM. The DPSG-NA and International Diabetes Federation (IDF) have recognized the significance of the HAPO results and support these recommendations.
Discussion and conclusion Screening guidelines for GDM remain inconsistent, which creates frustration for providers. Providers are responsible for incorporating practice guidelines that are evidence-based, meaningful, and reflect the characteristics of their particular population. This study examined two groups of patients who failed the screening 1-h glucose test and took the diagnostic OGTT. Risk characteristics that correlate to the diagnosis of GDM, were identified. Single characteristics of advanced age, obesity, and personal history of GDM had the strongest association. Also, the combination of advanced age and BMI had a correlation but this was not a significant increase over individual risk factors. These results give support to reconsider our current practice of universal screening for GDM. As ACOG and the ADA suggest, screening selec332
Acknowledgments Special thanks to Jill Stoltz, PhD, Director, Institute of Research, St. Luke’s Hospital and Health Network. Acknowledgment of Mary Cothran, PhD, Academic advisor, Robert Morris University.
References American College of Obstetricians and Gynecologists (ACOG). (2010). Clinical management guideline for obstetricians and gynecologists. (Practice Bulletin No. 30). Washington, DC: Author. Carpenter, M. W., & Constant, D. R. (1982). Criteria for screening tests for gestational diabetes. American Journal of Obstetrics and Gynecology, 144, 768–773. Cheng, Y. W., & Caughey, A. B. (2008). Gestational diabetes: Diagnosis and management. Journal of Perinatology, 28, 657–664.
J. E. Crete & J. N. Anasti
Constant, D. R., Lowe, L. P., & Metzer, B. E. (2010). The hyperglycemia and adverse pregnancy outcome (HAPO) study: Can we use the results as a basis for change? Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 204–209. doi:10.3109/14767050903550667 Ecker, J. L., & Greene, M. F. (2008). Gestational diabetes: Setting limits, exploring treatments. New England Journal of Medicine, 358, 2061–2063. Getahun, D., Fassett, M. J., & Jacobsen, S. J. (2010). Gestational diabetes: Risk of recurrence in subsequent pregnancies. American Journal of Obstetrics and Gynecology, 203, 1e1–1e6. doi:10.1016/j.ajog.2010.05.032 Griffin, M. E., Coffey, M, Johnson, H., Scanlon, P., Foley, M., Stronge, J., . . . Firth, R. G. (2000). Universal vs. risk factor-based screening for gestational diabetes mellitus: Detection rates, gestation at diagnosis and outcome. Diabetic Medicine, 17, 26–32. Hadar, E., Oats, J., & Hod, M. (2009). Towards new diagnostic criteria for diagnosing GDM—The HAPO study. Journal of Perinatal Medicine, 37, 447–449. doi:10.151S/JPM-2009.114 Hollander, M. H., Paarlberg, M. K., & Huisjes, J. M. (2007). Gestational diabetes: A review of the current literature and guidelines. Obstetrics and Gynecological Survey, 62(2), 125–136. Javanovic, L. (2009). Definition, size of the problem, screening and diagnostic criteria: Who should be screened, cost-effectiveness, and feasibility of screening. International Journal of Gynecology and Obstetrics, 104, S17–S19. doi:10.1016/j.ijgo.2008,11..023 Langer, O. (2010). Changing the diagnosis criteria of type 2 diabetes in pregnancy: Do the ends justify the means? Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 234–238. doi:10.3109/14767050903550907 Langer, O., Miodovnik, M., Reece, A. E., & Rosenn, B. M. (2010). The proceedings of the diabetes in pregnancy study group of North American 2009 conference. Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 196–198. doi:10.3109/14767050903550634 Metzger, B. E., Buchanan, T. A., Constant, D. R., DeLeiva, A., Dunger, D. B., Hadden, D. R., . . . Zoupas, C. (2007). Summary and recommendations of the fifth international workshop-conference on gestational diabetes mellitus. Diabetes Care, 30(Suppl. 2), S251–S260. doi:10.2337/dc07-s225 Moore, T. (2010). Can the findings of recent randomized trials of treatment or no treatment of gestational diabetes be used for changing current treatment approaches? Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 214–218. doi:10.3109/14767050903550634
