Associate at the Center for Fetal Medicine and Women’s Ultrasound, Los Angeles, Califoria and a Clinical Faculty in the Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of California, Los Angeles.

Christina S Han, MD

Type 1 diabetes mellitus (T1DM) is an autoimmune condition that causes pancreatic beta-cell destruction and complete absence of native insulin production. Risk factors for development of T1DM include family history, genetic predisposition (specific human leukocyte antigen gene variants), and viral triggers.

Han is a Maternal-Fetal Medicine Specialist in Los Angeles at the Center for Fetal Medicine and Women’s Ultrasound. She is the MFM Fellowship Program Director at the University of California, Los Angeles.

While T1DM affects only 5% of patients with pregestational diabetes and less than 1% of overall pregnancies, it is important that obstetricians are aware of the pathophysiology, basic management principles, and rapidly evolving insulin pump and continuous glucose monitoring (CGM) technologies.

Richley is an ob/gyn in Los Angeles at the University of California, Los Angeles Medical Center and practices at Ronald Reagan UCLA Medical Center and Santa Monica Medical Center.

Patients with poorly controlled T1DM have higher risks of preeclampsia, fetal growth abnormalities, polyhydramnios, intrauterine fetal demise, diabetic ketoacidosis, cesarean delivery, and wound infections.

In order to optimize glycemic control and pre-conception hemoglobin A1c, referral to both a maternal-fetal medicine physician and an endocrinologist is important for all individuals with reproductive potential. 

Preemptive optimization of comorbidities and hemoglobin A1c levels may decrease the risk of congenital birth defects and miscarriage.

Over the past 20 years, there has been a paradigm shift in management of T1DM from self-monitoring of fingerstick capillary blood glucose to CGM and from multiple daily injections (MDI) of insulin to continuous subcutaneous insulin infusion (CSII, colloquially known as “insulin pumps”). 

This review will discuss the science and rationale behind these technologies.

CGMs were first introduced in 1999, with the clinical intent of improving patient’s self-awareness of real-time glycemic control, and reducing the cumulative burden of disease management.

 These monitors utilize a glucose-oxidase enzyme reaction to measure the glucose concentration in interstitial fluid and converts the value via algorithms to expected glucose concentration in the blood.

These devices calculate the capillary blood sugar every 5 minutes, then report the value in real-time via short-range wireless communication to a receiver, mobile device, or insulin pump. The data is reported as 288 distinct data points per day.

The analytics generated from the data stream allows both the patient and the physician to visualize trends and changes in the blood sugar in either real-time or retrospectively. 

The ability to pinpoint specific time points of glucose perturbations and nutrition deficits may explain periods of poor glucose control, and allow for better management strategies. Drilling down to this level allows the provider to adjust insulin doses based on patient physiology, diurnal cycles, time of day, and specific meals. 

Furthermore, impending hypo- and hyperglycemia can trigger an audible alarm that allows for preemptive self-treatment with oral glucose or insulin as indicated.

While the data is mixed, use of CGM has been shown in multiple trials to improve outcomes in both pregnant and non-pregnant patients being treated with CSII or MDI of insulin.

 Notable clinical improvements with CGM use include decrease in hemoglobin A1c levels, increased times within target glucose goals, and decreased frequency of hypoglycemic events.

Some data also suggest that CGM also may confer benefit for patients with type 2 diabetes. Initiation of CGM is strongly recommended for pregnant individuals with T1DM.

 Some studies have found lower rates of preeclampsia, improved hemoglobin A1c, and decreased rates of fetal macrosomia.

 There have been no adverse events related to the use of CGMs and no side effects or adverse events have ever been associated with using CGMs in or outside of pregnancy.

Technological advances and limitations with CGM

Both hardware and algorithmic improvements are continuously being instated for CGMs.

 Traditionally, CGMs required the user to periodically obtain fingerstick glucose levels via a glucometer to calibrate the sensor signal. 

Modern CGM devices have improved in insertion, calibration, and accuracy. Newer models allow patient self-insertion subcutaneously every 7 to 10 days, have internal calibration systems, which eliminate the need for finger sticks, and have significantly decreased the mean absolute relative difference from the gold standard (25% in older models, down to 9-12%). 

