Vintzileos, Anthony M. MD; Finamore, Peter S. MD; Ananth, Cande V. PhD, MPH
The typical obstetric and gynecologic “history and physical” includes patient age, gravidity, parity, number of previous preterm births, number of abortions or miscarriages, number of living children, and race and ethnic identification in the opening statement of the history of present illness. The chief reason for including these descriptors in the opening statement of the history of present illness and in the situation, background, assessment, and recommendation communications1 is to provide the health care provider with a “snapshot of predictors.” It is hoped that such descriptions will immediately stimulate the thought process regarding differential or confirmed diagnosis, possible complications, and appropriate treatments or interventions. These descriptors are important for safe, effective, and consistent communication among health care professionals who provide care to obstetrics and gynecology patients.
These traditional descriptors have been extremely useful prognosticators and have facilitated communication for many years for obstetricians and gynecologists. However, given the increasing prevalence of obesity in many developed countries2 and its reported associations with adverse health outcomes and conditions, it makes sense to view body mass index (BMI, calculated as weight (kg)/[height (m)]2) as an emerging important descriptor.
The aim of this study was to determine whether BMI is associated frequently enough with obstetric and gynecologic adverse outcomes and conditions to be included with the other, more traditional descriptors used in the history of present illness or in situation, background, assessment, and recommendation communications.
MATERIALS AND METHODS
A PubMed search (performed May 16, 2012) using the key words “obesity and pregnancy” yielded 7,345 articles published between 1947 and 2012; 49% (n=3,600) of these articles were published over the past 7 years (2005–2012). Similarly, a PubMed search (performed May 23, 2012) using the key words “obesity and gynecology” yielded 2,268 articles published between 1954 and 2012; 60% (n=1,350) of these were published over the past 7 years (2005–2012). Through repeated PubMed searches of the abstracts published in years 2005–2012, we sought to identify abstracts in English, including those electronically published ahead of print, using as key words each of the descriptors (age, gravidity, parity, history of preterm births, history of abortions, race and ethnic status, and BMI) and the adverse outcomes or conditions that are depicted in Tables 1 and 2. The most recently published (starting from 2012) and relevant abstracts were reviewed to identify positive associations. Emphasis was given in the identification of articles describing more than one association or review articles that were reviewed in their entirety along with their references (19 such articles, published between 1993 and 2012, were reviewed). In instances in which the review resulted in questionable associations, the articles were discussed between the first two authors (A.M.V. and P.F.), and both had to be in agreement for the final disposition of the reported association being positive or negative. If no well-confirmed positive associations were identified after review of the 10 most recent and relevant abstracts, the associations were considered as being negative. Searches were not performed for associations judged to be well-known negative or common-sense negative. Although approval was sought from the Institutional Review Board of Winthrop University Hospital as an “exempt” status, the study was classified as “non-human subjects” research; so no Institutional Review Board was necessary.
The rate of association of each descriptor with each of the adverse outcomes or conditions was expressed as number and percentage. To compare the proportion of positive associations between each traditional descriptor and the adverse outcomes and conditions compared with the positive associations between BMI and the adverse outcomes and conditions, we constructed a series of two-by-two tables. The significances of associations from these two-by-two tables were based on χ2 test or the Fisher exact probability test. From these tables, we also derived the odds ratio and 95% confidence interval for each association, with BMI as the referent descriptor. Because all associations were planned a priori, none of the associations was corrected for multiple comparisons.
Table 1 describes the most frequent adverse obstetric outcomes or conditions and their association with the various descriptors, including maternal age, gravidity, parity, history of preterm delivery, history of abortions, race and ethnicity, and prepregnancy BMI. Of the 34 listed adverse obstetric outcomes or conditions, maternal age was linked to 20 (59%),3–15 gravidity was linked to 11 (32%),10,12,13,16–21 parity was linked to 14 (41%),10–12,14,18,19,22 history of preterm birth was linked to 18 (53%),9,20,22,23 history of abortions was linked to 8 (24%),17,19–21 and race and ethnicity were linked to 17 (50%).15,23–27 As shown in Table 3, BMI had the strongest association (32 [94%]) with adverse pregnancy outcomes or conditions.27–41 Most importantly, there was a dose--response relationship between increasing maternal BMI categories and outcomes with morbid obesity (BMI higher than 40) associated with increased rates of multiple adverse pregnancy outcomes such as fetuses large for gestational age (odds ratio [OR] 3.82), preeclampsia (OR 4.82), cesarean delivery (OR 2.69), antepartum stillbirth (OR 2.79), shoulder dystocia (OR 3.14), instrumental delivery (OR 1.34), meconium aspiration (OR 2.85), fetal distress (OR 2.52), and early neonatal death (OR 3.41), as compared with mothers with normal weights; the associations were similar for women with BMI between 35.1 and 40, but to a lesser degree.41
Table 2 shows the association of the descriptors with gynecologic outcomes or conditions. In two instances, the first two authors had to convene and discuss the final disposition of these two associations. Of the 23 listed gynecologic conditions, age is linked to 19 (83%),42–55 gravidity is linked to eight (35%),42,45,54,56 parity is linked to 10 (43%),42,45,48,50–52,54,56 history of preterm birth is linked to four (17%),55–58 history of abortion is linked to six (26%),54–56,58 and race and ethnicity are linked to nine (39%).42,47,49,51,52,56,59–61 The descriptor with the highest rate of association was BMI, with 21 of 23 (91%)42,44–48,50–56,58,62–67 of the gynecologic conditions.