Diagnosis of gestational diabetes mellitus
Mulholland, C., Njoroge, T., Mersereau, P., & Williams, J. (2007). Comparison of guidelines available in the United States for diagnosis and management of diabetes before, during and after pregnancy. Journal of Women’s Health, 16(6), 790–800. doi:10.10089/jwh.2007.CDC7 Naylor, D. C., Sermer, M., Chen, E., & Farine, D. (1997). Selective screening for gestation diabetes mellitus. New England Journal of Medicine, 337(22), 1591–1596. Palkhivala, A. (2009). IDF releases new guidelines on diabetes management. Retrieved from http://cme.medscape.com/viewarticle/711266 Perkins, J. M., Dunn, J. P., & Shubhada, J. M. (2007). Perspectives in gestational diabetes mellitus: A review of screening, diagnosis, and treatment. Clinical Diabetes, 25(2), 57–62. Reece, A. E. (2010). The fetal and maternal consequences of gestational diabetes mellitus. Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 199–203. doi:10.3109/14767050903550659 Robinson, S., & Dornhorst, A. (2007). ABCD position statement on screening for gestation diabetes mellitus. Practical Diabetes International, 143(4), 192–195. Russell, M. A., Carpenter, M. W., & Courstan, D. R. (2007). Screening and diagnosis of gestational diabetes mellitus. Clinical Obstetrics & Gynecology, 50, 949–958. O’Sullivan, J. B., Mahan, C. M., Charles, D., & Dandrow, R. V. (1973). Screening criteria for high-risk gestational diabetic patients. American Journal of Obstetrics and Gynecology, 116, 895. U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality. (2010). The guide to clinical preventive services (AHRQ Pub. No. 09-IP006). Retrieved from http://www.ahrq.gov/clinic/ pocketgd1011/pocketgd1011.pdf van Leeuwen, M., Zweers, E. J., Opmeer, B. C., van Ballegooie, E., Brugge, H. G., & de Valk, H. W. (2007). Comparison of accuracy measures of two screening tests for gestational diabetes mellitus. Diabetes Care, 30, 2779–2784. Williams, C. B., Iqbal, S., Zawacki, C. M., Yu, D., Brown, M. B., Herman, W. H. (1999). Effect of selective screening of gestational diabetes. Diabetes Care, 22, 418–421. World Health Organization. (2000). Obesity: Preventing and managing the global epidemic: Report of a WHO consultation (WHO Technical Report Series 894). WHO, Geneva, Switzerland.
333
Diagnosis of gestational diabetes mellitus: Can we avoid the glucose challenge test? Joan E. Crete, DNP, MSN, WHNP (Women’s Health Nurse Practitioner) & James N. Anasti, MD (Residency Program Director) Department of Obstetrics and Gynecology, St. Luke’s Hospital and Health Network, Bethlehem, Pennsylvania
Keywords Diabetes; risk factors; screening; obstetrics and gynecology (OB/GYN). Correspondence Joan E. Crete, DNP, MSN, WHNP, Department of Obstetrics and Gynecology, Tripler Army Medical Center, 1 Jarrett White Rd., Honolulu, HI 96859-5000. Tel: 808-433-6661; E-mail: [email protected] Received: March 2011; accepted: June 2011 doi: 10.1111/j.1745-7599.2012.00792.x
Abstract Objective: To identify risk factors for gestational diabetes mellitus (GDM) in patients who had either a normal or abnormal glucose tolerance test (GTT) after failing the initial glucose challenge test (GCT). If identified, consideration can be given to circumvent the glucose challenge test for those at risk. Data sources: A chart review was performed on 557 patients with abnormal GCT, 278 had an abnormal GTT (cases), and 279 had normal GTT (controls). The following risk factors were extracted: patients’ age, body mass index (BMI), ethnicity, selected personal history, and family history. A primary logistic regression and secondary exploratory logistic regression were used to analyze the data. Conclusions: Of the risk factors reviewed age, BMI, and prior history of GDM were predictive of GDM in the current pregnancy. Age 30–34 had an odds ratio (OR) of 1.95, 95% confidence interval (CI) [1.25,3.05] and over 35 had an OR 3.87 CI [2.12,7.05]. BMI over 30 had an OR 1.95, CI [1.25,3.05] and prior GDM had an OR 2.82 CI [1.55,5.13]. The combination of age and BMI had a significant OR, but not a significant increase over individual risk factors. Implications for practice: Screening by risk factors to circumvent glucose challenge testing may cause unnecessary testing and cost.