Despite the improvements in calibration, intermittent capillary blood glucose checks still are recommended to make sure the readings are accurate.

Of note, healthcare providers need to be aware that acetaminophen is contraindicated in all patients on CGMs because the drug binds to, and interferes with, the sensor and will significantly alter the values reported. Newer models of the Dexcom CGM have begun to address this particularity in the sensors.

There are currently four major CGM products available in the United States: Medtronic, Dexcom, FreeStyle Libre, and Senseonics Eversense 

. Dexcom is the only model that currently has Food and Drug Administration (FDA) approval to make insulin management decisions independent of a closed-loop system. 

The remaining models require an additional point-of-care glucose check to confirm the glucose level before insulin or sugar can be administered. The Dexcom and Medtronic CGMs can be used in closed-loop insulin systems, which are described below. 

It is important to note that none of the products have FDA approval for use in pregnancy.

Continuous subcutaneous insulin infusion (insulin pumps)

Most CSII have a reservoir of insulin that is connected to an electronic pump. The pump is connected to tubing that delivers the insulin subcutaneously via an external port placed subcutaneously by the patient. The anatomical sites used are typically abdomen, anterior thigh, and posterior upper arm, all regions that remain accessible to the pregnant individual.

The exception to this model is the Omnipod. 

This specific CSII has the infusion site, insulin reservoir, and pump all at the level of the skin, eliminating the need for tubing. It also has a separate device that allows the patient to control the pump via Bluetooth wireless communication.

Most CSII now have built-in insulin bolus calculators, based on the prescribed insulin-to-carbohydrate ratios of the patient. This feature eliminates human math errors that can lead to incorrect dosing of insulin, a potentially dangerous event.

The basal rates and bolus amounts are fine-tuned across all CSII to a meticulous level of accuracy. The basal rates can be fine-tuned to a level of 0.1 units/hour (or 10 microliters/hour) and bolus volumes can be calculated to a level of 0.05 units to 0.1 units, depending on the type of CSII. 

This level of accuracy is superior to traditional insulin injections that have an accuracy of 0.5 units per dose.

But even with all the advancements in device accuracy and technology, are pumps truly better than daily MDI in pregnancy? 

Within the last 10 years, most studies looking at CSII vs MDIs have found superior glycemic control outside of pregnancy,

 and one study in pregnancy showed superior glycemic control but no improvement in neonatal or obstetrical outcomes.

 However, this study was not powered to evaluate pregnancy outcomes. 

Literature remains sparse on the efficacy of CSII in pregnancy.

Patient characteristics to consider in recommendation and initiation of CSII in a patient requires evaluation of several factors, listed in 

. It is also important to note that physicians need to ensure that the patient has achieved adequate glycemic control prior to the transition to a CSII, as premature initiation during a period of poor control can be potentially dangerous for the patient. 

This increased risk is secondary to the exclusive use of short-acting insulins in CSIIs, which require that the user be savvy and experienced in adjustments. 

A potential lapse in dosing or temporary interruption due to inexperienced use can increase risk of diabetic ketoacidosis or other complications, compared to patients on MDI who have coverage with some basal insulin throughout the day.

There are five major manufacturers of CSII today, and all have similar efficacy. The differences are the presence of minor features that come down to patient preference of the interface (

Closed-loop insulin systems represent the pinnacle of T1DM technology. In closed-loop systems, the CGM communicates directly with the CSII to adjust basal rates and provide micro-boluses throughout the day, further reducing the daily burden of disease management and potential for human error. 

The governing algorithms can see trends in glucose readings and decrease basal rates when hypoglycemia may occur and can give micro-boluses when a trend towards hyperglycemia is detected. This leads to less corrective dosing regimens in the future and overall better control.

There have been multiple studies on closed-loop systems outside of pregnancy that have shown decreased rates of hypoglycemic episodes, more time in glycemic target range, and improved hemoglobin A1c.

One study in pregnant women showed more time in target range compared to previous standard of care.

While no study has looked at neonatal and pregnancy outcomes, given the ability to improve blood sugar control, there may be benefit to the closed-loop system in pregnancy.