Table 3 describes the frequency of association of each descriptor with obstetric, gynecologic, and combined conditions, and also demonstrates a comparison using BMI as the referent. Body mass index had the highest rate of association and reached statistical significance against all other descriptors for the combined obstetric and gynecologic conditions.
The primary finding of this study is that BMI is most frequently associated with combined obstetric and gynecologic adverse outcomes and conditions compared with other descriptors used in the past in the opening statement of the history of present illness. This finding is important, given the fact that most industrialized countries are currently experiencing an epidemic of obesity. Recent statistics indicate that more than one-third of women in the United States are obese,2 and that the prevalence of obesity is continuing to increase. As a result, the prevalence of obesity during pregnancy has dramatically increased and is now associated with an array of adverse obstetric and perinatal outcomes. The literature on obesity and its adverse effects on pregnancy outcome has expanded over the past 12 years. A Google search using the key words “obesity and pregnancy” resulted in 25,400,000 hits (performed May 16, 2012). Similarly, a Google search using the words “gynecology and obesity” resulted in 3,600,000 hits (performed May 23, 2012). There have been thousands of reports of associations of obesity with almost every adverse outcome. Because there is a documented linear dose--response relationship between frequently seen adverse outcomes, such as preeclampsia,68 it makes sense to use the specific BMI number in our communications rather than using the BMI categories. Despite these strong and associations, BMI is not currently included in the brief list of descriptors accompanying the opening statement of the history of present illness or in the “situation” part of the situation, background, assessment, and recommendation communication.
Currently, residents in obstetrics and gynecology at Winthrop University Hospital, Mineola, New York, are taught to include BMI in the opening statement of the history of present illness, as well as in all hand-off communications. The Joint Commission on Accreditation of Hospitals considers “standardized communication” as a prerequisite for patient safety and recommends situation, background, assessment, and recommendation communication as the best practice.69 The situation, background, assessment, and recommendation communication technique includes four components: situation (description of the patient by name, age, sex, gravidity, parity, ethnicity, hopefully BMI, and the reason for report); background (presenting symptom and a brief summary of the medical history); assessment (vital signs and clinical impression); and recommendation (specific action to be taken and urgency). The need for accurate communication among health care providers occurs constantly under a variety of settings such as emergency room, labor and delivery suite, or during changing shifts when specific patient information is passed from one caregiver to another. To ensure safe and effective care, the situation, background, assessment, and recommendation communication should provide the most consistent and precise exchange of patient information by using information-rich descriptors. In our view, BMI is the most informative descriptor, as compared with the other traditional descriptors that we use in everyday practice in obstetrics and gynecology. Given the recent obesity epidemic, it is prudent to include BMI along with our other traditional markers in the situation part of the situation, background, assessment, and recommendation communications. Our management should be drastically altered when a “36-year-old, white female, G3P1Ab1, BMI 42” presents for medical care as compared with a “36-year-old, white female, G3P1Ab1” with the same symptoms.
It is certain that similar convincing arguments can be made for BMI to be included in the history of present illness, as well as in the situation portion of the situation, background, assessment, and recommendation communication, in all communications regarding patients of other medical specialties. However, it is important for the obstetricians and gynecologists to lead this effort. Therefore, it will be prudent for all health care providers in obstetrics and gynecology to be encouraged to include BMI, along with the other typical descriptors, at the start of their communications to enhance awareness of potential complications and their prevention. This can only lead to improvement in women’s health care.
1. Beckett CD, Kipnis G. Collaborative communication: integrating SBAR to improve quality/patient safety outcomes. J Healthc Qual 2009;31:19–28.