Gestational diabetes mellitus (GDM) is identified in pregnancy regardless of preexisting diabetes or of persistence of diabetes subsequent to pregnancy. The diagnosis is associated with maternal and fetal complications of varying degrees (Cheng & Caughey, 2008; Ecker & Greene, 2008; Hollander, Paarlberg, & Huisjes, 2007; Reece, 2010). This medical disorder affects 5%–12% of pregnancies (Getahun, Fassett, & Jacobsen, 2010; Perkins, Dunn, & Shubhada, 2007). The incidence of GDM may be associated with undiagnosed pregestational conditions in the general population (Langer, 2010; Metzger et al., 2007; Palkhivala, 2009; Robinson & Dornhorst, 2007). Langer (2010) took this association further to suggest the possibility that GDM and type 2 diabetes are the same disease representing different points on the glucose tolerance continuum. Of additional concern is the evidence associating GDM with the development of type 2 diabetes later in life (Ecker & Greene, 2008; van Leeuwen et al., 2007). The literature is replete with information reflecting the incidence and impact of GDM but screening recom-
mendations remain inconsistent. Consensus is lacking regarding guidelines thus creating frustration for both patient and provider. Support for universal screening at 26–28 weeks gestation exists, reflecting known physiologic changes. Insulin resistance develops during this time in pregnancy and for some women the increased demand on the pancreas exposes a transient abnormality in carbohydrate tolerance (Cheng & Caughey, 2008; Griffin et al. (2000); Perkins et al., 2007; Russell, Carpenter, & Constant, 2007). Other authors have identified specific characteristics associated with the risk of developing GDM and support selective screening based on these characteristics. The characteristics include older age, diverse ethnic groups, marked obesity, a family history of diabetes, and previous adverse pregnancy outcomes including gestational diabetes or the birth of an infant with macrosomia (Naylor, Sermer, Chen, & Farine, 1997; Perkins et al., 2007; Williams et al. 1999). A recent study by Getahun et al. (2010) concluded that a subsequent pregnancy, after a past history of GDM, has a 41.3% increased risk of a
C 2012 The Author(s) Journal of the American Association of Nurse Practitioners 25 (2013) 329–333
C 2012 American Association of Nurse Practitioners
329
Diagnosis of gestational diabetes mellitus
repeat diagnosis. Screening by risk factors is common in the United Kingdom where 80% of perinatal units utilize this method (Russell et al., 2007). A third option, offered by Hollander et al. (2007), defends the suspension of all screening until a consensus is reached. Lastly, a pragmatic approach suggested by Robinson and Dornhorst (2007) is for antenatal units to review their patients’ characteristics and trends to develop guidelines that reflect their particular patient population and outcomes. Development of GDM screening guidelines remains a primary focus of obstetrics and diabetes specialty groups. The International Workshop-Conference on GDM supports studies that examine the incidence of GDM and risk factors associated with the disorder (Metzer et al., 2007). Collaboration among researchers is required to develop high-level evidence. In response to this need, The Diabetes in Pregnancy Group of North America (DPSGNA) was established in 1997 (Langer, Miodovnik, Reece, & Rosenn, 2010). This group has taken on the task of monitoring new developments in the area of GDM. Major medical organizations also maintain a close watch on the research and continue to modify their recommendations based on current data. The United States Preventative Services Task Force (USPSTF; United States Department of Health and Human Services, 2010) states that current evidence is insufficient to assess the balance of benefits and harm of screening for GDM. These updated 2010 recommendations also recognize populations at risk for GDM and suggest providers consider screening on a case-by-case basis. They encourage dialogue between provider and patient to arrive at screening decisions. According to the most recent American College of Obstetricians and Gynecologists (ACOG) Practice Bulletin No. 30 (2010), screening all women for GDM, whether by patient’s history, clinical risk factors, or a laboratory testing