To date, no randomized control trials have evaluated the use of intravenous insulin versus subcutaneous injections versus CSII therapy for intrapartum management. Patient and fetal/neonatal safety are of the highest priority while patients are in labor, so a standardized protocol determined by a multidisciplinary team of endocrinologists and maternal-fetal medicine specialists is recommended. 

While CSII may lead to better control of blood sugars and patient autonomy, labor floor providers who are unfamiliar with CSII may choose to transition to insulin drips. 

Further studies of intrapartum management of T1DM need to be performed to further assess safety of continuation of CSII in various labor and delivery models and populations.

One of the most significant barriers to obtaining this technology is variable insurance coverage. Most, but not all, insurance plans will cover CSII and CGMs for T1DM patients. If coverage is not provided by the insurance company the cost of the CSII system can be between $4,500-$6,000 with annual costs of about $3,500 for supplies.

As research continues to demonstrate the superior control and improved long-term outcomes, we anticipate that more insurance companies will cover this essential technology.

From a societal perspective, there have been multiple cost-effectiveness studies evaluating CSII. The data remains conflicting on the optimal management protocols, with differing models yielding different cost-effectiveness outcomes when comparing MDI, CSII with CGM, and closed-loop systems.

When evaluating CGM alone, in the absence of an insulin delivery system, most studies have found that there is a cost-benefit of using CGMs compared to multiple point-of-care glucose tests throughout the day.

But, given the heterogeneous data, it is unclear if there is a cost-benefit of using CSII and CGMs in combination. 

However, if these technologies can potentially reduce the risk of chronic sequelae, such as kidney disease, vascular disease, cardiac disease, and neurologic disease, there may be a long-term cost-benefit analysis. These long-term outcomes have not been evaluated yet as these technologies are relatively new.

A recent poll of T1DM patients revealed about one-third currently use an insulin pump.

 This number is expected to increase given the emerging evidence of superior control, increased safety with use, and expansion of insurance coverage. 

There is also a movement toward placing poorly controlled T1DM on closed-loop systems because of the reduction of human error with this type of technology. It is now up to the obstetrics community to ensure that these technologies and their continued advancements are studied adequately in pregnant individuals.

 is a Maternal-Fetal Medicine Specialist in Los Angeles at the Center for Fetal Medicine and Women’s Ultrasound. She is the MFM Fellowship Program Director at the University of California, Los Angeles.

 is an ob/gyn in Los Angeles at the University of California, Los Angeles Medical Center and practices at Ronald Reagan UCLA Medical Center and Santa Monica Medical Center.

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Articles

A review of the science and technology behind current insulin pumps and
CGMs for managing type 1 diabetes mellitus in pregnancy.

Advancing technology in diabetes management

Ever wonder how self-driving cars recognize a ball in the road? How about when Amazon magically knows what items you need before you do? This is all thanks to pattern recognition of artificial intelligence (AI). Analytical AI refers to the general process by which machines or computers replicate and replace human tasks and cognition. Machine learning is a branch of AI in which algorithms, inspired by the human brain, encourage the computer to continue recognizing patterns automatically (Figure 1). A further subset is deep learning in which massive amounts of neuronal networks interpret and use large amounts of data for deeper “cognitive” capabilities. It has also been called a 

 (CNN) in part due to its resemblance to the neurons and connections in our cerebral cortexes. Deep learning has led to breakthroughs in healthcare, specifically in radiologic image recognition.


	One of the most common uses of AI in healthcare is computer-aided diagnosis (CAD) which has already been widely studied in many fields including prostate, breast and cardiac imaging.

 Many AI applications are used to develop and implement protocols, thereby shortening imaging time, optimizing staffing, and reducing costs.

They also have become instrumental in helping physicians make decisions about patient care.

 Ob/gyn, while late to the game, given the ubiquitous involvement of ultrasound in care of nearly every reproductive-aged woman in the modern world, has the potential to climb the ranks as the specialty most instrumental to use and development of AI. 

“I can’t believe you have to calculate all those measurements yourselves. Doesn’t the machine recognize the baby’s head?” 