2. Flegal KM, Caroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Lindqvist P, Dahlback B, Marsal K. Thrombotic risk during pregnancy: a population study. Obstet Gynecol 1999;94:595–9.
4. Montan S. Increased risk in the elderly parturient. Curr Opin Obstet Gynecol 2007;19:110–2.
5. Ojule JD, Ibe VC, Fiebai PO. Pregnancy outcome in elderly primigravidae. Ann Afr Med 2011;10:204–8.
6. Favilli A, Pericoli S, Acanfora MM, Bini V, Di Renzo GC, Gerli S. Pregnancy outcome in women aged 40 years or more. J Matern Fetal Neonatal Med 2012;25:1260–3.
7. Dildy GA, Jackson GM, Fowers GK, Oshiro BT, Varner MW, Clark SL. Very advanced maternal age: pregnancy after age 45. Am J Obstet Gynecol 1996;175:668–74.
8. Koo YJ, Ryu HM, Yang JH, Lim JH, Lee JE, Kim MY, et al.. Pregnancy outcomes according to increasing maternal age. Taiwan J Obstet Gynecol 2012;51:60–5.
9. Aliyu MH, Luke S, Wilson RE, Saidu R, Alio AP, Salihu HM, et al.. Obesity in older mothers, gestational weight gain, and risk estimates for preterm phenotypes. Maturitas 2010;66:88–93.
10. Abu-Heija AT, Jallad MF, Abukteish F. Maternal and perinatal outcome of pregnancies after the age of 45. J Obstet Gynaecol Res 2000;26:27–30.
11. Dulitzki M, Soriano D, Schiff E, Chetrit A, Mashiach S, Seidman DS. Effect of very advanced maternal age on pregnancy outcome and rate of cesarean delivery. Obstet Gynecol 1998;92:935–9.
12. Abu-Heija AT, El-Jallad F, Ziadeh S. Placenta previa: effect of age, gravidity, parity and previous caesarean section. Gynecol Obstet Invest 1999;47:6–8.
13. Hossain GA, Islam SM, Mahmood S, Chakraborty RK, Akhter N, Sultana S. Placenta previa and its relation with maternal age, gravidity and cesarean section. Mymensingh Med J 2004;13:143–8.
14. Ludford I, Scheil W, Tucker G, Grivell R. Pregnancy outcomes for nulliparous women of advanced maternal age in South Australia, 1998-2008. Aust N Z Obstet Gynaecol 2012;52:235–41.
15. Eden KB, McDonagh M, Denman MA, Marshall N, Emeis C, Fu R, et al.. New insights on vaginal birth after cesarean: can it be predicted? Obstet Gynecol 2012;116:967–81.
16. Materna-Kiryluk A, Wieckowska B, Wisniewska K, Borszewska-Kornacka MK, Godula-Stuglik U, Limon J, et al.. Maternal reproductive history and the risk of isolated congenital malformations. Paediatr Perinat Epidemiol 2011;25:135–43.
17. Blanco-Munoz J, Lacasana M, Borja-Aburto VH. Maternal miscarriage history and risk of anencephaly. Paediatr Perinat Epidemiol 2006;20:210–8.
18. Ananth CV, Demissie K, Smulian JC, Vintzileos AM. Placenta previa in singleton and twin births in the United States, 1989 through 1998: a comparison of risk factor profiles and associated conditions. Am J Obstet Gynecol 2003;188:275–81.
19. Tuzovic L, Djelmis J, Ilijic M. Obstetric risk factors associated with placenta previa development: case-control study. Croat Med J 2003;44:728–33.
20. Greenwood R, Samms-Vaughan M, Golding J, Ashley D. Past obstetric history and risk of perinatal death in Jamaica. Paediatr Perinat Epidemiol 1994;8:40–53.
21. Bocciolone L, Parazzini F, Fedele L, Acaia B, Candiani GB. Epidemiology of spontaneous abortion: a review of the literature. Ann Ostet Ginecol Med Perinat 1989;110:323–34.
22. Ananth CV, Peltier MR, Getahun D, Kirby RS, Vintzileos AM. Primiparity: an “intermediate” risk group for spontaneous and medically indicated preterm birth. J Matern Fetal Neonatal Med 2007;20:605–11.
23. Anum EA, Brown HL, Strauss JF 3rd. Health disparities in risk for cervical insufficiency. Hum Reprod 2010;25:2894–900.