is recommended. ACOG states that selective screening based on risk is appropriate and low-risk women may be excluded. They also acknowledge that high-risk women may benefit from earlier screening. The American Diabetes Association’s (ADA) position is aligned with the ACOG guidelines (Metzer et al., 2007), but the World Health Organization (WHO) has a slightly different recommendation. It suggests screening all women at 24– 28 weeks with earlier screening for those considered high risk (Mulholland, Njoroge, Mersereau, & Williams, 2007). In an attempt to provide the evidence needed in this debate, the recent hyperglycemia and adverse pregnancy outcome (HAPO) study has started to release its findings. Preliminary review of the data suggest a continuum of risk for GDM patients resulting in adverse outcomes (Constant, Lowe, & Metzger, 2010). The data also cor330
J. E. Crete & J. N. Anasti
relate earlier diagnosis to more severe forms of glucose intolerance (Langer, 2010; Moore, 2010). These findings may provide, in part, the convincing data that the medical organizations have been searching for concerning the incidence of GDM and outcomes (Javanovic, 2009; Moore 2010). The purpose of this study was to identify risk factors for GDM in patients who had either a normal or abnormal GTT, after failing the initial glucose challenge test (GCT). If identified, consideration can be given to selectively screen by risk factors, and move directly to the diagnostic 3-h oral GTT (OGTT) for those considered high risk. Additionally, we can consider not performing glucose screening on those who are at low risk. Currently, our institution, like most in this country, performs universal screening for GDM at 28 weeks using a two-step method. The first step is a nonfasting, 1 h 50 g oral glucola test. Patients who fail this first step with a value between 135 and 179 mg/dL, (Carpenter & Constant, 1982) proceed on to the diagnostic test. This second step is a fasting, 3 h 100 g OGTT. Patients with a 1-h glucola value of ≥ 180 mg/dL are diagnosed with GDM and are not given an OGTT. The upper limit cut-off values for the 3-h OGTT is: fasting— 95 mg/dL, 1 h—180 mg/dL, 2 h—155 mg/dL, 3 h— 140 mg/dL (O’Sullivan, Mahan, Charles, & Dandrow, 1973). The diagnosis of GDM is made if two of the four values are elevated.
Methods Study design, sample, and measures This was a descriptive study consisting of a retrospective patient chart review. The patients received prenatal care and delivered in a community hospital in eastern Pennsylvania between 2001 and 2009. Services were provided by private practice groups affiliated with the hospital or by the in-house staff in the hospital’s outpatient women’s health center. The results of 1-h glucola and 3-h OGTT test results were collected from the hospital’s laboratory data bank and yielded 1700 results of patients who had failed the screening nonfasting 1 h 50 g oral glucose test and had a follow-up diagnostic 100 g OGTT. A random sample of 600 patients (300 case and 300 control) was chosen. The cases failed the 3-h OGTT with two or more elevated values and the controls passed the 3-h OGTT with a minimum of three normal values, using the guidelines cited. Excluded from the study were patients with a 1-h 50 g glucose results ≥ 180 mg/dL and patients with known pregestational diabetes or a current multiple gestation pregnancy. Selected independent predictor variables consisting of the patient’s age, BMI, ethnicity, personal history, and
Diagnosis of gestational diabetes mellitus
J. E. Crete & J. N. Anasti
Table 1 Independent variable and subgroups (N = 557) AGE ≤ 24 years 25–29 years 30–34 years ≥35 years BMI ≤24: NL 25–29: Overweight ≥30: Obese ETHNIC CATEGORY African American Caucasian Hispanic/Mexican Asian, Indian/Eastern, or Othera Family history Negative Diabetes mellitus Personal history Negative Macrosomia Gestational diabetes mellitus PIH/HTN Nullipara IUFD, PTL/PTD, PCOSa TOTAL
Table 2 Predicted probability of GDM—primary logistic regression re-
GDM
No GDM
28 56 86 99
67 77 90 54
68 83 118
106 85 97
12 215 31 11
19 205 53 11
217 52
244 44
107 27 42 10 55 28 269
168 20 21 19 44 16 288
a
Combined because of small numbers. IUFD, intrauterine fetal demise; PTL, preterm labor; PTD, preterm delivery; PCOS, polycystic ovary syndrome; BMI, body mass index.