-asks an inquisitive patient. A Tesla automobile costs roughly the same as a Voluson E10, yet the former can recognize multiple and simultaneous moving objects in the road but the latter has yet to reliably figure out how to recognize fetal organs, which are more or less identical in 97% of fetuses. While the initial task of learning to perform fetal ultrasound requires dexterity and rote hand skills, it seems plausible that a semi-trained sonographer can learn to place the probe in the correct location and computer aid can help identify the correct plane, prompt organ identification, and calculate many of the measurements automatically. 

In many new machines, automatic image recognition is already being used during biometric measurement of the fetal biparietal diameter (BPD), head circumference (HC), femur length (FL) and abdominal circumference (AC). In these instances, the sonographer is responsible for identifying the proper landmarks in the plane of choice, and once prompted, the machine will label and measure the desired biometric value. If correct, this saves time. On the contrary, if the image quality is poor, due to fetal position or maternal obesity for example, it may erroneously over or under measure the organ. One 

 compared 100 manual biometric measurements to 100 automated measurements and showed a time-saving of about 20 seconds and seven steps on each 20-minute anatomic survey.

 While the time-saving in each individual patient encounter may be insignificant, at the end of a busy day, every second counts towards improving sonographer efficiency and decreasing fatigue. 

Can AI improve accuracy and efficiency of mammography?

Three- and four-dimensional (3D/4D) ultrasound have further revolutionized the ability to acquire and process images, especially in the arena of CAD and image recognition. Many major ultrasound manufacturers have developed their own software, such as 

 (Virtual Organ Computer-aided Analysis) from GE or 

 for breast imaging from Samsung. Image recognition has boomed in fetal ultrasonography with groups studying virtually every plane and fetal organ. 

One of the earliest interests was 3D computer-aided analysis of the fetal heart. One study showed that satisfactory views of the four-chamber heart, outflow tracts, and stomach were only obtained in 43% to 65% of cases, and less in settings of obesity or fetal spine up.

 More recently, spatiotemporal image correlation (STIC) volume data sets have been used to identity nine standard fetal echocardiographic views with up to 98% sensitivity for screening congenital heart disease.

 Some other examples include the fetal thymus, for which CAD has assisted with border identification and accurate volume measurement of this complex pyramidal structure in 77% of cases.

 CAD has even been studied for identification of key characteristics of placenta accreta spectrum

 and cervical length, funneling, and sludge to predict preterm labor in patients with short cervix.

 Gestational age is no longer a limiting factor as these techniques have expanded to the first trimester in 

 to analyze the motion of multiple points on the fetal heart to provide information on its size, shape, and function. Calculation of this complex algorithm requires a simple 2D video clip of the beating four-chamber view and 3 minutes of post-production analysis, with the results potentially yielding a volume of information on fetal cardiac function.

 (Figure 2) developed by GE Healthcare and 

 Both use deep learning application to take one 3D sweep of the fetal brain and automatically recognize and measure the essential structures through the posterior fossa, ventricles, BPD, and HC. As it stands now, however, most providers use image recognition as a second pass or confirmation to increase their diagnostic accuracy and are not looking for replacement of their clinical acumen and years of training.

Data and a lot of it is the fundamental requirement for creating a successful deep learning application. One of the practical limitations of software developers is ethically and efficiently obtaining de-identified patient data to create such a thing. One of the biggest players on the AI block is the UK-based company Intelligent Ultrasound, which acquired over 

 from real obstetric scans to develop algorithms for the software 

. The goals of ScanNav are to provide real-time guidance to sonographers by automatically capturing the six correct images as recommended by the UK fetal anomaly-screening program and provide an audit showing that all the images were obtained. In a sense, this provides a layer of quality improvement to ensure that optimal patient care is being delivered. 

The software is still in development and some limitations include real-time guidance for probe placement, especially with unique patient considerations such as obesity. A crucial aspect to consider in these situations is patient privacy. While individual data may be de-identified, further advances in machine learning may be able to identify individuals if appropriate safety measures aren’t taken with data security.