24. Ogunyemi D, Brown P, Willis-Hassan R, Fukushima T. Racial and ethnic determinants of birth defects and infant mortality in a low income minority hospital population: a review of 67,349 deliveries. J Matern Fetal Neonatal Med 1993;2:124–8.
25. Orr M, Bove F, Kaye W, Stone M. Elevated birth defects in racial or ethnic minority children of women living near hazardous waste sites. Int J Hyg Environ Health 2002;205:19–27.
26. Yang Q, Wu Wen S, Caughey S, Krewski D, Sun L, Walker MC. Placenta previa: its relationship with race and the country of origin among Asian women. Acta Obstet Gynecol Scand 2008;87:612–6.
27. Kim SY, England L, Sappenfield W, Wilson HG, Bish CL, Salihu HM, et al.. Racial/ethnic differences in the percentage of gestational diabetes mellitus cases attributable to overweight and obesity, Florida, 2004-2007. Prev Chronic Dis 2012;9:110249.
28. Davies GAL, Maxwell C, McLeod L. Obesity in pregnancy. SOGC Clinical Practice Guideline. JOGC 2010;239:165–73.
29. Alanis MC, Goodnight WH, Hill EG, Robinson CJ, Villers MS, Johnson DD. Maternal super-obesity (body mass index ≥ or +50) and adverse pregnancy outcomes. Acta Obstet Gynecol Scand 2010;89:924–30.
30. Salihu HM, Lynch O, Alio AP, Kornosky JL, Clayton HB, Mbah AK. Extreme obesity and risk of placental abruption. Hum Reprod 2009;244:38–44.
31. Becker T, Vermeulen MJ, Wyatt PR, Meier C, Ray JG Maternal obesity and the risk of placental vascular disease. J Obstet Gynaecol Can 2008;30:1132–6.
32. Aliyu MH, Alio AP, Lynch O, Mbah A, Sailhu HM. Maternal pre-gravid body weight and risk for placental abruption among twin pregnancies. J Matern Fetal Neonatal Med 2009;22:745–50.
33. Walker ID. Venus and arterial thrombosis during pregnancy: epidemiology. Semin Vasc Med 2003;3:25–32.
34. Lu GC, Rouse DJ, DuBard M, Cliver S, Kimberlin D, Hauth JC. The effect of the increasing prevalence of maternal obesity on perinatal morbidity. Am J Obstet Gynecol 2001;185:845–9.
35. Blomberg MK, Bengt K. Maternal obesity and morbid obesity: the risk for birth defects in the offspring. Birth Defects Res A Clin Mol Teratol 2010;88:36–40.
36. Olivarez SA, Ferres M, Mattewal AK, Maheshwari B, Sangl-Haghpeykar H, Aagaard-Tillery K. Obstructive sleep apnea screening in pregnancy, perinatal outcomes, and impact of maternal obesity. Am J Perinatol 2011;28:651–8.
37. Fung AM, Wilson DL, Barnes M, Walker SP. Obstructive sleep apnea and pregnancy: the effect on perinatal outcomes. J Perinatol 2012;32:399–406.
38. Castro LC, Avina RL. Maternal obesity and pregnancy outcomes. Curr Opin Obstet Gynecol 2002;14:801–6.
39. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med 2008;6:550–4.
40. Stones RW, Paterson CM, Saunders NJ. Risk factors for major obstetric haemorrhage. Eur J Obstet Gynecol Reprod Biol 1993;48:15–8.
41. Cedergren MI. Maternal morbid obesity and the risk of adverse pregnancy outcome. Obstet Gynecol 2004;103:219–24.
42. Nygaard I, Barber M. Prevalence of symptomatic pelvic floor disorders in US Women. JAMA 2008;300:1311–6.
43. Laupland KB, Ross T, Pitout JD, Church DL, Gregson DB. Community-onset urinary tract infections: a population-based assessment. Infection 2007;35:150–3.
44. Ulmer H, Bjørge T, Concin H, Lukanova A, Manjer J, Hallmans G, et al.. Metabolic risk factors and cervical cancer in the metabolic syndrome and cancer project (Me-Can). Gynecol Oncol 2012;125:330–5.
45. Cisko B, Pochwalowski M, St Garbryś M. Risk factors and clinical characteristic patients with vulvar cancer. Ginekol Pol 2006;77:914–21.
46. Ray A, Cleary MP. Obesity and breast cancer: a clinical biochemistry perspective. Clin Biochem 2012;45:189–97.
47. Uppot RN, Sahani DV, Hahn PF, Gervais D, Mueller PR. Impact of obesity on medical imaging and image-guided intervention. AJR Am J Roentgenol 2007;188:433–40.