family history were entered into Excel spread sheets and coded by the principal investigator. To adhere to HIPAA guidelines, the patients’ personal identifying information was excluded. The spreadsheets were kept on the computers of the investigators and statistician who were all hospital employees and compliant with HIPAA guidelines. The research study received approval from both the hospital’s IRB committee and Robert Morris University’s IRB committee with expedited status. The five variables were all self-reported. They included patients’ age, prepregnant BMI, ethnicity, selected personal history, and family history. Each variable also included subgroups (see Table 1). The patient’s age at time of delivery was taken from the date of birth on record. The four age subgroups consisted of ≤24, 25–29, 30–34, and ≥35 years of age. The BMI (WHO, 2000) was calculated from the patient’s prepregnant weight and height and formed three subgroups: normal with a BMI of ≤ 24, overweight with a BMI between 25 and 29 and obese with a BMI of ≥ 30. Six ethnic groups were included: African American, Asian, Caucasian, Hispanic/Mexican/Latino, Eastern Indian, and Other. The patients’ personal obstetrical history included previous gestational diabetes, delivery of a macrosomic infant (wt.
sults (N = 557) Variable Age 30–34 Age ≥ 35 BMI 25–29 BMI ≥ 30 Phx GDM
Adjusted OR 95% CI
p value
2.04 (1.15, 3.61) 3.87 (2.12, 7.05) 1.53 (.96, 2.43) 1.95 (1.25, 3.05) 2.82 (1.55, 5.13)
.015 .000 .075 .003 .001
≥ 8.5 lbs), gestational hypertension or pregnancy induced hypertension, fetal demise, preterm labor or delivery, and the gynecologic diagnosis of polycystic ovary syndrome.
Data analysis Direct logistic regression analysis was conducted using PASW (formally known as SPSS) statistical software version 17. The Omnibus Tests of Model Coefficients was used to determine overall model fit, followed by reporting of adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). The primary logistic regression was calculated on the single variables and subgroups (see Table 1). Of the 600 charts, 557 were included (N = 557) while 43 (7.3%) were excluded based on missing or incomplete data. The group split was 269 cases and 288 controls. For exploratory purposes, a second multiple logistic regression (ML) was calculated with a multicategorical predictor variable combining age and BMI in addition to the previously included variables of ethnicity, personal history, and family history. The multicategorical predictor was distributed as follows: the ML. 1 group (n = 68) was aged 30–34 years with a BMI ≥ 30 and the ML. 2 group (n = 63) was aged ≥ 35 years with a BMI ≥ 30. This exploratory analysis had an N = 586 with 14 (2.3%) charts excluded because of missing data. The group split was 291 cases and 295 controls.
Results Primary logistic regression Testing of the full model (Omnibus Tests of Model Coefficients) with all predictors, compared with the constantonly model, yielded statistically significant results (χ 2 = 69.38, p < .0001), indicating that the predictor variables taken together differentiate the case and control groups in a reliable manner and suggesting good overall model fit. Table 2 presents results for the individual predictors. The two age groups of 30–34 and ≥ 35 years old had adjusted ORs of 2.04, 95% CI [1.15, 3.61], p = .015 and 3.87, 95% CI [2.12, 7.05], p < .0001, respectively, 331
Diagnosis of gestational diabetes mellitus
J. E. Crete & J. N. Anasti
Table 3 Predicted probability of GDM—exploratory logistic regression results (N = 586) Variable Age 30–34/BMI ≥ 30 Age ≥ 35/BMI ≥ 30
Adjusted OR 95% CI
p value
1.52 (.88, 2.59) 2.54 (1.41, 4.57)
.131 .002
indicating statistically significant prediction of gestational diabetes. The BMI category of ≥30 also had a statistically significant adjusted OR of 1.95, 95% CI [1.25, 3.05], p = .003, as did the personal history of gestational diabetes, with an adjusted OR of 2.82, 95% CI [1.55, 5.13], p = .001. The BMI category 25–29 showed a trend toward statistical significance with an adjusted OR of 1.53, 95% CI [.96, 2.43], p = .075. However, neither ethnicity nor family history of gestational diabetes significantly predicted gestational diabetes, p > .05.