At the end of 2018, SonoScape medical (Shenzhen, China) announced development of their 

 algorithm, designed for the S60 ultrasound system, which will scan the entire fetus with a single cine loop. Thousands of real images were used to develop algorithms to identify appropriate landmarks and accurate measurements. In addition, in true deep learning fashion, the system continues to fine-tune its analysis with each additional exam it performs. The S-fetus software will select the best images and automatically measure key growth components. This software will consolidate the multistep process of obtaining fetal biometry to a single push of a button. In addition to saving an immense amount of time and keystrokes for each patient, it will alert the sonographer if manual adjustments or measurements do not meet image standards, thus providing the sonographer feedback and resulting in better images. 

Fetal ultrasonography is a mainstay of routine prenatal care. Significant advancements have been made over the years to improve image quality and diagnostic accuracy while maintaining the ease, reproducibility, and efficiency for sonographers performing and physicians interpreting the images. One of AI’s greatest benefits is removing its dependency on the operator and standardizing our approach to improve patient safety, especially in low-resources settings where expertise may otherwise be lacking. Keep your eyes and ears open as the data and hype about this technology are only going to skyrocket in our field. The preliminary schedule for 

 in Berlin in October of this year includes courses on how large data and AI may impact our field, and the sessions are sure to be well attended. AI and deep learning certainly warrants all the buzz and energy surrounding it, but realistically, is not yet sophisticated enough to replace obstetricians, maternal-fetal medicine specialists or radiologists. Rest assured, we don’t have to worry about our job security quite yet. 

The authors report no potential conflicts of interest with regard to this article.

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Moon WK, Chen IL, Chang JM, Shin SU, Lo CM, Chang RF. The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound. 

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Cohen L, Mangers K, Grobman WA, et al. Three-dimensional fast acquisition with sonographically based volume computer-aided analysis for imaging of the fetal heart at 18 to 22 weeks’ gestation. 

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Rizzo G, Capponi A, Persico N, et al. 5D CNS+ Software for automatically imaging axial, sagittal, and coronal planes of normal and abnormal second-trimester fetal brains. 

Na L, Yang C, Lo CC, Zhao F, Fukuoka Y, Aswani A. Feasibility of reidentifying individuals in large national physical activity data sets from which protected health information has been removed with use of machine learning. 

Ob/gyn, while late to the game, has the potential to climb the ranks as the specialty most instrumental to the use and development of AI.

Artificial intelligence in ob/gyn ultrasound

Obesity has become an overwhelming epidemic in the United States and contributes an estimated $147 to nearly $210 billion per year in healthcare costs. It is estimated that over 60% of the US female population is considered overweight, with 45% of US women between ages 15 and 49 considered obese.

This prevalent disorder increases both risk of infertility and complications during pregnancy, and has become a major clinical risk factor for adverse outcomes for both mother and fetus.

 Women with obesity experience increased risks of maternal complications of gestational diabetes, preeclampsia, cesarean delivery, thromboembolism, and postpartum hemorrhage, and fetal complications, such as prematurity, congenital anomalies, macrosomia, birth complications, and stillbirth.

Maternal obesity is also associated with increased risk of fetal structural anomalies, especially cardiac defects, neural tube defects, sacral agenesis, anal atresia, and limb reduction abnormalites.

 Prevalence of cardiac defects in women with body mass index (BMI) > 40 kg/m

 is increased (RR 3.63; 95% CI 1.33- 9.91).


	In addition to the risk of structural anomalies, technical challenges associated with sonographic imaging result in a significantly lower detection rate for cardiac anomalies, as well as an increased risk of missing markers of aneuploidy.

 The percentage of overweight women who have suboptimal fetal echocardiography is 17.4% when compared to 6.4% in normal-weight patients.

Many factors contribute to suboptimal imaging in women with higher BMI. Sonographic visualization of fetal structures is degraded by depth of the body fat layer, with resultant increased depth of insonation, absorption of energy by the adipose tissue, and higher back-scatter from refraction.

 Over 50% of fetal anatomic surveys in women with obesity cannot be completed during the first ultrasound examination.

 Studies of second-
	trimester ultrasound examinations have shown that as BMI increases, there is a steady degradation in scan quality, and rates of poor visualization of fetal heart and spine correlate linearly with the degree of obesity.

 One study found that if a repeat ultrasound is performed at 21 weeks, then visualization of the heart improved; however, in 20% of exams, the fetal heart could not be adequately assessed despite several ultrasounds.