48. Short VL, Totten PA, Ness RB, Astete SG, Kelsey SF, Murray P, et al.. The demographic, sexual health and behavioral correlates of Mycoplasma genitalium infection among women with clinically suspected pelvic inflammatory disease. Sex Transm Infect 2010;86:29–31.
49. Hartmann KA, Lerance SJ, Jay MS. Tubo-ovarian abscess in virginal adolescents: exposure of the underlying etiology. J Pediatr Adolesc Gynecol 2009;22:e13–6.
50. Wise L, Plamer JR, Spiegelman D, et al.. Influence of body size and body fat distribution on risk of uterine leiomyomata in U.S. black women. Epidemiology 2005;16:346–54.
51. Grasinger CC, Wild RA, Parker IJ. Vulvar acanthosis nigricans: a marker for insulin resistance in hirsute women. Fertil Steril 1993;59:583–6.
52. Pandey S, Bhattacharya S. Impact of obesity on gynecology. Womens Health (Lond Engl) 2010;6:107–17.
53. Motta AB. The role of obesity in the development of polycystic ovary syndrome. Curr Pharm Design 2012;18:2482–91.
54. Olsson A, Selva-Nayagam P, Oehler MK. Postmenopausal vulval disease. Menopause Int 2008;14:169–72.
55. Walker JL, Piedmonte MR, Spirtos NM, Eisenkop SM, Schlaerth JB, Mannel RS, et al.. Laparoscopy compared with laparotomy for comprehensive surgical staging of uterine cancer: gynecologic oncology group study LAP2. J Clin Oncol 2009;27:5331–6.
56. Unluhizarci K, Kaltsas G, Kelestimur F. Non polycystic ovary syndrome-related endocrine disorders associated with hirsutism. Eur J Clin Invest 2012;42:86–94.
57. Chesson HW, Kent CK, Owusu-Edusei K, Leichliter JS, Aral SO. Disparities in sexually transmitted disease rates across the “eight Americas.” Sex Transm Dis 2012;39:458–64.
58. Latthe P, Mignini L, Gray R, Hills R, Khan K. Factors predisposing women to chronic pelvic pain: systematic review. BMJ 2006;332:749–55.
59. Semins MJ, Shore AD, Makary MA, Weiner J, Matlaga BR. The impact of obesity on urinary tract infection risk. Urology 2012;79:266–9.
60. Johnson HL, Ghanem KG, Zenilman JM, Erbelding EJ. Sexually transmitted infections and adverse pregnancy outcomes among women attending inner city public sexually transmitted diseases clinics. Sex Transm Dis 2011;38:167–71.
61. Simms I, Stephenson JM, Maillinson H, Peeling RW, Thomas K, Gokhale R, et al.. Risk factors associated with pelvic inflammatory disease. Sex Transm Infect 2006;82:452–7.
62. McGrother CW, Donaldson MM, Thompson J, Wagg A, Tincello DG, Manktelow BN. Etiology of overactive bladder: a diet and lifestyle model for diabetes and obesity in older women. Neurourol Urodyn 2012;31:487–95.
63. Cramer DW. The epidemiology of endometrial and ovarian cancer. Hematol Oncol Clin North Am 2012;26:1–12.
64. Ferrero S, Anserini P, Remorgida V, Ragni N. Body mass index in endometriosis. Eur J Obstet Gynecol Reprod Biol 2005;121:94–8.
65. Yilmaz N, Kilic S, Kanat-Pektas M, Gulerman C, Mollamahmutoglu L. The relationship between obesity and fecundity. J Womens Health (Larchmt) 2009;18:633–6.
66. Modesitt SC, van Nagell JR. The impact of obesity on the incidence and treatment of gynecologic cancers: a review. Obstet Gynecol Surv 2005;60:683–92.
67. Mazor-Dray E, Levy A, Schlaeffer F, Sheiner E. Maternal urinary tract infection: is it independently associated with adverse pregnancy outcome? J Matern Fetal Neonatal Med 2009;22:124–8.
68. Getahun D, Ananth CV, Oyelese Y, Chavez M, Kirby RS, Smulian JC. Primary preeclampsia in the second pregnancy. Effects of changes in prepregnancy body mass index between pregnancies. Obstet Gynecol 2007;110:1319–25.
69. Woods MS. Effective handoff communication, Part 1.: developing and implementing new SBAR tool. Jt Comm Perspect Patient Saf 2010;10:1–11.