Exploratory logistic regression Testing of the full model (Omnibus Tests of Model Coefficients) with all predictors, compared with the constantonly model, yielded statistically significant results, (χ 2 ) = 47.99, p < .0001, indicating that the predictor variables taken together differentiate the case and control groups in a reliable manner and suggesting good overall model fit. Of the multicategorical variables (see Table 3) reflecting both age and BMI, only the age ≥ 35 years with a BMI of ≥ 30 were significantly more likely to have gestational diabetes with an adjusted OR of 2.54, 95% CI [1.41, 4.57], p = .002. None of the remaining variables were significant predictors, p > .05.
tively based on risk factors, is a logical approach and would avoid unnecessary testing. Also, it is reasonable to offer earlier screening to those identified as higher risk. Using risk factors alone to circumvent the glucose challenge and proceed directly to the diagnostic OGTT is not supported and may cause unnecessary testing and cost to the care of the pregnant women. The results did not correlate family history of diabetes, personal history of poor obstetrical outcomes, or those from ethnically diverse populations with a significantly higher risk of GDM. One possible explanation for these results is the small sample size in each of the given subgroups. Previous studies have demonstrated an increased risk in these groups. The primary strength of this study was that the number of subjects was large enough to provide significant results and that the laboratory testing was done at the same institution. Several potential limitations to this study exist. All of the variables were self-reported by the subjects and the accuracy is dependent upon the historian’s memory. In addition, documentation on the chart by the hospital staff was not verified. As mentioned earlier, there were small numbers in some of the subgroups. Replicating this study in other populations is recommended. It is important to mention that since the start of this study the HAPO data have been published with the authors’ recommendations. The one step 75 g OGTT, popular in other countries and used in the HAPO study, may soon gain acceptance in the United States. Several authors support adoption of this test (Ecker & Greene, 2008; Hadar et al, 2009). Also reinforced is the importance of early diagnosis for populations at risk. Furthermore, based on these results, Constant et al. (2010) recommended lowering the threshold values for the diagnosis of GDM. The DPSG-NA and International Diabetes Federation (IDF) have recognized the significance of the HAPO results and support these recommendations.
Discussion and conclusion Screening guidelines for GDM remain inconsistent, which creates frustration for providers. Providers are responsible for incorporating practice guidelines that are evidence-based, meaningful, and reflect the characteristics of their particular population. This study examined two groups of patients who failed the screening 1-h glucose test and took the diagnostic OGTT. Risk characteristics that correlate to the diagnosis of GDM, were identified. Single characteristics of advanced age, obesity, and personal history of GDM had the strongest association. Also, the combination of advanced age and BMI had a correlation but this was not a significant increase over individual risk factors. These results give support to reconsider our current practice of universal screening for GDM. As ACOG and the ADA suggest, screening selec332
Acknowledgments Special thanks to Jill Stoltz, PhD, Director, Institute of Research, St. Luke’s Hospital and Health Network. Acknowledgment of Mary Cothran, PhD, Academic advisor, Robert Morris University.
References American College of Obstetricians and Gynecologists (ACOG). (2010). Clinical management guideline for obstetricians and gynecologists. (Practice Bulletin No. 30). Washington, DC: Author. Carpenter, M. W., & Constant, D. R. (1982). Criteria for screening tests for gestational diabetes. American Journal of Obstetrics and Gynecology, 144, 768–773. Cheng, Y. W., & Caughey, A. B. (2008). Gestational diabetes: Diagnosis and management. Journal of Perinatology, 28, 657–664.