	Images obtained while scanning a patient with obesity are often of poor resolution secondary to backscatter, artifacts, and noise.

 There are several techniques that can result in better sonographic imaging of these patients. The primary goal is to reduce the distance between the transducer and the fetus, and to leverage advanced ultrasound technologies for post-processing.  


	The panniculus limits visualization due to absorption of ultrasound energy by the adipose tissue, as well as increasing depth of insonation. Because the basic tenet of good ultrasound technique is placement of the organ of interest as close as possible to the transducer, scanning at a point away from the thickest portion of the pannus (between the umbilicus and pubic symphysis) may improve image quality.

 Placing patients in alternate positions, such as lateral decubitus, oblique, semi-recumbent or upright may allow the sonographer to scan away from the areas of most depth, yielding better images. The decubitus position causes the pannus to fall away, allowing the practitioner to use the lateral flank as a window, while pointing the transducer medially. The pannus can also be lifted by an assistant or by the patient herself, and scanning can be performed from the infrapanicular crease to minimize the distance to the target of the ultrasound, the fetus.

 Using multiple positions throughout the course of a single visit can result in more optimal views of fetal anatomy and maternal structures. In addition, after the first trimester, it may be necessary to move the fetus into a different position, using a free hand.

 Figures 1 and 2 show examples of the importance of appropriate positioning of the patient during ultrasound. 


	It is also helpful to have the patient maintain a full bladder, which displaces the uterus cephalad, where the adipose tissue of the anterior abdominal wall may be thinner.


	A lower-frequency transducer (e.g. 1 MHz), as opposed to the traditional 2-to 5-MHz abdominal probe, can be combined with beam-forming algorithms to allow for better modeling in overweight patients.

 Because absorption is proportional to ultrasound frequency, lower-frequency transducers allow for less absorption, less attenuation, and more penetration, at the expense of less resolution. Using tissue aberration programs that correct and adjust for the speed changes that occur in adipose tissue also allows for improved resolution and greater depth of penetration. Real-time multiplanar imaging provided by isotropic and new crystal array transducers also be helpful in patients who are obese.


	Because the vaginal probe can be placed closer to the fetal anatomy when used with a transvaginal approach, it is particularly useful in patients with excess adiposity. A transvaginal approach should be considered routine in suboptimal imaging in the first trimester and in scenarios where fetal parts close to the cervix are suboptimally visualized (i.e., intracranial anatomy in a cephalic fetus, or fetal extremities and sacral spine in a breech fetus). The vaginal probe can also be placed directly in the umbilicus, using it as an acoustic window, and has been shown to improve the fetal cardiac exam.


	Use of specialized advanced imaging technologies allows for post-image capture enhancements. Increased signal-to-noise ratios and improved margin definition are easier with post-processing techniques. Use of compound imaging and tissue harmonic imaging has also vastly improved the quality of images that can be obtained from standard ultrasound techniques.

 The principle behind compound imaging is to combine multiple slices of images that have been obtained from different angles to generate an improved composite sonographic image.

 Tissue harmonic sonography, on the other hand, is a gray-scale sonographic technique that markedly improves sonographic contrast resolution, particularly in patients who are difficult to image with conventional techniques.

Ergonomic tips to prevent injury to sonographer/physician


	Taking into account good ergonomic practices is especially important for a practitioner performing ultrasonography on patients who are obese. When performing ultrasound regularly, repetitive injury is common, due to forceful or awkward positions, applying pressure for extended periods of time, and improper positioning. Musculoskeletal injuries are common in sonographers who work in the ob/gyn setting, and it is imperative that they remain aware of hand positions and proper posture and avoid awkward twisting movements while doing scans to avoid injury.

Avoiding bias against patients who are obese


	In our society, obesity is stigmatized, and even though one-third of the adult US population is obese, patients with it are frequently the targets of negative or derogatory comments, prejudice and poor treatment in many settings, including healthcare. There is a growing amount of evidence that supports the notion that physicians and other healthcare workers hold strong implicit bias about patients with obesity,

 and those biases need to be addressed at the systematic and training levels. Because many of our patients have already experienced negative reactions from others, it is even more important to provide them with compassionate care, and to avoid any unconscious bias against patients with obesity, especially because they are more likely to have complications during pregnancy and will need to be able to trust that their physician treats them with respect and dignity. 

Obesity is a growing epidemic in the United States. Patients who are obese are at increased risk of fetal anomalies and overall, adverse outcomes are increased in both mothers who are obese and their fetuses. Pregnant patients who are obese present significant technical challenges for those performing ultrasonography. Providers should leverage a patient position, optimize probe selection, and acquire an understanding of post-processing abilities of their ultrasound machines to maximize image quality and facilitate appropriate counseling of pregnant women who are obese.

 Patients who are obese often require additional ultrasounds to complete anatomical surveys, and have a higher utilization of healthcare resources during and after their pregnancies. The economic impacts of obesity cannot be ignored and working with our patients to improve their health before, during, and after their pregnancies can improve the health of society over the long term.

Hendler I, Blackwell S, Treadwekk M, Bujold E, Sokol R, Sorokin Y. Does advanced ultrasound equipment improve the adequacy of ultrasound visualization of fetal cardiac structures in the obese gravid woman? Am J Obstet Gynecol. 2004;190:1616-1620.

Hendler I, Blackwell S, Treadwekk M, Bujold E, Sokol R, Sorokin Y. The impact of maternal obesity on midtrimester sonographic visualization of fetal cardiac and craniospinal structures. Int J Obes. 2004;28:1607-1611.

Benacerraf B. The use of obstetrical ultrasound in the obese gravida. Sem Perinatol. 2013;37:345-347.

Maxwell C, Glanc P. Imaging and obesity: a perspective during pregnancy. Am J Roentgenol. 2011;196:311-319.

Brite J, Laughon SK, Troendle J, Mills J. Maternal overweight and obesity and risk of congenital heart defects in offspring. Int J Obes. 2014 Jun;38(6):878-882.

Uhden M, Knippel AJ, Stressig R, Hammer R, Siegmann H, Froelich S, Kozlowski P. Impact of maternal obesity and maternal overweight on the detection rate of fetal heart defects and the image quality of prenatal echocardiography. Ultraschall Med. 2011 Dec; 32(S2):E108-14.

Rosenberg JC, Guzman ER, Vintzileos AM, Knuppel RA. Transumbilical placement of the vaginal probe in obese pregnant women. Obstet Gyncecol. 1995;85:132-134.

Oktar SO, Yucel C, Ozdemir H, Uluturk A, Isik S. Comparison of conventional sonography, real-time compound sonography, tissue harmonic sonography, and  tissue harmonic compound sonography of abdominal and pelvic lesions. Am J Roentg. Nov 2003;181(5):1341-1347.

Phelan SM, Burges DJ, Yeazel MW, Hellerstedt WL, Griffin JM, van Ryn M. Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes Rev. 2015;16:319-326.

Proper positioning, careful choice of probe, and leveraging of post-processing capabilities all can improve ultrasound quality in this patient population.

How to optimize imaging in the obese gravida

	Preconception care is primary prevention. The goal is to affirm pregnancy intention, reduce any potential harm, and recognize modifiable risk factors related to pregnancy while stratifying pregnancies on a continuum of low- to high-risk. The Centers for Disease Control and Prevention (CDC) has defined preconception care as “a set of interventions that aim to identify and modify medical, behavioral, and social risks to a woman’s health or pregnancy outcome through prevention and management.”

	A recent consensus statement from the National Preconception Health and Health Care Initiative defined 9 specific preconception wellness measures to confirm high-quality preconception care: 1) pregnancy intention; 2) access to care; 3) preconception multivitamin with folic acid; 4) tobacco avoidance; 5) absence of uncontrolled depression; 6) healthy weight; 7) absence of sexually transmitted infections (STIs); 8) optimal glycemic control in women with pregestational diabetes; and 9) teratogenic medication avoidance.

Recommended: Should women with intrahepatic cholestasis of pregnancy be delivered early?

Preconception care is primary prevention. The goal is to affirm pregnancy intention, reduce any potential harm, and recognize modifiable risk factors related to pregnancy while stratifying pregnancies on a continuum of low- to high-risk.

Christina S Han, MD

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