J. E. Crete & J. N. Anasti
Constant, D. R., Lowe, L. P., & Metzer, B. E. (2010). The hyperglycemia and adverse pregnancy outcome (HAPO) study: Can we use the results as a basis for change? Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 204–209. doi:10.3109/14767050903550667 Ecker, J. L., & Greene, M. F. (2008). Gestational diabetes: Setting limits, exploring treatments. New England Journal of Medicine, 358, 2061–2063. Getahun, D., Fassett, M. J., & Jacobsen, S. J. (2010). Gestational diabetes: Risk of recurrence in subsequent pregnancies. American Journal of Obstetrics and Gynecology, 203, 1e1–1e6. doi:10.1016/j.ajog.2010.05.032 Griffin, M. E., Coffey, M, Johnson, H., Scanlon, P., Foley, M., Stronge, J., . . . Firth, R. G. (2000). Universal vs. risk factor-based screening for gestational diabetes mellitus: Detection rates, gestation at diagnosis and outcome. Diabetic Medicine, 17, 26–32. Hadar, E., Oats, J., & Hod, M. (2009). Towards new diagnostic criteria for diagnosing GDM—The HAPO study. Journal of Perinatal Medicine, 37, 447–449. doi:10.151S/JPM-2009.114 Hollander, M. H., Paarlberg, M. K., & Huisjes, J. M. (2007). Gestational diabetes: A review of the current literature and guidelines. Obstetrics and Gynecological Survey, 62(2), 125–136. Javanovic, L. (2009). Definition, size of the problem, screening and diagnostic criteria: Who should be screened, cost-effectiveness, and feasibility of screening. International Journal of Gynecology and Obstetrics, 104, S17–S19. doi:10.1016/j.ijgo.2008,11..023 Langer, O. (2010). Changing the diagnosis criteria of type 2 diabetes in pregnancy: Do the ends justify the means? Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 234–238. doi:10.3109/14767050903550907 Langer, O., Miodovnik, M., Reece, A. E., & Rosenn, B. M. (2010). The proceedings of the diabetes in pregnancy study group of North American 2009 conference. Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 196–198. doi:10.3109/14767050903550634 Metzger, B. E., Buchanan, T. A., Constant, D. R., DeLeiva, A., Dunger, D. B., Hadden, D. R., . . . Zoupas, C. (2007). Summary and recommendations of the fifth international workshop-conference on gestational diabetes mellitus. Diabetes Care, 30(Suppl. 2), S251–S260. doi:10.2337/dc07-s225 Moore, T. (2010). Can the findings of recent randomized trials of treatment or no treatment of gestational diabetes be used for changing current treatment approaches? Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 214–218. doi:10.3109/14767050903550634
Diagnosis of gestational diabetes mellitus
Mulholland, C., Njoroge, T., Mersereau, P., & Williams, J. (2007). Comparison of guidelines available in the United States for diagnosis and management of diabetes before, during and after pregnancy. Journal of Women’s Health, 16(6), 790–800. doi:10.10089/jwh.2007.CDC7 Naylor, D. C., Sermer, M., Chen, E., & Farine, D. (1997). Selective screening for gestation diabetes mellitus. New England Journal of Medicine, 337(22), 1591–1596. Palkhivala, A. (2009). IDF releases new guidelines on diabetes management. Retrieved from http://cme.medscape.com/viewarticle/711266 Perkins, J. M., Dunn, J. P., & Shubhada, J. M. (2007). Perspectives in gestational diabetes mellitus: A review of screening, diagnosis, and treatment. Clinical Diabetes, 25(2), 57–62. Reece, A. E. (2010). The fetal and maternal consequences of gestational diabetes mellitus. Journal of Maternal-Fetal and Neonatal Medicine, 23(3), 199–203. doi:10.3109/14767050903550659 Robinson, S., & Dornhorst, A. (2007). ABCD position statement on screening for gestation diabetes mellitus. Practical Diabetes International, 143(4), 192–195. Russell, M. A., Carpenter, M. W., & Courstan, D. R. (2007). Screening and diagnosis of gestational diabetes mellitus. Clinical Obstetrics & Gynecology, 50, 949–958. O’Sullivan, J. B., Mahan, C. M., Charles, D., & Dandrow, R. V. (1973). Screening criteria for high-risk gestational diabetic patients. American Journal of Obstetrics and Gynecology, 116, 895. U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality. (2010). The guide to clinical preventive services (AHRQ Pub. No. 09-IP006). Retrieved from http://www.ahrq.gov/clinic/ pocketgd1011/pocketgd1011.pdf van Leeuwen, M., Zweers, E. J., Opmeer, B. C., van Ballegooie, E., Brugge, H. G., & de Valk, H. W. (2007). Comparison of accuracy measures of two screening tests for gestational diabetes mellitus. Diabetes Care, 30, 2779–2784. Williams, C. B., Iqbal, S., Zawacki, C. M., Yu, D., Brown, M. B., Herman, W. H. (1999). Effect of selective screening of gestational diabetes. Diabetes Care, 22, 418–421. World Health Organization. (2000). Obesity: Preventing and managing the global epidemic: Report of a WHO consultation (WHO Technical Report Series 894). WHO, Geneva, Switzerland.
333
